Current opinion in structural biology最新文献

Language models for protein design

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-03-06 DOI: 10.1016/j.sbi.2025.103027

Jin Sub Lee , Osama Abdin , Philip M. Kim

引用次数: 0

Extending the reach of single-particle cryoEM.

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-03-03 DOI: 10.1016/j.sbi.2025.103005

Ardan Patwardhan, Richard Henderson, Christopher J Russo

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Super-resolving chromatin in its own terms: Recent approaches to portray genomic organization

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-03-03 DOI: 10.1016/j.sbi.2025.103021

Manuel Fernández Merino , Maria Pia Cosma , Maria Victoria Neguembor

引用次数: 0

Allosteric modulation of protein–protein interactions in signal transduction with Nanobodies

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-03-01 DOI: 10.1016/j.sbi.2025.103022

Els Pardon , Alex Wohlkönig , Jan Steyaert

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Structural insights into the recognition of native nucleosomes by pioneer transcription factors

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-03-01 DOI: 10.1016/j.sbi.2025.103024

Bing-Rui Zhou, Benjamin Orris, Ruifang Guan, Tengfei Lian, Yawen Bai

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Predicting the structure-altering mechanisms of disease variants

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-02-27 DOI: 10.1016/j.sbi.2025.102994

Matteo Arnaudi , Mattia Utichi , Matteo Tiberti , Elena Papaleo

{"title":"Predicting the structure-altering mechanisms of disease variants","authors":"Matteo Arnaudi , Mattia Utichi , Matteo Tiberti , Elena Papaleo","doi":"10.1016/j.sbi.2025.102994","DOIUrl":"10.1016/j.sbi.2025.102994","url":null,"abstract":"<div><div>Missense variants can affect the severity of disease, choice of treatment, and treatment outcomes. While the number of known variants has been increasing at a rapid pace, available evidence of their clinical effect has been lagging behind, constituting a challenge for clinicians and researchers. Multiplexed assays of variant effects (MAVEs) are important to close the gap; nonetheless, computational predictions of pathogenicity are still often the only available data for scoring variants. Such methods are not designed to provide a mechanistic explanation for the effect of amino acid substitutions. To this purpose, we propose structure-based frameworks as ensemble methodologies, with each method tailored to predict a different aspect among those exerted by amino acid substitutions to link predicted pathogenicity to mechanistic indicators. We review available frameworks, as well as advancements in underlying structure-based methods that predict variant effects on several protein features, such as protein stability, biomolecular interactions, allostery, post-translational modifications, and more.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102994"},"PeriodicalIF":6.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510181","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

The “three body solution”: Structural insights into molecular glues

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-02-26 DOI: 10.1016/j.sbi.2025.103007

Aidan C.A. Tomlinson , John E. Knox , Luc Brunsveld , Christian Ottmann , Jason K. Yano

引用次数: 0

Recent advances in AI-driven protein-ligand interaction predictions

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-02-24 DOI: 10.1016/j.sbi.2025.103020

Jaemin Sim , Dongwoo Kim , Bomin Kim , Jieun Choi , Juyong Lee

{"title":"Recent advances in AI-driven protein-ligand interaction predictions","authors":"Jaemin Sim , Dongwoo Kim , Bomin Kim , Jieun Choi , Juyong Lee","doi":"10.1016/j.sbi.2025.103020","DOIUrl":"10.1016/j.sbi.2025.103020","url":null,"abstract":"<div><div>Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligand binding site prediction, protein-ligand binding pose estimation, scoring function development, and virtual screening. In this review, we summarize the recent AI-driven advances in various protein-ligand interaction prediction tasks. Traditional docking methods based on empirical scoring functions often lack accuracy, whereas AI models, including graph neural networks, mixture density networks, transformers, and diffusion models, have enhanced predictive performance. Ligand binding site prediction has been refined using geometric deep learning and sequence-based embeddings, aiding in the identification of potential druggable target sites. Binding pose prediction has evolved with sampling-based and regression-based models, as well as protein-ligand co-generation frameworks. AI-powered scoring functions now integrate physical constraints and deep learning techniques to improve binding affinity estimation, leading to more robust virtual screening strategies. Despite these advances, generalization across diverse protein-ligand pairs remains a challenge. As AI technologies continue to evolve, they are expected to revolutionize molecular docking and affinity prediction, increasing both the accuracy and efficiency of structure-based drug discovery.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"92 ","pages":"Article 103020"},"PeriodicalIF":6.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474446","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

