J. S. Zhang, W. K. Li, M. Zeng, X. M. Meng, L. Kurgan, F. X. Wu, and M. Li, NetEPD: A network-based essential protein discovery platform, Tsinghua Science and Technology, vol. 25, no. 4, pp. 542–552, 2020.
D. S. Marks, T. A. Hopf, and C. Sander, Protein structure prediction from sequence variation, Nat. Biotechnol., vol. 30, no. 11, pp. 1072–1080, 2012.
B. Adhikari, D. Bhattacharya, R. Z. Cao, and J. L. Cheng, CONFOLD: Residue-residue contact-guided ab initio protein folding, Proteins: Struct., Funct., Bioinformatics, vol. 83, no. 8, pp. 1436–1449, 2015.
J. B. Xu, Distance-based protein folding powered by deep learning, Proc. Natl. Acad. Sci. USA, vol. 116, no. 34, pp. 16856–16865, 2019.
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. L. Qin, A. Žídek, A. W. R. Nelson, A. Bridgland, H. Penedones, et al., Improved protein structure prediction using potentials from deep learning, Nature, vol. 577, no. 7792, pp. 706–710, 2020.
J. Y. Yang, I. Anishchenko, H. Park, Z. L. Peng, S. Ovchinnikov, and D. Baker, Improved protein structure prediction using predicted interresidue orientations, Proc. Natl. Acad. Sci. USA, vol. 117, no. 3, pp. 1496–1503, 2020.
M. Baek, F. Dimaio, I. Anishchenko, J. Dauparas, S. Ovchinnikov, G. R. Lee, J. Wang, Q. Cong, L. N. Kinch, R. D. Schaeffer, et al., Accurate prediction of protein structures and interactions using a three-track neural network, Science, vol. 373, no. 6557, pp. 871–876, 2021.
A. Raval, S. Piana, M. P. Eastwood, and D. E. Shaw, Assessment of the utility of contact-based restraints in accelerating the prediction of protein structure using molecular dynamics simulations, Protein Sci., vol. 25, no. 1, pp. 19–29, 2016.
E. A. Lubecka and A. Liwo, Introduction of a bounded penalty function in contact-assisted simulations of protein structures to omit false restraints, J. Comput. Chem., vol. 40, no. 25, pp. 2164–2178, 2019.
Q. Cong, I. Anishchenko, S. Ovchinnikov, and D. Baker, Protein interaction networks revealed by proteome coevolution, Science, vol. 365, no. 6449, pp. 185–189, 2019.
D. D. Pollock and W. R. Taylor, Effectiveness of correlation analysis in identifying protein residues undergoing correlated evolution, Protein Eng. Des. Sel., vol. 10, no. 6, pp. 647–657, 1997.
S. D. Dunn, L. M. Wahl, and G. B. Gloor, Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction, Bioinformatics, vol. 24, no. 3, pp. 333–340, 2007.
B. C. Lee and D. Kim, A new method for revealing correlated mutations under the structural and functional constraints in proteins, Bioinformatics, vol. 25, no. 19, pp. 2506–2513, 2009.
R. Rajgaria, S. R. McAllister, and C. A. Floudas, Towards accurate residue-residue hydrophobic contact prediction for α helical proteins via integer linear optimization, Proteins: Struct., Funct., Bioinformatics, vol. 74, no. 4, pp. 929–947, 2009.
R. Rajgaria, Y. Wei, and C. A. Floudas, Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3D structure prediction method ASTRO-FOLD, Proteins: Struct., Funct., Bioinformatics, vol. 78, no. 8, pp. 1825–1846, 2010.
J. L. Cheng and P. Baldi, Improved residue contact prediction using support vector machines and a large feature set, BMC Bioinformatics, vol. 8, no. 1, p. 113, 2007.
A. N. Tegge, Z. Wang, J. Eickholt, and J. L. Cheng, NNcon: Improved protein contact map prediction using 2D-recursive neural networks, Nucl. Acids Res., vol. 37, no. S2, pp. W515–W518, 2009.
S. T. Wu and Y. Zhang, A comprehensive assessment of sequence-based and template-based methods for protein contact prediction, Bioinformatics, vol. 24, no. 7, pp. 924–931, 2008.
Z. Y. Wang and J. B. Xu, Predicting protein contact map using evolutionary and physical constraints by integer programming, Bioinformatics, vol. 29, no. 13, pp. i266–i273, 2013.
H. L. Zhang, Q. S. Huang, Z. D. Bei, Y. J. Wei, and C. A. Floudas, COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming, Proteins: Struct., Funct., Bioinformatics, vol. 84, no. 3, pp. 332–348, 2016.
M. Weigt, R. A. White, H. Szurmant, J. A. Hoch, and T. Hwa, Identification of direct residue contacts in protein-protein interaction by message passing, Proc. Natl. Acad. Sci. USA, vol. 106, no. 1, pp. 67–72, 2009.
D. T. Jones, D. W. A. Buchan, D. Cozzetto, and M. Pontil, PSICOV: Precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments, Bioinformatics, vol. 28, no. 2, pp. 184–90, 2012.
F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. S. Marks, C. Sander, R. Zecchina, J. N. Onuchic, T. Hwa, and M. Weigt, Direct-coupling analysis of residue coevolution captures native contacts across many protein families, Proc. Natl. Acad. Sci. USA, vol. 108, no. 49, pp. E1293–E1301, 2011.
