Skip to main content
NCBI home page
ACS Medicinal Chemistry Letters logoLink to ACS Medicinal Chemistry Letters
. 2024 Jun 5;15(7):1017–1025. doi: 10.1021/acsmedchemlett.4c00047

Deciphering the Selectivity of CBL-B Inhibitors Using All-Atom Molecular Dynamics and Machine Learning

Feng Zhou †, Haolin Du †, Yang Wang †, Weiqiang Fu †, Bingchen Zhao †, Jielong Zhou †,*, Yingsheng J Zhang †,*
PMCID: PMC11247639  PMID: 39015275

Abstract

graphic file with name ml4c00047_0006.jpg

We employ a combination of accelerated molecular dynamics and machine learning to unravel how the dynamic characteristics of CBL-B and C–CBL confer their binding affinity and selectivity for ligands from subtle structural disparities within their binding pockets and dissociation pathways. Our predictive model of dissociation rate constants (koff) demonstrates a moderate correlation between predicted koff and experimental IC50 values, which is consistent with experimental koff and τ-random accelerated molecular dynamics (τRAMD) results. By employing a linear regression of dissociation trajectories, we identified key amino acids in binding pockets and along the dissociation paths responsible for activity and selectivity. These amino acids are statistically significant in achieving activity and selectivity and contribute to the primary structural discrepancies between CBL-B and C-CBL. Moreover, the binding free energies calculated from molecular mechanics with generalized Born and surface area solvation (MM/GBSA) highlight the ΔG difference between CBL-B and C-CBL. The koff prediction, together with the key amino acids, provides important guides for designing drugs with high selectivity.

Keywords: Selectivity, Molecular Dynamics (MD), τRAMD, Dissociation Rate Constant (koff), Interaction Fingerprints, Machine Learning (ML)

Casitas B-lineage lymphomas (Cbls) proteins are a group of RING (really interesting new gene) finger E3 ubiquitin ligases, which contain C-CBL, CBL-B and CBL-c.1−4 Multiple biological processes and cellular signal transduction are regulated by Cbls proteins.5 Immune response and protein metabolism are closely related by Cbls proteins through their diverse functions.6,7 In particular, CBL-B regulates chimeric antigen receptor (CAR) T cell activation and immune tolerance and has a higher activity selectively against T cell receptor complexes.8,9 In contrast, C-CBL exhibits higher substrate affinity toward signaling proteins, such as the epidermal growth factor receptor (EGFR).10,11 Ubiquitination of the Cbls is effectuated by the phosphorylation of Y363 of CBL-B and Y371 of C-CBL in the highly flexible linker helix region (LHR).12,13 The conformational changes of CBL-B and C-CBL are controlled by LHR and RING domains.14 The close or inactive states of CBL-B and C-CBL are unphosphorylated and adopt an autoinhibition conformation in which the RING and tyrosine kinase binding (TKB) domain are in close contact with each other.12,15,16 Upon binding to target substrates, the RING domain brings E2 into close contact with the target substrate. Meanwhile, the phosphorylation sites of CBL-B and C-CBL are exposed to corresponding kinases, and the resulting phosphorylation induces a large conformational change.12,13,17

CAR T cell therapies are vital in cancer treatment.7,17 CBL-B as a major negative regulator of T cells is beneficial to corresponding therapies when its gene is knocked down, which has been shown in many experiments.5,14 Therefore, CBL-B is potentially a significant therapeutic target in cancer immunotherapies. One study showed that inhibition of CBL-B by a small molecule inhibitor enhanced antitumor immunity and improved the efficacy of immune checkpoint blockade therapy in a melanoma model.18 The precise molecular mechanisms of Cbls protein functions are still not fully understood. Several studies hypothesized that selective CBL-B inhibition over C-CBL is the key in solid tumor treatment.19,20 However, high similarities in the sequence and structure of CBL-B and C-CBL make the design of selective CBL-B inhibitors a daunting challenge.7,8,17,21 CBL-B and C-CBL share high sequence homology in tyrosine kinase binding (TKB), LHR, and RING domain.12−15,17,22 A study identified a few amino acids difference in the linker region between the N-terminal TKB domain and the C-terminal RING finger domain with 86% amino acid identity of CBL-B and C-CBL, respectively.16,17 Therefore, how to exploit the little structural and sequence differences and their subtle dynamic and kinetic differences will be crucial to achieve selectivity. The binding affinities (Kd values) of known CBL-B inhibitors range from low-micromolar to nanomolar depending on assay conditions.23 There are a few koff values (dissociation rates) of CBL-B inhibitors in the literature, which are key kinetic properties of inhibitors.24,25 Exploiting the structural difference, coupled with kinetics via their different dissociation behavior and their unique interaction along the dissociation pathways in CBL-B drug design, has not been well elaborated.

In this paper, we combine τ-random accelerated molecular dynamics (τRAMD26,27), koff prediction model,28 and machine learning (ML) to decipher the subtle structural differences around the binding pocket and the key amino acids for the activity and selectivity of CBL-B and C-CBL.

