Table 1.

Summary of representative computational approaches for ΔΔG prediction.

Categories	Names	Webservers/Softwares	Unique features/advantages	References
Energy-based	FoldX	http://foldxsuite.crg.eu	Uses a rotamer library	[24, 25]
	Rosetta	-	Uses a rotamer library, focusing on alanine mutations	[26]
	CC/PBSA	-	Considers structural flexibility	[27]
	ZEMu	https://simtk.org/projects/rnatoolbox	Uses a multiscale method which models flexibility of mutation region	[28]
	Flex ddG	https://github.com/Kortemme-Lab/flex_ddG_tutorial	Samples conformational diversity using “backrub” to generate an ensemble of models	[29]
	BeAtMuSiC	http://babylone.ulb.ac.be/beatmusic	Uses the coarse-grained representation of protein structures	[30]
	Contact potentials-based model	-	Uses atomic and residue contact potentials	[31]
	BindProfX	https://zhanglab.dcmb.med.umich.edu/BindProfX	Calculates ΔΔG as the logarithm of relative probability of mutant residues over wild-type ones	[32]
	SSIPe	https://zhanglab.ccmb.med.umich.edu/SSIPe https://github.com/tommyhuangthu/SSIPe	Combines interface profiles derived from structural and sequence homology searches with a physics-based energy	[33]
Machine learning-based	mCSM-PPI2	http://biosig.unimelb.edu.au/mcsm_ppi2	Integrates mCSM graph-based signatures, evolutionary information, inter-residue non-covalent interaction networks analysis and energetic terms with ETs	[36]
	iSEE	https://github.com/haddocking/iSee	Combines structural, evolutionary, and energetic features with RF	[37]
	TopNetTree	https://codeocean.com/capsule/2202829/tree/v1	Integrates topological describtors with CNN-assisted GBDT	[38]
	MutaBind2	https://lilab.jysw.suda.edu.cn/research/mutabind2	Combines a set of scoring functions with RF	[39]
	ELASPIC2	http://elaspic.kimlab.org https://gitlab.com/elaspic/elaspic2	Incoraporates features generated using pre-trained TNN and GNN, and employs GBDT with a ranking object function	[40]
	ProAffiMuSeq	https://web.iitm.ac.in/bioinfo2/proaffimuseq	Considers the functional classes	[41]
	MuPIPR	https://github.com/guangyu-zhou/MuPIPR	An end-to-end deep learning framework using Bi-LSTM, RCNN and MLP without the need of hand-crafted features	[23]
	SAAMBE-SEQ	http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started	Employs GBDT on a set of features, and doesn’t require the knowledge of interfacial residue	[42]