发布求助
文献互助 智能选刊 最新文献

Current opinion in structural biology最新文献

筛选
英文 中文
Deep learning for RNA structure prediction
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-10 DOI: 10.1016/j.sbi.2025.102991
Jiuming Wang , Yimin Fan , Liang Hong, Zhihang Hu, Yu Li
{"title":"Deep learning for RNA structure prediction","authors":"Jiuming Wang ,&nbsp;Yimin Fan ,&nbsp;Liang Hong,&nbsp;Zhihang Hu,&nbsp;Yu Li","doi":"10.1016/j.sbi.2025.102991","DOIUrl":"10.1016/j.sbi.2025.102991","url":null,"abstract":"<div><div>Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure prediction with increasingly higher accuracy and efficiency. With an increase in the number of models proposed for this task, this review presents a timely summary of the applications of AI, particularly deep learning, in RNA structure prediction, highlighting their methodology advances as well as the challenges and opportunities for further work in this field.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102991"},"PeriodicalIF":6.1,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376934","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
Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence–based methods
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-08 DOI: 10.1016/j.sbi.2025.103000
Akashnathan Aranganathan , Xinyu Gu , Dedi Wang , Bodhi P. Vani , Pratyush Tiwary
{"title":"Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence–based methods","authors":"Akashnathan Aranganathan ,&nbsp;Xinyu Gu ,&nbsp;Dedi Wang ,&nbsp;Bodhi P. Vani ,&nbsp;Pratyush Tiwary","doi":"10.1016/j.sbi.2025.103000","DOIUrl":"10.1016/j.sbi.2025.103000","url":null,"abstract":"<div><div>This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2, there has been a shift toward more accurate and efficient sampling of structural ensembles. The review discusses the integration of AI with traditional molecular dynamics techniques as well as experiments, the challenges of conformational sampling, and future directions for AI-driven research in structural biology, particularly in drug discovery and protein dynamics.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103000"},"PeriodicalIF":6.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350214","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
Simulation-based inference of single-molecule experiments
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-07 DOI: 10.1016/j.sbi.2025.102988
Lars Dingeldein , Pilar Cossio , Roberto Covino
{"title":"Simulation-based inference of single-molecule experiments","authors":"Lars Dingeldein ,&nbsp;Pilar Cossio ,&nbsp;Roberto Covino","doi":"10.1016/j.sbi.2025.102988","DOIUrl":"10.1016/j.sbi.2025.102988","url":null,"abstract":"<div><div>Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) is a powerful framework for analyzing complex experimental data, integrating statistical inference, physics-based simulators, and machine learning. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep learning–based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102988"},"PeriodicalIF":6.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348004","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
Are protein language models the new universal key?
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-07 DOI: 10.1016/j.sbi.2025.102997
Konstantin Weissenow , Burkhard Rost
{"title":"Are protein language models the new universal key?","authors":"Konstantin Weissenow ,&nbsp;Burkhard Rost","doi":"10.1016/j.sbi.2025.102997","DOIUrl":"10.1016/j.sbi.2025.102997","url":null,"abstract":"<div><div>Protein language models (pLMs) capture some aspects of the grammar of the language of life as written in protein sequences. The so-called pLM embeddings implicitly contain this information. Therefore, embeddings can serve as the exclusive input into downstream supervised methods for protein prediction. Over the last 33 years, evolutionary information extracted through simple averaging for specific protein families from multiple sequence alignments (MSAs) has been the most successful universal key to the success of protein prediction. For many applications, MSA-free pLM-based predictions now have become significantly more accurate. The reason for this is often a combination of two aspects. Firstly, embeddings condense the <em>grammar</em> so efficiently that downstream prediction methods succeed with small models, i.e., they need few free parameters in particular in the era of exploding deep neural networks. Secondly, pLM-based methods provide protein-specific solutions. As additional benefit, once the pLM pre-training is complete, pLM-based solutions tend to consume much fewer resources than MSA-based solutions. In fact, we appeal to the community to rather optimize foundation models than to retrain new ones and to evolve incentives for solutions that require fewer resources even at some loss in accuracy. Although pLMs have not, yet, succeeded to entirely replace the body of solutions developed over three decades, they clearly are rapidly advancing as the universal key for protein prediction.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102997"},"PeriodicalIF":6.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348001","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
