Rapid protein stability prediction using deep learning representations

Lasse M Blaabjerg; Maher M Kassem; Lydia L Good; Nicolas Jonsson; Matteo Cagiada; Kristoffer E Johansson; Wouter Boomsma; Amelie Stein; Kresten Lindorff-Larsen

doi:10.7554/eLife.82593

. 2023 May 15;12:e82593. doi: 10.7554/eLife.82593

Rapid protein stability prediction using deep learning representations

Lasse M Blaabjerg ¹, Maher M Kassem ², Lydia L Good ¹, Nicolas Jonsson ¹, Matteo Cagiada ¹, Kristoffer E Johansson ¹, Wouter Boomsma ^2,^✉, Amelie Stein ^1,^✉, Kresten Lindorff-Larsen ^1,^✉

Editors: José D Faraldo-Gómez³, Detlef Weigel⁴

PMCID: PMC10266766 PMID: 37184062

Abstract

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available—including via a Web interface—and enables large-scale analyses of stability in experimental and predicted protein structures.

Research organism: None

Introduction

Protein stability, as measured by the thermodynamic free energy difference between the native and the unfolded state, is an important feature of protein structure and therefore function. Protein stability plays a critical role when trying to understand the molecular mechanisms of evolution and has been found to be an important driver of human disease (Casadio et al., 2011; Martelli et al., 2016; Nielsen et al., 2020). Furthermore, optimisation of protein stability is a fundamental part of protein engineering and design (Rocklin et al., 2017).

For a given protein, single amino acid substitutions can have substantial effects on protein stability depending on the particular substitution and the surrounding atomic environment. Assessing such variant effects can be done experimentally for example via thermal or chemical denaturation assays (Lindorff-Larsen and Teilum, 2021). This process, however, may be laborious and time-consuming for even a modest number of amino acid substitutions. In contrast, computational methods have been developed that can predict protein stability changes. Such methods include well-established energy-function-based methods such as for example FoldX (Schymkowitz et al., 2005) and Rosetta (Kellogg et al., 2011), or methods based on molecular dynamics simulations (Gapsys et al., 2016).

Machine learning models have also been developed to predict changes in protein stability and can roughly be split into two types: supervised and self-supervised models. In supervised models, experimental protein stability measurements are used as targets for model predictions (Li et al., 2020; Benevenuta et al., 2021; Pires et al., 2014; Chen et al., 2020). Supervised models are immediately appealing as they are trained directly on experimental data and are able to make predictions at the correct absolute scale. Supervised models may, however, suffer from systematic biases, which can be hard to overcome with limited experimental data. These biases relate to issues of model overfitting of the training data, biases present in the experimental data towards destabilising mutations, biases of the types of amino acid changes that have been probed experimentally, and a lack of self-consistency in model predictions (Yang et al., 2018; Pucci et al., 2018; Usmanova et al., 2018; Fang, 2019; Stein et al., 2019; Caldararu et al., 2020).

In contrast, self-supervised models can be trained without the use of experimental protein stability measurements. Typically, self-supervised models are trained to predict masked amino acid labels from structure or sequence information, thereby learning a likelihood distribution over possible amino acid types at a particular position. This learned likelihood distribution can subsequently be used directly to predict the effects of mutations on protein stability (Lui and Tiana, 2013; Boomsma and Frellsen, 2017; Riesselman et al., 2018; Shin et al., 2021; Elnaggar et al., 2021; Meier et al., 2021). Self-supervised models sidestep many of the challenges associated with training on experimental data and have shown impressive results in recent years. These models, however, often require substantial computational resources during training—and sometimes during evaluation—and generally do not make predictions at an absolute scale, which limits their practical application.

Due to the complementary strengths and weaknesses of supervised and self-supervised models, the two perspectives can often be fruitfully combined. Here we present such an approach for rapid protein stability-change predictions, combining pre-trained representations of molecular environments with supervised fine-tuning. Our method, RaSP (Rapid Stability Prediction) provides both fast and accurate predictions of protein stability changes and thus enables large-scale, proteome-wide applications.

Results

Development of the RaSP model

We trained the RaSP model in two steps (Figure 1). First, we trained a self-supervised representation model to learn an internal representation of protein structure. We selected a 3D convolutional neural network architecture for the representation model, as this has previously been shown to yield predictions that correlate well with changes in protein stability (Boomsma and Frellsen, 2017). Second, we trained a supervised downstream model using the learned structure representation as input to predict protein stability changes on an absolute scale. The task of the downstream model is therefore to re-scale and refine the input from the representation model. The supervised downstream model was trained on a set of calculated protein stability changes to enable the generation of a larger and more powerful training data set while minimizing the effects from experimental data biases. We chose to estimate protein variant stability values using the Rosetta ‘cartesian_ddg’ protocol, which has shown to be a reliable and well-established predictor of stability (Park et al., 2016; Frenz et al., 2020), and as shown below the model generalizes to a wide range of situations. We expect that similar results could have been obtained using other variant stability prediction methods such as FoldX.

Building on earlier work (Boomsma and Frellsen, 2017), we first trained a self-supervised 3D convolutional neural network on a large, homology-reduced set of high-resolution structures. This model was trained to predict the wild type amino acid labels of a protein given its local atomic environment (see Methods and Figure 2—figure supplement 1 for further details). In the second step of developing RaSP, we trained a downstream supervised fully-connected neural network to predict stability changes; this model uses the internal representation of the 3D convolutional neural network as well as the corresponding wild type and mutant amino acid labels and frequencies as input. We trained this model on $Δ Δ G$ values generated by saturation mutagenesis using Rosetta to limit the effects of biases from experimental assays and amino acid compositions; as described further below we validated the model using a range of different experiments. In the training of the supervised model, we used a custom loss function to focus the model on $Δ Δ G$ values in the range from approximately -1 to 7 where Rosetta is known to be most accurate (Kellogg et al., 2011) and where many loss-of-function variants relevant for human disease can be identified (Casadio et al., 2011; Cagiada et al., 2021).

After training (Figure 2a), the downstream model achieves a Pearson correlation coefficient of 0.82 and a mean absolute error (MAE) of 0.73 kcal/mol on a test data set comprised of 10 proteins with full saturation mutagenesis which was not seen by the model until after it had been developed and trained (Figure 2b and Table 1). In practice, the downstream model predictions were made using the median of an ensemble of 10 model predictions with each model trained using different initialization seeds. The accuracy of the model is relatively uniform for the different types of amino acid substitutions, with bigger errors in particular when substituting glycine residues, or when changing residues to proline (Figure 2—figure supplement 2). In terms of location of the residue in the protein structure, the model accuracy is slightly better at exposed relative to buried residues (Figure 2—figure supplement 3), although we note also that the distribution of stability changes are different in the two different locations.

Figure 2—figure supplement 1. — (A) Learning curve for training of the RaSP downstream model, with Pearson correlation coefficients ( $ρ$ ) and mean absolute error ( ${MAE}_{F}$ ) of RaSP predictions. During training we transformed the target $Δ Δ G$ data using a switching (Fermi) function, and ${MAE}_{F}$ refers to this transformed data (see Methods for further details). Error bars represent the standard deviation of 10 independently trained models, that were subsequently used in ensemble averaging. Val: validation set; Train: training set. (B) After training, we applied the RaSP model to an independent test set to predict $Δ Δ G$ values for a full saturation mutagenesis of 10 proteins. Pearson correlation coefficients and mean absolute errors (MAE) were for this figure computed using only variants with Rosetta $Δ Δ G$ values in the range [–1;7] kcal/mol.

Table 1. Overview of RaSP model test set prediction results including benchmark comparison with the Rosetta protocol.

When comparing RaSP to Rosetta (column: "Pearson | $ρ$ | RaSP vs. Ros."), we only compute the Pearson correlation coefficients for variants with a Rosetta $Δ Δ G$ value in the range [–1;7] kcal/mol. Experimental data is from Kumar, 2006; Ó Conchúir et al., 2015; Nisthal et al., 2019; Matreyek et al., 2018; Suiter et al., 2020.

