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Deep learning methods for proteome-scale interaction prediction 蛋白质组级相互作用预测的深度学习方法。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102981
Min Su Yoon , Byunghyun Bae , Kunhee Kim , Hahnbeom Park , Minkyung Baek
{"title":"Deep learning methods for proteome-scale interaction prediction","authors":"Min Su Yoon ,&nbsp;Byunghyun Bae ,&nbsp;Kunhee Kim ,&nbsp;Hahnbeom Park ,&nbsp;Minkyung Baek","doi":"10.1016/j.sbi.2024.102981","DOIUrl":"10.1016/j.sbi.2024.102981","url":null,"abstract":"<div><div>Proteome-scale interaction prediction is essential for understanding protein functions and disease mechanisms. Traditional experimental methods are often limited by scale and complexity, driving the need for computational approaches. Deep learning has emerged as a powerful tool, enabling high-throughput, accurate predictions of protein interactions. This review highlights recent advances in deep learning methods for protein–protein and protein-ligand interaction screening, along with datasets used for model training. Despite the progress with deep learning, challenges such as data quality and validation biases remain. We also discuss the increasing importance of integrating structural information to enhance prediction accuracy and how structure-based deep learning approaches can help overcome current limitations, ultimately advancing biological research and drug discovery.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102981"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advancing protein structure prediction beyond AlphaFold2 超越AlphaFold2推进蛋白质结构预测。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102985
Sanggeun Park , Sojung Myung , Minkyung Baek
{"title":"Advancing protein structure prediction beyond AlphaFold2","authors":"Sanggeun Park ,&nbsp;Sojung Myung ,&nbsp;Minkyung Baek","doi":"10.1016/j.sbi.2025.102985","DOIUrl":"10.1016/j.sbi.2025.102985","url":null,"abstract":"<div><div>Accurate prediction of protein structures is essential for understanding their biological functions. The release of AlphaFold2 in 2021 marked a significant breakthrough, delivering unprecedented accuracy. However, challenges remain, particularly for proteins with limited evolutionary data or complex molecular interactions. This review explores efforts to enhance AlphaFold2’s performance through advanced sequence search techniques and alternative approaches, including protein language models and frameworks that integrate diverse biomolecular interactions. We propose that future progress will depend on developing models grounded in fundamental physicochemical principles, offering more accurate and comprehensive predictions across a wider spectrum of biological systems.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102985"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143037410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Different applications and differentiated libraries for crystallographic fragment screening 晶体碎片筛选的不同应用和不同文库。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102982
Jessica Watt , Mathew P. Martin , Jane A. Endicott , Martin.E.M. Noble
{"title":"Different applications and differentiated libraries for crystallographic fragment screening","authors":"Jessica Watt ,&nbsp;Mathew P. Martin ,&nbsp;Jane A. Endicott ,&nbsp;Martin.E.M. Noble","doi":"10.1016/j.sbi.2024.102982","DOIUrl":"10.1016/j.sbi.2024.102982","url":null,"abstract":"<div><div>Macromolecular X-ray crystallography allows detection and characterisation of the binding of small, low-affinity chemical fragments. Here we review the utility of fragment screening for drug discovery, its potential for use in discovery science, as well as some of the distinct types of fragments that have been compiled into libraries.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102982"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial overview: Protein networks in health and disease 编辑概述：健康和疾病中的蛋白质网络。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102953
Elizabeth A. Komives, Gabriela Chiosis
{"title":"Editorial overview: Protein networks in health and disease","authors":"Elizabeth A. Komives,&nbsp;Gabriela Chiosis","doi":"10.1016/j.sbi.2024.102953","DOIUrl":"10.1016/j.sbi.2024.102953","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102953"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142791253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modern machine learning methods for protein property prediction 蛋白质性质预测的现代机器学习方法。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102990
Arjun Dosajh , Prakul Agrawal , Prathit Chatterjee, U. Deva Priyakumar
{"title":"Modern machine learning methods for protein property prediction","authors":"Arjun Dosajh ,&nbsp;Prakul Agrawal ,&nbsp;Prathit Chatterjee,&nbsp;U. Deva Priyakumar","doi":"10.1016/j.sbi.2025.102990","DOIUrl":"10.1016/j.sbi.2025.102990","url":null,"abstract":"<div><div>Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to new inputs, they make predictions based on patterns and relationships previously observed in the training set. Further, generative artificial intelligence (GenAI) can be used to accurately generate protein structure or sequence from specific selected properties. This review specifically focuses on the applications of AI/ML in predicting important functional properties of proteins, and the potential prospects of reverse-engineering in depicting the sequence and structure, from available protein-property information.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102990"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Binding mechanisms of intrinsically disordered proteins: Insights from experimental studies and structural predictions 内在无序蛋白质的结合机制：来自实验研究和结构预测的见解。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102958
Thibault Orand, Malene Ringkjøbing Jensen
{"title":"Binding mechanisms of intrinsically disordered proteins: Insights from experimental studies and structural predictions","authors":"Thibault Orand,&nbsp;Malene Ringkjøbing Jensen","doi":"10.1016/j.sbi.2024.102958","DOIUrl":"10.1016/j.sbi.2024.102958","url":null,"abstract":"<div><div>Advances in the characterization of intrinsically disordered proteins (IDPs) have unveiled a remarkably complex and diverse interaction landscape, including coupled folding and binding, highly dynamic complexes, multivalent interactions, and even interactions between entirely disordered proteins. Here we review recent examples of IDP binding mechanisms elucidated by experimental techniques such as nuclear magnetic resonance spectroscopy, single-molecule Förster resonance energy transfer, and stopped-flow fluorescence. These techniques provide insights into the structural details of transition pathways and complex intermediates, and they capture the dynamics of IDPs within complexes. Furthermore, we discuss the growing role of artificial intelligence, exemplified by AlphaFold, in identifying interaction sites within IDPs and predicting their bound-state structures. Our review highlights the powerful complementarity between experimental methods and artificial intelligence-based approaches in advancing our understanding of the intricate interaction landscape of IDPs.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102958"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial overview: New perspectives on the structure and dynamics of protein-nucleic acid interactions 编辑概述：蛋白质-核酸相互作用的结构和动力学的新观点。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102957
Junji Iwahara, David C. Williams Jr.
