Machine learning-based prediction of proteins’ architecture using sequences of amino acids and structural alphabets

References

• Abbass, J., & Nebel, J.-C. (2015). Customised fragments libraries for protein structure prediction based on structural class annotations. BMC Bioinformatics, 16(1), 136. https://doi.org/10.1186/s12859-015-0576-2
   PubMedGoogle Scholar
• Abdennaji, I., Zaied, M., & Girault, J. M. (2021). Prediction of protein structural class based on symmetrical recurrence quantification analysis. Computational Biology and Chemistry, 92, 107450. https://doi.org/10.1016/J.COMPBIOLCHEM.2021.107450
   PubMed Web of Science ®Google Scholar
• Allam, I., Flatters, D., Caumes, G., Regad, L., Delos, V., Nuel, G., & Camproux, A.-C. (2018). SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information. PLoS One, 13(7), e0198854. https://doi.org/10.1371/journal.pone.0198854
   PubMed Web of Science ®Google Scholar
• Andreeva, A. (2016). Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling. Biochemical Society Transactions, 44(3), 937–943. https://doi.org/10.1042/BST20160053
   PubMedGoogle Scholar
• Andreeva, A., & Murzin, A. G. (2010). Structural classification of proteins and structural genomics: New insights into protein folding and evolution. Acta Crystallographica. Section F, Structural Biology and Crystallization Communications, 66(Pt 10), 1190–1197. doi: 10.1107/S1744309110007177/https://journals.iucr.org/services/termsofuse.html.
   PubMedGoogle Scholar
• Andreeva, A., Howorth, D., Chothia, C., Kulesha, E., & Murzin, A. G. (2014). SCOP2 prototype: A new approach to protein structure mining. Nucleic Acids Research., 42(D1), D310–D314.
   PubMed Web of Science ®Google Scholar
• Andreeva, A., Kulesha, E., Gough, J., & Murzin, A. G. (2020). The SCOP database in 2020: Expanded classification of representative family and superfamily domains of known protein structures. Nucleic Acids Research, 48(D1), D376–D382. https://doi.org/10.1093/NAR/GKZ1064
   PubMed Web of Science ®Google Scholar
• Antipov, D., Raiko, M., Lapidus, A., & Pevzner, P. A. (2020). MetaviralSPAdes: Assembly of viruses from metagenomic data. Bioinformatics, 36(14), 4126–4129. https://doi.org/10.1093/BIOINFORMATICS/BTAA490
   PubMed Web of Science ®Google Scholar
• Barnoud, J., Santuz, H., Craveur, P., Joseph, A. P., Jallu, V., de Brevern, A. G., & Poulain, P. (2017). PBxplore: A tool to analyze local protein structure and deformability with Protein Blocks. PeerJ., 5(11), e4013. https://doi.org/10.7717/peerj.4013
   PubMedGoogle Scholar
• Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, TGod., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., Ignatchenko, A., Insana, G., Ishtiaq, R., & Zhang, J. (2023). UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Research, 51(D1), D523–D531. https://doi.org/10.1093/nar/gkac1052
   PubMed Web of Science ®Google Scholar
• Bepler, T., & Berger, B. (2021). Learning the protein language: Evolution, structure, and function. Cell Systems, 12(6), 654–669.e3. https://doi.org/10.1016/J.CELS.2021.05.017
   PubMed Web of Science ®Google Scholar
• Berman, H. M. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=102472&tool=pmcentrez&rendertype=abstract https://doi.org/10.1093/nar/28.1.235
   PubMed Web of Science ®Google Scholar
• Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022). Using deep learning to annotate the protein universe. Nature Biotechnology, 40(6), 932–937. https://doi.org/10.1038/s41587-021-01179-w
   PubMed Web of Science ®Google Scholar
