Abstract
Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein–protein and protein–ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein–protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.
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All the data and materials are available to the corresponding author upon reasonable request.
References
Wu, K., Karapetyan, E., Schloss, J., Vadgama, J., & Wu, Y. (2023). Advancements in small molecule drug design: A structural perspective. Drug Discovery Today, 28(10), 103730. https://doi.org/10.1016/j.drudis.2023.103730
Kermani, A. A., Aggarwal, S., & Ghanbarpour, A. (2023). Advances in X-ray crystallography methods to study structural dynamics of macromolecules. In P. Saudagar & D. Tripathi (Eds.), Advanced spectroscopic methods to study biomolecular structure and dynamics (pp. 309–355). Elsevier.
Peng, C.-X., Liang, F., Xia, Y.-H., Zhao, K.-L., Hou, M.-H., & Zhang, G.-J. (2024). Recent advances and challenges in protein structure prediction. Journal of Chemical Information and Modeling, 64(1), 76–95. https://doi.org/10.1021/acs.jcim.3c01324
Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D., & Bates, P. A. (2023). Critical assessment of methods for predicting the 3D structure of proteins and protein complexes. Annual Review of Biophysics, 52(1), 183–206. https://doi.org/10.1146/annurev-biophys-102622-084607
Zhang, H., Bei, Z., Xi, W., Hao, M., Ju, Z., SarFSavanan, K. M., & Wei, Y. (2021). Evaluation of residue-residue contact prediction methods: From retrospective to prospective. PLoS Computational Biology, 17(5), e1009027. https://doi.org/10.1371/journal.pcbi.1009027
Nithiyanandam, S., Sangaraju, V. K., Manavalan, B., & Lee, G. (2023). Computational prediction of protein folding rate using structural parameters and network centrality measures. Computers in Biology and Medicine, 155, 106436. https://doi.org/10.1016/j.compbiomed.2022.106436
Bhatia, H., Aydin, F., Carpenter, T. S., Lightstone, F. C., Bremer, P.-T., Ingólfsson, H. I., & Streitz, F. H. (2023). The confluence of machine learning and multiscale simulations. Current Opinion in Structural Biology, 80, 102569. https://doi.org/10.1016/j.sbi.2023.102569
Chong, J. W. R., Khoo, K. S., Chew, K. W., Vo, D.-V.N., Balakrishnan, D., Banat, F., & Show, P. L. (2023). Microalgae identification: Future of image processing and digital algorithm. Bioresource Technology, 369, 128418. https://doi.org/10.1016/j.biortech.2022.128418
Chong, J. W. R., Khoo, K. S., Chew, K. W., Ting, H.-Y., & Show, P. L. (2023). Trends in digital image processing of isolated microalgae by incorporating classification algorithm. Biotechnology Advances, 63, 108095. https://doi.org/10.1016/j.biotechadv.2023.108095
Mondal, P. P., Galodha, A., Verma, V. K., Singh, V., Show, P. L., Awasthi, M. K., & Jain, R. (2023). Review on machine learning-based bioprocess optimization, monitoring, and control systems. Bioresource Technology, 370, 128523. https://doi.org/10.1016/j.biortech.2022.128523
Dixit, R., Khambhati, K., Supraja, K. V., Singh, V., Lederer, F., Show, P.-L., & Jain, R. (2023). Application of machine learning on understanding biomolecule interactions in cellular machinery. Bioresource Technology, 370, 128522. https://doi.org/10.1016/j.biortech.2022.128522
Greener, J. G., Kandathil, S. M., & Jones, D. T. (2019). Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints. Nature Communications, 10(1), 3977. https://doi.org/10.1038/s41467-019-11994-0
Xu, J., & Wang, S. (2019). Analysis of distance-based protein structure prediction by deep learning in CASP13. Proteins, 87(12), 1069–1081. https://doi.org/10.1002/prot.25810
Du, Z., Su, H., Wang, W., Ye, L., Wei, H., Peng, Z., & Yang, J. (2021). The trRosetta server for fast and accurate protein structure prediction. Nature Protocols, 16(12), 5634–5651. https://doi.org/10.1038/s41596-021-00628-9
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., & Velankar, S. (2022). AlphaFold protein structure database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, 50(D1), D439–D444. https://doi.org/10.1093/nar/gkab1061
Tanaka, S., & Scheraga, H. A. (1975). Model of protein folding: Inclusion of short, medium, and long range interactions. Proceedings of the National Academy of Sciences of the United States of America, 72(10), 3802–3806. https://doi.org/10.1073/pnas.72.10.3802
Gromiha, M. M. (2010). Protein bioinformatics. In M. Michael Gromiha (Ed.), Protein bioinformatics. From sequence to function (1st edition). Elsevier.
Go, N., & Taketomi, H. (1978). Respective roles of short- and long-range interactions in protein folding. Proceedings of the National Academy of Sciences of the United States of America, 75(2), 559–563. https://doi.org/10.1073/pnas.75.2.559
Go, N. (1983). Theoretical studies of protein folding. Annual Review of Biophysics and Bioengineering, 12, 183–210. https://doi.org/10.1146/annurev.bb.12.060183.001151
Karplus, M., & Weaver, D. L. (1976). Protein-folding dynamics. Nature, 260(5550), 404–406. https://doi.org/10.1038/260404a0
Rollins, G. C., & Dill, K. A. (2014). General Mechanism of two-state protein folding kinetics. Journal of the American Chemical Society, 136(32), 11420–11427. https://doi.org/10.1021/ja5049434
Guo, J. S., Mi, D., & Sun, Y. Q. (2010). Folding kinetics of two-state proteins based on the model of general random walk in native contact number space. Physica A: Statistical Mechanics and its Applications, 389(4), 761–766. https://doi.org/10.1016/j.physa.2009.10.026
Kuwajima, K. (2020). The molten globule, and two-state vs non-two-state folding of globular proteins. Biomolecules, 10(3), 407. https://doi.org/10.3390/biom10030407
Eaton, W. A. (2021). Modern kinetics and mechanism of protein folding: A retrospective. The Journal of Physical Chemistry B, 125(14), 3452–3467. https://doi.org/10.1021/acs.jpcb.1c00206
Gromiha, M. M., & Selvaraj, S. (1997). Influence of medium and long range interactions in different structural classes of globular proteins. Journal of Biological Physics, 23(3), 151–162. https://doi.org/10.1023/A:1004981409616
Gromiha, M. M., & Selvaraj, S. (1999). Importance of long-range interactions in protein folding. Biophysical Chemistry, 77(1), 49–68. https://doi.org/10.1016/S0301-4622(99)00010-1
Selvaraj, S., & Gromiha, M. M. (2000). Inter-residue interactions in protein structures. Current Science, 78(2), 129–131.