A building blocks perspective on protein emergence and evolution
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-06 DOI: 10.1016/j.sbi.2025.102996
Yishi Ezerzer , Moran Frenkel-Pinter , Rachel Kolodny , Nir Ben-Tal
{"title":"A building blocks perspective on protein emergence and evolution","authors":"Yishi Ezerzer ,&nbsp;Moran Frenkel-Pinter ,&nbsp;Rachel Kolodny ,&nbsp;Nir Ben-Tal","doi":"10.1016/j.sbi.2025.102996","DOIUrl":"10.1016/j.sbi.2025.102996","url":null,"abstract":"<div><div>Recent findings increasingly suggest the emergence of proteins by mix and match of short peptides, or ‘building blocks’. What are these building blocks, and how did they evolve into contemporary proteins? We review two complementary approaches to tackling these questions. First, a bottom-up approach that involves identifying putative components of primordial peptides, and the synthetic routes through which these peptides may have emerged. Second, searches in protein space to reveal building blocks that make up the contemporary protein repertoire; proteins that are not closely related to one another may nevertheless have certain parts in common, suggesting common ancestry. Identifying such shared building blocks, and characterizing their functions, can shed light on the ancient molecules from which proteins emerged, and hint at the mechanisms that govern their evolution. A key challenge lies in merging these two approaches to create a cohesive narrative of how proteins emerged and continue to evolve.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102996"},"PeriodicalIF":6.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143277755","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
Single-point mutations in disordered proteins: Linking sequence, ensemble, and function
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-05 DOI: 10.1016/j.sbi.2025.102987
Eduardo Flores , Nirbhik Acharya , Carlos A. Castañeda , Shahar Sukenik
{"title":"Single-point mutations in disordered proteins: Linking sequence, ensemble, and function","authors":"Eduardo Flores ,&nbsp;Nirbhik Acharya ,&nbsp;Carlos A. Castañeda ,&nbsp;Shahar Sukenik","doi":"10.1016/j.sbi.2025.102987","DOIUrl":"10.1016/j.sbi.2025.102987","url":null,"abstract":"<div><div>Mutations in genomic DNA often result in single-point missense mutations in proteins. For folded proteins, the functional effect of these missense mutations can often be understood by their impact on structure. However, missense mutations in intrinsically disordered protein regions (IDRs) remain poorly understood. In IDRs, function can depend on the structural ensemble– the collection of accessible, interchanging conformations that is encoded in their amino acid sequence. We argue that, analogously to folded proteins, single-point mutations in IDRs can alter their structural ensemble, and consequently alter their biological function. To make this argument, we first provide experimental evidence from the literature showcasing how single-point missense mutations in IDRs affect their ensemble dimensions. Then, we use genomic data from patients to show that disease-linked missense mutations occurring in IDRs can, in many cases, significantly alter IDR structural ensembles. We hope this analysis prompts further study of disease-linked, single-point mutations in IDRs.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102987"},"PeriodicalIF":6.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143277709","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
Protein folding by the CCT/TRiC chaperone complex
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-05 DOI: 10.1016/j.sbi.2025.102999
Peter S. Shen , Barry M. Willardson
{"title":"Protein folding by the CCT/TRiC chaperone complex","authors":"Peter S. Shen ,&nbsp;Barry M. Willardson","doi":"10.1016/j.sbi.2025.102999","DOIUrl":"10.1016/j.sbi.2025.102999","url":null,"abstract":"<div><div>The chaperonin-containing TCP-1 (CCT) complex, also known as TRiC, is an abundant and essential molecular chaperone responsible for folding a significant portion of the eukaryotic proteome. Prominent CCT folding clients include cytoskeletal proteins such as actin and tubulin, and proteins with β-propeller folds. Recent advances in cryo-EM have provided unprecedented insights into CCT's substrate-specific folding mechanisms. This review summarizes these discoveries, emphasizing how CCT utilizes its unique structural features to recognize and fold diverse substrates.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102999"},"PeriodicalIF":6.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143277756","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 ligand structure prediction: From empirical to deep learning approaches