The 3D structure of bound CBL-B in its inactive state is shown in Figure 1A. It is evident that CBL-B and C-CBL share the same amino acids in their binding sites. However, the spatial distribution of amino acids outside of the binding pockets is not identical, as shown in Figure 1B. Even within the pocket, because of influence of the distal amino acids, residues, like GLU268, PHE263, TYR260, TYR363, etc., and the ligand are not exactly overlapped even though they have the same orientation for CBL-B (PDB ID: 8GCY) and C-CBL (PDB ID: 2Y1M(29)). The original residue index in C-CBL was shifted by eight to be consistent with the sequence of CBL-B (Figure S1). We listed the functional groups and corresponding amino acids within 5 Å of the pocket for CBL-B and C-CBL, respectively, in Table 1 and Figure 1C, which were determined mainly through experiments using the cocrystal structures of the template and MD simulations for other ligands (details are shown below). It is clear that the amino acids outside of the pockets do influence binding kinetics, and the ones along the dissociation pathway have a significant impact on koff, which in turn contributes to selectivity and binding affinity toward CBL-B and C-CBL.

Figure 1.

Figure 1

Open in a new tab

Cartoon representation of CBL-B protein bound with ligand with the important amino acids in the pocket shown in licorice mode (PDB ID: 8GCY). (A) 3D structure of CBL-B protein; the amino acids different from C-CBL (PDB ID: 2Y1M) are shown in blue. (B) The cartoon representation of the pocket for CBL-B (red) and C-CBL (blue) (two proteins were superimposed on Cα of the pocket). The key amino acids are shown in licorice mode with green carbon for CBL-B and blue carbon for C-CBL. (C) The 2D kekule structure of the ligand in 8GCY with the surrounding amino acids within 5 Å from the ligand. The amino acids involved in both CBL-B and C-CBL are shown in black, and the ones involved in CBL-B or C-CBL are shown in red and blue, respectively.

Table 1. Major Protein–Ligand Interactions and the Corresponding Amino Acids and the Functional Groups As Labeled in Figure 1.

amino acids in CBL-B amino acids in C-CBL functional groups interaction type
PRO71   Z hydrophobic (HY)
PRO72 PRO74 Z hydrophobic (HY)
ARG141 ARG141 X hydrophobic (HY)
THR144 THR144 X, Z hydrophobic (HY)
LYS145 LYS145 X, Y2 hydrophobic (HY)
LEU148 LEU148 X, Y2, Z hydrophobic (HY)
ILE149 ILE149 R3 hydrophobic (HY)
HIS152 HIS152 R3 aromatic (AR)
THR219 THR219 R3, L hydrophobic (HY)
LEU222 LEU222 R3, L hydrophobic (HY)
TYR260 TYR260 Q hydrogen bond donor (HD)
PHE263 PHE263 Y4, Y5, Z hydrogen bond donor (HD)
hydrogen bond acceptor (HA)
aromatic (AR)
LEU264 LEU264 Z hydrophobic (HY)
GLU268 GLU268 Z hydrogen bond acceptor (HA)
LEU287 LEU287 L positively ionic salt bridges (IP)
TYR363 TYR363 L, Q hydrogen bond donor (HD)
aromatic (AR)
hydrophobic (HY)
MET366 MET366 Y2 hydrogen bond acceptor (HA)
hydrophobic (HY)
Open in a new tab

To further analyze the structural and dynamic differences between CBL-B and C-CBL in detail, we performed full-atom molecular dynamics (MD) analyses for both CBL-B and C-CBL complexes. For CBL-B, we used its cocrystal structure (PDB ID: 8GCY) as the initial structure, while for C-CBL, by searching the PDB database, we only found two unbound structures for the inactive state (PDB ID: 2Y1N and 2Y1M).29 The key difference between 2Y1N and 2Y1M is that 2Y1N has a narrower pocket than 2Y1M characterized by the shorter distance between PHE263 and GLY367. On the basis of the structural features, 2Y1M is more holo-like and 2Y1N is apo-like. To further prove 2Y1M is holo-like and suitable for ligand binding, we explored the phase space of C-CBL using 2Y1M as the initial structure using the replica exchange molecular dynamics (REMD). In 16 × 4 μs REMD, there are two well separated states for C-CBL, as shown in the Supporting Information (Figure S2). By clustering and superimposing, the first state (clusters 1 and 3) has a longer PHE263-GLY367 distance and matches well with 2Y1M and 8GCY and is holo-like. The second state (clusters 2 and 4) has a shorter PHE263-GLY367 distance and matches well with the apo-like 2Y1N. The fact that C-CBL without a bound ligand can have two stable states has also been validated by crystallography experiments (2Y1N and 2Y1M). Compared with 2Y1N, 2Y1M is more holo-like and aligns well with the holo state of 8GCY. We used 2Y1M as template and modeled the ligand from 8GCY to build the initial structure for the C-CBL complex. Three 100 ns production runs were performed for the bound CBL-B and C-CBL complex, respectively (simulation details are in the Supporting Information.). The root mean square deviation (RMSD), which indicated the stabilization of the protein–ligand complex, is plotted in Figure S3.