Deller, M. C., Kong, L., & Rupp, B. (2016). Protein stability: A crystallographer’s perspective. Acta Crystallographica Section F, 72(2), 72–95. https://doi.org/10.1107/S2053230X15024619
Selvaraj, S., & Gromiha, M. M. (1998). Importance of long-range interactions in (α/β)8 barrel fold. Journal of Protein Chemistry, 17(7), 691–697. https://doi.org/10.1007/BF02780972
Selvaraj, S., & Gromiha, M. M. (2003). Role of hydrophobic clusters and long-range contact networks in the folding of (α/β)8 barrel proteins. Biophysical Journal, 84(3), 1919–1925. https://doi.org/10.1016/S0006-3495(03)75000-0
Gromiha, M. M., & Selvaraj, S. (2001). Role of medium- and long-range interactions in discriminating globular and membrane proteins. International Journal of Biological Macromolecules, 29(1), 25–34. https://doi.org/10.1016/S0141-8130(01)00150-7
Krigbaum, W. R., & Komoriya, A. (1979). Local interactions as a structure determinant for protein molecules: II. BBA: Protein Structure, 576(1), 204–228. https://doi.org/10.1016/0005-2795(79)90498-7
Narayana, S. V. L., & Argos, P. (1984). Residue contacts in protein structures and implications for protein folding. International Journal of Peptide and Protein Research, 24(1), 25–39. https://doi.org/10.1111/j.1399-3011.1984.tb00924.x
Crippen, G. M. (1991). Prediction of protein folding from amino acid sequence over discrete conformation spaces. Biochemistry, 30(17), 4232–4237. https://doi.org/10.1021/bi00231a018
Gugolya, Z., Dosztányi, Z., & Simon, I. (1997). Interresidue interactions in protein classes. Proteins, 27(3), 360–366.
Gromiha, M. M., & Selvaraj, S. (2001). Comparison between long-range interactions and contact order in determining the folding rate of two-state proteins: Application of long-range order to folding rate prediction. Journal of Molecular Biology, 310(1), 27–32. https://doi.org/10.1006/jmbi.2001.4775
Kumarevel, T. S., Gromiha, M. M., Selvaraj, S., Gayatri, K., & Kumar, P. K. R. (2002). Influence of medium- and long-range interactions in different folding types of globular proteins. Biophysical Chemistry, 99(2), 189–198. https://doi.org/10.1016/S0301-4622(02)00183-7
Tüdos, É., Fiser, A., Simon, Á., Dosztányi, Z., Fuxreiter, M., Magyar, C., & Simon, I. (2004). Noncovalent cross-links in context with other structural and functional elements of proteins. Journal of Chemical Information and Computer Sciences, 44(2), 347–351. https://doi.org/10.1021/ci030409i
Faísca, P. F. N., Telo Da Gama, M. M., & Nunes, A. (2005). The Gō model revisited: Native structure and the geometric coupling between local and long-range contacts. Proteins, 60(4), 712–722. https://doi.org/10.1002/prot.20521
Mounce, B. C., Kurt, N., Ellison, P. A., & Cavagnero, S. (2009). Nonrandom distribution of intramolecular contacts in native single-domain proteins. Proteins, 75(2), 404–412. https://doi.org/10.1002/prot.22258
Rishya Kulya, M., & Saravanan, K. (2014). Computational structural analysis of C-terminal residues of proteins containing transmembrane regions. International Journal for Computational Biology, 4(1), 44. https://doi.org/10.34040/ijcb.4.1.2014.47
Chen, P., & Li, J. (2010). Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers. BMC Structural Biology, 10(Suppl 1), S2. https://doi.org/10.1186/1472-6807-10-S1-S2
Esque, J., Oguey, C., & De Brevern, A. G. (2011). Comparative analysis of threshold and tessellation methods for determining protein contacts. Journal of Chemical Information and Modeling, 51(2), 493–507. https://doi.org/10.1021/ci100195t
Eickholt, J., Wang, Z., & Cheng, J. (2011). A conformation ensemble approach to protein residue-residue contact. BMC Structural Biology, 11(1), 38. https://doi.org/10.1186/1472-6807-11-38
Yuan, C., Chen, H., & Kihara, D. (2012). Effective inter-residue contact definitions for accurate protein fold recognition. BMC Bioinformatics, 13(1), 292. https://doi.org/10.1186/1471-2105-13-292
Yang, J., Jin, Q. Y., Zhang, B., & Shen, H. B. (2016). R2C: Improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter. Bioinformatics, 32(16), 2435–2443. https://doi.org/10.1093/bioinformatics/btw181
Zhang, H., Huang, Y., Bei, Z., Ju, Z., Meng, J., Hao, M., & Xi, W. (2022). Inter-residue distance prediction from duet deep learning models. Frontiers in Genetics, 13, 887491. https://doi.org/10.3389/fgene.2022.887491
Zhang, H., Hao, M., Wu, H., Ting, H.-F., Tang, Y., Xi, W., & Wei, Y. (2022). Protein residue contact prediction based on deep learning and massive statistical features from multi-sequence alignment. Tsinghua Science and Technology, 27(5), 843–854. https://doi.org/10.26599/TST.2021.9010064
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., & Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
Fiser, A., & Šali, A. (2003). MODELLER: Generation and refinement of homology-based protein structure models. Methods in Enzymology, 374, 461–491. https://doi.org/10.1016/S0076-6879(03)74020-8
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017). Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Computational Biology, 13(1), e1005324. https://doi.org/10.1371/journal.pcbi.1005324
Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H., & Xu, J. (2012). Template-based protein structure modeling using the RaptorX web server. Nature Protocols, 7(8), 1511–1522. https://doi.org/10.1038/nprot.2012.085
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020). Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences, 117(3), 1496–1503. https://doi.org/10.1073/pnas.1914677117
