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Using a Restricted Boltzmann Machine (RBM), we learn a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force-field method (FoldX). Our approach is applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assess the functionality of 71 variants generated to explore a range of RBM and FoldX energies. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21\/71 sequences designed with our method were functional. Interestingly, 6\/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011621","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T13:37:55Z","timestamp":1700228275000},"page":"e1011621","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality 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Pseudolikelihood Maximization for Direct-Coupling Analysis of Protein Structure from Many Homologous Amino-Acid Sequences","volume":"276","author":"M Ekeberg","year":"2014","journal-title":"Journal of Computational Physics"},{"issue":"1","key":"pcbi.1011621.ref008","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1073\/pnas.0805923106","article-title":"Identification of Direct Residue Contacts in Protein\u2013Protein Interaction by Message Passing","volume":"106","author":"M Weigt","year":"2009","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"1","key":"pcbi.1011621.ref009","doi-asserted-by":"crossref","first-page":"5800","DOI":"10.1038\/s41467-021-25756-4","article-title":"Efficient Generative Modeling of Protein Sequences Using Simple Autoregressive Models","volume":"12","author":"J Trinquier","year":"2021","journal-title":"Nature 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Moffat","year":"2021","journal-title":"Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design"},{"key":"pcbi.1011621.ref024","author":"M Jendrusch","year":"2021","journal-title":"AlphaDesign: A de Novo Protein Design Framework Based on AlphaFold"},{"key":"pcbi.1011621.ref025","author":"Z Gao","year":"2022","journal-title":"AlphaDesign: A Graph Protein Design Method and Benchmark on AlphaFoldDB"},{"key":"pcbi.1011621.ref026","first-page":"643","article-title":"Learning Algorithms for the Classification Restricted Boltzmann Machine","volume":"13","author":"H Larochelle","year":"2012","journal-title":"The Journal of Machine Learning Research"},{"issue":"2","key":"pcbi.1011621.ref027","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.cels.2020.11.005","article-title":"RBM-MHC: a semi-supervised machine-learning method for sample-specific prediction of antigen presentation by HLA-I alleles","volume":"12","author":"B Bravi","year":"2021","journal-title":"Cell 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Othman","year":"2020","journal-title":"Biochemical and Biophysical Research Communications"},{"key":"pcbi.1011621.ref031","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.ijbiomac.2020.05.243","article-title":"Enhancing the Thermostability of Rhizopus Chinensis Lipase by Rational Design and MD Simulations","volume":"160","author":"R Wang","year":"2020","journal-title":"International Journal of Biological Macromolecules"},{"issue":"6213","key":"pcbi.1011621.ref032","doi-asserted-by":"crossref","first-page":"1258096","DOI":"10.1126\/science.1258096","article-title":"The New Frontier of Genome Engineering with CRISPR-Cas9","volume":"346","author":"JA Doudna","year":"2014","journal-title":"Science"},{"issue":"5","key":"pcbi.1011621.ref033","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.cell.2014.02.001","article-title":"Crystal Structure of Cas9 in Complex with Guide RNA and Target DNA","volume":"156","author":"H Nishimasu","year":"2014","journal-title":"Cell"},{"issue":"1","key":"pcbi.1011621.ref034","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1038\/s41467-019-08395-8","article-title":"Engineer Chimeric Cas9 to Expand PAM Recognition Based on Evolutionary Information","volume":"10","author":"D Ma","year":"2019","journal-title":"Nature Communications"},{"issue":"4","key":"pcbi.1011621.ref035","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.molcel.2018.12.003","article-title":"A compact, high-accuracy Cas9 with a dinucleotide PAM for in vivo genome editing","volume":"73","author":"A Edraki","year":"2019","journal-title":"Molecular cell"},{"key":"pcbi.1011621.ref036","doi-asserted-by":"crossref","first-page":"e77825","DOI":"10.7554\/eLife.77825","article-title":"Closely related type II-C Cas9 orthologs recognize diverse PAMs","volume":"11","author":"J Wei","year":"2022","journal-title":"eLife"},{"issue":"1","key":"pcbi.1011621.ref037","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41467-020-20633-y","article-title":"CRISPR Technologies and the Search for the PAM-free Nuclease","volume":"12","author":"D Collias","year":"2021","journal-title":"Nature Communications"},{"key":"pcbi.1011621.ref038","first-page":"21","article-title":"Learning and Evaluating Boltzmann Machines","volume":"2","author":"R Salakhutdinov","year":"2008","journal-title":"Utml Tr"},{"key":"pcbi.1011621.ref039","doi-asserted-by":"crossref","unstructured":"Tieleman T. Training Restricted Boltzmann Machines Using Approximations to the Likelihood Gradient. In: Proceedings of the 25th International Conference on Machine Learning. ICML\u2019 08. Helsinki, Finland: Association for Computing Machinery; 2008. p. 1064\u20131071.","DOI":"10.1145\/1390156.1390290"},{"issue":"4","key":"pcbi.1011621.ref040","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41592-021-01100-y","article-title":"Low-N protein engineering with data-efficient deep learning","volume":"18","author":"S Biswas","year":"2021","journal-title":"Nature methods"},{"issue":"1","key":"pcbi.1011621.ref041","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1186\/s13059-021-02495-9","article-title":"PAM-repeat Associations and Spacer Selection Preferences in Single and Co-Occurring CRISPR-Cas Systems","volume":"22","author":"JNA