C. Baldassi, M. Zamparo, C. Feinauer, A. Procaccini, R. Zecchina, M. Weigt, and A. Pagnani, Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners, PLoS One, vol. 9, no. 3, p. e92721, 2014.
M. Ekeberg, C. Lövkvist, Y. H. Lan, M. Weigt, and E. Aurell, Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models, Phys. Rev.E, vol. 87, no. 1, p. 012707, 2013.
H. Kamisetty, S. Ovchinnikov, and D. Baker, Assessing the utility of coevolution-based residue-residue contact predictions in a sequence-and structure-rich era, Proc. Natl. Acad. Sci. USA, vol. 110, no. 39, pp. 15674–15679, 2013.
S. Seemayer, M. Gruber, and J. Söding, CCMpred-fast and precise prediction of protein residue-residue contacts from correlated mutations, Bioinformatics, vol. 30, no. 21, pp. 3128–3130, 2014.
M. J. Skwark, A. Abdel-Rehim, and A. Elofsson, PconsC: Combination of direct information methods and alignments improves contact prediction, Bioinformatics, vol. 29, no. 14, pp. 1815–1816, 2013.
D. T. Jones, T. Singh, T. Kosciolek, and S. Tetchner., MetaPSICOV: Combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins, Bioinformatics, vol. 31, no. 7, pp. 999–1006, 2015.
B. He, S. M. Mortuza, Y. T. Wang, H. B. Shen, and Y. Zhang, NeBcon: Protein contact map prediction using neural network training coupled with naïve Bayes classifiers, Bioinformatics, vol. 33, no. 15, pp. 2296–2306, 2017.
D. T. Jones and S. M. Kandathil, High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features, Bioinformatics, vol. 34, no. 19, pp. 3308–3315, 2018.
S. Wang, S. Q. Sun, Z. Li, R. Y. Zhang, and J. B. Xu, Accurate de novo prediction of protein contact map by ultra-deep learning model, PLoS Comput. Biol., vol. 13, no. 1, p. e1005324, 2017.
W. Z. Ding, W. Z. Mao, D. Shao, W. X. Zhang, and H. P. Gong, DeepConPred2: An improved method for the prediction of protein residue contacts, Comput. Struct. Biotechnol. J., vol. 16. pp. 503–510, 2018.
B. Adhikari, J. Hou, and J. L. Cheng, DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks, Bioinformatics, vol. 34, no. 9, pp. 1466–1472, 2018.
B. Adhikari, DEEPCON: Protein contact prediction using dilated convolutional neural networks with dropout, Bioinformatics, vol. 36, no. 2, pp. 470–477, 2020.
J. Hanson, K. Paliwal, T. Litfin, Y. D. Yang, and Y. Q. Zhou, Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks, Bioinformatics, vol. 34, no. 23, pp. 4039–4045, 2018.
Q. Wu, Z. L. Peng, I. Anishchenko, Q. Cong, D. Baker, and J. Y. Yang, Protein contact prediction using metagenome sequence data and residual neural networks, Bioinformatics, vol. 36, no. 1, pp. 41–48, 2020.
A. Lo, Y. Y. Chiu, E. A. Rødland, P. C. Lyu, T. Y. Sung, and W. L. Hsu, Predicting helix-helix interactions from residue contacts in membrane proteins, Bioinformatics, vol. 25, no. 8, pp. 996–1003, 2009.
T. Nugent and D. T. Jones, Predicting transmembrane helix packing arrangements using residue contacts and a force-directed algorithm, PLoS Comput. Biol., vol. 6, no. 3, p. e1000714, 2010.
H. L. Zhang, Z. D. Bei, W. H. Xi, M. Hao, Z. Ju, K. M. Saravanan, H. P. Zhang, N. Guo, and Y. J. Wei, Evaluation of residue-residue contact prediction methods: From retrospective to prospective, PLoS Comput. Biol., vol. 17, no. 5, p. e1009027, 2021.
D. Kozma, I. Simon, and G. E. Tusnády, PDBTM: Protein data bank of transmembrane proteins after 8 years, Nucl. Acids Res., vol. 41, no. D1, pp. D524–D529, 2013.
Y. Zhang, J. W. T. Chan, F. Y. L. Chin, H. F. Ting, D. S. Ye, F. Zhang, and J. Y. Shi, Constrained pairwise and center-star sequences alignment problems, J. Comb. Optim., vol. 32, no. 1, pp. 79–94, 2016.
W. T. Chan, Y. Zhang, S. P. Y. Fung, D. S. Ye, and H. Zhu, Efficient algorithms for finding a longest common increasing subsequence, J. Comb. Optim., vol. 13, no. 3, pp. 277–288, 2007.
C. X. Zhang, W. Zheng, S. M. Mortuza, Y. Li, and Y. Zhang, DeepMSA: Constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins, Bioinformatics, vol. 36, no. 7, pp. 2105–2112, 2020.
A. J. Hockenberry and C. O. Wilke, Evolutionary couplings detect side-chain interactions, PeerJ, vol. 7, p. e7280, 2019.
M. Chonofsky, S. H. P. De Oliveira, K. Krawczyk, and C. M. Deane, The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing, Bioinformatics, vol. 36, no. 6, pp. 1750–1756, 2020.