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
Skolnick, J., Gao, M., Zhou, H., & Singh, S. (2021). AlphaFold 2: Why it works and its implications for understanding the relationships of protein sequence, structure, and function. Journal of Chemical Information and Modeling, 61(10), 4827–4831. https://doi.org/10.1021/acs.jcim.1c01114
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., & Hassabis, D. (2021). Applying and improving AlphaFold at CASP14. Proteins, 89(12), 1711–1721. https://doi.org/10.1002/prot.26257
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387
Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J. J., & Rocklin, G. J. (2023). Mega-scale experimental analysis of protein folding stability in biology and design. Nature, 620(7973), 434–444. https://doi.org/10.1038/s41586-023-06328-6
Englander, S. W., & Mayne, L. (2017). The case for defined protein folding pathways. Proceedings of the National Academy of Sciences, 114(31), 8253–8258. https://doi.org/10.1073/pnas.1706196114
Jackson, S. E., & Fersht, A. R. (1991). Folding of Chymotrypsin Inhibitor 2.1. Evidence for a two-state transition. Biochemistry, 30(43), 10428–10435. https://doi.org/10.1021/bi00107a010
Jackson, S. E. (1998). How do small single-domain proteins fold? Folding and Design, 3(4), R81–R91. https://doi.org/10.1016/S1359-0278(98)00033-9
Zwanzig, R. (1997). Two-state models of protein folding kinetics. Proceedings of the National Academy of Sciences of the United States of America, 94(1), 148–150. https://doi.org/10.1073/pnas.94.1.148
Fersht, A. R., & Daggett, V. (2002). Protein Folding and unfolding at atomic resolution. Cell, 108(4), 573–582. https://doi.org/10.1016/S0092-8674(02)00620-7
Plaxco, K. W., Simons, K. T., & Baker, D. (1998). Contact order, transition state placement and the refolding rates of single domain proteins. Journal of Molecular Biology, 277(4), 985–994. https://doi.org/10.1006/jmbi.1998.1645
Muñoz, V., & Eaton, W. A. (1999). A simple model for calculating the kinetics of protein folding from three-dimensional structures. Proceedings of the National Academy of Sciences of the United States of America, 96(20), 11311–11316. https://doi.org/10.1073/pnas.96.20.11311
Debe, D. A., & Goddard, W. A. (1999). First principles prediction of protein folding rates. Journal of Molecular Biology, 294(3), 619–625. https://doi.org/10.1006/jmbi.1999.3278
Zhou, H., & Zhou, Y. (2002). Folding rate prediction using total contact distance. Biophysical Journal, 82(1), 458–463. https://doi.org/10.1016/S0006-3495(02)75410-6
Makarov, D. E., Keller, C. A., Plaxco, K. W., & Metiu, H. (2002). How the folding rate constant of simple, single-domain proteins depends on the number of native contacts. Proceedings of the National Academy of Sciences of the United States of America, 99(6), 3535–3539. https://doi.org/10.1073/pnas.052713599
Makarov, D. E. (2003). The topomer search model: A simple, quantitative theory of two-state protein folding kinetics. Protein Science, 12(1), 17–26. https://doi.org/10.1110/ps.0220003
Micheletti, C. (2003). Prediction of folding rates and transition-state placement from native-state geometry. Proteins, 51(1), 74–84. https://doi.org/10.1002/prot.10342
Zhang, L., Li, J., Jiang, Z., & Xia, A. (2003). Folding rate prediction based on neural network model. Polymer, 44(5), 1751–1756. https://doi.org/10.1016/S0032-3861(03)00021-1
Weikl, T. R., & Dill, K. A. (2003). Folding rates and low-entropy-loss routes of two-state proteins. Journal of Molecular Biology, 329(3), 585–598. https://doi.org/10.1016/S0022-2836(03)00436-4
Gromiha, M. M. (2003). Importance of native-state topology for determining the folding rate of two-state proteins. Journal of Chemical Information and Computer Sciences, 43(5), 1481–1485. https://doi.org/10.1021/ci0340308
Kuznetsov, I. B., & Rackovsky, S. (2004). Class-specific correlations between protein folding rate, structure-derived, and sequence-derived descriptors. Proteins, 54(2), 333–341. https://doi.org/10.1002/prot.10518
Gromiha, M. M., Saraboji, K., Ahmad, S., Ponnuswamy, M. N., & Suwa, M. (2004). Role of non-covalent interactions for determining the folding rate of two-state proteins. Biophysical Chemistry, 107(3), 263–272. https://doi.org/10.1016/j.bpc.2003.09.008
Bai, Y., Zhou, H., & Zhou, Y. (2004). Critical nucleation size in the folding of small apparently two-state proteins. Protein Science, 13(5), 1173–1181. https://doi.org/10.1110/ps.03587604
Paci, E., Lindorff-Larsen, K., Dobson, C. M., Karplus, M., & Vendruscolo, M. (2005). Transition state contact orders correlate with protein folding rates. Journal of Molecular Biology, 352(3), 495–500. https://doi.org/10.1016/j.jmb.2005.06.081
Dixit, P. D., & Weikl, T. R. (2006). A simple measure of native-state topology and chain connectivity predicts the folding rates of two-state proteins with and without crosslinks. Proteins, 64(1), 193–197. https://doi.org/10.1002/prot.20976
Bagler, G., & Sinha, S. (2007). Assortative mixing in protein contact networks and protein folding kinetics. Bioinformatics, 23(14), 1760–1767. https://doi.org/10.1093/bioinformatics/btm257
Gromiha, M. M. (2009). Multiple contact network is a key determinant to protein folding rates. Journal of Chemical Information and Modeling, 49(4), 1130–1135. https://doi.org/10.1021/ci800440x
Jiang, Y., Iglinski, P., & Kurgan, L. (2009). Prediction of protein folding rates from primary sequences using hybrid sequence representation. Journal of Computational Chemistry, 30(5), 772–783. https://doi.org/10.1002/jcc.21096