• Bonidia, R. P., Domingues, D. S., Sanches, D. S., & De Carvalho, A. C. P. L. F. (2022). MathFeature: Feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. Briefings in Bioinformatics, 23(1), 1–10. https://doi.org/10.1093/BIB/BBAB434
   Web of Science ®Google Scholar
• Bonidia, R. P., Sampaio, L. D. H., Domingues, D. S., Paschoal, A. R., Lopes, F. M., de Carvalho, A. C. P. L. F., & Sanches, D. S. (2021). Feature extraction approaches for biological sequences: A comparative study of mathematical features. Briefings in Bioinformatics, 22(5), 1–20. https://doi.org/10.1093/BIB/BBAB011
   PubMed Web of Science ®Google Scholar
• Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., Dutta, S., Feng, Z., Ganesan, S., Goodsell, D. S., Ghosh, S., Green, R. K., Guranović, V., Guzenko, D., Hudson, B. P., … Zhuravleva, M. (2021). RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Research, 49(D1), D437–D451. https://doi.org/10.1093/NAR/GKAA1038
   PubMed Web of Science ®Google Scholar
• Cai, Y. D., Feng, K. Y., Lu, W. C., & Chou, K.-C. (2006). Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology, 238(1), 172–176. https://doi.org/10.1016/j.jtbi.2005.05.034
   PubMed Web of Science ®Google Scholar
• Camproux, A. C., & Tufféry, P. (2005). Hidden Markov Model-derived structural alphabet for proteins: The learning of protein local shapes captures sequence specificity. Biochimica et Biophysica Acta, 1724(3), 394–403. https://doi.org/10.1016/J.BBAGEN.2005.05.019
   PubMed Web of Science ®Google Scholar
• Camproux, A. C., Gautier, R., & Tufféry, P. (2004). A hidden Markov model derived structural alphabet for proteins. Journal of Molecular Biology, 339(3), 591–605. https://doi.org/10.1016/j.jmb.2004.04.005
   PubMed Web of Science ®Google Scholar
• Camproux, A. C., Tuffery, P., Chevrolat, J. P., Boisvieux, J. F., & Hazout, S. (1999). Hidden Markov model approach for identifying the modular framework of the protein backbone. Protein Engineering, 12(12), 1063–1073. https://doi.org/10.1093/protein/12.12.1063
   PubMedGoogle Scholar
• Chen, Z., Zhao, P., Li, F., Marquez-Lago, T. T., Leier, A., Revote, J., Zhu, Y., Powell, D. R., Akutsu, T., Webb, G. I., Chou, K.-C., Smith, A. I., Daly, R. J., Li, J., & Song, J. (2020). iLearn: An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Briefings in Bioinformatics, 21(3), 1047–1057. https://doi.org/10.1093/BIB/BBZ041
   PubMed Web of Science ®Google Scholar
• Chothia, C., Gough, J., Vogel, C., & Teichmann, S. A. (2003). Evolution of the protein repertoire. Science, 300(5626), 1701–1703. https://doi.org/10.1126/SCIENCE.1085371
   PubMed Web of Science ®Google Scholar
• Chou, K.-C. (2005). Progress in protein structural class prediction and its impact to bioinformatics and proteomics. Current Protein & Peptide Science, 6(5), 423–436. https://doi.org/10.2174/138920305774329368
   PubMed Web of Science ®Google Scholar
• Chou, K.-C., & Zhang, C. T. (1995). Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology, 30(4), 275–349. https://doi.org/10.3109/10409239509083488
   PubMed Web of Science ®Google Scholar
• Dawson, N. L., Lewis, T. E., Das, S., Lees, J. G., Lee, D., Ashford, P., Orengo, C. A., & Sillitoe, I. (2016). CATH: An expanded resource to predict protein function through structure and sequence. Nucleic Acids Research, 45(D1), D289–D295. https://doi.org/10.1093/nar/gkw1098
   PubMed Web of Science ®Google Scholar
• de Brevern, A. G. (2005). New assessment of a structural alphabet. In Silico Biology., 5(3), 283–289.