Data set	Protein name	PDB, chain	Pearson \|ρ\| RaSP vs. Ros.	Pearson \|ρ\| RaSP vs. Exp.	Pearson \|ρ\| Ros. vs. Exp.
RaSP test set	MEN1	3U84, A	0.85	-	-
	F8	2R7E, A	0.71	-	-
	ELANE	4WVP, A	0.81	-	-
	ADSL	2J91, A	0.84	-	-
	GCK	4DCH, A	0.84	-	-
	RPE65	4RSC, A	0.84	-	-
	TTR	1F41, A	0.88	-	-
	ELOB	4AJY, B	0.87	-	-
	SOD1	2CJS, A	0.84	-	-
	VANX	1R44, A	0.83	-	-
ProTherm test set	Myoglobin	1BVC, A	0.91	0.71	0.76
	Lysozyme	1LZ1, A	0.80	0.57	0.65
	Chymotrypsin inhib.	2CI2, I	0.79	0.65	0.68
	RNAse H	2RN2, A	0.78	0.79	0.71
Protein G	Protein G	1PGA, A	0.90	0.72	0.72
MAVE test set	NUDT15	5BON, A	0.83	0.50	0.54
	TPMT	2H11, A	0.86	0.48	0.49
	PTEN	1D5R, A	0.87	0.52	0.53

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Validating RaSP using experimental data

After having shown that the RaSP model can successfully reproduce Rosetta $Δ Δ G$ values, we proceeded to test it on experimental measurements for five proteins. Specifically, we selected four proteins with stability measurements from ProTherm (Kumar, 2006; Ó Conchúir et al., 2015) and the B1 domain of protein G for which stability measurements are available for almost all possible single amino acid substitutions (Nisthal et al., 2019). We compared RaSP predictions for the five test proteins to Rosetta as a baseline (Figure 3, Figure 3—figure supplement 1). Pearson correlation coefficients of RaSP model predictions versus experimental stability measurements ranged from 0.79 (RNAse H, Rosetta baseline: 0.71)–0.57 (lysozyme, Rosetta baseline: 0.65). Although these correlations are not very high on an absolute scale, our results indicate that the RaSP model is approximately as accurate as the Rosetta protocol for predicting experimental $Δ Δ G$ values (Table 1). We also note that this accuracy is close to what is computationally achievable given the available data; it has been shown that there exists a natural upper bound on the achievable accuracy when predicting experimental $Δ Δ G$ values due to variations between experiments (Montanucci et al., 2019). Furthermore, we find the bias of the predictions (assessed via the mean error) is roughly the same between RaSP and Rosetta.

Figure 3. — Predictions of changes in stability obtained using (A) RaSP and (B) Rosetta are compared to experimental data on five test proteins; myoglobin (1BVC), lysozyme (1LZ1), chymotrypsin inhibitor (2CI2), RNAse H (2RN2) and Protein G (1PGA) (Kumar, 2006; Ó Conchúir et al., 2015; Nisthal et al., 2019). Metrics used are Pearson correlation coefficient ( $ρ$ ), mean absolute error (MAE) and mean error (ME). In the experimental study of Protein G, 105 variants were assigned a $Δ Δ G$ value of at least 4 kcal/mol due to low stability, presence of a folding intermediate, or lack expression (Nisthal et al., 2019).

Figure 3—figure supplement 1. — Predictions of changes in stability obtained using (A) RaSP and (B) Rosetta are compared to experimental data on five test proteins; myoglobin (1BVC), lysozyme (1LZ1), chymotrypsin inhibitor (2CI2), RNAse H (2RN2) and Protein G (1PGA) (Kumar, 2006; Ó Conchúir et al., 2015; Nisthal et al., 2019). Metrics used are Pearson correlation coefficient ( $ρ$ ), mean absolute error (MAE) and mean error (ME). In the experimental study of Protein G, 105 variants were assigned a $Δ Δ G$ value of at least 4 kcal/mol due to low stability, presence of a folding intermediate, or lack expression (Nisthal et al., 2019).

In order to further validated RaSP, we compared its performance versus other methods on the recently published S669 experimental direct data set (Pancotti et al., 2022; Figure 3—figure supplement 2). The data set includes 669 variants and 94 experimental structures. We observe that RaSP performs as well as Rosetta in terms of Pearson correlation on this particular data set (Figure 3—figure supplement 2) and that RaSP performs relatively close to several of the best performing methods (Table 2). We also test the performance of RaSP on a high-accuracy version of the recently published experimental mega-scale data set from Rocklin and collaborators (Tsuboyama et al., 2022). We observe that RaSP achieves a zero-shot Pearson correlation coefficient of 0.62 and an MAE of 0.94 (Figure 3—figure supplement 3). Taken together, these results suggest the RaSP performs on-par with other computational methods although differences in training and parameterization makes direct comparison difficult. For example, a given method might have been parameterized either directly or independently from experimental data. In the case of RaSP, parameterization is made indirectly from experimental data since the Rosetta $Δ Δ G$ itself has been parameterized using a range of different types of experimental data (Park et al., 2016).

Table 2. Benchmark performance of RaSP versus other structure-based methods on the S669 direct experimental data set (Pancotti et al., 2022).

Results for methods other than RaSP have been copied from Pancotti et al., 2022. We speculate that the higher RMSE and MAE values for Rosetta relative to RaSP are due to missing scaling of Rosetta output onto a scale similar to kcal/mol.

Method	S669, direct
Method	Pearson $ρ$	RMSE [kcal/mol]	MAE [kcal/mol]
Structure-based
ACDC-NN	0.46	1.49	1.05
DDGun3D	0.43	1.60	1.11
PremPS	0.41	1.50	1.08
RaSP	0.39	1.63	1.14
ThermoNet	0.39	1.62	1.17
Rosetta	0.39	2.70	2.08
Dynamut	0.41	1.60	1.19
INPS3D	0.43	1.50	1.07
SDM	0.41	1.67	1.26
PoPMuSiC	0.41	1.51	1.09
MAESTRO	0.50	1.44	1.06
FoldX	0.22	2.30	1.56
DUET	0.41	1.52	1.10
I-Mutant3.0	0.36	1.52	1.12
mCSM	0.36	1.54	1.13
Dynamut2	0.34	1.58	1.15

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A common challenge for many machine learning-based methods is the ability to satisfy the anti-symmetry condition, which states that the $Δ Δ G$ of a single point mutation must equal its reverse mutation with opposite sign (Pancotti et al., 2022). In order to assess this property, we tested RaSP on the Ssym+ data set, which includes both a direct and a reverse data set (Pancotti et al., 2022). The direct data set includes 352 variants and 19 experimental structures while the reverse data set includes 352 experimental structures for each of the corresponding reverse variants. RaSP achieves a Pearson correlation of 0.58 and a MAE of 1.01 kcal/mol on the direct data set, while it only achieves a Pearson correlation of 0.18 and a MAE of 1.82 kcal/mol on the reverse data set (Figure 3—figure supplement 2). The failure of RaSP to accurately capture anti-symmetric effects is expected following the one-sided training method and provides an interesting avenue for further method development.

Multiplexed assays of variant effects (MAVEs, also known as deep mutational scanning assays) leverage recent developments in high-throughput DNA synthesis and sequencing to probe large (e.g. thousands per protein) libraries of protein variants (Kinney and McCandlish, 2019). A particular type of MAVE termed ‘Variant Abundance by Massively Parallel sequencing’ (VAMP-seq) probes variant effects on cellular protein abundance (Matreyek et al., 2018), and correlates with both in vitro measurements (Matreyek et al., 2018; Suiter et al., 2020) and computational predictions (Cagiada et al., 2021) of protein stability. We therefore compared RaSP calculations with VAMP-seq data for three proteins and find that it correlates about as well as Rosetta calculations (Table 1 and Figure 3—figure supplement 4).

Prediction of protein stability-change using computationally modelled structures

For maximal utility for proteome-wide stability predictions, our model should be generally robust to the quality of input protein structures (Caldararu et al., 2021). To test this, we used template-based (homology) modelling to generate structures of the four proteins selected from ProTherm that we analysed above. To minimize issues of leakage between training and testing data, we have recently used MODELLER (Martí-Renom et al., 2000; Webb and Sali, 2016) to construct models of the four proteins, using templates with decreasing sequence identities to the original structures (Valanciute et al., 2023). We used these structures as input to RaSP in order to calculate $Δ Δ G$ values to compare with experiments; we also performed similar tests using $Δ Δ G$ values calculated using Rosetta (Figure 4). Overall we find that both RaSP and Rosetta are relatively insensitive to the input models, with some decrease in accuracy for both methods when models are created using more distant homologues.

Figure 4. — Pearson correlation coefficients ( $ρ$ ) between experimental stability measurements and predictions using protein homology models with decreasing sequence identity to the target sequence. Pearson correlation coefficients were computed in the range of [–1;7] kcal/mol.

The development of AlphaFold 2 (Jumper et al., 2021) and other protein structure prediction algorithms has enabled large-scale generation of accurate structural models (Varadi et al., 2022). Recently, it has been shown that high-confidence models generated by AlphaFold 2 are sufficiently accurate to be used as input for protein stability predictions using for example FoldX and Rosetta (Akdel et al., 2022). We therefore tested whether AlphaFold 2 structures could similarly be used as input to RaSP. More specifically, we asked whether $Δ Δ G$ could be predicted as accurately from AlphaFold 2 structures as from crystal structures. We selected three proteins that each had two high-resolution crystal structures and used these as well as an AlphaFold 2 model to predict $Δ Δ G$ . Overall we find a high correlation between the RaSP predictions from the pairs of crystal structures ( $\bar{ρ} = 0.97$ ), only slightly greater than the correlation between values predicted from a crystal structure and an AlphaFold 2 structure ( $\bar{ρ} = 0.94$ ) (Table 3). This correlation is highest in regions of the proteins where AlphaFold 2 is most confident about the structure prediction (i.e. residues with high pLDDT scores). Interpreting this observation is, however, complicated by the fact that low pLDDT scores are often found in loop regions that might have naturally lower $Δ Δ G$ scores, thus increasing the effects of noise and minor outliers on correlation scores. Overall, these results, together with those using the homology models above, indicate that RaSP can also deliver accurate predictions using computationally generated protein structures; we note that similar results have been obtained using other stability prediction methods (Akdel et al., 2022; Valanciute et al., 2023; Lihan et al., 2023; Keskin Karakoyun et al., 2023).