Harihar, B., & Selvaraj, S. (2009). Refinement of the long-range order parameter in predicting folding rates of two-state proteins. Biopolymers, 91(11), 928–935. https://doi.org/10.1002/bip.21281
Gong, H., Isom, D. G., Srinivasan, R., & Rose, G. D. (2003). Local secondary structure content predicts folding rates for simple, two-state proteins. Journal of Molecular Biology, 327(5), 1149–1154. https://doi.org/10.1016/S0022-2836(03)00211-0
Nölting, B., Schälike, W., Hampel, P., Grundig, F., Gantert, S., Sips, N., & Qi, P. X. (2003). Structural determinants of the rate of protein folding. Journal of Theoretical Biology, 223(3), 299–307. https://doi.org/10.1016/S0022-5193(03)00091-2
Zhang, L., & Sun, T. (2005). Folding rate prediction using n-order contact distance for proteins with two- and three-state folding kinetics. Biophysical Chemistry, 113(1), 9–16. https://doi.org/10.1016/j.bpc.2004.07.036
Prabhu, N. P., & Bhuyan, A. K. (2006). Prediction of folding rates of small proteins: Empirical relations based on length, secondary structure content, residue type, and stability. Biochemistry, 45(11), 3805–3812. https://doi.org/10.1021/bi0521137
Huang, J. T., Cheng, J. P., & Chen, H. (2007). Secondary structure length as a determinant of folding rate of proteins with two- and three-state kinetics. Proteins, 67(1), 12–17. https://doi.org/10.1002/prot.21282
Gromiha, M. M. (2005). A statistical model for predicting protein folding rates from amino acid sequence with structural class information. Journal of Chemical Information and Modeling, 45(2), 494–501. https://doi.org/10.1021/ci049757q
Gromiha, M. M., Thangakani, A. M., & Selvaraj, S. (2006). FOLD-RATE: Prediction of protein folding rates from amino acid sequence. Nucleic Acids Research, 34, W70–W74. https://doi.org/10.1093/nar/gkl043
Ma, B. G., Guo, J. X., & Zhang, H. Y. (2006). Direct correlation between proteins’ folding rates and their amino acid compositions: An ab initio folding rate prediction. Proteins, 65(2), 362–372. https://doi.org/10.1002/prot.21140
Huang, J. T., & Tian, J. (2006). Amino acid sequence predicts folding rate for middle-size two-state proteins. Proteins, 63(3), 551–554. https://doi.org/10.1002/prot.20911
Huang, L. T., & Gromiha, M. M. (2008). Analysis and prediction of protein folding rates using quadratic response surface models. Journal of Computational Chemistry, 29(10), 1675–1683. https://doi.org/10.1002/jcc.20925
Ouyang, Z., & Liang, J. (2008). Predicting protein folding rates from geometric contact and amino acid sequence. Protein Science, 17(7), 1256–1263. https://doi.org/10.1110/ps.034660.108
Gao, J., Zhang, T., Zhang, H., Shen, S., Ruan, J., & Kurgan, L. (2010). Accurate prediction of protein folding rates from sequence and sequence-derived residue flexibility and solvent accessibility. Proteins, 78(9), 2114–2130. https://doi.org/10.1002/prot.22727
Chang, L., Wang, J., & Wang, W. (2010). Composition-based effective chain length for prediction of protein folding rates. Physical Review E, 82(5), 051930. https://doi.org/10.1103/PhysRevE.82.051930
Guo, J., & Rao, N. (2011). Predicting protein folding rate from amino acid sequence. Journal of Bioinformatics and Computational Biology, 9(1), 1–13. https://doi.org/10.1142/S0219720011005306
Galzitskaya, O. V., Garbuzynskiy, S. O., Ivankov, D. N., & Finkelstein, A. V. (2003). Chain length is the main determinant of the folding rate for proteins with three-state folding kinetics. Proteins, 51(2), 162–166. https://doi.org/10.1002/prot.10343
Ivankov, D. N., Garbuzynskiy, S. O., Alm, E., Plaxco, K. W., Baker, D., & Finkelstein, A. V. (2003). Contact order revisited: Influence of protein size on the folding rate. Protein Science, 12(9), 2057–2062. https://doi.org/10.1110/ps.0302503
Ma, B. G., Chen, L. L., & Zhang, H. Y. (2007). What determines protein folding type? An investigation of intrinsic structural properties and its implications for understanding folding mechanisms. Journal of Molecular Biology, 370(3), 439–448. https://doi.org/10.1016/j.jmb.2007.04.051
Huang, J. T., & Cheng, J. P. (2008). Differentiation between two-state and multi-state folding proteins based on sequence. Proteins, 72(1), 44–49. https://doi.org/10.1002/prot.21893
Ivankov, D. N., Bogatyreva, N. S., Lobanov, M. Y., & Galzitskaya, O. V. (2009). Coupling between properties of the protein shape and the rate of protein folding. PLoS ONE, 4(8), e6476. https://doi.org/10.1371/journal.pone.0006476
Guo, J., Rao, N., Liu, G., Yang, Y., & Wang, G. (2011). Predicting protein folding rates using the concept of Chou’s pseudo amino acid composition. Journal of Computational Chemistry, 32(8), 1612–1617. https://doi.org/10.1002/jcc.21740
Ittah, V., & Haas, E. (1995). Nonlocal interactions stabilize long range loops in the initial folding intermediates of reduced bovine pancreatic trypsin inhibitor. Biochemistry, 34(13), 4493–4506. https://doi.org/10.1021/bi00013a042
Klein-Seetharaman, J., Oikawa, M., Grimshaw, S. B., Wirmer, J., Duchardt, E., Ueda, T., & Schwalbe, H. (2002). Long-range interactions within a nonnative protein. Science, 295(5560), 1719–1722. https://doi.org/10.1126/science.1067680
Lietzow, M. A., Jamin, M., Jane Dyson, H., & Wright, P. E. (2002). Mapping long-range contacts in a highly unfolded protein. Journal of Molecular Biology, 322(4), 655–662. https://doi.org/10.1016/S0022-2836(02)00847-1
Mizuguchi, M., Kroon, G. J., Wright, P. E., & Dyson, H. J. (2003). Folding of a β-sheet protein monitored by real-time NMR spectroscopy. Journal of Molecular Biology, 328(5), 1161–1171. https://doi.org/10.1016/S0022-2836(03)00349-8
