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Prediction of nucleic acid binding residues in protein sequences: Recent advances and future prospects 蛋白质序列中核酸结合残基的预测：最新进展和未来展望
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-21 DOI: 10.1016/j.sbi.2025.103085
Sushmita Basu , Yuedong Yang , Lukasz Kurgan
{"title":"Prediction of nucleic acid binding residues in protein sequences: Recent advances and future prospects","authors":"Sushmita Basu ,&nbsp;Yuedong Yang ,&nbsp;Lukasz Kurgan","doi":"10.1016/j.sbi.2025.103085","DOIUrl":"10.1016/j.sbi.2025.103085","url":null,"abstract":"<div><div>Computational prediction of DNA-binding residues (DBRs) and the RNA-binding residues (RBRs) in protein sequences is an active area of research, with about 90 predictors and 20 that were published over the last two years. The new predictors rely on sophisticated deep neural networks and protein language models, produce accurate predictions, and are conveniently available as code and/or web servers. However, we identified shortage of tools that predict these interactions in intrinsically disordered regions and tools capable of predicting residues that interact with specific RNA and DNA types. Moreover, cross-predictions between RBRs and DBRs should be quantified and minimized to ensure that future tools accurately differentiate between these two distinct types of nucleic acids.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103085"},"PeriodicalIF":6.1,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331061","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
Artificial intelligence in therapeutic antibody design: Advances and future prospects 人工智能在治疗性抗体设计中的应用：进展和未来展望
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-18 DOI: 10.1016/j.sbi.2025.103084
Sujin Park , Wooyeop Jeong , Yubeen Kim , Chang-Han Lee , Chaok Seok
{"title":"Artificial intelligence in therapeutic antibody design: Advances and future prospects","authors":"Sujin Park ,&nbsp;Wooyeop Jeong ,&nbsp;Yubeen Kim ,&nbsp;Chang-Han Lee ,&nbsp;Chaok Seok","doi":"10.1016/j.sbi.2025.103084","DOIUrl":"10.1016/j.sbi.2025.103084","url":null,"abstract":"<div><div>In the few years since AlphaFold 2 revolutionized protein structure prediction, AI technologies have demonstrated strong potential for practical application in therapeutic antibody development, a key area in the pharmaceutical industry. This mini-review provides a concise overview of AI-driven approaches designed to precisely optimize antibody properties critical for successful therapeutics. In particular, protein structure prediction-based antibody design AI is advancing rapidly, facilitating the effective targeting of protein hotspots, as demonstrated in a few reported cases. These advancements are expected to streamline experimental workflows, reduce reliance on trial-and-error screening, and enable the efficient discovery of novel molecules that would be challenging to identify through traditional methods. Additionally, this review explores emerging AI methodologies aimed at optimizing Fc function, immunogenicity, and developability, offering insights into future directions in the field.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103084"},"PeriodicalIF":6.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306871","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
Entropy, enthalpy, and evolution: Adaptive trade-offs in protein binding thermodynamics 熵、焓和进化：蛋白质结合热力学中的适应性权衡
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-17 DOI: 10.1016/j.sbi.2025.103080
Rosemary Georgelin , Colin J. Jackson
{"title":"Entropy, enthalpy, and evolution: Adaptive trade-offs in protein binding thermodynamics","authors":"Rosemary Georgelin ,&nbsp;Colin J. Jackson","doi":"10.1016/j.sbi.2025.103080","DOIUrl":"10.1016/j.sbi.2025.103080","url":null,"abstract":"<div><div>Proteins are central to biological complexity as their ligand binding processes, shaped by thermodynamics, have driven evolutionary adaptation throughout Earth’s history. Despite extensive research into protein–ligand interactions, the evolution of their binding thermodynamics, particularly regarding enthalpy–entropy trade-offs, remains underexplored. This review compares experimental and computational findings to illustrate how the balance of thermodynamics influences protein structure and function over time. We hypothesize that ancient proteins likely exhibit entropically favored, flexible binding modes, while modern proteins increasingly rely on enthalpically driven specificity. Evolutionary trajectories, including those from ancestral sequence reconstruction studies and modern viral evolution, reveal that thermodynamic trade-offs allow proteins to adapt to diverse functions. Our evolutionary perspective on the existing research demonstrates that binding thermodynamics not only govern ligand affinity and specificity but also fundamentally shape protein evolution and inform potential protein engineering strategies.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103080"},"PeriodicalIF":6.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298654","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
Elucidating double stranded DNA viral scaffolding protein structures through advances in cryogenic electron microscopy data processing 通过低温电子显微镜数据处理的进展来阐明双链DNA病毒支架蛋白结构
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-16 DOI: 10.1016/j.sbi.2025.103081
Makayla N. Leroux , Garrett S. Skidds , Carolyn M. Teschke