Table 3. Comparing RaSP predictions from crystal and AlphaFold 2 (AF2) structures.

Pearson correlation coefficients ( $ρ$ ) between RaSP $Δ Δ G$ predictions using either two different crystal structures or a crystal structure and an AlphaFold 2 structure for six test proteins: PRMT5 (X1: 6V0P_A, X2: 4GQB_A), PKM (X1: 6B6U_A, X2: 6NU5_A), FTH1 (X1: 4Y08_A, X2: 4OYN_A), FTL (X1: 5LG8_A, X2: 2FFX_J), PSMA2 (X1: 5LE5_A, X2: 5LE5_O) and GNB1 (X1: 6CRK_B, X2: 5UKL_B). We also divided the analysis into residues with high (pLDDT ≥ 0.9) and medium-low (pLDDT <0.9) pLDDT scores from AlphaFold 2.

Protein	All [ $ρ$ ]			High AF2 pLDDT [ $ρ$ ]			Medium-Low AF2 pLDDT [ $ρ$ ]
Protein	X1-X2	X1-AF2	X2-AF2	X1-X2	X1-AF2	X2-AF2	X1-X2	X1-AF2	X2-AF2
PRMT5	0.93	0.89	0.95	-	0.90	0.95	-	0.66	0.89
PKM	0.99	0.95	0.95	-	0.95	0.95	-	0.88	0.89
FTH1	0.99	0.97	0.97	-	0.97	0.97	-	0.92	0.95
FTL	0.97	0.96	0.97	-	0.96	0.97	-	0.96	0.94
PSMA2	0.99	0.95	0.95	-	0.96	0.96	-	0.78	0.80
GNB1	0.96	0.94	0.94	-	0.94	0.94	-	0.93	0.89

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Large-scale calculations of stability-changes and analysis of disease-causing missense variants

Protein stability has been shown to be an important driver of human disease (Casadio et al., 2011; Martelli et al., 2016; Nielsen et al., 2020) and knowledge about changes in protein stability can provide important mechanistic insights into the molecular origins of a disease (Abildgaard et al., 2023; Cagiada et al., 2021). In particular, predictions of changes in protein stability can help separate variants into mechanistic classes depending on how they affect protein function (Cagiada et al., 2022). In this light, developing accurate predictors of $Δ Δ G$ values is an important step in understanding the mechanisms behind human diseases.

Large-scale predictions often require models that are specifically suited for the task. As such, we designed RaSP to be fast, yet retain the accuracy of the Rosetta protocol. We benchmarked the speed of RaSP against both Rosetta and FoldX (well-established energy-function-based methods) (Kellogg et al., 2011; Schymkowitz et al., 2005) as well as two recent machine learning-based methods; ACDC-NN and ThermoNet (Benevenuta et al., 2021; Li et al., 2020; Table 4). We find that RaSP enables saturation mutagenesis $Δ Δ G$ calculations at a speed of ∼0.4 s per position—independent of total protein length—after pre-processing of the protein structure. At this speed, saturation mutagenesis scans are possible for most proteins in a few minutes—orders of magnitude faster than Rosetta (Table 4; note differences in hardware used for the two sets of calculations). Furthermore, we find that RaSP is also faster than the other machine learning-based methods that have not been optimized for large-scale applications. The ACDC-NN model is the closest performing model to RaSP in terms of speed, and ACDC-NN has also shown to have good predictive performance on experimental test data (Table 2). ACDC-NN is, however, somewhat slower than RaSP both in time for pre-processing and for computing $Δ Δ G$ ; it also uses information from multiple sequence alignments, which might not always be available.

Table 4. Run-time comparison of RaSP and four other methods for three test proteins ELOB (PDB: 4AJY_B, 107 residues), GCK (PBD: 4DCH_A, 434 residues) and F8 (PDB: 2R7E_A, 693 residues).

The RaSP model is in total 480–1,036 times faster than Rosetta. RaSP, ACDC-NN and ThermoNet computations were performed using a single NVIDIA V100 16 GB GPU machine, while Rosetta and FoldX computations were parallelized and run on a server using 64 2.6 GHz AMD Opteron 6380 CPU cores. The number of $Δ Δ G$ computations per mutation was set to 3 for both Rosetta and FoldX. For ThermoNet, we expect that the pre-processing speed can be made comparable to Rosetta via parallelization.

Method	Protein	Wall-clock time [s]
Method	Protein	Pre-processing	$Δ Δ G$	$Δ Δ G$ / residue
RaSP	ELOB	7	41	0.4
	GCK	11	173	0.4
	F8	20	270	0.4
Rosetta	ELOB	677	44,324	414.2
	GCK	7,996	118,361	272.7
	F8	17,211	133,178	192.2
FoldX	ELOB	78	42,237	394.7
	GCK	728	309,762	713.7
	F8	1,306	559,050	806.7
ACDC-NN	ELOB	81	158	1.5
	GCK	169	619	1.4
	F8	325	1,080	1.6
ThermoNet	ELOB	80,442	884	8.3
	GCK	4,586,522	4,227	9.7
	F8	11,627,433	8,732	12.6

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To demonstrate how the speed of RaSP enables large-scale applications, we used it to perform saturation scans for 1381 experimentally determined structures of human proteins or domains (see Methods for further details on selection criteria), yielding a data set of ∼ 8.8 million $Δ Δ G$ values. The $Δ Δ G$ values predicted by RaSP follow the expected distribution (Tokuriki et al., 2007; Nisthal et al., 2019) with a small number of stabilizing variants, a large number of slightly destabilizing variants and a long tail of very destabilizing variants (Figure 5—figure supplement 1).

As an example of the kinds of analyses that these large-scale calculations enable, we examined variants that have been observed in the human population. Specifically, we extracted validated disease-causing and benign variants from ClinVar (Landrum et al., 2018) and variants observed more broadly in the human population from the Genome Aggregation Database (gnomAD) (Karczewski et al., 2020), and annotated them with their predicted $Δ Δ G$ values (Figure 5). We find that benign variants generally only have a smaller effect on stability (median $Δ Δ G$ 0.54 kcal/mol, and 95% within the range –0.9–2.7 kcal/mol; Figure 5A) whereas many disease causing missense variants are predicted to be destabilizing (median $Δ Δ G$ 1.4 kcal/mol, and 95% within the range –1.4–6.7 kcal/mol; Figure 5A). Thus, a substantial fraction of the pathogenic variants are predicted to be destabilized to an extent that is likely to cause lowered abundance (Cagiada et al., 2021; Høie et al., 2022). We observe that this difference in median $Δ Δ G$ values between the benign and the pathogenic group is statistically significant using bootstrap sampling. We resample each of the pathogenic and benign distributions with replacement 10⁴ times and compute the difference in medians between the two groups along with a 95% confidence interval (CI). Using this method, the difference in medians is estimated at 0.82 kcal/mol (CI: 0.73 kcal/mol – 0.93 kcal/mol).

Figure 5. — The grey distribution shown in the background of all plots represents the distribution of $Δ Δ G$ values calculated using RaSP for all single amino acid changes in the 1,366 proteins that we analysed (15 of the 1381 proteins that we calculated $Δ Δ G$ for did not have variants in ClinVar or gnomAD and were therefore not included in this analysis). Each plot is also labelled with the median $Δ Δ G$ of the subset analysed as well as a range of $Δ Δ G$ values that cover 95% of the data in that subset (box plot shows median, quartiles and outliers). The plots only show values between –1 and 7 kcal/mol (for the full range see Figure 5—figure supplement 2). (A) Distribution of RaSP $Δ Δ G$ values for benign (blue) and pathogenic (tan) variants extracted from the ClinVar database (Landrum et al., 2018). We observe that the median RaSP $Δ Δ G$ value is significantly higher for pathogenic variants compared to benign variants using bootstrapping. (B) Distribution of RaSP $Δ Δ G$ values for variants with different allele frequencies (AF) extracted from the gnomAD database Karczewski et al., 2020 in the ranges (i) AF>10^-2 (green), (ii) 10^-2 > AF>10^-4 (orange), and (iii) AF<10^-4 (purple). We observe a gradual shift in the median RaSP $Δ Δ G$ going from common variants (AF>10^-2) towards rarer ones (AF<10^-4).