   PubMedGoogle Scholar
• De Brevern, A. G., Etchebest, C., & Hazout, S. (2000). Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Proteins: Structure, Function, and Genetics, 41(3), 271–287. https://doi.org/10.1002/1097-0134(20001115)41:3<271::AID-PROT10>3.0.CO;2-Z
   PubMed Web of Science ®Google Scholar
• Dehzangi, A., Paliwal, K., Lyons, J., Sharma, A., & Sattar, A. (2014). Proposing a highly accurate protein structural class predictor using segmentation-based features. BMC Genomics, 15(Suppl 1), S2. https://doi.org/10.1186/1471-2164-15-S1-S2
   PubMedGoogle Scholar
• Dehzangi, A., Paliwal, K., Sharma, A., Dehzangi, O., & Sattar, A. (2013). A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(3), 564–575. https://doi.org/10.1109/TCBB.2013.65
   PubMed Web of Science ®Google Scholar
• Déraspe, M., Boisvert, S., Laviolette, F., Roy, P. H., & Corbeil, J. (2022). Flexible protein database based on amino acid k-mers. Scientific Reports, 12(1), 9101. https://doi.org/10.1038/s41598-022-12843-9
   PubMed Web of Science ®Google Scholar
• Ding, S., Li, Y., Shi, Z., & Yan, S. (2014). A protein structural classes prediction method based on predicted secondary structure and PSI-BLAST profile. Biochimie, 97(1), 60–65. https://doi.org/10.1016/j.biochi.2013.09.013
   PubMedGoogle Scholar
• Ding, Y.-S., Zhang, T.-L., & Chou, K.-C. (2007). Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein and Peptide Letters, 14(8), 811–815. https://doi.org/10.2174/092986607781483778
   PubMed Web of Science ®Google Scholar
• Dong, Q. W., Wang, X. L., & Lin, L. (2007). Methods for optimizing the structure alphabet sequences of proteins. Computers in Biology and Medicine, 37(11), 1610–1616. https://doi.org/10.1016/j.compbiomed.2007.03.002
   PubMed Web of Science ®Google Scholar
• Dubinkina, V. B., Ischenko, D. S., Ulyantsev, V. I., Tyakht, A. V., & Alexeev, D. G. (2016). Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis. BMC Bioinformatics, 17(1), 38. doi: 10.1186/S12859-015-0875-7/FIGURES/3.
   PubMedGoogle Scholar
• Dudev, M., & Lim, C. (2007). Discovering structural motifs using a structural alphabet: Application to magnesium-binding sites. BMC Bioinformatics, 8(1), 1–12. https://doi.org/10.1186/1471-2105-8-106/FIGURES/6
   PubMedGoogle Scholar
• Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., Bhowmik, D., & Rost, B. (2022). ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 7112–7127. https://doi.org/10.1109/TPAMI.2021.3095381
   PubMed Web of Science ®Google Scholar
• Etchebest, C., Benros, C., Bornot, A., Camproux, A. C., & De Brevern, A. G. (2007). A reduced amino acid alphabet for understanding and designing protein adaptation to mutation. European Biophysics Journal, 36(8), 1059–1069. doi: 10.1007/S00249-007-0188-5/TABLES/4.
   PubMed Web of Science ®Google Scholar
• Faure, G., Bornot, A., & de Brevern, A. G. (2008). Protein contacts, inter-residue interactions and side-chain modelling. Biochimie, 90(4), 626–639. https://doi.org/10.1016/j.biochi.2007.11.007
   PubMed Web of Science ®Google Scholar
• Faure, G., Joseph, A. P., Craveur, P., Narwani, T. J., Srinivasan, N., Gelly, J.-C., Rebehmed, J., & de Brevern, A. G. (2019). IPBAvizu: A PyMOL plugin for an efficient 3D protein structure superimposition approach. Source Code for Biology and Medicine, 14(1), 5. https://doi.org/10.1186/s13029-019-0075-3
   PubMed Web of Science ®Google Scholar
• Fetrow, J. S., Palumbo, M. J., & Berg, G. (1997). Patterns, structures, and amino acid frequencies in structural building blocks, a protein secondary structure classification scheme. Proteins, 27(2), 249–271. https://doi.org/10.1002/(SICI)1097-0134(199702)27:2<249::AID-PROT11>3.0.CO;2-M
   PubMed Web of Science ®Google Scholar
• Fox, N. K., Brenner, S. E., & Chandonia, J. M. (2015). The value of protein structure classification information – Surveying the scientific literature. Proteins, 83(11), 2025–2038. https://doi.org/10.1002/PROT.24915