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-05 DOI: 10.1016/j.sbi.2025.102998
Guangfeng Zhou, Frank DiMaio
{"title":"Protein ligand structure prediction: From empirical to deep learning approaches","authors":"Guangfeng Zhou,&nbsp;Frank DiMaio","doi":"10.1016/j.sbi.2025.102998","DOIUrl":"10.1016/j.sbi.2025.102998","url":null,"abstract":"<div><div>Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening and lead optimization. Traditional empirical approaches use explicit scoring functions and conformational search techniques to predict protein-ligand structures and binding affinities. With the recent advent of deep learning (DL) methods, DL-based models learn both the scoring function and conformational sampling by approximating the underlying data distribution from training data. In this review, we first discuss the key components of both empirical and DL-based structure prediction methods to provide a unified view. We categorize these computational methods into two main groups based on whether a template protein structure is required, and briefly overview the important methods in each category. Finally, we discuss the major challenges and opportunities, focusing on the future development of DL-based approaches.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102998"},"PeriodicalIF":6.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143277757","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
Advances in structure-based allosteric drug design 基于结构的变构药物设计研究进展。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102974
Rui Li , Xinheng He , Chengwei Wu , Mingyu Li , Jian Zhang
{"title":"Advances in structure-based allosteric drug design","authors":"Rui Li ,&nbsp;Xinheng He ,&nbsp;Chengwei Wu ,&nbsp;Mingyu Li ,&nbsp;Jian Zhang","doi":"10.1016/j.sbi.2024.102974","DOIUrl":"10.1016/j.sbi.2024.102974","url":null,"abstract":"<div><div>The identification of allosteric binding sites forms a critical connection between structural and computational biology, substantially advancing the discovery of allosteric drugs. However, the prevailing strategies for allosteric drug development predominantly rely on high-throughput screening, which suffers from high failure rates due to a limited understanding of allosteric mechanisms. This review collects insights from case studies on allosteric mechanisms, protein structure databases and computation algorithm developments, aiming to enhance our comprehension of allostery and guide more effective allosteric drug development. A crucial element in this area is the integration of structural biology with computational biology, which is vital for translating three-dimensional structural datasets into available drug discovery knowledge. These datasets and AI algorithms underpin the establishment of the allosteric binding site identification leading to structure–activity relationships (SARs) and are fueling the development of computational algorithms tailored for allosteric proteins, thereby driving forward the field of allosteric drug discovery.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102974"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906648","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
On the emergence of machine-learning methods in bottom-up coarse-graining 论自下而上粗粒度学习中机器学习方法的出现。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-01 DOI: 10.1016/j.sbi.2024.102972
Patrick G. Sahrmann, Gregory A. Voth
{"title":"On the emergence of machine-learning methods in bottom-up coarse-graining","authors":"Patrick G. Sahrmann,&nbsp;Gregory A. Voth","doi":"10.1016/j.sbi.2024.102972","DOIUrl":"10.1016/j.sbi.2024.102972","url":null,"abstract":"<div><div>Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which ‘coarse-grain’ electronic structure have inspired similar applications to the thermodynamic coarse-graining of chemical and biological systems. In this review, we discuss the current viability and challenges in using machine-learning methods to represent coarse-grained force-fields, as well as the utility of machine-learning in various aspects of coarse-grained modeling.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"90 ","pages":"Article 102972"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142926858","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信