{"title":"Elucidating double stranded DNA viral scaffolding protein structures through advances in cryogenic electron microscopy data processing","authors":"Makayla N. Leroux ,&nbsp;Garrett S. Skidds ,&nbsp;Carolyn M. Teschke","doi":"10.1016/j.sbi.2025.103081","DOIUrl":"10.1016/j.sbi.2025.103081","url":null,"abstract":"<div><div>Icosahedral double stranded DNA (dsDNA) virus assembly first necessitates the formation of a precursor capsid (procapsid) into which the DNA is packaged. Direct interactions between the major capsid protein (MCP) and a scaffolding protein promote proper procapsid assembly. The scaffolding protein can be an independent protein or a scaffolding-like domain covalently attached to the MCP that is comparable in structure and function. A full understanding of scaffolding protein structures has been limited by their intrinsically disordered nature. Advances in cryogenic electron microscopy (cryoEM) data processing techniques have provided new methodologies to help solve the structures of scaffolding proteins within procapsids. These structural insights further our understanding of how scaffolding proteins interact with the other assembly proteins to correctly construct the procapsid.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103081"},"PeriodicalIF":6.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298655","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
Large-scale protein clustering in the age of deep learning 深度学习时代的大规模蛋白质聚类
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-13 DOI: 10.1016/j.sbi.2025.103078
Joana Pereira , Lorenzo Pantolini , Janani Durairaj , Torsten Schwede
{"title":"Large-scale protein clustering in the age of deep learning","authors":"Joana Pereira ,&nbsp;Lorenzo Pantolini ,&nbsp;Janani Durairaj ,&nbsp;Torsten Schwede","doi":"10.1016/j.sbi.2025.103078","DOIUrl":"10.1016/j.sbi.2025.103078","url":null,"abstract":"<div><div>Proteins within a family sharing sequence and structure similarity due to a common evolutionary origin often also share functional similarities. Clustering of proteins therefore offers valuable insights, enabling the transfer of features and annotations from well-studied proteins to less-investigated ones. On a local scale, clustering helps identify patterns within specific protein families. On a larger scale, it provides insights into the entire protein universe, showcasing relationships that may not be immediately apparent. Traditionally, this was done at the sequence level or with the use of experimentally resolved protein structures, but the advent of deep learning in protein bioinformatics has brought new options to the table, increasing the breadth, depth, and diversity of similarity metrics and clustering approaches.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103078"},"PeriodicalIF":6.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281131","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
Databases and web-based tools for studying structures of protein-nucleic acid complexes 研究蛋白质-核酸复合物结构的数据库和基于网络的工具
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-11 DOI: 10.1016/j.sbi.2025.103079
Justas Dapkūnas, Česlovas Venclovas
{"title":"Databases and web-based tools for studying structures of protein-nucleic acid complexes","authors":"Justas Dapkūnas,&nbsp;Česlovas Venclovas","doi":"10.1016/j.sbi.2025.103079","DOIUrl":"10.1016/j.sbi.2025.103079","url":null,"abstract":"<div><div>Structural data on protein-DNA and protein-RNA interactions are indispensable in molecular biology research. In this article, we review available databases and other web-based resources devoted to 3D structures of protein-nucleic acid complexes. First, we describe the core databases that collect and disseminate experimental data. We then review derivative databases focused specifically on structural data on protein-nucleic acid interactions. Finally, we provide an overview of several useful web servers for structure prediction, analysis and comparison. Tools for investigating protein-nucleic acid complexes are relatively scarce. This is primarily because the methods that integrate structural information from both proteins and nucleic acids are in short supply. However, the emerging AI-driven techniques for structure prediction are expected to boost the development of such methods.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103079"},"PeriodicalIF":6.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262728","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
Artificial intelligence methods for protein folding and design 蛋白质折叠与设计的人工智能方法
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-11 DOI: 10.1016/j.sbi.2025.103066
Zhidian Zhang , Chenxi Ou , Yehlin Cho , Yo Akiyama , Sergey Ovchinnikov
{"title":"Artificial intelligence methods for protein folding and design","authors":"Zhidian Zhang ,&nbsp;Chenxi Ou ,&nbsp;Yehlin Cho ,&nbsp;Yo Akiyama ,&nbsp;Sergey Ovchinnikov","doi":"10.1016/j.sbi.2025.103066","DOIUrl":"10.1016/j.sbi.2025.103066","url":null,"abstract":"<div><div>Machine learning has revolutionized protein structure prediction and design. This review discusses current methods for protein folding and inverse folding challenges. Models like AlphaFold2 (AF2), RoseTTAFold, and ESMFold excel at leveraging evolutionary information to accurately predict protein structures while still struggling to capture the physics of protein folding. Their repurposing for protein design has led to innovations such as RFdiffusion, AF2-design, and relaxed sequence optimization. ProteinMPNN and ESM-IF design sequences based on structure, so they are frequently referred to as “inverse folding’ methods. By examining the potential and limitations of current protein design methods and metrics, we provide perspectives on developing models that fully characterize energy landscapes associated with amino acid sequences. Such advances would enable more accurate structure prediction and the design of proteins with specified conformational dynamics, potentially transforming our ability to engineer novel proteins for biotechnological applications.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"93 ","pages":"Article 103066"},"PeriodicalIF":6.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253797","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