Leveraging protein structural information to improve variant effect prediction 利用蛋白质结构信息改进变异效应预测

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-02-22 DOI: 10.1016/j.sbi.2025.103023

Lukas Gerasimavicius , Sarah A. Teichmann , Joseph A. Marsh

{"title":"Leveraging protein structural information to improve variant effect prediction","authors":"Lukas Gerasimavicius , Sarah A. Teichmann , Joseph A. Marsh","doi":"10.1016/j.sbi.2025.103023","DOIUrl":"10.1016/j.sbi.2025.103023","url":null,"abstract":"<div><div>Despite massive sequencing efforts, understanding the difference between human pathogenic and benign variants remains a challenge. Computational variant effect predictors (VEPs) have emerged as essential tools for assessing the impact of genetic variants, although their performance varies. Initially, sequence-based methods dominated the field, but recent advances, particularly in protein structure prediction technologies like AlphaFold, have led to an increased utilization of structural information by VEPs aimed at scoring human missense variants. This review highlights the progress in integrating structural information into VEPs, showcasing novel models such as AlphaMissense, PrimateAI-3D, and CPT-1 that demonstrate improved variant evaluation. Structural data offers more interpretability, especially for non-loss-of-function variants, and provides insights into complex variant interactions <em>in vivo</em>. As the field advances, utilizing biomolecular complex structures will be pivotal for future VEP development, with recent breakthroughs in protein-ligand and protein-nucleic acid complex prediction offering new avenues.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"92 ","pages":"Article 103023"},"PeriodicalIF":6.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471354","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

Dynamics-based drug discovery by time-resolved cryo-EM

IF 6.1 2区生物学

Current opinion in structural biology Pub Date : 2025-02-21 DOI: 10.1016/j.sbi.2025.103001

Youdong Mao

{"title":"Dynamics-based drug discovery by time-resolved cryo-EM","authors":"Youdong Mao","doi":"10.1016/j.sbi.2025.103001","DOIUrl":"10.1016/j.sbi.2025.103001","url":null,"abstract":"<div><div>Rational structure-based drug design (SBDD) depends on high-resolution structural models of target macromolecules or their complexes. However, the lack of atomic-level functional molecular dynamics hinders the applications of SBDD and limits their effective translation into clinically successful therapeutics. Time-resolved cryo-electron microscopy (cryo-EM) has emerged as a powerful tool in structural biology, capable of capturing high-resolution snapshots of biomolecular machines in action. Unlike molecular dynamics (MD) simulations, time-resolved cryo-EM can visualize rare intermediate states across a broader range of timescales, providing invaluable insights into drug-binding kinetics, dynamic protein-ligand interactions, and allosteric regulation. Integration of time-resolved cryo-EM with machine learning (ML) and artificial intelligence (AI) expands SBDD into a dynamics-based approach, allowing for more accurate pharmacological modeling of challenging drug targets that are beyond the reach of MD simulations. Time-resolved cryo-EM can help researchers to identify novel druggable conformations, overcome drug resistance, and reduce the time and cost of clinical translations. Despite current challenges, the future development of time-resolved cryo-EM with AI and <em>in situ</em> imaging strategy, such as cryo-electron tomography, holds the potential to revolutionize drug discovery by revealing <em>in vivo</em> molecular dynamics of drug actions at an unprecedented spatiotemporal scale.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103001"},"PeriodicalIF":6.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455034","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