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Current opinion in structural biology最新文献

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Prediction of structural variation
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-20 DOI: 10.1016/j.sbi.2025.103003
Yogesh Kalakoti, Airy Sanjeev, Björn Wallner
{"title":"Prediction of structural variation","authors":"Yogesh Kalakoti,&nbsp;Airy Sanjeev,&nbsp;Björn Wallner","doi":"10.1016/j.sbi.2025.103003","DOIUrl":"10.1016/j.sbi.2025.103003","url":null,"abstract":"<div><div>Proteins are dynamic molecules that transition between conformational states to perform their functions, and characterizing the protein ensemble is important for understanding biology and therapeutic applications. While recent breakthroughs in machine learning have enabled the prediction of high-quality static models of individual proteins, generating reliable estimates of their conformational ensembles remains a challenge. Several recent methods have tried to utilize the evolutionary and structural features captured by effective sequence-to-structure models to enhance conformational diversity in generated models. Most of these approaches involve adapting existing inference pipelines, such as AlphaFold 2, combined with sampling techniques to induce the generation of diverse conformational states. Here, we describe the general problem of predicting structural variations in protein systems, explain the methods designed to address this challenge, explore why they are effective, discuss their limitations, and suggest potential future directions.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103003"},"PeriodicalIF":6.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455037","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
Chromatin domains in the cell: Phase separation and condensation
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-20 DOI: 10.1016/j.sbi.2025.103006
Shin Fujishiro , Masaki Sasai , Kazuhiro Maeshima
{"title":"Chromatin domains in the cell: Phase separation and condensation","authors":"Shin Fujishiro ,&nbsp;Masaki Sasai ,&nbsp;Kazuhiro Maeshima","doi":"10.1016/j.sbi.2025.103006","DOIUrl":"10.1016/j.sbi.2025.103006","url":null,"abstract":"<div><div>Negatively charged genomic DNA wraps around positively charged core histone octamers to form nucleosomes, which, along with proteins and RNAs, self-organize into chromatin within the nucleus. In eukaryotic cells, chromatin forms loops that collapse into chromatin domains and serve as functional units of the genome. Chromatin domains vary in physical properties based on gene activity and are assembled into A (euchromatin) and B (heterochromatin) compartments. Since various factors—such as chromatin-binding proteins, histone modifications, transcriptional states, depletion attraction, and cations—can significantly impact chromatin organization, the formation processes of these hierarchical structures remain unclear. No single imaging, genomics, or modeling method can provide a complete picture of the process. Beautiful models can sometimes fool our thinking. In this short review, we critically discuss the formation mechanisms of the chromatin domain in the cell from a physical point of view, including phase separation and condensation.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103006"},"PeriodicalIF":6.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445627","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
Toward deep learning sequence–structure co-generation for protein design
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-20 DOI: 10.1016/j.sbi.2025.103018
Chentong Wang , Sarah Alamdari , Carles Domingo-Enrich , Ava P. Amini , Kevin K. Yang
{"title":"Toward deep learning sequence–structure co-generation for protein design","authors":"Chentong Wang ,&nbsp;Sarah Alamdari ,&nbsp;Carles Domingo-Enrich ,&nbsp;Ava P. Amini ,&nbsp;Kevin K. Yang","doi":"10.1016/j.sbi.2025.103018","DOIUrl":"10.1016/j.sbi.2025.103018","url":null,"abstract":"<div><div>Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today’s models focus on generating either sequences or structures, emerging co-generation methods promise more accurate and controllable protein design, ideally achieved by modeling both modalities simultaneously. Here we review recent advances in deep generative models for protein design, with a particular focus on sequence-structure co-generation methods. We describe the key methodological and evaluation principles underlying these methods, highlight recent advances from the literature, and discuss opportunities for continued development of sequence-structure co-generation approaches.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103018"},"PeriodicalIF":6.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455036","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
Foundation models of protein sequences: A brief overview
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-20 DOI: 10.1016/j.sbi.2025.103004
Andreas Bjerregaard , Peter Mørch Groth , Søren Hauberg , Anders Krogh , Wouter Boomsma