By analyzing the trajectories of CBL-B and C-CBL, we identified remarkable orientation discrepancy of three amino acids, LYS145, GLU268 and PHE263, in spatial distribution in their binding pockets. We chose another three residues that are close to and form interactions with these three residues. The distance between each residue–residue pair was statistically analyzed to show the structural difference between CBL-B and C-CBL. First, LYS145 has an opposite orientation for CBL-B and C-CBL. The longer LYS145-LEU222 distance indicates an “out” conformation of LYS145, while the shorter one indicates an “in” conformation. From Figure 2B, CBL-B has more “out” conformation of LYS145 than C-CBL has. The LYS145 in “out” will interact with the group X (−CF3 in Figure 1) of the ligand, and that in “in” will not. Second, GLU268, which interacts with functional group Z of the ligand, also has a unique dynamic behavior for CBL-B and C-CBL and leads to different binding affinity of the ligand. As shown in Figure 2D, the negatively charged GLU268 has more probability to interact with positively charged ARG272 for C-CBL than for CBL-B because of the distal amino acid, TYR276 for CBL-B and PHE276 for C-CBL. TYR276 forms a stronger interaction with ARG272 than PHE276 does, thus weakening the interaction between ARG272 and GLU268 and resulting in a stronger and more stable interaction with the ligand at group Z. Third, as shown in Figure 2F, PHE263 has a stronger interaction with the deeply buried LEU222 for CBL-B than for C-CBL, which will influence the selectivity of the ligands for CBL-B over C-CBL. LYS145 and PHE263 were also proven important by Yang et al. in their study in the allosteric pathways of C-CBL.30

Figure 2.

Figure 2

Open in a new tab

Cartoon representation of the local structure of the pocket for (A) LYS145, (C) GLU268, and (E) PHE263 for both CBL-B and C-CBL. The red color in the ribbon shows the same amino acids for CBL-B and C-CBL, and the blue color shows the different amino acids for CBL-B and C-CBL. The key amino acids are shown in licorice mode with green-colored carbon for CBL-B and blue-colored carbon for C-CBL. The ligand is shown in black color for carbon. The distribution of the (B) LYS145-LEU222 distances, (D) GLU268-ARG272 distances, and (F) PHE263-LEU222 distances.

We have discussed the different structural and dynamic behaviors between CBL-B and C-CBL in the previous section observed from the equilibrium dynamics. To further study their unique binding and unbinding kinetics, we performed τRAMD to track the ligand dissociation and the trajectories from which both the bound states and the dissociation pathways can be analyzed simultaneously. We assembled a list of 44 CBL-B inhibitors with a wide range of activity and selectivity from patents of Nurix,24,31,32 Hotspots,25,33,34 Genentech,35,36 Nimbus,37,38 and Xiansheng.39,40 The SMILES strings and functional groups of the list are included in Supporting Information (Tables S1 and S2). The structures were prepared using RDKit41 and docked into the binding pocket using LEDOCK.42 Ensemble docking procedure was applied to ensure the correct docking poses by comparing top-ranked docking poses with the cocrystal structure. The interaction fingerprints (IFPs) along the dissociation pathway were retrieved from 90 τRAMD trajectories for each ligand, and machine learning (ML) was used to determine the key amino acids that influence activity or selectivity. The activity is defined by IC50, i.e., half-maximal inhibitory concentration (nM) in experiment, ranges from 5 to 12 000 nM for the 44 inhibitors. The selectivity is defined as the ratio of the CBL-B activity (IC50) of the compound against the off-target protein, C-CBL. Considering the small size of data, we divided the 44 data into two categories, i.e., less activity/selectivity (labeled 0) or higher activity/selectivity (labeled 1) and used the leave-one-out cross-validation (LOOCV)43 to evaluate the model performance. For the two sets of data created, we performed the fitting of logistic regression (LR)44 on the basis of LOOCV and sorted and derived the weights corresponding to the more important features (Figure 3B). The obtained coefficients are used to explain the contribution of each feature to the results, and they are highly explanatory. This LR model can decipher features playing key roles in classification. The detailed protocols of ensemble docking, pose selection, and IFP generation are included in the Supporting Information, and the complete IFPs data can be downloaded from the Supporting Information.

Figure 3.

Figure 3

Open in a new tab

Key amino acids in the binding pocket and their importance in activity and selectivity. (A) The position of the amino acids (carbon in green) in the pocket. The carbons of the reference ligand (Nimbus-1) are colored black. (B) The LR coefficient of top-ranked amino acids has the most impact on the activity (red color) or selectivity (blue color).

From Figure 3, we identified key IFPs to the activity or selectivity. These IFPs, together with the corresponding amino acids and functional groups, can be found in Table 1. Interestingly, the hydrogen bond donor of PHE263 (HD_PHE263) has a positive impact on activity but a negative impact on selectivity. One ligand from Nimbus,38 in which the carbonyl group at Y4 is a nitrogen instead of carbonyl to weaken the hydrogen bond of PHE263, increases the selectivity at the price of losing some activity. Amino acids from 71 to 74 are different in CBL-B and C-CBL, especially in PRO71 and PRO72. Though far away from the binding pockets, they have positive impacts on the activity and selectivity, respectively, through dynamic perturbation.