   PubMed Web of Science ®Google Scholar
• Gelly, J. C., Joseph, A. P., Srinivasan, N., & De Brevern, A. G. (2011). IPBA: A tool for protein structure comparison using sequence alignment strategies. Nucleic Acids Research, 39(Web Server issue), W18–W23. https://doi.org/10.1093/nar/gkr333
   PubMedGoogle Scholar
• Ghiurcuta, C. G., & Moret, B. M. E. (2014). Evaluating synteny for improved comparative studies. Bioinformatics, 30(12), i9–18. https://doi.org/10.1093/BIOINFORMATICS/BTU259
   PubMed Web of Science ®Google Scholar
• Ghouzam, Y., Postic, G., De Brevern, A. G., & Gelly, J. C. (2015). Improving protein fold recognition with hybrid profiles combining sequence and structure evolution. Bioinformatics, 31(23), 3782–3789. https://doi.org/10.1093/BIOINFORMATICS/BTV462
   PubMed Web of Science ®Google Scholar
• Ghouzam, Y., Postic, G., Guerin, P. E., De Brevern, A. G., & Gelly, J. C. (2016). ORION: A web server for protein fold recognition and structure prediction using evolutionary hybrid profiles. Scientific Reports, 6(1), 28268. https://doi.org/10.1038/srep28268
   PubMed Web of Science ®Google Scholar
• Gribskov, M., McLachlan, A. D., & Eisenberg, D. (1987). Profile analysis: Detection of distantly related proteins. Proceedings of the National Academy of Sciences of the United States of America, 84(13), 4355–4358. https://doi.org/10.1073/pnas.84.13.4355
   PubMed Web of Science ®Google Scholar
• Guyon, F., Camproux, A. C., Hochez, J., & Tufféry, P. (2004). SA-Search: A web tool for protein structure mining based on a Structural Alphabet. Nucleic Acids Research, 32(Web Server issue), W545–W548. https://doi.org/10.1093/NAR/GKH467
   PubMedGoogle Scholar
• Harris, R. S., & Medvedev, P. (2020). Improved representation of sequence bloom trees. Bioinformatics, 36(3), 721–727. https://doi.org/10.1093/BIOINFORMATICS/BTZ662
   PubMed Web of Science ®Google Scholar
• Hayat, M., & Khan, A. (2012). MemHyb: Predicting membrane protein types by hybridizing SAAC and PSSM. Journal of Theoretical Biology, 292, 93–102. https://doi.org/10.1016/j.jtbi.2011.09.026
   PubMed Web of Science ®Google Scholar
• Hayat, M., Khan, A., & Yeasin, M. (2012). Prediction of membrane proteins using split amino acid and ensemble classification. Amino Acids, 42(6), 2447–2460. https://doi.org/10.1007/s00726-011-1053-5
   PubMed Web of Science ®Google Scholar
• Iqbal, Z., Caccamo, M., Turner, I., Flicek, P., & McVean, G. (2012). De novo assembly and genotyping of variants using colored de Bruijn graphs. Nature Genetics, 44(2), 226–232. https://doi.org/10.1038/ng.1028
   PubMed Web of Science ®Google Scholar
• Jahandideh, S., Abdolmaleki, P., Jahandideh, M., & Asadabadi, E. B. (2007). Novel two-stage hybrid neural discriminant model for predicting proteins structural classes. Biophysical Chemistry, 128(1), 87–93. https://doi.org/10.1016/j.bpc.2007.03.006
   PubMed Web of Science ®Google Scholar
• Joseph, A. P., Srinivasan, N., & De Brevern, A. G. (2012). Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies. Biochimie, 94(9), 2025–2034. https://doi.org/10.1016/J.BIOCHI.2012.05.028
   PubMed Web of Science ®Google Scholar
• Kelley, D. R., Schatz, M. C., & Salzberg, S. L. (2010). Quake: Quality-aware detection and correction of sequencing errors. Genome Biology, 11(11), R116. https://doi.org/10.1186/GB-2010-11-11-R116/FIGURES/6
   PubMed Web of Science ®Google Scholar
• Khorsand, P., & Hormozdiari, F. (2021). Nebula: Ultra-efficient mapping-free structural variant genotyper. Nucleic Acids Research, 49(8), e47–e47. https://doi.org/10.1093/NAR/GKAB025
   PubMed Web of Science ®Google Scholar
• Ku, S. Y., & Hu, Y. J. (2008). Protein structure search and local structure characterization. BMC Bioinformatics, 9(1), 349. doi: 10.1186/1471-2105-9-349/FIGURES/7.
