Table 1.

A list of participating teams and submitted predictive models. Detailed model descriptions provided by participants are available as supplemental data. PSSM, position specific scoring matrices; MSA, multiple sequence alignments; ML, machine learning; SVM, support-vector machine; PMD, Protein Mutation Database; HGMD, Human Gene Mutation Database.

PI	Model Name	PubMed	PolyPhen/SIFT/Provean Based Features	Structure Based Features	PSSM/MSA Based Features	ML Method	Training Database
Bromberg	SNAP-1	Bromberg & Rost, 2007	Yes	Yes	Yes	Neural Network	PMD
Bromberg	SNAP-2	Bromberg & Rost, 2007	Yes	Yes	Yes	Neural Network	PMD
Moult	Moult Consensus	Yin, Kundu, Pal, & Moult, 2017	Yes	Yes	Yes	Support Vector Regression
Lichtarge	Evolutionary Action	Katsonis & Lichtarge, 2014	No	No	Yes	None
Wei	iFish	Wang & Wei, 2016	Yes	Yes	Yes	SVM
Mooney	MutPred	Li et al., 2009	Yes	No	Yes	Random Forest	HGMD
Mooney	MutPred2 w/o homology	Pejaver et al., 2017	Yes	No	Yes	Neural Network Ensemble	HGMD 2013
Mooney	MutPred2 w homology	Pejaver et al., 2017	Yes	No	Yes	Neural Network Ensemble	HGMD 2013
Jones	HHblits w/ real contacts	Remmert, Biegert, Hauser, & Soding, 2011	No	Yes	Yes	Logistic regression
Jones	HHblits w/ predicted contacts	Remmert et al., 2011	No	No	Yes	Logistic regression
Jones	HHblits w/o contacts	Remmert et al., 2011	No	No	No	Logistic regression
Jones	PAM250 PSSM		No	No	Yes	Logistic regression
Ford	PolyPhen2 Random Forest	Ford, Uppal, Nodzak, & Shi, 2019	Yes	No	Yes	Random Forest	1000 Genomes, NCBI, wANNOVAR
Casadio	INPS3D	Savojardo, Fariselli, Martelli, & Casadio, 2016	No	Yes	Yes	SVM
Casadio	SNPs&GO	Capriotti et al., 2013	No	Yes	Yes	SVM
Zhou	EASE-MM	Folkman, Stantic, Sattar, & Zhou, 2016	No	No	Yes	Support Vector Regression	ProTherm
Dunbrack	Dunbrack-SVM	Wei, Xu, & Dunbrack, 2013	Yes	Yes	Yes	SVM