Journal of Molecular Biology最新文献_第9页

RNA-modification by Base Exchange: Structure, Function and Application of tRNA-guanine Transglycosylases.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-15 DOI: 10.1016/j.jmb.2025.168980

Klaus Reuter, Ralf Ficner

{"title":"RNA-modification by Base Exchange: Structure, Function and Application of tRNA-guanine Transglycosylases.","authors":"Klaus Reuter, Ralf Ficner","doi":"10.1016/j.jmb.2025.168980","DOIUrl":"https://doi.org/10.1016/j.jmb.2025.168980","url":null,"abstract":"<p><p>tRNA-guanine transglycosylases (TGT) occur in all domains of life. They are unique among RNA-modifying enzymes as they exchange a guanine base in the primary RNA transcript by various 7-substituted 7-deazaguanines leading to the modified nucleosides queuosine and archaeosine. Archaeosine is found in the D-loop of archaeal tRNAs, queuosine in the anticodon of bacterial and eukaryotic tRNAs specific for Asp, Asn, His and Tyr. Structural and functional studies revealed a common base-exchange mechanism for all TGTs. Nonetheless, there are also significant differences between TGTs, which will be discussed here. It concerns the specificity for different 7-deazaguanine substrates as well as the recognition of substrate tRNAs. For queuosine TGT an anticodon stem-loop containing the UGU recognition motif is a minimal substrate sufficient for binding to the active site, however, full-length tRNA is bound with higher affinity due to multiple interactions with the dimeric enzyme. Archaeal TGT also binds tRNAs as homodimer, even though the interaction pattern is very different and results in a large change of tRNA conformation. Interestingly, a closely related enzyme, DpdA, exchanges guanine by 7-cyano-7-deazguanine (preQ<sub>0</sub>) in double stranded DNA of several bacteria. Bacterial TGT is a target for structure-based drug design, as the virulence of Shigella depends on TGT activity, and mammalian TGT has been used for the treatment of murine experimental autoimmune encephalomyelitis, a model for chronic multiple sclerosis. Furthermore, TGT has become a valuable tool in nucleic acid chemistry, as it facilitates the incorporation of non-natural bases in tRNA molecules, e.g. for labelling or cross-linking purposes.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"168980"},"PeriodicalIF":4.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431999","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

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-15 DOI: 10.1016/j.jmb.2025.169010

Weimin Zhu, Xiaohan Ding, Hong-Bin Shen, Xiaoyong Pan

{"title":"Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models","authors":"Weimin Zhu, Xiaohan Ding, Hong-Bin Shen, Xiaoyong Pan","doi":"10.1016/j.jmb.2025.169010","DOIUrl":"10.1016/j.jmb.2025.169010","url":null,"abstract":"<div><div>RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule binding sites. However, accurate and efficient computational methods for identifying these interactions are still lacking. Recently, advances in large language models (LLMs), previously successful in DNA and protein research, have spurred the development of RNA-specific LLMs. These models leverage vast unlabeled RNA sequences to autonomously learn semantic representations with the goal of enhancing downstream tasks, particularly those constrained by limited annotated data. Here, we develop RNABind, an embedding-informed geometric deep learning framework to detect RNA-small molecule binding sites from RNA structures. RNABind integrates RNA LLMs into advanced geometric deep learning networks, which encodes both RNA sequence and structure information. To evaluate RNABind, we first compile the largest RNA-small molecule interaction dataset from the entire multi-chain complex structure instead of single-chain RNAs. Extensive experiments demonstrate that RNABind outperforms existing state-of-the-art methods. Besides, we conduct an extensive experimental evaluation of eight pre-trained RNA LLMs, assessing their performance on the binding site prediction task within a unified experimental protocol. In summary, RNABind provides a powerful tool on exploring RNA-small molecule binding site prediction, which paves the way for future innovations in the RNA-targeted drug discovery.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"437 8","pages":"Article 169010"},"PeriodicalIF":4.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439670","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 Data Bank Japan: Computational Resources for Analysis of Protein Structures.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-15 DOI: 10.1016/j.jmb.2025.169013

Gert-Jan Bekker, Chioko Nagao, Matsuyuki Shirota, Tsukasa Nakamura, Toshiaki Katayama, Daisuke Kihara, Kengo Kinoshita, Genji Kurisu