After discussing the impact of sequence and structure differences of CBL-B and C-CBL to activity and selectivity in view of the equilibrium and nonequilibrium dynamics, we shift our focus to the contribution of binding free energy (ΔG) and kinetic dissociation rate (koff) to their activity and selectivity. Applying our ML koff prediction model,28 we predicted the koff values of 44 CBL-B inhibitors. The inputs of the koff prediction is a set of molecular representation features extracted from our previously published pretraining model (G2GT)45 based on retrosynthetic reaction data. The main merit of these features is their quantum and physical molecular information derived from these data. We have proven in our previous paper that the predicted koff has a high correlation with the experimental koff value and can be used to guide drug design.28 For the CBL-B inhibitors, the predicted koff values are highly correlated (Pearson correlation coefficient rP is 0.71) with the simulated koff values from τRAMD and moderately correlated with the experimental pIC50 (negative log of the IC50; Pearson correlation coefficient rP is 0.48) (Figure S4). To further validate our results, we compared the predicted koff with the experimental data from Nurix24 and Hotspots.25 The experimental retention time (RT = 1/koff) is ∼90–180 s for the Nurix series and 104 s for the Hotspots series. Our predicted RT is in the range of ∼40–315 s for the Nurix series and ∼0.6–1.2 × 104 s for the Hotspots series, which match well with these experimental data. It should be noted that koff is a kinetic property and cannot be treated as the sole factor for the activity. However, koff has a tight correlation with activity by the equation Kd = koff/kon. Therefore, the predicted koff can be used to evaluate the activity. Figure 3 shows the relationship of koff and the dissociation pathway with the selectivity by applying IFP analysis derived from the dissociation trajectories from CBL-B inhibitors. To elaborate how the koff influences the selectivity, we show the IFP composition along the dissociation pathway of a CBL-B inhibitor from Hotspots Therapeutics46 in Figure 4. The inhibitor gives both high activity (IC50 = 6 nM) and selectivity (10-fold selectivity) compared with other inhibitors.

Figure 4.

Figure 4

Open in a new tab

A representative visualization of IFP composition along the dissociation pathway of the Hotspot ligand of CBL-B and C-CBL. Cluster 1 is the bound state, and Cluster 8 is the unbound state. The key amino acids that make the difference for CBL-B and C-CBL are indicated by red arrow.

From Figure 4, we identified several impactful amino acids along the dissociation pathway. For CBL-B, most IFPs (except PRO71 and PRO72, which are at the entrance) along the dissociation pathway overlap with the bound state. For example, HY_MET366 has strong interaction with the ligand for CBL-B until the full dissociation, while for C-CBL, it is instead HY_THR369, and the interaction is much weaker. Both HA_GLU268 and IP_GLU268 contribute to the binding and increase the RT of ligand in CBL-B but not in C-CBL. At the entrance of the binding pocket, PRO72 forms a hydrophobic interaction with the ligand in CBL-B, and in C-CBL, this interaction comes from PRO74 and is weaker. All these effects lead to a lower energy basin of the bound state and longer RT for ligands in CBL-B than in C-CBL. The simulated −log(koff) is 2.33 for CBL-B and 1.78 for C-CBL. Nimbus-1 is another ligand showing high selectivity; its IFPs along the dissociation pathway are given in the Supporting Information (Figure S5). As a negative control, we also added the IFP composition along the dissociation pathway for ligand Genentech-21, which has only onefold selectivity between CBL-B and C-CBL, as shown in Figure 5.

Figure 5.

Figure 5

Open in a new tab

A representative visualization of IFP composition along the dissociation pathway of Genentech-21 of CBL-B and C-CBL. Cluster 1 is the bound state, and Cluster 8 is the unbound state. The key amino acids that make the difference for CBL-B and C-CBL are indicated by red arrow.

In Figure 5, there are eight differentiable IFPs between CBL-B and C-CBL. In CBL-B, there are HY_THR265, HY_TYR363, HY_MET366, and HA_MET366, which are absent in C-CBL. In C-CBL, there are HY_PRO74, AR_TYR260, HA_GLY367, and HY_THR369, which are absent in CBL-B. Contrary to inhibitors with high selectivity, the IFP composition for Genentech-21 does not show overwhelming advantage of CBL-B over C-CBL. Each ligand has a different pattern of dissociation IFPs, and we will not detail the IFPs analyses for all the 44 inhibitors studied in this paper. We applied a ML model to find the important interactions from abundant trajectories that affect the activity or selectivity, as shown in Figure 3.

Furthermore, we calculated the binding free energy via molecular mechanics with generalized Born and surface area solvation (MM/GBSA) method47 using gmx_PBSA scripts48 for high selectivity inhibitors, as shown in Table 2. Table 2 lists the ligands with high selectivity and in a wide range of binding affinities and dissociation rates between CBL-B and C-CBL. It is evident that these highly selective inhibitors differ significantly in their calculated ΔG for CBL-B and C-CBL. The ΔΔG between CBL-B and C-CBL is in the range of 2–9 kcal/mol and is consistent with the respective simulated RTs. These results demonstrate that the kinetic properties can be the differentiator of selectivity and binding affinity for CBL-B and C-CBL. The correlation between predicted koff and the experimental pIC50 for all the 44 inhibitors can be found in the Supporting Information (Figure S4).