Dedmon, M. M., Lindorff-Larsen, K., Christodoulou, J., Vendruscolo, M., & Dobson, C. M. (2005). Mapping long-range interactions in α-synuclein using spin-label NMR and ensemble molecular dynamics simulations. Journal of the American Chemical Society, 127(2), 476–477. https://doi.org/10.1021/ja044834j
Saravanan, K. M., Zhang, H., Zhang, H., Xi, W., & Wei, Y. (2020). On the conformational dynamics of β-Amyloid forming peptides: A computational perspective. Frontiers in Bioengineering and Biotechnology, 8, 532. https://doi.org/10.3389/fbioe.2020.00532
Mok, K. H., Kuhn, L. T., Goez, M., Day, I. J., Lin, J. C., Andersen, N. H., & Hore, P. J. (2007). A pre-existing hydrophobic collapse in the unfolded state of an ultrafast folding protein. Nature, 447(7140), 106–109. https://doi.org/10.1038/nature05728
Felitsky, D. J., Lietzow, M. A., Dyson, H. J., & Wright, P. E. (2008). Modeling transient collapsed states of an unfolded protein to provide insights into early folding events. Proceedings of the National Academy of Sciences of the United States of America, 105(17), 6278–6283. https://doi.org/10.1073/pnas.0710641105
Meier, S., Blackledge, M., & Grzesiek, S. (2008). Conformational distributions of unfolded polypeptides from novel NMR techniques. The Journal of Chemical Physics, 128(5), 052204. https://doi.org/10.1063/1.2838167
Zarrine-Afsar, A., Wallin, S., Neculai, A. M., Neudecker, P., Howell, P. L., Davidson, A. R., & Hue, S. C. (2008). Theoretical and experimental demonstration of the importance of specific nonnative interactions in protein folding. Proceedings of the National Academy of Sciences of the United States of America, 105(29), 9999–10004. https://doi.org/10.1073/pnas.0801874105
Nabuurs, S. M., De Kort, B. J., Westphal, A. H., & Van Mierlo, C. P. M. (2010). Non-native hydrophobic interactions detected in unfolded apoflavodoxin by paramagnetic relaxation enhancement. European Biophysics Journal, 39(4), 689–698. https://doi.org/10.1007/s00249-009-0556-4
Hagen, S. J., Hofrichter, J., Szabo, A., & Eaton, W. A. (1996). Diffusion-limited contact formation in unfolded cytochrome c: Estimating the maximum rate of protein folding. Proceedings of the National Academy of Sciences of the United States of America, 93(21), 11615–11617. https://doi.org/10.1073/pnas.93.21.11615
Gellman, S. H. (1998). Minimal model systems for β sheet secondary structure in proteins. Current Opinion in Chemical Biology, 2(6), 717–725. https://doi.org/10.1016/S1367-5931(98)80109-9
Krieger, F., Fierz, B., Bieri, O., Drewello, M., & Kiefhaber, T. (2003). Dynamics of unfolded polypeptide chains as model for the earliest steps in protein folding. Journal of Molecular Biology, 332(1), 265–274. https://doi.org/10.1016/S0022-2836(03)00892-1
Mirny, L. A., Abkevich, V. I., & Shakhnovich, E. I. (1998). How evolution makes proteins fold quickly. Proceedings of the National Academy of Sciences of the United States of America, 95(9), 4976–4981. https://doi.org/10.1073/pnas.95.9.4976
Dokholyan, N. V., Li, L., Ding, F., & Shakhnovich, E. I. (2002). Topological determinants of protein folding. Proceedings of the National Academy of Sciences of the United States of America, 99(13), 8637–8641. https://doi.org/10.1073/pnas.122076099
Fuxreiter, M., & Simon, I. (2002). Role of stabilization centers in 4 helix bundle proteins. Proteins, 48(2), 320–326. https://doi.org/10.1002/prot.10167
Mukherjee, A., & Bagchi, B. (2003). Correlation between rate of folding, energy landscape, and topology in the folding of a model protein HP-36. Journal of Chemical Physics, 118(10), 4733–4747. https://doi.org/10.1063/1.1542599
Papoian, G. A., Ulander, J., Eastwood, M. P., Luthey-Schulten, Z., & Wolynes, P. G. (2004). Water in protein structure prediction. Proceedings of the National Academy of Sciences of the United States of America, 101(10), 3352–3357. https://doi.org/10.1073/pnas.0307851100
Otzen, D. E. (2002). Protein unfolding in detergents: Effect of micelle structure, ionic strength, pH, and temperature. Biophysical Journal, 83(4), 2219–2230. https://doi.org/10.1016/S0006-3495(02)73982-9
Otzen, D. E., & Oliveberg, M. (2002). Burst-phase expansion of native protein prior to global unfolding in SDS. Journal of Molecular Biology, 315(5), 1231–1240. https://doi.org/10.1006/jmbi.2001.5300
Manning, M., & Colón, W. (2004). Structural basis of protein kinetic stability: Resistance to sodium dodecyl sulfate suggests a central role for rigidity and a bias toward β-sheet structure. Biochemistry, 43(35), 11248–11254. https://doi.org/10.1021/bi0491898
Plaxco, K. W., Simons, K. T., Ruczinski, I., & Baker, D. (2000). Topology, stability, sequence, and length: Defining the determinants of two-state protein folding kinetics. Biochemistry, 39(37), 11177–11183. https://doi.org/10.1021/bi000200n
Dinner, A. R., & Karplus, M. (2001). The roles of stability and contact order in determining protein folding rates. Nature Structural Biology, 8(1), 21–22. https://doi.org/10.1038/83003
Jung, J., Buglass, A. J., & Lee, E. K. (2010). Topological quantities determining the folding/unfolding rate of two-state folding proteins. Journal of Solution Chemistry, 39(7), 943–958. https://doi.org/10.1007/s10953-010-9556-3
Jung, J., Lee, J., & Moon, H. T. (2005). Topological determinants of protein unfolding rates. Proteins, 58(2), 389–395. https://doi.org/10.1002/prot.20324