   PubMedGoogle Scholar
• Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D., Harris, K., Heaford, A., Howland, J., Kann, L., Lehoczky, J., LeVine, R., McEwan, P., . . . Szustakowki, J. (2001). Initial sequencing and analysis of the human genome. Nature, 409, 860–921.
   Web of Science ®Google Scholar
• Léonard, S., Joseph, A. P., Srinivasan, N., Gelly, J. C., & De Brevern, A. G. (2014). MulPBA: An efficient multiple protein structure alignment method based on a structural alphabet. Journal of Biomolecular Structure & Dynamics, 32(4), 661–668. https://doi.org/10.1080/07391102.2013.787026
   PubMed Web of Science ®Google Scholar
• Levitt, M., & Chothia, C. (1976). Structural patterns in globular proteins. Nature, 261(5561), 552–558. https://doi.org/10.1038/261552a0
   PubMed Web of Science ®Google Scholar
• Lewis, T. E., Sillitoe, I., Andreeva, A., Blundell, T. L., Buchan, D. W. A., Chothia, C., Cuff, A., Dana, J. M., Filippis, I., Gough, J., Hunter, S., Jones, D. T., Kelley, L. A., Kleywegt, G. J., Minneci, F., Mitchell, A., Murzin, A. G., Ochoa-Montaño, B., Rackham, O. J. L., … Orengo, C. (2013). Genome3D: A UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains. Nucleic Acids Research, 41(Database issue), D499–D507. https://doi.org/10.1093/NAR/GKS1266
   PubMedGoogle Scholar
• Lewis, T. E., Sillitoe, I., Dawson, N., Lam, S. D., Clarke, T., Lee, D., Orengo, C., & Lees, J. (2018). Gene3D: Extensive prediction of globular domains in proteins. Nucleic Acids Research, 46(D1), D435–D439. https://doi.org/10.1093/NAR/GKX1069
   PubMed Web of Science ®Google Scholar
• Li, Q., Zhou, C., & Liu, H. (2009). Fragment-based local statistical potentials derived by combining an alphabet of protein local structures with secondary structures and solvent accessibilities. Proteins, 74(4), 820–836. https://doi.org/10.1002/PROT.22191
   PubMed Web of Science ®Google Scholar
• Li, Z.-C., Zhou, X.-B., Lin, Y.-R., & Zou, X.-Y. (2008). Prediction of protein structure class by coupling improved genetic algorithm and support vector machine. Amino Acids, 35(3), 581–590. https://doi.org/10.1007/s00726-008-0084-z
   PubMed Web of Science ®Google Scholar
• Liang, Y., & Zhang, S. (2017). Predict protein structural class by incorporating two different modes of evolutionary information into Chou’s general pseudo amino acid composition. Journal of Molecular Graphics & Modelling, 78, 110–117. https://doi.org/10.1016/J.JMGM.2017.10.003
   PubMed Web of Science ®Google Scholar
• Liu, B., Gao, X., & Zhang, H. (2019). BioSeq-Analysis2.0: An updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Research, 47(20), e127–e127. https://doi.org/10.1093/NAR/GKZ740
   PubMed Web of Science ®Google Scholar
• Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., & Chou, K. C. (2015). Pse-in-One: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Research, 43(W1), W65–W71. https://doi.org/10.1093/NAR/GKV458
   PubMed Web of Science ®Google Scholar
• Liu, T., & Jia, C. (2010). A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. Journal of Theoretical Biology, 267(3), 272–275. https://doi.org/10.1016/j.jtbi.2010.09.007
   PubMed Web of Science ®Google Scholar
• Lo, C. C., & Chain, P. S. G. (2014). Rapid evaluation and quality control of next generation sequencing data with FaQCs. BMC Bioinformatics, 15(1), 366. doi: 10.1186/S12859-014-0366-2/TABLES/3.
   PubMedGoogle Scholar
• Lu, J., Rincon, N., Wood, D. E., Breitwieser, F. P., Pockrandt, C., Langmead, B., Salzberg, S. L., & Steinegger, M. (2022). Metagenome analysis using the Kraken software suite. Nature Protocols, 17(12), 2815–2839. https://doi.org/10.1038/s41596-022-00738-y
   PubMed Web of Science ®Google Scholar
• Luo, R., Liu, B., Xie, Y., Li, Z., Huang, W., Yuan, J., He, G., Chen, Y., Pan, Q., Liu, Y., Tang, J., Wu, G., Zhang, H., Shi, Y., Liu, Y., Yu, C., Wang, B., Lu, Y., Han, C., . . . Wang, J. (2012). SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler. Gigascience, 1(1), 18.