Figure 5—figure supplement 1. — The grey distribution shown in the background of all plots represents the distribution of $Δ Δ G$ values calculated using RaSP for all single amino acid changes in the 1,366 proteins that we analysed (15 of the 1381 proteins that we calculated $Δ Δ G$ for did not have variants in ClinVar or gnomAD and were therefore not included in this analysis). Each plot is also labelled with the median $Δ Δ G$ of the subset analysed as well as a range of $Δ Δ G$ values that cover 95% of the data in that subset (box plot shows median, quartiles and outliers). The plots only show values between –1 and 7 kcal/mol (for the full range see Figure 5—figure supplement 2). (A) Distribution of RaSP $Δ Δ G$ values for benign (blue) and pathogenic (tan) variants extracted from the ClinVar database (Landrum et al., 2018). We observe that the median RaSP $Δ Δ G$ value is significantly higher for pathogenic variants compared to benign variants using bootstrapping. (B) Distribution of RaSP $Δ Δ G$ values for variants with different allele frequencies (AF) extracted from the gnomAD database Karczewski et al., 2020 in the ranges (i) AF>10^-2 (green), (ii) 10^-2 > AF>10^-4 (orange), and (iii) AF<10^-4 (purple). We observe a gradual shift in the median RaSP $Δ Δ G$ going from common variants (AF>10^-2) towards rarer ones (AF<10^-4).

Similarly, variants that are common in the human population (i.e. with an allele frequency in gnomAD > 10^-2) generally only have a small effect on the stability (median $Δ Δ G$ 0.41 kcal/mol), whereas rarer variants have a broader distribution including more destabilizing variants (Figure 5B). Thus, in line with previous observations (Casadio et al., 2011; Martelli et al., 2016; Stein et al., 2019) we find that loss of protein stability is likely to be the underlying cause for loss of function for a large fraction of pathogenic variants. Using the bootstrapping method described above, we observe that the difference in median $Δ Δ G$ values between the common (gnomAD > 10^-2) and the rare (gnomAD < 10^-4) variants is estimated at 0.35 kcal/mol (CI: 0.27 kcal/mol – 0.43 kcal/mol).

Following the prediction of ∼8.8 million RaSP $Δ Δ G$ values, we decided to expand our analysis to the entire human proteome corresponding to ∼ 300 million predicted RaSP $Δ Δ G$ values across 23,391 protein structures obtained from the AlphaFold2 database (Varadi et al., 2022; Figure 5—figure supplement 3); because of overlapping fragments in AlphaFold predictions for large proteins, this corresponds to ca. 230 million unique variants. We note that RaSP has only been trained and benchmarked on soluble, globular and folded proteins and may therefore not expected to perform equally well on proteins outside this category including e.g. membrane proteins and intrinsically disordered proteins. Furthermore, AlphaFold2 may not predict reliable structural models in regions that are highly flexible and disordered (Jumper et al., 2021; Varadi et al., 2022). For convenience, we however provide the data for all human proteins. Despite these caveats, we observe that the RaSP predictions follow the same expected distributional shape as described above. We believe that these predictions could serve as a fruitful starting point for large-scale studies of protein stability and have therefore made these RaSP predictions freely available online (see ‘Data and code availability’).

Discussion

Here, we have presented a novel deep-learning-based model for making rapid predictions of protein stability changes using a combination of supervised and self-supervised methods. Our model uses the latent-space representation of a 3D convolutional neural network for protein structures as input to a supervised model to predict $Δ Δ G$ . Our results show that the RaSP model performs on-par with the Rosetta protocol that we used as target for our optimization, but at a speed that is several orders of magnitude faster. We analysed RaSP’s sensitivity towards using predicted protein structures, and applied it to generate a unique and large set of stability-change predictions for 8.8 million variants of 1,381 human proteins. As an example of the kinds of applications that RaSP may enable, we analysed the role of protein stability for genetic diseases, and hope that this data and the RaSP model will be a broadly useful tool to study the effects of amino acid changes on protein stability.

Methods

A convolutional neural network for protein structures

We use a 3D convolutional neural network as a model to represent protein structures based on the architectures presented in Boomsma and Frellsen, 2017. During training, the model is tasked with predicting the amino acid type given its local 3D atomic environment. Specifically, for each residue in the polypeptide chain, the model aims to predict the amino acid type based on the local atomic environment lying inside a sphere centered on the $C_{α}$ atom of the target residue to be predicted with a radius of 9 Å. Before prediction, all atoms in the target residue are removed from the environment. Each local spherical environment is parameterized using a Cartesian grid and so divided into multiple cubic voxels each with a volume of $(1 \overset{˚}{A})^{3}$ . Each cubic voxel has six input channels corresponding to the one-hot encoding of C, N, O, H, S, and P atoms. The model consists of a series of three 3D convolutional layers that is applied to the local spherical atomic environment. All convolutional filters have a size of $(3 \overset{˚}{A})^{3}$ and the number of filters at each convolutional layer is 16, 32, and 64, respectively. The three 3D convolutional layers are connected using leaky ReLU activation (He et al., 2015), max pooling (Riesenhuber and Poggio, 1999) and batch normalization functions (Ioffe and Szegedy, 2015). We apply Gaussian blurring to the input local environment before the convolutional layers. After the convolutional layers, the output is flattened and the dimensionality is reduced by two fully connected layers with 100 and 20 nodes, respectively. Model predictions are optimized by minimizing the cross-entropy between the predicted 20-length vector and the one-hot encoded amino acid label.

We trained the above model on a data set of 2336 high resolution protein structures obtained using the PISCES server with maximal sequence identity set to 30% (Wang and Dunbrack, 2003). All structures were pre-processed using the Reduce program (Word et al., 1999) to add missing hydrogen atoms and OpenMM PDBFixer (Eastman et al., 2013) to correct for common PDB errors; during pre-processing, three proteins gave errors and were thus discarded. Out of the remaining 2333 proteins, 2099 proteins were used for training while 234 were withheld for validation. Optimization was performed using Adam (Kingma and Ba, 2014) with a learning rate of $3 \cdot 10^{- 4}$ and a batch size of 100 residues. After training, the representation model achieves a wild-type amino acid classification accuracy of 63% on a validation set (Figure 2—figure supplement 1).

Downstream model architecture and training

We used the convolutional neural network described above to represent protein structures when predicting protein stability changes. Specifically, for each variant, the downstream model takes as input (i) a flattened 100-dimensional representation of the atomic environment extracted for the wild-type structure using the self-supervised model, (ii) one-hot encoded labels of the wild type and variant amino acid, and (iii) the amino acid frequencies of the wild type and variant amino acid in the PISCES set of proteins. These inputs are concatenated and passed through three fully connected layers with 128, 64, and 16 nodes, respectively, resulting in a scalar value. During training, this value is passed through a sigmoid function corresponding to the transformation of the target data described below. Between each layer, we apply batch normalization (Ioffe and Szegedy, 2015) and leaky ReLU activation (He et al., 2015). We train the model by minimizing the mean absolute error between the predicted values of the stability-change and Rosetta $Δ Δ G$ values, after being transformed using a switching function (see below).

We trained the downstream model using $Δ Δ G$ values from saturation mutagenesis of 35 training proteins and 10 validation proteins, and tested using 10 different proteins. Before training, we transformed all Rosetta $Δ Δ G$ values using a switching (Fermi) function to focus model training on the $Δ Δ G$ range from -1 to 7, as this is where Rosetta is expected to be most accurate (Kellogg et al., 2011), where most experimental values lie (Kumar, 2006; Ó Conchúir et al., 2015; Nisthal et al., 2019), and covering an important range for detecting missense variants that cause disease via loss of protein stability and abundance (Stein et al., 2019; Cagiada et al., 2021; Høie et al., 2022). The Fermi function was defined as:

F (Δ Δ G) = \frac{1}{1 + e^{- β (Δ Δ G - α)}}

(1)

with $β = 0.4$ and $α = 3.0$ . Optimization was performed using Adam (Kingma and Ba, 2014) with a learning rate of $5 \cdot 10^{- 4}$ and a batch size of 40 variants. Stability changes for (disulfide-bonded) cystine residues were removed from the training, validation and test set as they are not predicted using our Rosetta protocol. In practice, this means that RaSP will tend to underestimate the magnitude of $Δ Δ G$ values for mutations at cysteine residues involved in disulfide-bonds and that these predicted $Δ Δ G$ values should therefore not be taken to be accurate.

Rosetta protocol

We used Rosetta (GitHub SHA1 99d33ec59ce9fcecc5e4f3800c778a54afdf8504) and the the Cartesian $Δ Δ G$ protocol (Park et al., 2016) to predict $Δ Δ G$ values. Values from Rosetta values were divided by 2.9 to convert to a scale corresponding to kcal/mol (Park et al., 2016).

Calculation of SASA

We analyse the differences in correlation between RaSP and Rosetta $Δ Δ G$ predictions at exposed and buried residues respectively (Figure 2—figure supplement 3). The solvent accessible surface area (SASA) was calculated using BioPython and default Sander and Rost values (Cock et al., 2009; Rost and Sander, 1994). The cut-off between exposed and buried residues was set at 0.2.

