A sequence space search engine for computational protein design to modulate molecular functionality

References

• Atiqullah, M. M. (2004). An efficient simple cooling schedule for simulated annealing. Paper presented at the International Conference on Computational Science and Its Applications, Springer, pp. 396–404.
   Google Scholar
• Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A., Pereira, J. H., Rodrigues, A. V., van Dijk, A. A., Ebrecht, A. C., … Baker, D. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science (New York, NY), 373(6557), 871–876. https://doi.org/10.1126/science.abj8754
   Google Scholar
• Bale, J. B., Gonen, S., Liu, Y., Sheffler, W., Ellis, D., Thomas, C., Cascio, D., Yeates, T. O., Gonen, T., King, N. P., & Baker, D. (2016). Accurate design of megadalton-scale two-component icosahedral protein complexes. Science (New York, NY), 353(6297), 389–394. https://doi.org/10.1126/science.aaf8818
   Google Scholar
• Banerjee, A., & Mitra, P. (2020). Ebola virus VP35 protein: Modeling of the tetrameric structure and an analysis of its interaction with human PKR. Journal of Proteome Research, 19(11), 4533–4542. https://doi.org/10.1021/acs.jproteome.0c00473
   PubMed Web of Science ®Google Scholar
• Banerjee, A., Pal, A., Pal, D., & Mitra, P. (2018). Ebolavirus interferon antagonists-protein interaction perspectives to combat pathogenesis. Briefings in Functional Genomics, 17(6), 392–401. https://doi.org/10.1093/bfgp/elx034
   PubMed Web of Science ®Google Scholar
• Banerjee, A., Pal, K., & Mitra, P. (2021). An evolutionary profile guided greedy parallel replica-exchange Monte Carlo search algorithm for rapid convergence in protein design. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(2), 489–499. https://doi.org/10.1109/TCBB.2019.2928809
   PubMed Web of Science ®Google Scholar
• Bazzoli, A., Tettamanzi, A. G., & Zhang, Y. (2011). Computational protein design and large-scale assessment by I-TASSER structure assembly simulations. Journal of Molecular Biology, 407(5), 764–776. https://doi.org/10.1016/j.jmb.2011.02.017
   PubMed Web of Science ®Google Scholar
• Berman, H., Henrick, K., Nakamura, H., & Markley, J. L. (2007). The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Research, 35(Database issue), D301–3. https://doi.org/10.1093/nar/gkl971
   PubMed Web of Science ®Google Scholar
• Boas, F. E., & Harbury, P. B. (2007). Potential energy functions for protein design. Current Opinion in Structural Biology, 17(2), 199–204. https://doi.org/10.1016/j.sbi.2007.03.006
   PubMed Web of Science ®Google Scholar
• Bommarius, A. S., & Paye, M. F. (2013). Stabilizing biocatalysts. Chemical Society Reviews, 42(15), 6534–6565. https://doi.org/10.1039/c3cs60137d
   PubMed Web of Science ®Google Scholar
• Chen, C. Y., Georgiev, I., Anderson, A. C., & Donald, B. R. (2009). Computational structure-based redesign of enzyme activity. Proceedings of the National Academy of Sciences of the United States of America, 106(10), 3764–3769. https://doi.org/10.1073/pnas.0900266106
   PubMed Web of Science ®Google Scholar
• Chevalier, A., Silva, D. A., Rocklin, G. J., Hicks, D. R., Vergara, R., Murapa, P., Bernard, S. M., Zhang, L., Lam, K. H., Yao, G., Bahl, C. D., Miyashita, S. I., Goreshnik, I., Fuller, J. T., Koday, M. T., Jenkins, C. M., Colvin, T., Carter, L., Bohn, A., … Baker, D. (2017). Massively parallel de novo protein design for targeted therapeutics. Nature, 550(7674), 74–79. https://doi.org/10.1038/nature23912
   PubMed Web of Science ®Google Scholar
• Dahiyat, B. I., & Mayo, S. L. (1997). De novo protein design: Fully automated sequence selection. Science (New York, NY), 278(5335), 82–87. https://doi.org/10.1126/science.278.5335.82
   Google Scholar
• Desmet, J., De Maeyer, M., Hazes, B., & Lasters, I. (1992). The dead-end elimination theorem and its use in protein side-chain positioning. Nature, 356(6369), 539–542. https://doi.org/10.1038/356539a0
   PubMed Web of Science ®Google Scholar