Vink","year":"2021","journal-title":"Genome Biology"},{"issue":"3","key":"pcbi.1011621.ref042","doi-asserted-by":"crossref","first-page":"034109","DOI":"10.1103\/PhysRevE.104.034109","article-title":"Barriers and Dynamical Paths in Alternating Gibbs Sampling of Restricted Boltzmann Machines","volume":"104","author":"C Roussel","year":"2021","journal-title":"Physical Review E"},{"key":"pcbi.1011621.ref043","author":"S Kumar","year":"2022","journal-title":"Constrained Sampling from Language Models via Langevin Dynamics in Embedding Spaces"},{"key":"pcbi.1011621.ref044","first-page":"1","article-title":"ColabFold: Making Protein Folding Accessible to All","author":"M Mirdita","year":"2022","journal-title":"Nature Methods"},{"issue":"4","key":"pcbi.1011621.ref045","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1002\/prot.20264","article-title":"Scoring function for automated assessment of protein structure template quality","volume":"57","author":"Y Zhang","year":"2004","journal-title":"Proteins: Structure, Function, and Bioinformatics"},{"key":"pcbi.1011621.ref046","article-title":"NetSurfP-2.0: Improved Prediction of Protein Structural Features by Integrated Deep Learning","author":"MS Klausen","year":"2018","journal-title":"Bioinformatics"},{"key":"pcbi.1011621.ref047","author":"E Asgari","year":"2019","journal-title":"DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences"},{"issue":"W1","key":"pcbi.1011621.ref048","doi-asserted-by":"crossref","first-page":"W438","DOI":"10.1093\/nar\/gky439","article-title":"COACH-D: Improved Protein\u2013Ligand Binding Sites Prediction with Refined Ligand-Binding Poses through Molecular Docking","volume":"46","author":"Q Wu","year":"2018","journal-title":"Nucleic acids research"},{"key":"pcbi.1011621.ref049","author":"ML Hekkelman","year":"2021","journal-title":"AlphaFill: Enriching the AlphaFold Models with Ligands and Co-Factors"},{"key":"pcbi.1011621.ref050","article-title":"Language models of protein sequences at the scale of evolution enable accurate structure prediction","author":"Z Lin","year":"2022","journal-title":"BioRxiv"},{"issue":"2","key":"pcbi.1011621.ref051","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1002\/wcms.1121","article-title":"An Overview of the Amber Biomolecular Simulation Package","volume":"3","author":"R Salomon-Ferrer","year":"2013","journal-title":"WIREs Computational Molecular Science"},{"key":"pcbi.1011621.ref052","doi-asserted-by":"crossref","unstructured":"Nijkamp E, Ruffolo J, Weinstein EN, Naik N, Madani A. Progen2: exploring the boundaries of protein language models. arXiv preprint arXiv:220613517. 2022;.","DOI":"10.1016\/j.cels.2023.10.002"},{"key":"pcbi.1011621.ref053","first-page":"8844","article-title":"MSA transformer. In: International Conference on Machine Learning","author":"RM Rao","year":"2021","journal-title":"PMLR"},{"key":"pcbi.1011621.ref054","first-page":"2023","article-title":"Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling","author":"A Elnaggar","year":"2023","journal-title":"bioRxiv"},{"key":"pcbi.1011621.ref055","first-page":"2021","article-title":"Function-guided protein design by deep manifold sampling","author":"V Gligorijevi\u0107","year":"2021","journal-title":"bioRxiv"},{"key":"pcbi.1011621.ref056","first-page":"14084","article-title":"Deep extrapolation for attribute-enhanced generation","volume":"34","author":"A Chan","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pcbi.1011621.ref057","first-page":"1","article-title":"Large language models generate functional protein sequences across diverse families","author":"A Madani","year":"2023","journal-title":"Nature Biotechnology"},{"key":"pcbi.1011621.ref058","doi-asserted-by":"crossref","first-page":"102571","DOI":"10.1016\/j.sbi.2023.102571","article-title":"Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies","volume":"80","author":"C Malbranke","year":"2023","journal-title":"Current Opinion in Structural Biology"},{"issue":"9","key":"pcbi.1011621.ref059","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1093\/bioinformatics\/btw006","article-title":"MMseqs Software Suite for Fast and Deep Clustering and Searching of Large Protein Sequence Sets","volume":"32","author":"M Hauser","year":"2016","journal-title":"Bioinformatics"},{"key":"pcbi.1011621.ref060","unstructured":"Glorot X, Bengio Y. Understanding the Difficulty of Training Deep Feedforward Neural Networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings; 2010. p. 249\u2013256."},{"key":"pcbi.1011621.ref061","article-title":"Fixing Weight Decay Regularization in Adam","author":"I Loshchilov","year":"2018","journal-title":"open review"},{"issue":"3","key":"pcbi.1011621.ref062","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1038\/s41564-020-00839-y","article-title":"The impact of genetic diversity on gene essentiality within the Escherichia coli species","volume":"6","author":"F Rousset","year":"2021","journal-title":"Nature microbiology"},{"issue":"7","key":"pcbi.1011621.ref063","doi-asserted-by":"crossref","first-page":"3022","DOI":"10.1093\/molbev\/msab120","article-title":"MEGA11: Molecular Evolutionary Genetics Analysis Version 11","volume":"38","author":"K Tamura","year":"2021","journal-title":"Molecular biology and evolution"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1011621","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011621","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T13:43:25Z","timestamp":1702993405000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011621"}},"subtitle":[],"editor":[{"given":"Joanna","family":"Slusky","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":63,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11,17]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1011621","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.03.20.533501","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,17]]}}}