Filtering of mega-scale data set

We tested the performance of RaSP on a recently published mega-scale folding stability experiment (Tsuboyama et al., 2022). We included variants with single amino acid substitutions with well-defined experimental $Δ Δ G$ values. As RaSP has only been trained on $Δ Δ G$ values from natural protein structures, we also decided to include only those in our test. Furthermore, we excluded any synonymous substitutions. The filtered data set contains a total of 164,524 variants across 164 protein domain structures.

Speed test benchmarking

We compare the speed of RaSP to other predictors of $Δ Δ G$ namely Rosetta, FoldX, ACDC-NN and ThermoNet (Kellogg et al., 2011; Schymkowitz et al., 2005; Benevenuta et al., 2021; Li et al., 2020). The Rosetta protocol was implemented as described above. The FoldX protocol was implemented using FoldX version 5.0 with a custom script to enable full saturation mutagenesis calculations. ACDC-NN was implemented using the GitHub source code (https://github.com/compbiomed-unito/acdc-nn, Computational Biology and Medicine Group, 2023 accessed January 2023). ThermoNet was implemnted using the GitHub source code (https://github.com/gersteinlab/ThermoNet, Gerstein Lab, 2022 accessed January 2023).

Selection of proteins and structures for large-scale analysis

Our selection of experimental structures was based on 5,557 human proteins annotated with cytosolic location. To make this list, we extracted UniProt identifiers from the human genome assembly GRCh38.p13 filtered for the Gene Ontology (Ashburner et al., 2000) term ‘cytosol’ (GO:0005829). We aligned the sequences of all chains of all PDB entries in the SIFTS map (Dana et al., 2019) (June 2021) to the UniProt sequences for the cytosolic proteins and calculated how much the protein structure in each PDB file covered the UniProt entries (explicitly considering mismatches, indels, residues that are not resolved in the structure, and modified residues). Based on these features, we calculated a score for each protein structure and the highest scoring structure was selected to represent a PDB entry. When calculating the score, we gave the highest priority to the coverage of the UniProt sequence and the experimental method. Specifically, given that RaSP was initially developed and benchmarked using crystal structures, we prioritized methods in the following order: (i) X-ray, (ii) cryo-electron microscopy, (iii) NMR. For our final selection, we chose 1,381 structures from the above selection, where the structure was of high quality in a canonical PDB format and where at least 50% of the UniProt sequence was covered by the structure.

Benchmarking on the S669 dataset

We used RaSP to predict stability changes for 669 variants using 94 experimental structures from a recently described dataset (Pancotti et al., 2022). We note that one of the proteins in the S669 set (1XXN.pdb) is highly similar to one of the proteins we used to train our downstream model on (1C5H.pdb). We also used the Ssym+ data set (Pancotti et al., 2022), which includes 352 variants and 19 experimental structures for the ‘forward’ substitutions, and 352 experimental structures for each of the corresponding reverse variants.

Selection of ClinVar and gnomAD variants

The extraction of variants from ClinVar and gnomAD is based on Tiemann et al., 2023. Briefly, we extracted data from gnomAD using an in-house database (scripts available at: https://github.com/KULL-Centre/PRISM/tree/main/software/make_prism_files, Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, 2021 release-tag v0.1.1). This was constructed using exome data from gnomAD v2 and whole-genome data from gnomAD v3, selecting exome GRCh38 liftover for v2 and whole-genome files for v3, and annotated with Variant Effect Predictor with the GRCh38 release 100 human transcripts set from Ensembl. As previously described (Tiemann et al., 2023), we computed gnomAD allele frequencies for all protein variants from the extracted exome- and genome data combined (if present, otherwise solely for the exome- or genome allele frequencies). The reported allele frequencies are calculated as the sum of exome- and genome allele counts divided by the sum of total exome- and genome allele counts, and all DNA variants leading to the same protein-level variant were combined (Tiemann et al., 2023). Similarly, ClinVar data were extracted from an in-house database constructed using the NCBI data source: here (May 2021). For our analysis, only single nucleotide variants with a mapping to GRCh38 and with a rating of at least one star were used. We did not find any variants in gnomAD or ClinVar for 15 of the 1381 proteins that we calculated $Δ Δ G$ for; the data shown in Figure 5 thus refers only to 1366 proteins.

Selection of data for human proteome analysis

The structures used for the human proteome analysis were obtained from the AlphaFold2 database UP000005640_9606_HUMAN_v2 (Varadi et al., 2022; Figure 5—figure supplement 3). The structures in the database were pre-processed following the RaSP protocol using Reduce and PDBFixer, but were otherwise unmodified so that each protein is defined by a single UniProt sequence and a single predicted structure. For longer proteins the AlphaFold2-predicted structures have been split into fragments; for those proteins we also split our RaSP predictions into corresponding fragments.

Data and code availability

Scripts and data to repeat our analyses are available via: https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg/, where we also provide a link to run RaSP via resources at Google Colaboratory.

Acknowledgements

Our research is supported by the PRISM (Protein Interactions and Stability in Medicine and Genomics) centre funded by the Novo Nordisk Foundation (NNF18OC0033950 to A.S. and K.L.L.), and by grants from the Novo Nordisk Foundation (NNF20OC0062606 and NNF18OC0052719 to W.B.) and the Lundbeck Foundation (R272-2017-4528 to A.S.).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Wouter Boomsma, Email: wb@di.ku.dk.

Amelie Stein, Email: amelie.stein@bio.ku.dk.

Kresten Lindorff-Larsen, Email: lindorff@bio.ku.dk.

José D Faraldo-Gómez, National Heart, Lung and Blood Institute, United States.

Detlef Weigel, Max Planck Institute for Biology Tübingen, Germany.

Funding Information

This paper was supported by the following grants:

Novo Nordisk Fonden NNF18OC0033950 to Amelie Stein, Kresten Lindorff-Larsen.
Novo Nordisk Fonden NNF20OC0062606 to Wouter Boomsma.
Novo Nordisk Fonden NNF18OC0052719 to Wouter Boomsma.
Lundbeckfonden R272-2017-4528 to Amelie Stein.

Additional information

Competing interests

No competing interests declared.

Author contributions

Data curation, Software, Formal analysis, Investigation, Methodology, Writing - original draft, Writing – review and editing.

Software, Methodology, Writing – review and editing.

Data curation, Formal analysis, Investigation, Writing – review and editing.

Formal analysis, Investigation, Visualization, Writing – review and editing.

Data curation, Formal analysis, Investigation, Writing – review and editing.

Resources, Data curation, Formal analysis, Writing – review and editing.

Conceptualization, Resources, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing.

Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing.

Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing – review and editing.

Additional files

MDAR checklist

elife-82593-mdarchecklist1.docx^{(101KB, docx)}

Data availability

Scripts and data to repeat our analyses are available via: https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg/ (copy archived at Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, 2023). Data is available at http://doi.org/10.17894/ucph.7f82bee1-b6ed-4660-8616-96482372e736. A browsable version is available at https://sid.erda.dk/sharelink/fFPJWflLeE.

The following dataset was generated:

Blaabjerg LM, Kassem MM, Good LL, Jonsson N, Cagiada M, Johansson KE, Boomsma W, Stein A, Lindorff-Larsen K. 2022. Supporting data for Blaabjerg et al. ERDA.

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eLife. doi: 10.7554/eLife.82593.sa0

Editor's evaluation

José D Faraldo-Gómez ¹

This is a valuable study of broad potential impact in structural biology, which will interest readers in various fields, including molecular biomedicine, molecular evolution, and protein engineering.

eLife. doi: 10.7554/eLife.82593.sa1

Decision letter

Editor: José D Faraldo-Gómez¹

Reviewed by: Nir Ben-Tal², Julian Echave³

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Rapid protein stability prediction using deep learning representations" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by José Faraldo-Gómez as the Senior Editor. Two of the reviewers agreed to reveal their identity, namely Nir Ben-Tal (Reviewer #1) and Julian Echave (Reviewer #2).

As you will see below, the reviewers conclude the manuscript is not suitable for publication for eLife in its current form, but they make specific recommendations to resolve their concerns. I recognize some of these revisions are substantial and will require considerable effort – nevertheless, I encourage you to take the time required to address these concerns convincingly, and then submit a revised version.

Reviewer #1 (Recommendations for the authors):

(1) Authors should compare the results to existing alternatives. For example, here is a recent method, also based on deep learning: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008291. And there are many studies using 'simple' machine learning methods. The prediction quality here is similar to what these "shallow learning" methods give, albeit on other datasets, so perhaps irrelevant comparison. Anyway, all these methods show prediction quality that is very close to the natural upper bound given how noisy the data is (PMID: 30329016; a paper that I co-authored). So it is not clear whether the deep learning helped at all.

(2) Why was ROSETTA used as gold standard? Clearly ROSETTA is very far from perfect. In fact it's not even clear that it outperforms FoldX.

(3) Estimating ddG based on model structures: Now that AlphaFold models are available essentially to all proteins, it is possible to do much more than only 3 proteins. The authors should take advantage of this and examine the dependence of the ddG prediction quality on model RMSD to experimental structure, and more importantly, the dependence on AlphaFold's estimated model accuracy.