Table 2. Simulated Binding Free Energy Difference ΔΔG(CBL-B–C-CBL) and RT Difference RTCBL-B/RTC-CBL Together with the Experimental IC50 and Selectivity between CBL-B and C-CBL for Nine Inhibitors.

compound IC50 for CBL-B (nM) IC50 for C-CBL (nM) selectivity simulated ΔΔG(CBL-B–C-CBL) (kcal/mol) simulated RTCBL-B/RTC-CBL
NRX-1 ∼5 ∼20 ∼4 5.61 1.5
Hotspot-1 ∼6 ∼60 ∼10 7.22 2.9
Xiansheng-1 ∼16.5 ∼165 ∼10 2.70 1.6
Genentech-1 ∼25 ∼150 ∼6 4.68 1.8
Genentech-2 ∼21 ∼120 ∼6 2.94 1.7
Genentech-3 ∼170 ∼1100 ∼6.5 2.08 2.0
Nimbus-1 ∼200 ∼10 000 ∼50 5.27 2.4
Nimbus-2 ∼200 ∼10 000 ∼50 5.17 1.9
Nimbus-3 ∼200 ∼10 000 ∼50 7.61 2.3
Open in a new tab

This work focuses on the challenging drug design tasks to achieve selectivity among highly similar homologues and other structures sharing high similarity in their binding pockets. We integrated accelerated MD and ML techniques to elucidate the distinct structural and kinetic attributes of CBL-B and C-CBL with their binding modes and selectivity against diverse chemical structures. These variances in activity and selectivity stem from nuanced disparities within the binding pocket’s structure influenced by distal residues outside of the pocket through kinetic and dynamic perturbation via binding and the trajectory of dissociation. Our koff predictive model exhibits a moderate correlation with experimental IC50 values and closely aligns with experimental koff data sets. MM/GBSA binding free energy calculation reveals the discernible ΔG variation between CBL-B and C-CBL even though they share identical amino acids in their binding pockets. Key amino acids responsible for activity and selectivity along the dissociation paths are identified through a regression strategy, which corresponds to the principal structural divergence between CBL-B and C-CBL. This approach and the results demonstrated the potential and benefit of including both dynamic and kinetic properties into drug design.

Experimental Procedures

The MD and τRAMD were performed using the Gromacs software.49 Logistic regression (LR) was used in fitting the data in Python’s scikit-learn library. Methodological details are given in the Supporting Information.

No unexpected or unusually high safety hazards were encountered.

Acknowledgments

This study was funded by the National Key R&D Program of China (grant number 2022YFF1203004). This study was also funded by Guangzhou Laboratory (grant number SRPG22-011).

Glossary

Abbreviations

ML

machine learning

MD

molecular dynamics

τRAMD

τ-random accelerated molecular dynamics

REMD

replica exchange molecular dynamics

MM/GBSA

molecular mechanics with generalized Born and surface area solvation

IFPs

protein–ligand interaction fingerprints

RT

retention time

Cbls

Casitas B-lineage lymphomas

Kd

dissociation constant

koff

dissociation rate constant

IC50

half-maximal inhibitory concentration

LR

logistic regression

LOO

leave-one-out

RMSD

root mean square deviation

PDB

Protein Data Bank

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsmedchemlett.4c00047.

  • Methods, sequence of amino acids of CBL-B and C-CBL, root mean square deviation (RMSD) of protein–ligand complex MD runs, REMD results of C-CBL, and predicted versus simulated −log(koff) and experimental pIC50 for the 44 inhibitors of CBL-B (PDF)

  • Data files for all IFPs in CSV file format for CBL-B and C-CBL (ZIP)

Author Contributions

The manuscript was written through the contributions of all authors. All authors have approved the final version of the manuscript.

The authors declare no competing financial interest.

Supplementary Material

ml4c00047_si_001.pdf (781.1KB, pdf)
ml4c00047_si_004.zip (38.1MB, zip)