{"title":"Editorial overview: New perspectives on the structure and dynamics of protein-nucleic acid interactions","authors":"Junji Iwahara,&nbsp;David C. Williams Jr.","doi":"10.1016/j.sbi.2024.102957","DOIUrl":"10.1016/j.sbi.2024.102957","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102957"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Major advances in protein function assignment by remote homolog detection with protein language models – A review 基于蛋白质语言模型的远程同源检测在蛋白质功能定位中的主要进展
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102984
Mesih Kilinc , Kejue Jia , Robert L. Jernigan
{"title":"Major advances in protein function assignment by remote homolog detection with protein language models – A review","authors":"Mesih Kilinc ,&nbsp;Kejue Jia ,&nbsp;Robert L. Jernigan","doi":"10.1016/j.sbi.2025.102984","DOIUrl":"10.1016/j.sbi.2025.102984","url":null,"abstract":"<div><div>There is an ever-increasing need for accurate and efficient methods to identify protein homologs. Traditionally, sequence similarity-based methods have dominated protein homolog identification for function identification, but these struggle when the sequence identity between the pairs is low. Recently, transformer architecture-based deep learning methods have achieved breakthrough performances in many fields. One type of model that uses transformer architecture is the protein language model (pLM). Here, we describe methods that use pLMs for protein homolog identification intended for function identification and describe their strengths and weaknesses. Several important ideas emerge, such as filtering the substitution matrix generated from embeddings, selecting specific pLM layers for specific purposes, compressing the embeddings, and dividing proteins into domains before searching for homologs that improve remote homolog detection accuracy considerably. All of these approaches produce huge numbers of new homologs that can reliably extend the reach of protein relationships for a deeper understanding of evolution and many other problems.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102984"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Challenges and compromises: Predicting unbound antibody structures with deep learning 挑战与妥协：用深度学习预测非结合抗体结构。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102983
Alexander Greenshields-Watson , Odysseas Vavourakis , Fabian C. Spoendlin , Matteo Cagiada , Charlotte M. Deane
{"title":"Challenges and compromises: Predicting unbound antibody structures with deep learning","authors":"Alexander Greenshields-Watson ,&nbsp;Odysseas Vavourakis ,&nbsp;Fabian C. Spoendlin ,&nbsp;Matteo Cagiada ,&nbsp;Charlotte M. Deane","doi":"10.1016/j.sbi.2025.102983","DOIUrl":"10.1016/j.sbi.2025.102983","url":null,"abstract":"<div><div>Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modelling of the CDRH3 loop, where inaccuracies are potentially worse due to a bias in structural data towards antibody-antigen complexes. This class imbalance provides a challenge for deep learning models trained on this data, potentially limiting generalisation to unbound forms.</div><div>Here we discuss the importance of unbound structures in antibody development pipelines. We explore how the latest generation of structure predictors can provide new insights and assess how conformational heterogeneity may influence binding kinetics. We hypothesise that generative models may address some of these issues. While prediction of antibodies in complex is essential, we should not ignore the need for progress in modelling the unbound form.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102983"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143037416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial overview: 3D Genome Chromatin organization and regulation 编辑概述：三维基因组染色质组织和调控。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102956
Eric Conway, Daniel R. Larson
{"title":"Editorial overview: 3D Genome Chromatin organization and regulation","authors":"Eric Conway,&nbsp;Daniel R. Larson","doi":"10.1016/j.sbi.2024.102956","DOIUrl":"10.1016/j.sbi.2024.102956","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102956"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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