{"title":"Foundation models of protein sequences: A brief overview","authors":"Andreas Bjerregaard ,&nbsp;Peter Mørch Groth ,&nbsp;Søren Hauberg ,&nbsp;Anders Krogh ,&nbsp;Wouter Boomsma","doi":"10.1016/j.sbi.2025.103004","DOIUrl":"10.1016/j.sbi.2025.103004","url":null,"abstract":"<div><div>Protein sequence models have evolved from simple statistics of aligned families to versatile foundation models of evolutionary scale. Enabled by self-supervised learning and an abundance of protein sequence data, such foundation models now play a central role in protein science. They facilitate rich representations, powerful generative design, and fine-tuning across diverse domains. In this review, we trace modeling developments and categorize them into methodological trends over the modalities they describe and the contexts they condition upon. Following a brief historical overview, we focus our attention on the most recent trends and outline future perspectives.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103004"},"PeriodicalIF":6.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445626","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
From part to whole: AI-driven progress in fragment-based drug discovery
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-18 DOI: 10.1016/j.sbi.2025.102995
Jinhyeok Yoo , Wonkyeong Jang , Woong-Hee Shin
{"title":"From part to whole: AI-driven progress in fragment-based drug discovery","authors":"Jinhyeok Yoo ,&nbsp;Wonkyeong Jang ,&nbsp;Woong-Hee Shin","doi":"10.1016/j.sbi.2025.102995","DOIUrl":"10.1016/j.sbi.2025.102995","url":null,"abstract":"<div><div>Fragment-based drug discovery is a technique that finds potent binding fragments to the binding hotspots and makes them a hit compound. The combination of fragments allows us to explore the large chemical space. Thus, it becomes an effective methodology for identifying lead compounds. Three concepts have been introduced to make the fragments into the compound: growing, merging, and linking. Recently, growing and merging techniques using AI have significantly improved the accuracy and efficiency of molecular design. In this review, recent techniques such as VAE, reinforcement learning, and SE(3)-equivariant models will be discussed. These methods enable precise molecular structure exploration and optimization. Additionally, we address techniques utilizing diffusion models, language models, and deep evolutionary learning. We also introduce linker optimization methods using reinforcement learning and deep learning-based models. This progress of fragment-based drug discovery methods with AI opens the possibility of discovering the vast chemical space with high efficiency.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102995"},"PeriodicalIF":6.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429491","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
Combining cryo-electron microscopy (cryo-EM) with orthogonal solution state methods to define the molecular basis of the phosphoprotein phosphatase family regulation and substrate specificity
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-13 DOI: 10.1016/j.sbi.2025.102992
Wolfgang Peti , Sathish K.R. Padi , Rebecca Page
{"title":"Combining cryo-electron microscopy (cryo-EM) with orthogonal solution state methods to define the molecular basis of the phosphoprotein phosphatase family regulation and substrate specificity","authors":"Wolfgang Peti ,&nbsp;Sathish K.R. Padi ,&nbsp;Rebecca Page","doi":"10.1016/j.sbi.2025.102992","DOIUrl":"10.1016/j.sbi.2025.102992","url":null,"abstract":"<div><div>Protein phosphatases are dynamic enzymes that exhibit complex regulatory mechanisms, with disruptions in these regulatory processes associated with disease. It is now clear that many phosphatases assemble into large macromolecular complexes via the interaction of phosphatase-specific regulatory proteins and substrates containing short linear motifs (SLiMs) or short helical motifs (SHelMs). Here, we review how cryo-electron microscopy (cryo-EM) integrated with orthogonal methods to study dynamic protein–protein interactions (NMR spectroscopy, hydrogen-deuterium exchange mass spectrometry, among others) is leading to new discoveries about the mechanisms controlling phosphatase assembly, substrate recruitment and dephosphorylation and, in turn, are providing novel strategies for targeting phosphatase-related diseases. This review focuses on the recently determined structures and regulation of the phosphoprotein phosphatase (PPP) family of ser/thr phosphatases—PP1, PP2A, Calcineurin and PP5.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 102992"},"PeriodicalIF":6.1,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394810","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
Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-02-12 DOI: 10.1016/j.sbi.2025.103002
Joelle Morgan Strom, Katja Luck
{"title":"Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces","authors":"Joelle Morgan Strom,&nbsp;Katja Luck","doi":"10.1016/j.sbi.2025.103002","DOIUrl":"10.1016/j.sbi.2025.103002","url":null,"abstract":"<div><div>A structural understanding of protein–protein interactions is a key component of many facets of applied molecular biology research. AlphaFold-Multimer (AF-MM) provided a breakthrough in the ability to predict protein–protein interface structure. However, the available training data for this model and the resulting benchmarking and validation efforts show a bias toward interactions between more ordered regions of proteins. Here we highlight some of the successes and limitations of AF-MM and discuss available methods and future directions to enable balanced prediction of all interface types.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"91 ","pages":"Article 103002"},"PeriodicalIF":6.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388119","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 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
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