(4) Figure 4A. Both disease-causing and benign mutations appear to destabilize proteins. Authors claim that, on average, disease-causing mutations are more destabilizing. The calculated ddG values ~0.5 vs. ~1.4 kcal/mol are not that different. Is the difference really significant? More importantly, that the benign mutations appear to be destabilizing suggests that there might be systematic error towards destabilization. Could the authors test that? Do experimental ddG data show a similar behavior?

Reviewer #2 (Recommendations for the authors):

## Suggestions for improved or additional experiments data or analyses

1. More thorough assessment of accuracy against experiment, as compared not only with Rosetta, but also with other current methods. I think this work is much more likely to have an impact if a more thorough assessment of RaSP is made. In particular, I suggest that RaSP is more thoroughly tested against experimental mutational stability changes. In addition, to help position RaSP in the landscape of current methods, I suggest the RaSP-vs-experiment performance is compared not only to Rosetta, but also to other current methods. Such extra work does not necessarily mean that other methods need to be run. For instance, running RaSP on the same datasets used by recent assessments it would probably be possible to compare RaSP with other methods by getting the accuracy of other methods from the literature (see e.g. [https://doi.org/10.1093/bib/bbab555](https://doi.org/10.1093/bib/bbab555) for a recent thorough comparison of several methods).

2. Assess whether the method satisfies the antisymmetry condition. I think it would be important to complement Pearson's correlation and MAE with other measures of accuracy. Of particular importance is how well RaSP satisfies the thermodynamic condition that the reverse stability change satisfies ΔΔG(B->A) = – ΔΔG(A->B). Violation of this condition is a well-known issue with most ΔΔG-prediction methods (and experimental values!) and it would be very interesting to know whether RaSP suffers from this problem.

3. Compare the accuracy of the method for different sites. In the paper, there is an interesting analysis of the relative performance of RaSP for different types of amino-acid substitution. Another issue the authors might want to consider is how performance depends on whether sites are buried or exposed.

4. Compare the speed of the method to that of other methods. A strength of this work is that it presents a rapid method. From the present manuscript, it is clear that it is much more rapid than Rosetta. However, it is not clear how the speed of RaSP compares with other currently available methods. If the authors could perform such a comparison, at least for a limited dataset of proteins, I think this may go a long way towards making this work more influential.

## Recommendations for improving the writing and presentation

In general, I think the paper is very well written. However, some of what I perceived to be weaknesses could probably be dealt with by revising the manuscript.

When considering these recommendations, please take into account that my take on this work is that it is about a new rapid and accurate ΔΔG-prediction method. Therefore, I think everything in the manuscript should serve the purpose of bolstering this point. For instance, from this perspective, the calculations performed on the dataset of human proteins should be included only because it serves the purpose of demonstrating the usefulness of this approach, not because the obtained results are interesting in themselves. Therefore, I make a few recommendations that tend toward giving less weight to this application and more weight to the method and its assessment.

1. Make the application to the human dataset more clearly support the main point: the method. I think what's interesting about the large-scale application is not that common variants tend to be more stable, rare and/or disease variants unstable, etc, which we already know (you and others have demonstrated this already in previous work), but that it can be done by RaSP in a very short time because RaSP is fast. Also, the fact that it confirms previous findings shows that its accuracy is enough to draw these important conclusions. I think this is pretty clear from the paper. However, it can be made even more clear. For instance, almost half the abstract talks about the conclusions of the application, rather than the method itself. Also, it is not perfectly clear, from the Abstract, whether the method was developed as a mere tool to perform these calculations, or the calculations were performed to further assess and illustrate the method's usefulness. I think some minor carefully thought-out revisions in the abstract, results, and Discussion sections may make the method more of a protagonist and give the application, in the present paper, a supporting role.

2. Improve the method's description. From the perspective that the method is the point of the paper, I think the description may be much improved. Figure 1 may be much more detailed, for example. To understand the method I had to read the 2017 Paper by Boomsa and Frellsen, which is very nice. It would be great if some versions of Figure 3 and Figure 5 of that paper can be added to Figure 1. Also, I don't think it says anywhere in the present manuscript what sort of grid was used in the 3D-CNN part of the mode (which of the 3 grids used in the 2017 paper was used here?)

3. Highlight comparison with experiment. To support that the method is "accurate", comparison with experimental data is paramount, in my view. As I said before, I suggest you do a more thorough comparison. However, even if you decide not to, I suggest that you highlight the assessment that you have performed, by moving Table S1 and Figures S2 and S3 into the main document (If there are any limitations to the number of figures, I would rather get rid of Figure 4 than have Table S1 and Figures S2 and S3 as supplementary). Perhaps you could merge all data in Figures S2, Figures S3, and Table S1 into a single multi-panel figure that deals with all the comparison-with-experiment data.

4. Position the method in the landscape of current methods. As I said before, I think this work will be much more impactful if RaSP is positioned in the context of current methods. I suggested above that you do this by running some extra analyses and leveraging recent thorough method comparisons from the literature. If you decide not to do so, I think you could add a more detailed discussion of how you expect RaSP to compare with other methods regarding accuracy and computation speed.

5. Improve the Discussion. The Discussion, (and the Abstract) may be improved with little changes that make clearer that the point is the method, its accuracy, and speed and that the application is an example that supports that the method is useful, accurate, and fast. Also, depending on whether or not extra analyses are run, some discussion may be added to put this method in relation to other current methods.

Reviewer #3 (Recommendations for the authors):

[1] The main advantage of the method the authors claim is the speed. But they don't really take advantage of this. For example, I think the impact of this work would be much greater if they were to provide ddG predictions for all substitutions in all human proteins and to make these predictions available through a website and downloadable table. I don't see any reason not to do this, given it is not much work.

[2] The comparison to existing methods is rather anecdotal. How does the performance and run time compare to foldX? To MuteCompute? To other methods? How well do state of the art protein language models predict ddGS? How well do variant effect predictors such as CADD or Eve with good performance on the task of distinguishing pathological variants perform for predicting ddGs?

[3] How well does RASP perform for discriminating pathological from benign variants? The authors approach this problem but don't actually present the predictive performance nor do they compare it to other methods.

[4] The reality is that none of the methods for predicting ddGs perform particularly well and in places the text overstates the utility of computational methods. I would suggest the authors are more critical in their evaluation of the current state of the field. The real challenge – which is not addressed here – still remains, which is how to improve the predictive performance beyond the ok/reasonable but not that useful for many tasks predictions of Rosetta etc.

eLife. 2023 May 15;12:e82593. doi: 10.7554/eLife.82593.sa2

Author response

Reviewer #1 (Recommendations for the authors):

(1) Authors should compare the results to existing alternatives. For example, here is a recent method, also based on deep learning: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008291. And there are many studies using 'simple' machine learning methods. The prediction quality here is similar to what these "shallow learning" methods give, albeit on other datasets, so perhaps irrelevant comparison. Anyway, all these methods show prediction quality that is very close to the natural upper bound given how noisy the data is (PMID: 30329016; a paper that I co-authored). So it is not clear whether the deep learning helped at all.

We agree that the comparisons to other methods are important, but (as the reviewer knows well) also difficult to interpret due to issues outlined above. As suggested by reviewer 2, we have now used RaSP on a dataset that other methods have also been tested on [https://doi.org/10.1093/bib/bbab555]. This gives the reader the ability to evaluate RaSP in the context of other methods though we note (also in the revised manuscript) that differences in training/parametrization methods and data makes it difficult to compare. Indeed, we note also that while RaSP is not trained directly on experiments, it is also not fully independent because Rosetta is in itself based on experiments (including to some extent stability data).

(2) Why was ROSETTA used as gold standard? Clearly ROSETTA is very far from perfect. In fact it's not even clear that it outperforms FoldX.

A simple answer—as also outlined above—is that while Rosetta is indeed not perfect, it has worked well for us in a range of applications (as has FoldX). We suspect that using for example FoldX would have led to a similar model.

A slightly more complex explanation, which we now realize we did not present well in the original paper, is that the training on Rosetta is to some extent only a “refinement” and “re-scaling” of the structure-based convolutional neural network (CNN) that we use as input to the downstream neural network. This is important because the CNN is only trained on protein structures and has never seen experimental ∆∆G values. Indeed, a raw CNN similar to the one that we use achieves a relatively good correlation with experiments [https://dl.acm.org/doi/10.5555/3294996.3295102], as also seen before training of the model against Rosetta data [Figure 2 of our paper]. Training of the downstream neural network thus acts to fine-tune the information contained in the CNN and to bring predictions onto a common, thermodynamic scale.

We now explain these points in the revised paper.

(3) Estimating ddG based on model structures: Now that AlphaFold models are available essentially to all proteins, it is possible to do much more than only 3 proteins. The authors should take advantage of this and examine the dependence of the ddG prediction quality on model RMSD to experimental structure, and more importantly, the dependence on AlphaFold's estimated model accuracy.