   Google Scholar
• Madera, M., & Bateman, A. (2008). Profile Comparer: A program for scoring and aligning profile hidden Markov models. Bioinformatics, 24(22), 2630–2631. https://doi.org/10.1093/BIOINFORMATICS/BTN504
   PubMed Web of Science ®Google Scholar
• Marçais, G., & Kingsford, C. (2011). A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics, 27(6), 764–770. https://doi.org/10.1093/BIOINFORMATICS/BTR011
   PubMed Web of Science ®Google Scholar
• Martin, F. J., Amode, M. R., Aneja, A., Austine-Orimoloye, O., Azov, A. G., Barnes, I., Becker, A., Bennett, R., Berry, A., Bhai, J., Bhurji, S. K., Bignell, A., Boddu, S., Branco Lins, P. R., Brooks, L., Ramaraju, S. B., Charkhchi, M., Cockburn, A., Da Rin Fiorretto, L., … Flicek, P. (2023). Ensembl 2023. Nucleic Acids Research, 51(D1), D933–D941. https://doi.org/10.1093/NAR/GKAC958
   PubMed Web of Science ®Google Scholar
• Mistry, J., Finn, R. D., Eddy, S. R., Bateman, A., & Punta, M. (2013). Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Research, 41(12), e121–e121. https://doi.org/10.1093/NAR/GKT263
   PubMed Web of Science ®Google Scholar
• Mizianty, M. J., & Kurgan, L. A. (2009). Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences. BMC Bioinformatics, 10(1), 414. https://doi.org/10.1186/1471-2105-10-414
   PubMedGoogle Scholar
• 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/10.1006/jmbi.1995.0159
   PubMed Web of Science ®Google Scholar
• Nallapareddy, V., Bordin, N., Sillitoe, I., Heinzinger, M., Littmann, M., Waman, V. P., Sen, N., Rost, B., & Orengo, C. (2023). CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models. Bioinformatics, 39(1), 29. https://doi.org/10.1093/BIOINFORMATICS/BTAD029
   Web of Science ®Google Scholar
• Narwani, T. J., Etchebest, C., Craveur, P., Léonard, S., Rebehmed, J., Srinivasan, N., Bornot, A., Gelly, J.-C., & de Brevern, A. G. (2019). In silico prediction of protein flexibility with local structure approach. Biochimie, 165, 150–155. https://doi.org/10.1016/J.BIOCHI.2019.07.025
   PubMed Web of Science ®Google Scholar
• Nguyen, L. A. T., Dang, X. T., Le, T. K. T., Saethang, T., Tran, V. A., Ngo, D. L., Gavrilov, S., Nguyen, N. G., Kubo, M., Yamada, Y., & Satou, K. (2014). Predicting βeta-turns and βeta-turn types using a novel over-sampling approach. Journal of Biomedical Science and Engineering, 07(11), 927–940. https://doi.org/10.4236/jbise.2014.711090
   Google Scholar
• Oates, M. E., Stahlhacke, J., Vavoulis, D. V., Smithers, B., Rackham, O. J. L., Sardar, A. J., Zaucha, J., Thurlby, N., Fang, H., & Gough, J. (2015). The SUPERFAMILY 1.75 database in 2014: A doubling of data. Nucleic Acids Research, 43(Database issue), D227–D233. https://doi.org/10.1093/NAR/GKU1041
   PubMedGoogle Scholar
• Ondov, B. D., Treangen, T. J., Melsted, P., Mallonee, A. B., Bergman, N. H., Koren, S., & Phillippy, A. M. (2016). Mash: Fast genome and metagenome distance estimation using MinHash. Genome Biology, 17(1), 132. doi: 10.1186/S13059-016-0997-X/FIGURES/5.
   PubMed Web of Science ®Google Scholar
• Orengo, C. A., & Taylor, W. R. (1996). SSAP: Sequential structure alignment program for protein structure comparison. Methods in Enzymology, 266, 617–635. doi: 10.1016/S0076-6879(96)66038-8.
