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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, 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
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
Challenges and compromises: Predicting unbound antibody structures with deep learning
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
Major advances in protein function assignment by remote homolog detection with protein language models – A review
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
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
AI-based methods for biomolecular structure modeling for Cryo-EM
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102989
Farhanaz Farheen , Genki Terashi , Han Zhu , Daisuke Kihara
{"title":"AI-based methods for biomolecular structure modeling for Cryo-EM","authors":"Farhanaz Farheen ,&nbsp;Genki Terashi ,&nbsp;Han Zhu ,&nbsp;Daisuke Kihara","doi":"10.1016/j.sbi.2025.102989","DOIUrl":"10.1016/j.sbi.2025.102989","url":null,"abstract":"<div><div>Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to derive three-dimensional structures from raw projections. Recent advancements in artificial intelligence (AI) including deep learning have significantly improved the performance of these processes. In this review, we discuss state-of-the-art AI-based techniques used in key steps of cryo-EM data processing, including macromolecular structure modeling and heterogeneity analysis.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102989"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045837","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
Solution NMR goes big: Atomic resolution studies of protein components of molecular machines and phase-separated condensates 溶液核磁共振大：分子机器和相分离凝聚物的蛋白质组分的原子分辨率研究。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102976
Alexander I.M. Sever , Rashik Ahmed , Philip Rößler , Lewis E. Kay
{"title":"Solution NMR goes big: Atomic resolution studies of protein components of molecular machines and phase-separated condensates","authors":"Alexander I.M. Sever ,&nbsp;Rashik Ahmed ,&nbsp;Philip Rößler ,&nbsp;Lewis E. Kay","doi":"10.1016/j.sbi.2024.102976","DOIUrl":"10.1016/j.sbi.2024.102976","url":null,"abstract":"<div><div>The tools of structural biology have undergone remarkable advances in the past decade. These include new computational and experimental approaches that have enabled studies at a level of detail – and ease – that were not previously possible. Yet, significant deficiencies in our understanding of biomolecular function remain and new challenges must be overcome to go beyond static pictures towards a description of function in terms of structural dynamics. Solution Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as a powerful technique for atomic resolution studies of the dynamics of a wide range of biomolecules, including molecular machines and the components of phase-separated condensates. Here we highlight some of the very recent advances in these areas that have been driven by NMR.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102976"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001536","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
Traversing the drug discovery landscape using native mass spectrometry
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2025.102993
Hannah M. Britt , Carol V. Robinson
{"title":"Traversing the drug discovery landscape using native mass spectrometry","authors":"Hannah M. Britt ,&nbsp;Carol V. Robinson","doi":"10.1016/j.sbi.2025.102993","DOIUrl":"10.1016/j.sbi.2025.102993","url":null,"abstract":"<div><div>As health needs in our society evolve, the field of drug discovery must undergo constant innovation and improvement to identify novel targets and drug candidates. Owing to its ability to simultaneously capture biological interactions and provide in-depth molecular characterisation of the species involved, native mass spectrometry is starting to play an important role in this endeavour. Here, we discuss recent contributions that native mass spectrometry has made to drug discovery including deciphering protein-small molecule interactions, unravelling biochemical pathways, and integrating with complementary structural approaches.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102993"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078796","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
Computational advances in discovering cryptic pockets for drug discovery 发现药物发现的隐口袋的计算进展。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102975
Martijn P. Bemelmans , Zoe Cournia , Kelly L. Damm-Ganamet , Francesco L. Gervasio , Vineet Pande
{"title":"Computational advances in discovering cryptic pockets for drug discovery","authors":"Martijn P. Bemelmans ,&nbsp;Zoe Cournia ,&nbsp;Kelly L. Damm-Ganamet ,&nbsp;Francesco L. Gervasio ,&nbsp;Vineet Pande","doi":"10.1016/j.sbi.2024.102975","DOIUrl":"10.1016/j.sbi.2024.102975","url":null,"abstract":"<div><div>A number of promising therapeutic target proteins have been considered “undruggable” due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of “cryptic” pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102975"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946100","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
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