Before answering this point in more detail, we first note (i) that we have recently tested the accuracy on Rosetta (and other stability prediction methods) on model quality using experimental data [https://doi.org/10.1101/2022.07.12.499700] with an analysis similar to that in Figure 3 of the paper and (ii) that stability calculations using Rosetta (and FoldX) using high-quality AlphaFold2 structures as input give as accurate results as using experimental structures [https://doi.org/10.1101/2021.09.26.461876]. Given these results (which suggest that high-quality predicted structures would work well as input to RaSP)—as well as the greater computational costs of running Rosetta on a large number of proteins—we initially decided only to validate on a smaller number of proteins.

Based on the reviewer’s comment, we have however now expanded this analysis. We note, however, that because estimated model accuracy (e.g. pLDDT) is correlated also to protein structure (e.g. disordered or mobile regions tend to have low pLDDT), it is not trivial to assess to what extent prediction accuracy depends on pLDDT and why. That is, low-pLDDT regions will often have small ∆∆G values, which might mean that the MSE would be small but correlations weak, simply because the dynamical range is different.

We have thus expanded the analyses we performed to six proteins, selecting again proteins for which at least two crystal structures exist to set a reasonable baseline to what a good prediction would look like. We now also break down the analysis depending on AlphaFold 2’s predicted accuracy (pLDDT score). The results show that ∆∆G predictions are more consistent (i.e. better correlated) in regions of the AlphaFold 2 structures that have high pLDDT compared to regions that have low pLDDT.

(4) Figure 4A. Both disease-causing and benign mutations appear to destabilize proteins. Authors claim that, on average, disease-causing mutations are more destabilizing. The calculated ddG values ~0.5 vs. ~1.4 kcal/mol are not that different. Is the difference really significant? More importantly, that the benign mutations appear to be destabilizing suggests that there might be systematic error towards destabilization. Could the authors test that? Do experimental ddG data show a similar behavior?

While the difference in the median is indeed only ca. 1 kcal/mol, this is highly statistically significant. For example, using bootstrapping we find that a lower bound of the difference in medians is 0.73 using a 95% confidence interval. These results have now been added to paper.

Concerning the point that many benign variants are (mildly) destabilizing, we note that mild (predicted) destabilization tends not to cause substantial loss of function in itself as assed both by multiplexed assays of variant effects

[https://doi.org/10.1016/j.celrep.2021.110207] or via analysis of pathogenicity in several proteins [https://doi.org/10.1371/journal.pgen.1006739 and https://doi.org/10.7554/eLife.49138 are two examples from our own research]. Thus, these results are not different from those using other stability predictors.

Regarding the point of whether there is a bias relative to experiments, this is difficult to answer because experimental data is sparse and noisy; specifically we do not know of any systematic analysis of measured ∆∆G values for a large range of pathogenic and benign variants. To examine this question, we instead calculated both the (signed) mean error and (unsigned) mean absolute error between experimental and calculated values for both Rosetta and RaSP using the data presented in Figure S2. The results show that RaSP does not appear to be biased relative to for example Rosetta.

Reviewer #2 (Recommendations for the authors):

## Suggestions for improved or additional experiments data or analyses

1. More thorough assessment of accuracy against experiment, as compared not only with Rosetta, but also with other current methods. I think this work is much more likely to have an impact if a more thorough assessment of RaSP is made. In particular, I suggest that RaSP is more thoroughly tested against experimental mutational stability changes. In addition, to help position RaSP in the landscape of current methods, I suggest the RaSP-vs-experiment performance is compared not only to Rosetta, but also to other current methods. Such extra work does not necessarily mean that other methods need to be run. For instance, running RaSP on the same datasets used by recent assessments it would probably be possible to compare RaSP with other methods by getting the accuracy of other methods from the literature (see e.g. [https://doi.org/10.1093/bib/bbab555](https://doi.org/10.1093/bib/bbab555) for a recent thorough comparison of several methods).

We thank the reviewer for this suggestion. We have now analysed the S669 dataset [from https://doi.org/10.1093/bib/bbab555] using RaSP, and report the results in the revised manuscript. These results enable us to compare RaSP to other methods.

2. Assess whether the method satisfies the antisymmetry condition. I think it would be important to complement Pearson's correlation and MAE with other measures of accuracy. Of particular importance is how well RaSP satisfies the thermodynamic condition that the reverse stability change satisfies ΔΔG(B->A) = – ΔΔG(A->B). Violation of this condition is a well-known issue with most ΔΔG-prediction methods (and experimental values!) and it would be very interesting to know whether RaSP suffers from this problem.

We have now also analysed RaSP on pairs of structures that differ by a single substitution including also the Ssym+ data set [from https://doi.org/10.1093/bib/bbab555], again noting the bias in that most original variants are deletion variants.

3. Compare the accuracy of the method for different sites. In the paper, there is an interesting analysis of the relative performance of RaSP for different types of amino-acid substitution. Another issue the authors might want to consider is how performance depends on whether sites are buried or exposed.

We have added an analysis on the accuracy (as assessed by agreement with Rosetta) depending on whether the sites are buried or exposed. Interpretation of the results is complicated by the fact that the ∆∆G range is very different in these two parts of proteins, and so we report both correlation coefficients and MAE values. We find that RaSP correlations with Rosetta are generally lower at buried residues compared those exposed on the surface – though this may also be due to the higher variance of the target Rosetta ∆∆G values in those regions.

4. Compare the speed of the method to that of other methods. A strength of this work is that it presents a rapid method. From the present manuscript, it is clear that it is much more rapid than Rosetta. However, it is not clear how the speed of RaSP compares with other currently available methods. If the authors could perform such a comparison, at least for a limited dataset of proteins, I think this may go a long way towards making this work more influential.

While we agree that this is an interesting question, we think that assessing the speed of a large number of other methods is outside the scope of our work. Such analyses are also complicated by the fact that many other methods are only available as webservices. Also, speed comparisons will depend on the chosen hardware since, for example, not all methods can exploit the parallel nature of GPUs. For these reasons, we have opted to examine three additional methods, namely the widely used FoldX, [https://doi.org/10.1093/nar/gki387] as well as two neural-network based methods ACDC-NN [https://doi.org/10.1088/13616463/abedfb] and ThermoNet [https://doi.org/10.1371/journal.pcbi.1008291]. The results are shown in the revised paper.

## Recommendations for improving the writing and presentation

In general, I think the paper is very well written. However, some of what I perceived to be weaknesses could probably be dealt with by revising the manuscript.

When considering these recommendations, please take into account that my take on this work is that it is about a new rapid and accurate ΔΔG-prediction method. Therefore, I think everything in the manuscript should serve the purpose of bolstering this point. For instance, from this perspective, the calculations performed on the dataset of human proteins should be included only because it serves the purpose of demonstrating the usefulness of this approach, not because the obtained results are interesting in themselves. Therefore, I make a few recommendations that tend toward giving less weight to this application and more weight to the method and its assessment.

1. Make the application to the human dataset more clearly support the main point: the method. I think what's interesting about the large-scale application is not that common variants tend to be more stable, rare and/or disease variants unstable, etc, which we already know (you and others have demonstrated this already in previous work), but that it can be done by RaSP in a very short time because RaSP is fast. Also, the fact that it confirms previous findings shows that its accuracy is enough to draw these important conclusions. I think this is pretty clear from the paper. However, it can be made even more clear. For instance, almost half the abstract talks about the conclusions of the application, rather than the method itself. Also, it is not perfectly clear, from the Abstract, whether the method was developed as a mere tool to perform these calculations, or the calculations were performed to further assess and illustrate the method's usefulness. I think some minor carefully thought-out revisions in the abstract, results, and Discussion sections may make the method more of a protagonist and give the application, in the present paper, a supporting role.

We agree that the paper is mostly about the method, realizing also that showing why we developed the method is an important point. We have attempted to update the manuscript with the suggestions by the reviewer in mind, and hopefully put the method itself in greater focus.

2. Improve the method's description. From the perspective that the method is the point of the paper, I think the description may be much improved. Figure 1 may be much more detailed, for example. To understand the method I had to read the 2017 Paper by Boomsa and Frellsen, which is very nice. It would be great if some versions of Figure 3 and Figure 5 of that paper can be added to Figure 1. Also, I don't think it says anywhere in the present manuscript what sort of grid was used in the 3D-CNN part of the mode (which of the 3 grids used in the 2017 paper was used here?)

We have expanded the description of the method, and also updated Figure 1 to give a better schematic description of the 3D CNN, while also keeping the context for the full model including training against Rosetta. The type of grid is now also explicitly specified.

3. Highlight comparison with experiment. To support that the method is "accurate", comparison with experimental data is paramount, in my view. As I said before, I suggest you do a more thorough comparison. However, even if you decide not to, I suggest that you highlight the assessment that you have performed, by moving Table S1 and Figures S2 and S3 into the main document (If there are any limitations to the number of figures, I would rather get rid of Figure 4 than have Table S1 and Figures S2 and S3 as supplementary). Perhaps you could merge all data in Figures S2, Figures S3, and Table S1 into a single multi-panel figure that deals with all the comparison-with-experiment data.