References

  1. Swaminathan G.; Tsygankov A. Y. The Cbl Family Proteins: Ring Leaders in Regulation of Cell Signaling. J. Cell. Physiol. 2006, 209, 21–43. 10.1002/jcp.20694. [DOI] [PubMed] [Google Scholar]
  2. Deshaies R. J.; Joazeiro C. A. P. RING Domain E3 Ubiquitin Ligases. Annu. Rev. Biochem. 2009, 78, 399–434. 10.1146/annurev.biochem.78.101807.093809. [DOI] [PubMed] [Google Scholar]
  3. Joazeiro C. A. P.; Wing S. S.; Huang H. K.; Leverson J. D.; Hunter T.; Liu Y. C. The Tyrosine Kinase Negative Regulator C-Cbl as a RING-Type, E2- Dependent Ubiquitin-Protein Ligase. Science (1979) 1999, 286 (5438), 309–312. 10.1126/science.286.5438.309. [DOI] [PubMed] [Google Scholar]
  4. Shao Y.; Yang C.; Elly C.; Liu Y. C. Differential Regulation of the B Cell Receptor-Mediated Signaling by the E3 Ubiquitin Ligase Cbl. J. Biol. Chem. 2004, 279 (42), 43646–43653. 10.1074/jbc.M404082200. [DOI] [PubMed] [Google Scholar]
  5. Duda D. M.; Scott D. C.; Calabrese M. F.; Zimmerman E. S.; Zheng N.; Schulman B. A. Structural Regulation of Cullin-RING Ubiquitin Ligase Complexes. Current Opinion in Structural Biology 2011, 21, 257–264. 10.1016/j.sbi.2011.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Levkowitz G.; Waterman H.; Ettenberg S. A; Katz M.; Tsygankov A. Y; Alroy I.; Lavi S.; Iwai K.; Reiss Y.; Ciechanover A.; Lipkowitz S.; Yarden Y. Ubiquitin Ligase Activity and Tyrosine Phosphorylation Underlie Suppression of Growth Factor Signaling by C-Cbl/Sli-1. Mol. Cell. 1999, 4, 1029–1040. 10.1016/S1097-2765(00)80231-2. [DOI] [PubMed] [Google Scholar]
  7. Kales S. C.; Ryan P. E.; Nau M. M.; Lipkowitz S. Cbl and Human Myeloid Neoplasms: The Cbl Oncogene Comes of Age. Cancer Res. 2010, 70, 4789–4794. 10.1158/0008-5472.CAN-10-0610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chiang Y. J.; Kole H. K.; Brown K.; Naramura M.; Fukuhara S.; Hu R.-J.; Jang I. K.; Gutkind J. S.; Shevach E.; Gu H. Cbl-b Regulates the CD28 Dependence of T-Cell Activation. Nature 2000, 403 (6766), 216–220. 10.1038/35003235. [DOI] [PubMed] [Google Scholar]
  9. Qiao G.; Li Z.; Molinero L.; Alegre M.-L.; Ying H.; Sun Z.; Penninger J. M.; Zhang J. T-Cell Receptor-Induced NF-ΚB Activation Is Negatively Regulated by E3 Ubiquitin Ligase Cbl-b. Mol. Cell. Biol. 2008, 28 (7), 2470–2480. 10.1128/MCB.01505-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Thien C. B. F.; Walker F.; Langdon W. Y. RING Finger Mutations That Abolish C-Cbl-Directed Polyubiquitination and Downregulation of the EGF Receptor Are Insufficient for Cell Transformation. Mol. Cell. 2001, 7, 355–365. 10.1016/S1097-2765(01)00183-6. [DOI] [PubMed] [Google Scholar]
  11. Mueller D. L. E3 Ubiquitin Ligases as T Cell Anergy Factors. Nat. Immunol. 2004, 5, 883–890. 10.1038/ni1106. [DOI] [PubMed] [Google Scholar]
  12. Kassenbrock C. K.; Anderson S. M. Regulation of Ubiquitin Protein Ligase Activity in C-Cbl by Phosphorylation-Induced Conformational Change and Constitutive Activation by Tyrosine to Glutamate Point Mutations. J. Biol. Chem. 2004, 279 (27), 28017–28027. 10.1074/jbc.M404114200. [DOI] [PubMed] [Google Scholar]
  13. Yang T.; Han L.; Huo S. Dynamics and Allosteric Information Pathways of Unphosphorylated C-Cbl. J. Chem. Inf Model 2022, 62 (23), 6148–6159. 10.1021/acs.jcim.2c01022. [DOI] [PubMed] [Google Scholar]
  14. Dou H.; Buetow L.; Hock A.; Sibbet G. J.; Vousden K. H.; Huang D. T. Structural Basis for Autoinhibition and Phosphorylation-Dependent Activation of c-Cbl. Nat. Struct Mol. Biol. 2012, 19 (2), 184–192. 10.1038/nsmb.2231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dou H.; Buetow L.; Hock A.; Sibbet G. J.; Vousden K. H.; Huang D. T. Structural Basis for Autoinhibition and Phosphorylation-Dependent Activation of c-Cbl. Nat. Struct Mol. Biol. 2012, 19 (2), 184–192. 10.1038/nsmb.2231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Peschard P.; Kozlov G.; Lin T.; Mirza I. A.; Berghuis A. M.; Lipkowitz S.; Park M.; Gehring K. Structural Basis for Ubiquitin-Mediated Dimerization and Activation of the Ubiquitin Protein Ligase Cbl-b. Mol. Cell 2007, 27 (3), 474–485. 10.1016/j.molcel.2007.06.023. [DOI] [PubMed] [Google Scholar]