   PubMed Web of Science ®Google Scholar
• Orengo, C. A., Michie, A. D., Jones, S., Jones, D. T., Swindells, M. B., & Thornton, J. M. (1997). CATH–a hierarchic classification of protein domain structures. Structure, 5(8), 1093–1108. doi: 10.1016/S0969-2126(97)00260-8.
   PubMed Web of Science ®Google Scholar
• Pandini, A., Fornili, A., & Kleinjung, J. (2010). Structural alphabets derived from attractors in conformational space. BMC Bioinformatics, 11(1), 97. https://doi.org/10.1186/1471-2105-11-97
   PubMedGoogle Scholar
• Pevzner, P. A., Tang, H., & Waterman, M. S. (2001). An Eulerian path approach to DNA fragment assembly. Proceedings of the National Academy of Sciences of the United States of America, 98(17), 9748–9753. doi: 10.1073/PNAS.171285098/ASSET/8F490F1C-A68A-45AD-A4A9-6166D702BD74/ASSETS/GRAPHIC/PQ1712850005.JPEG.
   PubMed Web of Science ®Google Scholar
• Rangavittal, S., Stopa, N., Tomaszkiewicz, M., Sahlin, K., Makova, K. D., & Medvedev, P. (2019). DiscoverY: A classifier for identifying y chromosome sequences in male assemblies. BMC Genomics, 20(1), 641. doi: 10.1186/S12864-019-5996-3/FIGURES/5.
   PubMed Web of Science ®Google Scholar
• Redfern, O. C., Harrison, A., Dallman, T., Pearl, F. M. G., & Orengo, C. A. (2007). CATHEDRAL: A fast and effective algorithm to predict folds and domain boundaries from multidomain protein structures. PLoS Computational Biology, 3(11), e232. https://doi.org/10.1371/JOURNAL.PCBI.0030232
   PubMed Web of Science ®Google Scholar
• Sander, O., Sommer, I., & Lengauer, T. (2006). Local protein structure prediction using discriminative models. BMC Bioinformatics, 7(1), 14. https://doi.org/10.1186/1471-2105-7-14/FIGURES/13.
   PubMedGoogle Scholar
• Schuchhardt, J., Schneider, G., Reichelt, J., Schomburg, D., & Wrede, P. (1996). Local structural motifs of protein backbones are classified by self-organizing neural networks. Protein Engineering, 9(10), 833–842. https://doi.org/10.1093/protein/9.10.833
   PubMedGoogle Scholar
• Sehnal, D., Bittrich, S., Deshpande, M., Svobodová, R., Berka, K., Bazgier, V., Velankar, S., Burley, S. K., Koča, J., & Rose, A. S. (2021). Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Research, 49(W1), W431–W437. https://doi.org/10.1093/nar/gkab314
   PubMed Web of Science ®Google Scholar
• Sillitoe, I., Bordin, N., Dawson, N., Waman, V. P., Ashford, P., Scholes, H. M., Pang, C. S. M., Woodridge, L., Rauer, C., Sen, N., Abbasian, M., Le Cornu, S., Lam, S. D., Berka, K., Varekova, I. H., Svobodova, R., Lees, J., & Orengo, C. A. (2021). CATH: Increased structural coverage of functional space. Nucleic Acids Research, 49(D1), D266–D273. https://doi.org/10.1093/NAR/GKAA1079
   PubMed Web of Science ®Google Scholar
• Steinegger, M., & Söding, J. (2017). MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, 35(11), 1026–1028. https://doi.org/10.1038/nbt.3988
   PubMed Web of Science ®Google Scholar
• Steinegger, M., & Söding, J. (2018). Clustering huge protein sequence sets in linear time. Nature Communications, 9(1), 2542. https://doi.org/10.1038/s41467-018-04964-5
   PubMedGoogle Scholar
• Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019). HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics, 20(1), 473. doi: 10.1186/S12859-019-3019-7/FIGURES/7.