As described above, we have added a more extensive comparisons to experiments. The results previously shown in Figures S2 are now shown in the main text.

4. Position the method in the landscape of current methods. As I said before, I think this work will be much more impactful if RaSP is positioned in the context of current methods. I suggested above that you do this by running some extra analyses and leveraging recent thorough method comparisons from the literature. If you decide not to do so, I think you could add a more detailed discussion of how you expect RaSP to compare with other methods regarding accuracy and computation speed.

Using the results on S669 dataset as well as the speed assessments for additional methods, we now provide an expanded discussion of how we think RaSP can be used.

5. Improve the Discussion. The Discussion, (and the Abstract) may be improved with little changes that make clearer that the point is the method, its accuracy, and speed and that the application is an example that supports that the method is useful, accurate, and fast. Also, depending on whether or not extra analyses are run, some discussion may be added to put this method in relation to other current methods.

As discussed above, we have focused the paper more on the method and validation.

Reviewer #3 (Recommendations for the authors):

[1] The main advantage of the method the authors claim is the speed. But they don't really take advantage of this. For example, I think the impact of this work would be much greater if they were to provide ddG predictions for all substitutions in all human proteins and to make these predictions available through a website and downloadable table. I don't see any reason not to do this, given it is not much work.

We have now applied RaSP to all human protein structures as predicted using AlphaFold 2, and now make these available for download and show an overall histogram in the manuscript. We note, however, that we do not expect RaSP’s accuracy to uniform across all of these proteins; for example RaSP hasn’t been trained to predict stability changes in membrane proteins. Also, ∆∆G predictions for disordered proteins (in their free state) are poorly defined and AlphaFold is not expected to give reasonable models for such disordered regions (or other regions with low confidence scores). We have thus added caveats to the uses of this data in the text.

[2] The comparison to existing methods is rather anecdotal. How does the performance and run time compare to foldX? To MuteCompute? To other methods? How well do state of the art protein language models predict ddGS? How well do variant effect predictors such as CADD or Eve with good performance on the task of distinguishing pathological variants perform for predicting ddGs?

There are two slightly different points here. First, regarding how well RaSP compares to predict stability changes relative to other methods, we have (as described in our response to reviewer 2) run RaSP on a larger validation set, which enables comparison with other methods. We have also performed comparisons to run times of other methods. As for the question on using non-stability predictors to predict stability changes, we have not analysed this. While these methods might predict stability changes, their scores and goal are very different, and we do not find compelling reasons to attempt to apply them to this problem.

[3] How well does RASP perform for discriminating pathological from benign variants? The authors approach this problem but don't actually present the predictive performance nor do they compare it to other methods.

The goal of our ∆∆G predictions for pathogenic/benign variants was not to assess how well RaSP performs as a variant effect predictor on its own. Indeed, we and others have previously shown (on a much smaller set of proteins) that these methods work relatively well, but not quite as well as methods that are for example based on analysis of multiple sequence alignments. Indeed, while loss of abundance due to loss of stability is one very common mechanism for pathogenicity, there are many others (loss of intrinsic function or binding to other proteins); only the first category is captured by RaSP, whereas evolutionary-based methods and various meta-predictors capture all of these mechanisms. However, as we have previously shown, combining stability predictions with evolutionary assessments enables both accurate predictions and assessment of mechanism. Thus, RaSP is an important step in finding those variants that cause disease via loss of abundance. We now explain these issues better in the revised manuscript, but as suggested by Reviewer 2 we also focus less on the application and more on the method.

[4] The reality is that none of the methods for predicting ddGs perform particularly well and in places the text overstates the utility of computational methods. I would suggest the authors are more critical in their evaluation of the current state of the field. The real challenge – which is not addressed here – still remains, which is how to improve the predictive performance beyond the ok/reasonable but not that useful for many tasks predictions of Rosetta etc.

We agree that none of the available methods work as well as one might hope, though also note (in the revised manuscript and in the responses above) that limitations in the data also limits how well we can assess the methods.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

Blaabjerg LM, Kassem MM, Good LL, Jonsson N, Cagiada M, Johansson KE, Boomsma W, Stein A, Lindorff-Larsen K. 2022. Supporting data for Blaabjerg et al. ERDA. [DOI] [PMC free article] [PubMed]

Supplementary Materials

MDAR checklist

elife-82593-mdarchecklist1.docx^{(101KB, docx)}

Data Availability Statement

Scripts and data to repeat our analyses are available via: https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg/, where we also provide a link to run RaSP via resources at Google Colaboratory.

The following dataset was generated:

Blaabjerg LM, Kassem MM, Good LL, Jonsson N, Cagiada M, Johansson KE, Boomsma W, Stein A, Lindorff-Larsen K. 2022. Supporting data for Blaabjerg et al. ERDA.

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PERMALINK

Rapid protein stability prediction using deep learning representations

Lasse M Blaabjerg

Maher M Kassem

Lydia L Good

Nicolas Jonsson

Matteo Cagiada

Kristoffer E Johansson

Wouter Boomsma

Amelie Stein

Kresten Lindorff-Larsen

Roles

Abstract

Introduction

Results

Development of the RaSP model

Figure 1. Overview of model training.

Figure 2. Overview of RaSP downstream model training and testing.

Figure 2—figure supplement 1. Learning curve for the self-supervised 3D convolutional neural network.

Figure 2—figure supplement 2. Mean absolute prediction error for RaSP on the validation set, split by amino acid type of the wild-type and variant residue.

Figure 2—figure supplement 3. RaSP versus Rosetta Δ⁢Δ⁢G values for a full saturation mutagenesis of 10 test proteins separated into either exposed (A) or buried (B) residues.

Table 1. Overview of RaSP model test set prediction results including benchmark comparison with the Rosetta protocol.

Validating RaSP using experimental data

Figure 3. Comparing RaSP and Rosetta predictions to experimental stability measurements.

Figure 3—figure supplement 1. Comparing RaSP and Rosetta predictions to experimental stability measurements.

Figure 3—figure supplement 2. RaSP performance on three recently published data sets (Pancotti et al., 2022): (A) The S669 data set, (B) The Ssym+ direct data set, (C) The Ssym+ reverse data set.

Figure 3—figure supplement 3. RaSP performance on the recently published mega-scale experiments (Tsuboyama et al., 2022).

Figure 3—figure supplement 4. Benchmarking RaSP and Rosetta using VAMP-seq data.

Table 2. Benchmark performance of RaSP versus other structure-based methods on the S669 direct experimental data set (Pancotti et al., 2022).

Prediction of protein stability-change using computationally modelled structures

Figure 4. Stability predictions from structures created by template-based modelling.

Table 3. Comparing RaSP predictions from crystal and AlphaFold 2 (AF2) structures.

Large-scale calculations of stability-changes and analysis of disease-causing missense variants

Table 4. Run-time comparison of RaSP and four other methods for three test proteins ELOB (PDB: 4AJY_B, 107 residues), GCK (PBD: 4DCH_A, 434 residues) and F8 (PDB: 2R7E_A, 693 residues).

Figure 5. Large-scale analysis of disease-causing variants and variants observed in the population.

Figure 5—figure supplement 1. Histogram of Δ⁢Δ⁢G values from saturation mutagenesis using RaSP on 1,366 PDB structures corresponding to ∼8.8 million predicted Δ⁢Δ⁢G values.

Figure 5—figure supplement 2. Large-scale analysis of disease-causing variants and variants observed in the population using the RaSP model.

Figure 5—figure supplement 3. Histogram of Δ⁢Δ⁢G values from saturation mutagenesis using RaSP on predicted structures of the entire human proteome corresponding to ∼300 million predicted Δ⁢Δ⁢G values predicted from 23,391 protein structures.

Discussion

Methods

A convolutional neural network for protein structures

Downstream model architecture and training

Rosetta protocol

Calculation of SASA

Filtering of mega-scale data set

Speed test benchmarking

Selection of proteins and structures for large-scale analysis

Benchmarking on the S669 dataset

Selection of ClinVar and gnomAD variants

Selection of data for human proteome analysis

Data and code availability

Acknowledgements

Funding Statement

Contributor Information

Funding Information

Additional information

Competing interests

Author contributions

Additional files

Data availability

References

Editor's evaluation

José D Faraldo-Gómez

Roles

Decision letter

Roles

Author response

Associated Data

Data Citations

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Figure 2—figure supplement 3. RaSP versus Rosetta $Δ Δ G$ values for a full saturation mutagenesis of 10 test proteins separated into either exposed (A) or buried (B) residues.

Figure 5—figure supplement 1. Histogram of $Δ Δ G$ values from saturation mutagenesis using RaSP on 1,366 PDB structures corresponding to ∼8.8 million predicted $Δ Δ G$ values.

Figure 5—figure supplement 3. Histogram of $Δ Δ G$ values from saturation mutagenesis using RaSP on predicted structures of the entire human proteome corresponding to ∼300 million predicted $Δ Δ G$ values predicted from 23,391 protein structures.