Capturing protein dynamics across timescales with site-directed spin labeling electron paramagnetic resonance spectroscopy 捕获蛋白质动态跨越时间尺度与定点自旋标记电子顺磁共振波谱
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-09 DOI: 10.1016/j.sbi.2025.103073
Patrick C. Brennan, Julian D. Grosskopf, Alexander M. Garces, Cassandra L. Trier, Michael T. Lerch
{"title":"Capturing protein dynamics across timescales with site-directed spin labeling electron paramagnetic resonance spectroscopy","authors":"Patrick C. Brennan,&nbsp;Julian D. Grosskopf,&nbsp;Alexander M. Garces,&nbsp;Cassandra L. Trier,&nbsp;Michael T. Lerch","doi":"10.1016/j.sbi.2025.103073","DOIUrl":"10.1016/j.sbi.2025.103073","url":null,"abstract":"<div><div>In the current age of protein structure prediction and determination, resolving the time dependence of structural transitions represents an exciting frontier. Time-resolved biophysical techniques possess the capability to directly observe dynamic structural changes of biomolecules in real time. Here, we review applications of site-directed spin labeling (SDSL) coupled with electron paramagnetic resonance (EPR) spectroscopy that cover a broad range of protein dynamics, from backbone fluctuations on the ps–ns timescale to protein complex assembly formation on the ms–s timescale. Recent developments in SDSL EPR methods allow for direct investigation of protein conformational exchange kinetics on the important μs–ms timescale, providing the time axis for structural transitions needed to define molecular mechanisms of complex biological phenomena.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"93 ","pages":"Article 103073"},"PeriodicalIF":6.1,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241907","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
Quantitative analysis methods for free diffusion single-molecule FRET experiments 自由扩散单分子FRET实验的定量分析方法
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-09 DOI: 10.1016/j.sbi.2025.103075
Irina V. Gopich, Hoi Sung Chung
{"title":"Quantitative analysis methods for free diffusion single-molecule FRET experiments","authors":"Irina V. Gopich,&nbsp;Hoi Sung Chung","doi":"10.1016/j.sbi.2025.103075","DOIUrl":"10.1016/j.sbi.2025.103075","url":null,"abstract":"<div><div>Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful technique for investigating the structure and dynamics of biomolecules. This review focuses on recent advances in quantitative methods to analyze freely diffusing molecules in smFRET. The methods include traditional approaches of analyzing FRET efficiency and advanced photon-by-photon techniques based on maximum likelihood estimation without binning photon sequences. More recently, methods explicitly accounting for molecular diffusion have been developed, addressing biases arising from variations in brightness and diffusivity among molecular states and species. Applications of these tools include studies of protein folding, DNA dynamics, and oligomerization processes of neurodegenerative proteins. These advancements expand the ability of free diffusion-based smFRET to elucidate the dynamic behavior of biomolecules on the timescales relevant to their biological processes.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"93 ","pages":"Article 103075"},"PeriodicalIF":6.1,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241908","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
HDX-MS in micelles and membranes for small molecule and biopharmaceutical development 用于小分子和生物制药开发的胶束和膜中的HDX-MS
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-06-06 DOI: 10.1016/j.sbi.2025.103077
Charlotte Guffick , Argyris Politis
{"title":"HDX-MS in micelles and membranes for small molecule and biopharmaceutical development","authors":"Charlotte Guffick ,&nbsp;Argyris Politis","doi":"10.1016/j.sbi.2025.103077","DOIUrl":"10.1016/j.sbi.2025.103077","url":null,"abstract":"<div><div>While protein characterisation is critical for continuing drug discovery and development, many techniques fall short of capturing the range of conformational dynamics that underpin the targeted protein activity. Hydrogen–deuterium exchange mass spectrometry (HDX-MS) is a powerful tool for investigation of protein structural dynamics, particularly for membrane proteins in different mimetic environments. This measurement of intrinsic protein behaviour is invaluable in outlining pathogenic protein dynamics, protein–ligand profiles, and druggable protein centres that are often inaccessible in other structural techniques. This minireview will cover how recent advances have been applied to HDX-MS of membrane proteins and peptides widening the use of HDX for drug discovery.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"Article 103077"},"PeriodicalIF":6.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230903","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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