• Ding, F., & Dokholyan, N. V. (2006). Emergence of protein fold families through rational design. PLoS Computational Biology, 2(7), e85. https://doi.org/10.1371/journal.pcbi.0020085
   PubMed Web of Science ®Google Scholar
• Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L. P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., Wiewiora, R. P., Brooks, B. R., & Pande, V. S. (2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Computational Biology, 13(7), e1005659. https://doi.org/10.1371/journal.pcbi.1005659
   PubMed Web of Science ®Google Scholar
• Fischman, S., & Ofran, Y. (2018). Computational design of antibodies. Current Opinion in Structural Biology, 51, 156–162. https://doi.org/10.1016/j.sbi.2018.04.007
   PubMed Web of Science ®Google Scholar
• Huang, X., Pearce, R., & Zhang, Y. (2020). FASPR: An open-source tool for fast and accurate protein side-chain packing. Bioinformatics (Oxford, England), 36(12), 3758–3765. https://doi.org/10.1093/bioinformatics/btaa234
   PubMed Web of Science ®Google Scholar
• Jones, D. T. (1994). De novo protein design using pairwise potentials and a genetic algorithm. Protein Science, 3(4), 567–574. https://doi.org/10.1002/pro.5560030405
   PubMed Web of Science ®Google Scholar
• Jorgensen, W. L., & Tirado-Rives, J. (1988). The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6), 1657–1666. https://doi.org/10.1021/ja00214a001
   PubMed Web of Science ®Google Scholar
• Kaynak, B. T., Zhang, S., Bahar, I., & Doruker, P. (2021). ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution. Bioinformatics, 37(21), 3956–3958. https://doi.org/10.1093/bioinformatics/btab496
   PubMed Web of Science ®Google Scholar
• Koehl, P., & Levitt, M. (1999). De novo protein design. I. In search of stability and specificity. Journal of Molecular Biology, 293(5), 1161–1181. https://doi.org/10.1006/jmbi.1999.3211
   PubMed Web of Science ®Google Scholar
• Kuhlman, B., & Baker, D. (2000). Native protein sequences are close to optimal for their structures. Proceedings of the National Academy of Sciences of the United States of America, 97(19), 10383–10388. https://doi.org/10.1073/pnas.97.19.10383
   PubMed Web of Science ®Google Scholar
• Leaver-Fay, A., O'Meara, M. J., Tyka, M., Jacak, R., Song, Y., Kellogg, E. H., Thompson, J., Davis, I. W., Pache, R. A., Lyskov, S., Gray, J. J., Kortemme, T., Richardson, J. S., Havranek, J. J., Snoeyink, J., Baker, D., & Kuhlman, B. (2013). Scientific benchmarks for guiding macromolecular energy function improvement. Methods in Enzymology, 523, 109–143.
   PubMed Web of Science ®Google Scholar
• Leaver-Fay, A., Tyka, M., Lewis, S. M., Lange, O. F., Thompson, J., Jacak, R., Kaufman, K. W., Renfrew, P. D., Smith, C. A., & Sheffler, W. (2011). ROSETTA3: An object-oriented software suite for the simulation and design of macromolecules. In Methods in enzymology (Vol. 487, pp. 545–574). Elsevier.
   Google Scholar
• Liu, Y., & Kuhlman, B. (2006). RosettaDesign server for protein design. Nucleic Acids Research, 34(Web Server issue), W235–8. https://doi.org/10.1093/nar/gkl163
   PubMedGoogle Scholar
• Mitra, P., Shultis, D., Brender, J. R., Czajka, J., Marsh, D., Gray, F., Cierpicki, T., & Zhang, Y. (2013a). An evolution-based approach to De Novo protein design and case study on Mycobacterium tuberculosis. PLoS Computational Biology, 9(10), e1003298. https://doi.org/10.1371/journal.pcbi.1003298
   PubMed Web of Science ®Google Scholar
• Mitra, P., Shultis, D., & Zhang, Y. (2013b). EvoDesign: De novo protein design based on structural and evolutionary profiles. Nucleic Acids Research, 41(Web Server issue), W273–80. https://doi.org/10.1093/nar/gkt384
   PubMedGoogle Scholar
• Murphy, P. M., Bolduc, J. M., Gallaher, J. L., Stoddard, B. L., & Baker, D. (2009). Alteration of enzyme specificity by computational loop remodeling and design. Proceedings of the National Academy of Sciences of the United States of America, 106(23), 9215–9220. https://doi.org/10.1073/pnas.0811070106
   PubMed Web of Science ®Google Scholar
