[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122999-en":3,"doc-seo-122999-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122999,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants","Protein function is governed by dynamic behavior that cannot be inferred from amino-acid sequence alone. A proposed machine-learning framework integrates peptide sequence, protein structure, and molecular dynamics-derived descriptors to improve prediction of protein variant function. The pipeline combines conventional sequence/structure features with simulation data to forecast how multiple point mutations affect fold improvements in activity for bovine enterokinase variants. Results support the use of structural plus dynamic information to guide protein engineering in industrial applications.","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/jcim](pubs.acs.org/jcim)  Article   \nMachine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants  \nNiccolo Alberto Elia Venanzi, Andrea Basciu, Attilio Vittorio Vargiu, Alexandros Kiparissides, Paul A. Dalby, and Duygu Dikicioglu *  \n Cite This: J. Chem. Inf. Model. 2024, 64, 2681−2694  \nRead Online  \nDownloaded via UNIV OF CAGLIARI on October 10, 2024 at 21:19:28 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.  \n■ INTRODUCTION  \nProteins are essential, powerful machines in biology and consequently find a wide range of application areas in biotechnology, including the manufacturing of targeted therapies. However, their development as a functional product is expensive, time-consuming, and frequently yields unsuccessful results.1 The estimated average cost of bringing a new protein-based therapy to market is between $1 and $3 billion, and the success rate of clinical trials is below 10%. To overcome these obstacles, researchers are investigating new methods for expediting the engineering of proteins with enhanced functionality and manufacturability. Protein engineering entails making precise alterations to the original sequence of a protein to identify variants with desirable properties while minimizing interference with its function. Thus, protein engineering has revolutionized the production and use of protein-based products.2 However, due to the vast number of possible amino acid combinations, exhaustive experimental exploration of the landscape of protein fitness remains nearly impossible.3−6 Theoretically, the scope of mutations could be restricted to include only the ostensibly significant fragments of the protein such as the binding sites. However, this remains a heuristic solution applicable to a limited number of cases, as the majority of protein properties depend on the entire sequence and structural conformation, not just a few amino acids, due to the presence of epistatic effects.7,8  \n© 2024 The Authors. Published by American Chemical Society  \nBiodescriptors are quantitative characteristics that shed light on a protein’s chemistry and structure. Algorithms can use the information contained in biodescriptors to predict the effects of amino acid substitutions on the properties of proteins. Recently, machine learning (ML) techniques have been applied to the classification of proteins and the prediction of the stability of protein−ligand complexes.9−13 Nevertheless, biodescriptors, which incorporate function-related properties associated with the macromolecules, were not employed in the investigation of the role of mutations introduced into the protein sequence on protein performance. 14−16  \nUnsupervised ML methods mostly use natural language processing (NLP) to obtain seq","cbCaityW2igj9r9X","https://ap.wps.com/l/cbCaityW2igj9r9X","pdf",5070330,1,14,"English","en",105,"# Abstract\n# Introduction\n## Limitations of sequence-only approaches\n## Biodescriptors and prior ML efforts\n## Role of molecular dynamics simulations\n# Related background and study gap\n# Methods overview (work described)","[{\"question\":\"Why can’t protein dynamics be captured using sequence information alone?\",\"answer\":\"Protein biological activity is directly governed by dynamic behavior, which is not fully revealed by amino-acid order in the sequence.\"},{\"question\":\"What inputs does the proposed machine-learning framework integrate?\",\"answer\":\"It integrates peptide sequence, protein structure, and protein dynamics descriptors derived from molecular dynamics simulations.\"},{\"question\":\"What is the study’s prediction target and application context?\",\"answer\":\"The model predicts how point mutations influence fold improvements in the activity of bovine enterokinase variants, supporting protein engineering challenges in industrial settings.\"}]","Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants | 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can’t protein dynamics be captured using sequence information alone?","Question",{"text":75,"@type":76},"Protein biological activity is directly governed by dynamic behavior, which is not fully revealed by amino-acid order in the sequence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inputs does the proposed machine-learning framework integrate?",{"text":80,"@type":76},"It integrates peptide sequence, protein structure, and protein dynamics descriptors derived from molecular dynamics simulations.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the study’s prediction target and application context?",{"text":84,"@type":76},"The model predicts how point mutations influence fold improvements in the activity of bovine enterokinase variants, supporting protein engineering challenges in industrial 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