Gromiha, M. M., Selvaraj, S., & Thangakani, A. M. (2006). A statistical method for predicting protein unfolding rates from amino acid sequence. Journal of Chemical Information and Modeling, 46(3), 1503–1508. https://doi.org/10.1021/ci050417u
Ji, G. S., Chun, H. L., Hao, R., Wei, Z. C., & Cun, X. W. (2008). Protein unfolding behavior studied by elastic network model. Biophysical Journal, 94(12), 4586–4596. https://doi.org/10.1529/biophysj.107.121665
Harihar, B., & Selvaraj, S. (2011). Application of long-range order to predict unfolding rates of two-state proteins. Proteins, 79(3), 880–887. https://doi.org/10.1002/prot.22925
Harihar, B., & Selvaraj, S. (2011). Analysis of rate-limiting long-range contacts in the folding rate of three-state and two-state proteins. Protein & Peptide Letters, 18(10), 1042–1052. https://doi.org/10.2174/092986611796378684
Samuel, S., & Balasubramanian, H. (2017). Long-range contacts in unfolding of two-state proteins. Protein & Peptide Letters, 24(3), 206–214. https://doi.org/10.2174/0929866523666161216123019
Hariha, B., & Selvaraj, S. (2012). Role of long-range contacts and structural classification in understanding the free energy of unfolding of two-state proteins. Current Bioinformatics, 7(2), 143–151. https://doi.org/10.2174/157489312800604372
Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995). SCOP: A structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology, 247(4), 536–540. https://doi.org/10.1016/S0022-2836(05)80134-2
Chothia, C., & Lesk, A. M. (1986). The relation between the divergence of sequence and structure in proteins. The EMBO Journal, 5(4), 823–826. https://doi.org/10.1002/j.1460-2075.1986.tb04288.x
Jabeen, A., Vijayram, R., & Ranganathan, S. (2021). BIO-GATS: A tool for automated GPCR template selection through a biophysical approach for homology modeling. Frontiers in Molecular Biosciences, 8, 617176. https://doi.org/10.3389/fmolb.2021.617176
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020). Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences of the United States of America, 117(3), 1496–1503. https://doi.org/10.1073/pnas.1914677117
Roche, R., Bhattacharya, S., & Bhattacharya, D. (2021). Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins. PLoS Computational Biology, 17(2), e1008753. https://doi.org/10.1371/journal.pcbi.1008753
Stebbings, L. A. (2004). HOMSTRAD: Recent developments of the homologous protein structure alignment database. Nucleic Acids Research, 32(90001), 203D – 207. https://doi.org/10.1093/nar/gkh027
Balaji, S. (2001). PALI–a database of phylogeny and ALIgnment of homologous protein structures. Nucleic Acids Research, 29(1), 61–65. https://doi.org/10.1093/nar/29.1.61
Holm, L., Kääriäinen, S., Rosenström, P., & Schenkel, A. (2008). Searching protein structure databases with DaliLite vol 3. Bioinformatics, 24(23), 2780–2781. https://doi.org/10.1093/bioinformatics/btn507
Knudsen, M., & Wiuf, C. (2010). The CATH database. Human Genomics, 4(3), 207. https://doi.org/10.1186/1479-7364-4-3-207
Nishikawa, K., Nakashima, H., Kanehisa, M., & Ooi, T. (1987). Detection of weak sequence homology of proteins for tertiary structure prediction. Protein Sequences & Data Analysis, 1, 107–116.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2
Dietmann, S., & Holm, L. (2001). Identification of homology in protein structure classification. Nature Structural Biology, 8(11), 953–957. https://doi.org/10.1038/nsb1101-953
Jin, X., Liao, Q., Wei, H., Zhang, J., & Liu, B. (2021). SMI-BLAST: A novel supervised search framework based on PSI-BLAST for protein remote homology detection. Bioinformatics, 37(7), 913–920. https://doi.org/10.1093/bioinformatics/btaa772
Liu, B., Li, C.-C., & Yan, K. (2020). DeepSVM-fold: Protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks. Briefings in Bioinformatics, 21(5), 1733–1741. https://doi.org/10.1093/bib/bbz098
Gao, S., Yu, S., & Yao, S. (2021). An efficient protein homology detection approach based on seq2seq model and ranking. Biotechnology & Biotechnological Equipment, 35(1), 633–640. https://doi.org/10.1080/13102818.2021.1892522
Chen, J., Long, R., Wang, X., Liu, B., & Chou, K.-C. (2016). dRHP-PseRA: Detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation. Scientific Reports, 6(1), 32333. https://doi.org/10.1038/srep32333
Zheng, W., Wuyun, Q., Li, Y., Mortuza, S. M., Zhang, C., Pearce, R., & Zhang, Y. (2019). Detecting distant-homology protein structures by aligning deep neural-network based contact maps. PLoS Computational Biology, 15(10), e1007411. https://doi.org/10.1371/journal.pcbi.1007411
Skolnick, J., Kihara, D., & Zhang, Y. (2004). Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm. Proteins, 56(3), 502–518. https://doi.org/10.1002/prot.20106
Bhattacharya, S., Roche, R., Shuvo, M. H., & Bhattacharya, D. (2021). Recent advances in protein homology detection propelled by inter-residue interaction map threading. Frontiers in Molecular Biosciences, 8, 643752. https://doi.org/10.3389/fmolb.2021.643752
Bhattacharya, S., & Bhattacharya, D. (2020). Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading. Scientific Reports, 10(1), 2908. https://doi.org/10.1038/s41598-020-59834-2
Du, Z., Pan, S., Wu, Q., Peng, Z., & Yang, J. (2020). CATHER: A novel threading algorithm with predicted contacts. Bioinformatics, 36(7), 2119–2125. https://doi.org/10.1093/bioinformatics/btz876
Zhang, H., & Shen, Y. (2020). Template-based prediction of protein structure with deep learning. BMC Genomics, 21(S11), 878. https://doi.org/10.1186/s12864-020-07249-8