  17. Kobashigawa Y.; Tomitaka A.; Kumeta H.; Noda N. N.; Yamaguchi M.; Inagaki F. Autoinhibition and Phosphorylation-Induced Activation Mechanisms of Human Cancer and Autoimmune Disease-Related E3 Protein Cbl-b. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 20579–20584. 10.1073/pnas.1110712108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gavali S.; Liu J.; Li X.; Paolino M. Ubiquitination in T-Cell Activation and Checkpoint Inhibition: New Avenues for Targeted Cancer Immunotherapy. Int. J. Mol. Sci. 2021, 22, 10800. 10.3390/ijms221910800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Zhou F.; Li Z.; Liu Y.; Yang G.; Mai W.; Yang W.; Xue L.; Chen P.; Tang R. Abstract 3474: Identification of the selective CBL-b inhibitors which effectively prevent T cells from exhaustion and demonstrate synergistic anti-tumor activity in combination with an anti-PD1 antibody. Cancer Res. 2023, 83, 3474. 10.1158/1538-7445.AM2023-3474. [DOI] [Google Scholar]
  20. Thien C. B. F.; Langdon W. Y. C-Cbl and Cbl-b Ubiquitin Ligases: Substrate Diversity and the Negative Regulation of Signalling Responses. Biochem. J. 2005, 391, 153–166. 10.1042/BJ20050892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Blaquiere N.; Villemure E.; Staben S. T. Medicinal Chemistry of Inhibiting RING-Type E3 Ubiquitin Ligases. J. Med. Chem. 2020, 63, 7957–7985. 10.1021/acs.jmedchem.9b01451. [DOI] [PubMed] [Google Scholar]
  22. Buetow L.; Tria G.; Ahmed S. F.; Hock A.; Dou H.; Sibbet G. J.; Svergun D. I.; Huang D. T. Casitas B-Lineage Lymphoma Linker Helix Mutations Found in Myeloproliferative Neoplasms Affect Conformation. BMC Biol. 2016, 14 (1), 76. 10.1186/s12915-016-0298-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Zhou L.; Yang J.; Zhang K.; Wang T.; Jiang S.; Zhang X. Rising Star in Immunotherapy: Development and Therapeutic Potential of Small-Molecule Inhibitors Targeting Casitas B Cell Lymphoma-b (Cbl-b). J. Med. Chem. 2024, 67, 816. 10.1021/acs.jmedchem.3c01361. [DOI] [PubMed] [Google Scholar]
  24. Sands A. T.; Bence N. F.; Zapf C. W.; Cohen F.; Wang C.; Cummins T.; Tanaka H.; Lawrenz M.; Cardozo M.; Weiss D.; Goslin J.. Urea, Amide, and Substituted Heterroaryl Compounds for CBL-B Inhibition. WO 2021021761 A1, 2021.
  25. Kuai J.; Bi Y.; Qi Y.; Conrady D.; Govindaraj R.; Hone G.; Aldrin Denny R.; Carson K.; Harriman G.; Wang F.. Identification of A Novel Allosteric Oral CBL-B Inhibitor that Augmented T Cell Response and Enhanced NK Cell Killing in vitro and in vivo. Society for Immunotherapy of Cancer (SITC) 36th Annual Meeting, Washington, DC, November 10–14, 2021. HotSpot Therapeutics, Inc. website, https://www.hotspotthera.com/wp-content/uploads/2023/01/POSTER-HotSpot-Therapeutics_SITC_2021.pdf (accessed 2024-06-03).
  26. Kokh D. B.; Amaral M.; Bomke J.; Grädler U.; Musil D.; Buchstaller H. P.; Dreyer M. K.; Frech M.; Lowinski M.; Vallee F.; Bianciotto M.; Rak A.; Wade R. C. Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations. J. Chem. Theory Comput 2018, 14 (7), 3859–3869. 10.1021/acs.jctc.8b00230. [DOI] [PubMed] [Google Scholar]
  27. Kokh D. B.; Doser B.; Richter S.; Ormersbach F.; Cheng X.; Wade R. C. A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprint Analysis of Ligand Trajectories. J. Chem. Phys. 2020, 153 (12), 125102. 10.1063/5.0019088. [DOI] [PubMed] [Google Scholar]
  28. Zhou F.; Yin S.; Xiao Y.; Lin Z.; Fu W.; Zhang Y. J. Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation. ACS Omega 2023, 8 (20), 18312–18322. 10.1021/acsomega.3c02294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Dou H.; Buetow L.; Hock A.; Sibbet G. J.; Vousden K. H.; Huang D. T. Structural Basis for Autoinhibition and Phosphorylation-Dependent Activation of c-Cbl. Nat. Struct Mol. Biol. 2012, 19 (2), 184–192. 10.1038/nsmb.2231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Yang T.; Han L.; Huo S. Dynamics and Allosteric Information Pathways of Unphosphorylated C-Cbl. J. Chem. Inf Model 2022, 62 (23), 6148–6159. 10.1021/acs.jcim.2c01022. [DOI] [PubMed] [Google Scholar]
  31. Whelan S.; Karim C.; Ye J.; Ingallinera T.; Cherala G.; Jameson K.; Williams A.; Sharp A.; Krebs M.; Pacey S.; Blagden S.; Plummer R.; Hochhauser D.; Evans J.; Bono J. De; Powers J. 777 Initial Clinical Characterization of Novel Proximal Biomarkers for NX-1607, a First-in-Class Oral CBL-B Inhibitor, in Patients with Advanced Malignancies. J. Immunother. Cancer 2022, 10, A808. 10.1136/jitc-2022-sitc2022.0777. [DOI] [Google Scholar]