   PubMed Web of Science ®Google Scholar
• Suresh, V., & Parthasarathy, S. (2014). SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures. Protein and Peptide Letters, 21(8), 736–742. https://doi.org/10.2174/09298665113209990064
   PubMed Web of Science ®Google Scholar
• Suresh, V., Ganesan, K., & Parthasarathy, S. (2013). A protein block based fold recognition method for the annotation of twilight zone sequences. Protein and Peptide Letters, 20(3), 249–254. https://doi.org/10.2174/0929866511320030003
   PubMed Web of Science ®Google Scholar
• Thomas, A., Deshayes, S., Decaffmeyer, M., Van Eyck, M. H., Charloteaux, B., & Brasseur, R. (2006). Prediction of peptide structure: How far are we? Proteins, 65(4), 889–897. https://doi.org/10.1002/PROT.21151
   PubMed Web of Science ®Google Scholar
• Tung, C. H., Huang, J. W., & Yang, J. M. (2007). Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database. Genome Biology, 8(3), R31. doi: 10.1186/GB-2007-8-3-R31/FIGURES/9.
   PubMed Web of Science ®Google Scholar
• Tung, C.-H., & Nacher, J. C. (2013). A complex network approach for the analysis of protein units similarity using structural alphabet. International Journal of Bioscience, Biochemistry and Bioinformatics, 3, 433–437. https://doi.org/10.7763/IJBBB.2013.V3.250
   Google Scholar
• Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G. J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S. A. A., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 590–596. https://doi.org/10.1038/s41586-021-03819-2
   PubMed Web of Science ®Google Scholar
• Unger, R., Harel, D., Wherland, S., & Sussman, J. L. (1989). A 3D building blocks approach to analyzing and predicting structure of proteins. Proteins, 5(4), 355–373. https://doi.org/10.1002/PROT.340050410
   PubMed Web of Science ®Google Scholar
• van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2023). Fast and accurate protein structure search with Foldseek. Nature Biotechnology, 42(2), 243–246. https://doi.org/10.1038/s41587-023-01773-0
   PubMedGoogle Scholar
• Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., Žídek, A., Green, T., Tunyasuvunakool, K., Petersen, S., Jumper, J., Clancy, E., Green, R., Vora, A., Lutfi, M., … 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
   PubMed Web of Science ®Google Scholar
• Vetrivel, I., Mahajan, S., Tyagi, M., Hoffmann, L., Sanejouand, Y.-H., Srinivasan, N., de Brevern, A. G., Cadet, F., & Offmann, B. (2017). Knowledge-based prediction of protein backbone conformation using a structural alphabet. PLoS One, 12(11), e0186215. https://doi.org/10.1371/journal.pone.0186215
   PubMed Web of Science ®Google Scholar
• Wang, Y., Fu, L., Ren, J., Yu, Z., Chen, T., & Sun, F. (2018). Identifying Group-Specific sequences for microbial communities using Long k-mer sequence signatures. Front Microbiol, 9(MAY), 329350. doi: 10.3389/FMICB.2018.00872/BIBTEX.
   Google Scholar
• Wenzheng, B., Yuehui, C., & Dong, W. (2014). Prediction of protein structure classes with flexible neural tree. Biomed Mater Eng, 24(6), 3797–3806. https://doi.org/10.3233/BME-141209
   PubMed Web of Science ®Google Scholar
• Zhu, L., Davari, M. D., & Li, W. (2021). Recent advances in the prediction of protein structural classes: Feature descriptors and machine learning algorithms. Crystals, 11(4), 324. 2021, Vol. 11, Page 324, Mar. https://doi.org/10.3390/cryst11040324
   Web of Science ®Google Scholar
• Zhu, X. J., Feng, C. Q., Lai, H. Y., Chen, W., & Hao, L. (2019). Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowledge-Based Systems, 163, 787–793. https://doi.org/10.1016/j.knosys.2018.10.007
   Web of Science ®Google Scholar
• Zimmermann, O., & Hansmann, U. H. E. (2008). LOCUSTRA: Accurate prediction of local protein structure using a two-layer support vector machine approach. Journal of Chemical Information and Modeling, 48(9), 1903–1908. doi: 10.1021/CI800178A/SUPPL_FILE/CI800178A_SI_001.PDF.
   PubMed Web of Science ®Google Scholar
• Zuo, Y. C., & Li, Q. Z. (2009). Using reduced amino acid composition to predict defensin family and subfamily: Integrating similarity measure and structural alphabet. Peptides, 30(10), 1788–1793. https://doi.org/10.1016/J.PEPTIDES.2009.06.032
   PubMed Web of Science ®Google Scholar