Liu, B., Zhang, D., Xu, R., Xu, J., Wang, X., Chen, Q., & Chou, K.-C. (2014). Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics, 30(4), 472–479. https://doi.org/10.1093/bioinformatics/Btt709
Jin, X., Liao, Q., & Liu, B. (2021). S2L-PSIBLAST: A supervised two-layer search framework based on PSI-BLAST for protein remote homology detection. Bioinformatics, 37(23), 4321–4327. https://doi.org/10.1093/bioinformatics/btab472
Rozewicki, J., Li, S., Amada, K. M., Standley, D. M., & Katoh, K. (2019). MAFFT-DASH: Integrated protein sequence and structural alignment. Nucleic acids Research, 47(W1), W5–W10. https://doi.org/10.1093/nar/gkz342
Chen, J., Guo, M., Wang, X., & Liu, B. (2018). A comprehensive review and comparison of different computational methods for protein remote homology detection. Briefings in Bioinformatics, 19(2), 231–244. https://doi.org/10.1093/bib/bbw108
Li, Y., Zhang, C., Zheng, W., Zhou, X., Bell, E. W., Yu, D.-J., & Zhang, Y. (2021). Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14. Proteins, 89(12), 1911–1921. https://doi.org/10.1002/prot.26211
Ju, F., Zhu, J., Shao, B., Kong, L., Liu, T.-Y., Zheng, W.-M., & Bu, D. (2021). CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction. Nature Communications, 12(1), 2535. https://doi.org/10.1038/s41467-021-22869-8
Hameduh, T., Haddad, Y., Adam, V., & Heger, Z. (2020). Homology modeling in the time of collective and artificial intelligence. Computational and Structural Biotechnology Journal, 18, 3494–3506. https://doi.org/10.1016/j.csbj.2020.11.007
Kilinc, M., Jia, K., & Jernigan, R. L. (2023). Improved global protein homolog detection with major gains in function identification. Proceedings of the National Academy of Sciences, 120(9), e2211823120. https://doi.org/10.1073/pnas.2211823120
Hamamsy, T., Morton, J. T., Blackwell, R., Berenberg, D., Carriero, N., Gligorijevic, V., & Bonneau, R. (2023). Protein remote homology detection and structural alignment using deep learning. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01917-2
Mandloi, S., & Chakrabarti, S. (2017). Protein sites with more coevolutionary connections tend to evolve slower, while more variable protein families acquire higher coevolutionary connections. F1000Research, 6(453), 453. https://doi.org/10.12688/f1000research.11251.2
Xie, J., Zhang, W., Zhu, X., Deng, M., & Lai, L. (2023). Coevolution-based prediction of key allosteric residues for protein function regulation. eLife, 12, e81850. https://doi.org/10.7554/eLife.81850
Saravanan, K. M., Suvaithenamudhan, S., Parthasarathy, S., & Selvaraj, S. (2017). Pairwise contact energy statistical potentials can help to find probability of point mutations. Proteins, 85(1), 54–64. https://doi.org/10.1002/prot.25191
Zhang, H., Saravanan, K. M., Lin, J., Liao, L., Ng, J.T.-Y., Zhou, J., & Wei, Y. (2020). DeepBindPoc: A deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ, 8, e8864. https://doi.org/10.7717/peerj.8864
Zhang, H., Liao, L., Saravanan, K. M., Yin, P., & Wei, Y. (2019). DeepBindRG: A deep learning based method for estimating effective protein-ligand affinity. PeerJ, 2019(7), e7362. https://doi.org/10.7717/peerj.7362
Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014). Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information. eLife, 3, e02030. https://doi.org/10.7554/eLife.02030
Rehman, A. U., Khurshid, B., Ali, Y., Rasheed, S., Wadood, A., Ng, H.-L., & Zhang, J. (2023). Computational approaches for the design of modulators targeting protein-protein interactions. Expert Opinion on Drug Discovery, 18(3), 315–333. https://doi.org/10.1080/17460441.2023.2171396
Vangone, A., Spinelli, R., Scarano, V., Cavallo, L., & Oliva, R. (2011). COCOMAPS: A web application to analyze and visualize contacts at the interface of biomolecular complexes. Bioinformatics, 27(20), 2915–2916. https://doi.org/10.1093/bioinformatics/btr484
Fischer, T. B., Holmes, J. B., Miller, I. R., Parsons, J. R., Tung, L., Hu, J. C., & Tsai, J. (2006). Assessing methods for identifying pair-wise atomic contacts across binding interfaces. Journal of Structural Biology, 153(2), 103–112. https://doi.org/10.1016/j.jsb.2005.11.005
Holm, L., & Sander, C. (1996). Mapping the protein universe. Science, 273(5275), 595–602. https://doi.org/10.1126/science.273.5275.595
Tina, K. G., Bhadra, R., & Srinivasan, N. (2007). PIC: Protein interactions calculator. Nucleic Acids Research, 35, W473–W476. https://doi.org/10.1093/nar/gkm423
Lensink, M. F., & Wodak, S. J. (2013). Docking, scoring, and affinity prediction in CAPRI. Proteins, 81(12), 2082–2095. https://doi.org/10.1002/prot.24428
Chermak, E., Petta, A., Serra, L., Vangone, A., Scarano, V., Cavallo, L., & Oliva, R. (2015). CONSRANK: A server for the analysis, comparison and ranking of docking models based on inter-residue contacts. Bioinformatics, 31(9), 1481–1483. https://doi.org/10.1093/bioinformatics/btu837
Durham, J., Zhang, J., Humphreys, I. R., Pei, J., & Cong, Q. (2023). Recent advances in predicting and modeling protein–protein interactions. Trends in Biochemical Sciences, 48(6), 527–538. https://doi.org/10.1016/j.tibs.2023.03.003
Oliva, R., Vangone, A., & Cavallo, L. (2013). Ranking multiple docking solutions based on the conservation of inter-residue contacts. Proteins, 81(9), 1571–1584. https://doi.org/10.1002/prot.24314
Vangone, A., Cavallo, L., & Oliva, R. (2013). Using a consensus approach based on the conservation of inter-residue contacts to rank CAPRI models. Proteins, 81(12), 2210–2220. https://doi.org/10.1002/prot.24423