  32. Gallotta M.; Gosling J.; Tenn-McClellan A.; Ranucci S.; Romo J. G.; Cohen F.; Hansen G.; Sands A.; Guiducci C.; Rountree R. 824 NX-1607, a Small Molecule Inhibitor of the CBL-B E3 Ubiquitin Ligase, Promotes T and NK Cell Activation and Enhances NK-Mediated ADCC in a Mouse Lymphoma Tumor Model. J. Immunother. Cancer 2022, 10, A446. 10.1136/jitc-2022-sitc2022.0824. [DOI] [Google Scholar]
  33. Qi Y.; Kuai J.; Bi Y.; Sun H.; Jaeger S.; Greco D.; Carson K.; Reilly T.; Harriman G.; Wang F. 424 An Allosteric, Orally Administered CBL-B Inhibitor Remodels the Tumor Microenvironment and Enhances Immune-Mediated Tumor Growth Inhibition. J. Immunother. Cancer 2022, 10, A445. 10.1136/jitc-2022-SITC2022.0424. [DOI] [Google Scholar]
  34. Kuai J.; Bi Y.; Qi Y.; Conrady D.; Govindaraj R.; Hone G.; Aldrin Denny R.; Carson K.; Harriman G.; Wang F. 864 Identification of a Novel Allosteric Oral Cbl-b Inhibitor That Augmented T Cell Response and Enhanced NK Cell Killing in Vitro and in Vivo. J. Immunother. Cancer 2021, 9 (Suppl 2), A905–A905. 10.1136/jitc-2021-sitc2021.864. [DOI] [Google Scholar]
  35. Sabnis R. W. Novel Lactams as Cbl-b Inhibitors for Treating Cancer. ACS Med. Chem. Lett. 2022, 13 (10), 1556–1557. 10.1021/acsmedchemlett.2c00413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Liang J.; Jakalian A.; Lambrecht M. J.; Larouche-Gauthier R.; Huestis M.; Ung M. U.; Wang X.; Yadav A.; Zbeig J. R.; Broccatelli F.. Lactams as CBL-B Inhibitors. WO 202216997 A1, 2022.
  37. Leit de Moradei S. M.; West A. V.; Baker T.; Arregui J. C.; Castagna D.; Greenwood J. R.; Rafi S.; McRobb F.; Zhang Y.. CBL-B Modulators and Uses Thereof. WO 2022217276 A1, 2022.
  38. Leit de Moradei S. M.; West A. V.; Baker T.; Arregui J. C.; Castagna D.; Greenwood J. R.; Rafi S.; McRobb F.; Zhang Y.. CBL-B Modulators and Uses Thereof. WO 2022272248, 2022.
  39. Mai W.; Liu X.; Shi W.; Deng Z.; Zhu W.; Li Z.; Zou H.. Polycyclic Compound as CBL-B Inhibitor. WO 2023072273 A1, 2023.
  40. Mai W.; Liu X.; Zhu W.; Zou H.; Li Z.. Tricyclic Compound As CBL-B Inhibitor. WO 2023036330 A1, 2023.
  41. Landrum G.RDKit Documentation (2019.09.1). RDKit Open-Source Cheminformatics and Machine Leraning, 2019. https://www.rdkit.org/.
  42. Wang Z.; Sun H.; Yao X.; Li D.; Xu L.; Li Y.; Tian S.; Hou T. Comprehensive Evaluation of Ten Docking Programs on a Diverse Set of Protein-Ligand Complexes: The Prediction Accuracy of Sampling Power and Scoring Power. Phys. Chem. Chem. Phys. 2016, 18 (18), 12964–12975. 10.1039/C6CP01555G. [DOI] [PubMed] [Google Scholar]
  43. Stone M. Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society. Series B (Methodological) 1974, 36 (2), 111–147. [Google Scholar]
  44. Pedregosa F.; Varoquaux G.; Gramfort A.; Michel V.; Thirion B.; Grisel O.; Blondel M.; Prettenhofer P.; Weiss R.; Dubourg V.; Vanderplas J.; Passos A.; Cournapeau D.; Brucher M.; Perrot M.; Duchernay É. Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research 2011, 12 (85), 2825–2830. [Google Scholar]
  45. Lin Z.; Yin S.; Shi L.; Zhou W.; Zhang Y. G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training. J. Chem. Inf Model 2023, 63 (7), 1894–1905. 10.1021/acs.jcim.2c01302. [DOI] [PubMed] [Google Scholar]
  46. Bi Y.Compounds, Compositions and Methods of Treating Cancer. WO 2022221704 A1, 2022.
  47. Kollman P. A.; Massova I.; Reyes C.; Kuhn B.; Huo S.; Chong L.; Lee M.; Lee T.; Duan Y.; Wang W.; Donini O.; Cieplak P.; Srinivasan J.; Case D. A.; Cheatham T. E. Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Acc. Chem. Res. 2000, 33 (12), 889–897. 10.1021/ar000033j. [DOI] [PubMed] [Google Scholar]
  48. Valdés-Tresanco M. S.; Valdés-Tresanco M. E.; Valiente P. A.; Moreno E. Gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J. Chem. Theory Comput 2021, 17 (10), 6281–6291. 10.1021/acs.jctc.1c00645. [DOI] [PubMed] [Google Scholar]
  49. Hess B.; Kutzner C.; van der Spoel D.; Lindahl E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput 2008, 4 (3), 435–447. 10.1021/ct700301q. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

ml4c00047_si_001.pdf (781.1KB, pdf)
ml4c00047_si_004.zip (38.1MB, zip)

Articles from ACS Medicinal Chemistry Letters are provided here courtesy of American Chemical Society

RESOURCES