• O'Meara, M. J., Leaver-Fay, A., Tyka, M. D., Stein, A., Houlihan, K., DiMaio, F., Bradley, P., Kortemme, T., Baker, D., Snoeyink, J., & Kuhlman, B. (2015). Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta. Journal of Chemical Theory and Computation, 11(2), 609–622. https://doi.org/10.1021/ct500864r
   PubMed Web of Science ®Google Scholar
• Pal, A., Mulumudy, R., & Mitra, P. (2022). Modularity-based parallel protein design algorithm with an implementation using shared memory programming. Proteins: Structure, Function, and Bioinformatics, 90(3), 658–669. https://doi.org/10.1002/prot.26263
   PubMed Web of Science ®Google Scholar
• Privett, H. K., Kiss, G., Lee, T. M., Blomberg, R., Chica, R. A., Thomas, L. M., Hilvert, D., Houk, K. N., & Mayo, S. L. (2012). Iterative approach to computational enzyme design. Proceedings of the National Academy of Sciences of the United States of America, 109(10), 3790–3795. https://doi.org/10.1073/pnas.1118082108
   PubMed Web of Science ®Google Scholar
• Pronk, S., Pall, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., Hess, B., & Lindahl, E. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics (Oxford, England), 29(7), 845–854. https://doi.org/10.1093/bioinformatics/btt055
   PubMed Web of Science ®Google Scholar
• Rajesh, Y., Banerjee, A., Pal, I., Biswas, A., Das, S., Dey, K. K., Kapoor, N., Ghosh, A. K., Mitra, P., & Mandal, M. (2019). Delineation of crosstalk between HSP27 and MMP-2/MMP-9: A synergistic therapeutic avenue for glioblastoma management. Biochimica et Biophysica Acta. General Subjects, 1863(7), 1196–1209. https://doi.org/10.1016/j.bbagen.2019.04.015
   PubMed Web of Science ®Google Scholar
• Roy, A., Yang, J., & Zhang, Y. (2012). COFACTOR: An accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, 40(Web Server issue), W471–7. https://doi.org/10.1093/nar/gks372
   PubMedGoogle Scholar
• Saunders, C. T., & Baker, D. (2005). Recapitulation of protein family divergence using flexible backbone protein design. Journal of Molecular Biology, 346(2), 631–644. https://doi.org/10.1016/j.jmb.2004.11.062
   PubMed Web of Science ®Google Scholar
• Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005). The FoldX web server: An online force field. Nucleic Acids Research, 33(Web Server issue), W382–8. https://doi.org/10.1093/nar/gki387
   PubMedGoogle Scholar
• Shultis, D., Mitra, P., Huang, X., Johnson, J., Khattak, N. A., Gray, F., Piper, C., Czajka, J., Hansen, L., Wan, B., Chinnaswamy, K., Liu, L., Wang, M., Pan, J., Stuckey, J., Cierpicki, T., Borchers, C. H., Wang, S., Lei, M., & Zhang, Y. (2019). Changing the apoptosis pathway through evolutionary protein design. Journal of Molecular Biology, 431(4), 825–841. https://doi.org/10.1016/j.jmb.2018.12.016
   PubMed Web of Science ®Google Scholar
• Slovic, A. M., Summa, C. M., Lear, J. D., & DeGrado, W. F. (2003). Computational design of a water-soluble analog of phospholamban. Protein Science, 12(2), 337–348. https://doi.org/10.1110/ps.0226603
   PubMed Web of Science ®Google Scholar
• Yan, R., Xu, D., Yang, J., Walker, S., & Zhang, Y. (2013). A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Scientific Reports, 3, 2619. https://doi.org/10.1038/srep02619
   PubMed Web of Science ®Google Scholar
• Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015). The I-TASSER Suite: Protein structure and function prediction. Nature Methods, 12(1), 7–8. https://doi.org/10.1038/nmeth.3213
   PubMed Web of Science ®Google Scholar
• Yin, H., Slusky, J. S., Berger, B. W., Walters, R. S., Vilaire, G., Litvinov, R. I., Lear, J. D., Caputo, G. A., Bennett, J. S., & DeGrado, W. F. (2007). Computational design of peptides that target transmembrane helices. Science (New York, NY), 315(5820), 1817–1822. https://doi.org/10.1126/science.1136782
   Google Scholar
• Zhang, Y., & Skolnick, J. (2005). TM-align: A protein structure alignment algorithm based on the TM-score. Nucleic Acids Research, 33(7), 2302–2309. https://doi.org/10.1093/nar/gki524
   PubMed Web of Science ®Google Scholar