Ramakrishna Reddy, P., Kulandaisamy, A., & Michael Gromiha, M. (2023). TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features. Methods, 218, 118–124. https://doi.org/10.1016/j.ymeth.2023.08.005
Khakzad, H., Igashov, I., Schneuing, A., Goverde, C., Bronstein, M., & Correia, B. (2023). A new age in protein design empowered by deep learning. Cell Systems, 14(11), 925–939. https://doi.org/10.1016/j.cels.2023.10.006
Salamanca Viloria, J., Allega, M. F., Lambrughi, M., & Papaleo, E. (2017). An optimal distance cutoff for contact-based protein structure networks using side-chain centers of mass. Scientific Reports, 7(1), 2838. https://doi.org/10.1038/s41598-017-01498-6
Ji, S., Oruç, T., Mead, L., Rehman, M. F., Thomas, C. M., Butterworth, S., & Winn, P. J. (2019). DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure. PLoS ONE, 14(1), e0205214. https://doi.org/10.1371/journal.pone.0205214
Zhang, H., Zhang, Q., Ju, F., Zhu, J., Gao, Y., Xie, Z., & Bu, D. (2019). Predicting protein inter-residue contacts using composite likelihood maximization and deep learning. BMC Bioinformatics, 20(1), 537. https://doi.org/10.1186/s12859-019-3051-7
Wittmann, B. J., Johnston, K. E., Wu, Z., & Arnold, F. H. (2021). Advances in machine learning for directed evolution. Current Opinion in Structural Biology, 69, 11–18. https://doi.org/10.1016/j.sbi.2021.01.008
Middendorf, L., & Eicholt, L. A. (2024). Random, de novo, and conserved proteins: How structure and disorder predictors perform differently. Proteins. https://doi.org/10.1002/prot.26652
Eickholt, J., & Cheng, J. (2012). Predicting protein residue–residue contacts using deep networks and boosting. Bioinformatics, 28(23), 3066–3072. https://doi.org/10.1093/bioinformatics/bts598
Xu, X.-L., Shi, J.-X., Wang, J., & Li, W. (2021). Long-range correlation and critical fluctuations in coevolution networks of protein sequences. Physica A: Statistical Mechanics and its Applications, 562, 125339. https://doi.org/10.1016/j.physa.2020.125339
Saravanan, K. M., Zhang, H., & Wei, Y. (2021). Identifying native and non-native membrane protein loops by using stabilizing energetic terms of three popular force fields. Current Chinese Science, 1(1), 14–21. https://doi.org/10.2174/2665997201999200729165146
Huang, H., & Gong, X. (2020). A review of protein inter-residue distance prediction. Current Bioinformatics. https://doi.org/10.2174/1574893615999200425230056
Li, Y., Zhang, C., Bell, E. W., Zheng, W., Zhou, X., Yu, D.-J., & Zhang, Y. (2021). Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. PLoS Computational Biology, 17(3), e1008865. https://doi.org/10.1371/journal.pcbi.1008865
Lin, P., Tao, H., Li, H., & Huang, S.-Y. (2023). Protein–protein contact prediction by geometric triangle-aware protein language models. Nature Machine Intelligence, 5(11), 1275–1284. https://doi.org/10.1038/s42256-023-00741-2
Ding, W., Nakai, K., & Gong, H. (2022). Protein design via deep learning. Briefings in Bioinformatics, 23(3), bbac102. https://doi.org/10.1093/bib/bbac102
Wang, Z., & Xu, J. (2013). Predicting protein contact map using evolutionary and physical constraints by integer programming. Bioinformatics, 29(13), i266–i273. https://doi.org/10.1093/bioinformatics/btt211
Shimomura, T., Nishijima, K., & Kikuchi, T. (2019). A new technique for predicting intrinsically disordered regions based on average distance map constructed with inter-residue average distance statistics. BMC Structural Biology, 19(1), 3. https://doi.org/10.1186/s12900-019-0101-3
Kikuchi, T. (1992). Similarity between average distance maps of structurally homologous proteins. Journal of Protein Chemistry, 11(3), 305–320. https://doi.org/10.1007/BF01024869
Li, Y., Liu, Y., & Yu, D.-J. (2022). Machine learning for protein inter-residue interaction prediction. Machine learning in bioinformatics of protein sequences (pp. 183–203). World Scientific.
Ishida, T., & Kinoshita, K. (2007). PrDOS: Prediction of disordered protein regions from amino acid sequence. Nucleic Acids Research, 35(suppl_2), W460–W464. https://doi.org/10.1093/nar/gkm363
Ward, J. J., McGuffin, L. J., Bryson, K., Buxton, B. F., & Jones, D. T. (2004). The DISOPRED server for the prediction of protein disorder. Bioinformatics, 20(13), 2138–2139. https://doi.org/10.1093/bioinformatics/bth195
Eronen, L., Hintsanen, P., & Toivonen, H. (2012). Biomine: A network-structured resource of biological entities for link prediction. In M. R. Berthold (Ed.), BT: Bisociative knowledge discovery: An introduction to concept, algorithms, tools, and applications (pp. 364–378). Springer.
Zheng, W., Zhou, X., Wuyun, Q., Pearce, R., Li, Y., & Zhang, Y. (2020). FUpred: Detecting protein domains through deep-learning-based contact map prediction. Bioinformatics, 36(12), 3749–3757. https://doi.org/10.1093/bioinformatics/btaa217
Anishchenko, I., Pellock, S. J., Chidyausiku, T. M., Ramelot, T. A., Ovchinnikov, S., Hao, J., & Baker, D. (2021). De novo protein design by deep network hallucination. Nature, 600(7889), 547–552. https://doi.org/10.1038/s41586-021-04184-w
Huang, S., & Huang, J. T. (2013). Inter-residue interaction is a determinant of protein folding kinetics. Journal of Theoretical Biology, 317, 224–228. https://doi.org/10.1016/j.jtbi.2012.10.003
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Harihar, B., Saravanan, K.M., Gromiha, M.M. et al. Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-024-01119-4
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DOI: https://doi.org/10.1007/s12033-024-01119-4