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Current opinion in structural biology最新文献

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Modern machine learning methods for protein property prediction.
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-01-28 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, Prakul Agrawal, Prathit Chatterjee, U Deva Priyakumar","doi":"10.1016/j.sbi.2025.102990","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.102990","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102990"},"PeriodicalIF":6.1,"publicationDate":"2025-01-28","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
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 : 2025-01-25 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, Kejue Jia, Robert L Jernigan","doi":"10.1016/j.sbi.2025.102984","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.102984","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102984"},"PeriodicalIF":6.1,"publicationDate":"2025-01-25","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
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 : 2025-01-25 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, Genki Terashi, Han Zhu, Daisuke Kihara","doi":"10.1016/j.sbi.2025.102989","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.102989","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102989"},"PeriodicalIF":6.1,"publicationDate":"2025-01-25","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
Advancing protein structure prediction beyond AlphaFold2.
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-01-24 DOI: 10.1016/j.sbi.2025.102985
Sanggeun Park, Sojung Myung, Minkyung Baek
{"title":"Advancing protein structure prediction beyond AlphaFold2.","authors":"Sanggeun Park, Sojung Myung, Minkyung Baek","doi":"10.1016/j.sbi.2025.102985","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.102985","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102985"},"PeriodicalIF":6.1,"publicationDate":"2025-01-24","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
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 : 2025-01-24 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, Odysseas Vavourakis, Fabian C Spoendlin, Matteo Cagiada, Charlotte M Deane","doi":"10.1016/j.sbi.2025.102983","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.102983","url":null,"abstract":"<p><p>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. 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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102983"},"PeriodicalIF":6.1,"publicationDate":"2025-01-24","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep learning methods for proteome-scale interaction prediction.
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-01-22 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, Byunghyun Bae, Kunhee Kim, Hahnbeom Park, Minkyung Baek","doi":"10.1016/j.sbi.2024.102981","DOIUrl":"https://doi.org/10.1016/j.sbi.2024.102981","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102981"},"PeriodicalIF":6.1,"publicationDate":"2025-01-22","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
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 : 2025-01-20 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, Rashik Ahmed, Philip Rößler, Lewis E Kay","doi":"10.1016/j.sbi.2024.102976","DOIUrl":"https://doi.org/10.1016/j.sbi.2024.102976","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102976"},"PeriodicalIF":6.1,"publicationDate":"2025-01-20","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
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 : 2025-01-18 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, Mathew P Martin, Jane A Endicott, Martin E M Noble","doi":"10.1016/j.sbi.2024.102982","DOIUrl":"https://doi.org/10.1016/j.sbi.2024.102982","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102982"},"PeriodicalIF":6.1,"publicationDate":"2025-01-18","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Molecular basis of conjugation-mediated DNA transfer by gram-negative bacteria. 革兰氏阴性菌偶联介导DNA转移的分子基础。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-01-16 DOI: 10.1016/j.sbi.2024.102978
Gabriel Waksman
{"title":"Molecular basis of conjugation-mediated DNA transfer by gram-negative bacteria.","authors":"Gabriel Waksman","doi":"10.1016/j.sbi.2024.102978","DOIUrl":"https://doi.org/10.1016/j.sbi.2024.102978","url":null,"abstract":"<p><p>Bacterial conjugation is the unidirectional transfer of DNA (often plasmids, but also other mobile genetic elements, or even entire genomes), from a donor cell to a recipient cell. In Gram-negative bacteria, it requires the formation of three complexes in the donor cell: i-a large, double-membrane-embedded transport machinery called the Type IV Secretion System (T4SS), ii-a long extracellular tube, the conjugative pilus, and iii-a DNA-processing machinery termed the relaxosome. While knowledge has expanded regarding molecular events in the donor cell, very little is known about the machinery involved in DNA transfer into the recipient cell. Here, focusing on systems principally involved in DNA transfer, we provide an update on progress made on various mechanistic aspects of conjugation.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102978"},"PeriodicalIF":6.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001518","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
Protein binding and folding through an evolutionary lens. 从进化的角度看蛋白质的结合和折叠。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-01-15 DOI: 10.1016/j.sbi.2024.102980
Per Jemth
{"title":"Protein binding and folding through an evolutionary lens.","authors":"Per Jemth","doi":"10.1016/j.sbi.2024.102980","DOIUrl":"https://doi.org/10.1016/j.sbi.2024.102980","url":null,"abstract":"<p><p>Protein-protein associations are often mediated by an intrinsically disordered protein region interacting with a folded domain in a coupled binding and folding reaction. Classic physical organic chemistry approaches together with structural biology have shed light on mechanistic aspects of such reactions. Further insight into general principles may be obtained by interpreting the results through an evolutionary lens. This review attempts to provide an overview on how the analysis of binding and folding reactions can benefit from an evolutionary approach, and is aimed at protein scientists without a background in evolution. Evolution constantly reshapes existing proteins by sampling more or less fit variants. Most new variants are weeded out as generations and new species come and go over hundreds to hundreds of millions of years. The huge ongoing genome sequencing efforts have provided us with a snapshot of existing adapted fit-for-purpose protein homologs in thousands of different organisms. Comparison of present-day orthologs and paralogs highlights general principles of the evolution of coupled binding and folding reactions and demonstrate a great potential for evolution to operate on disordered regions and modulate affinity and specificity of the interactions.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"102980"},"PeriodicalIF":6.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001533","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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