{"title":"Protein Data Bank Japan: Computational Resources for Analysis of Protein Structures.","authors":"Gert-Jan Bekker, Chioko Nagao, Matsuyuki Shirota, Tsukasa Nakamura, Toshiaki Katayama, Daisuke Kihara, Kengo Kinoshita, Genji Kurisu","doi":"10.1016/j.jmb.2025.169013","DOIUrl":"https://doi.org/10.1016/j.jmb.2025.169013","url":null,"abstract":"<p><p>Protein Data Bank Japan (PDBj, https://pdbj.org/) is the Asian hub of three-dimensional macromolecular structure data, and a founding member of the worldwide Protein Data Bank. We have accepted, processed, and distributed experimentally determined biological macromolecular structures for over two decades. Although we collaborate with RCSB PDB and BMRB in the United States, PDBe and EMDB in Europe and recently PDBc in China for our data-in activities, we have developed our own unique services and tools for searching, exploring, visualizing, and analyzing protein structures. We have also developed novel archives for computational data and raw crystal diffraction images. Recently, we introduced the Sequence Navigator Pro service to explore proteins using experimental and computational approaches, which enables experimental structural biologists to increase their insight to help them to design their experimental studies more efficiently. In addition, we also introduced a new UniProt-integrated portal to provide users with a quick overview of their target protein and it shows a recommended structure and integrates data from various internal and external resources. With these new additions, we have enhanced our service portfolio to benefit both experimental as computational structural biologists in their search to interpret protein structures, their dynamics and function.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169013"},"PeriodicalIF":4.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708040","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

Queuine: A Bacterial Nucleobase Shaping Translation in Eukaryotes.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-15 DOI: 10.1016/j.jmb.2025.168985

Ann E Ehrenhofer-Murray

引用次数: 0

RNAproDB: A Webserver and Interactive Database for Analyzing Protein-RNA Interactions.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-15 DOI: 10.1016/j.jmb.2025.169012

Raktim Mitra, Ari S Cohen, Wei Yu Tang, Hirad Hosseini, Yongchan Hong, Helen M Berman, Remo Rohs

引用次数: 0

htFuncLib: Designing Libraries of Active-site Multipoint Mutants for Protein Optimization.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-15 DOI: 10.1016/j.jmb.2025.169011

Rosalie Lipsh-Sokolik, Sarel J Fleishman

{"title":"htFuncLib: Designing Libraries of Active-site Multipoint Mutants for Protein Optimization.","authors":"Rosalie Lipsh-Sokolik, Sarel J Fleishman","doi":"10.1016/j.jmb.2025.169011","DOIUrl":"https://doi.org/10.1016/j.jmb.2025.169011","url":null,"abstract":"<p><p>Protein function relies on accurate and densely packed constellations of amino acids within the active site. The high density in the active site optimizes activity but reduces tolerance to mutations, thereby frustrating efforts to engineer or design new or dramatically improved activity. Introducing new activities may therefore require simultaneous multipoint mutations. Still, in a phenomenon known as epistasis, the outcome of combinations of mutations can differ significantly-and even reverse-the impact of the individual mutations, limiting predictability. To address these challenges we previously developed FuncLib, a method for the computational design of multipoint mutants in active sites. We recently extended FuncLib to enable the design of large combinatorial mutation libraries for high-throughput screening in a method called htFuncLib that generates compatible sets of mutations likely to yield functional multipoint mutants. htFuncLib enables scalable library design and experimental screening of hundreds and up to millions of active-site variants. This approach has generated thousands of active enzymes and fluorescent proteins with diverse functional properties. We have updated the FuncLib web server (https://FuncLib.weizmann.ac.il/) to support htFuncLib and introduced an electronic notebook (https://github.com/Fleishman-Lab/htFuncLib-web-server) for customizable library design, making those tools easily accessible for protein engineering and design. The new FuncLib web server enables reliable and scalable design of function for low-, medium- and high-throughput experiments through a single computational platform. We envision that this server will accelerate the optimization and discovery of function in enzymes, antibodies, and other proteins.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169011"},"PeriodicalIF":4.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708023","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

Serum Amyloid A Binding to Glycosaminoglycans is Synergistic with Amyloid Formation: Therapeutic Targeting in the Inflammation-linked Amyloidosis

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-13 DOI: 10.1016/j.jmb.2025.169007

Shobini Jayaraman , Angela Urdaneta , Marcus Fandrich , Olga Gursky

{"title":"Serum Amyloid A Binding to Glycosaminoglycans is Synergistic with Amyloid Formation: Therapeutic Targeting in the Inflammation-linked Amyloidosis","authors":"Shobini Jayaraman , Angela Urdaneta , Marcus Fandrich , Olga Gursky","doi":"10.1016/j.jmb.2025.169007","DOIUrl":"10.1016/j.jmb.2025.169007","url":null,"abstract":"<div><div>Serum amyloid A (SAA), a small lipophilic plasma protein elevated in inflammation, is a precursor of amyloid A (AA) amyloidosis, the major life-threatening complication of chronic inflammation in animals and humans. Although heparan sulfate (HS) is a potent amyloid agonist, particularly in AA amyloidosis, therapeutic targeting of SAA-HS interactions using a small-molecule HS/heparin decoy was unsuccessful. To understand molecular underpinnings, we used recombinant lipid-free human and murine SAA1 and human SAA2 to explore their interactions with various glycosaminoglycans at pH 5.5–7.4 during amyloid formation, from native protein to amyloid oligomers and fibrils. Effects of pH and glycosaminoglycan sulfation/charge supported by prior computational studies indicate electrostatic origin of SAA-glycosaminoglycan interactions. HS/heparin can promote amyloidogenesis by inducing non-native β-sheet and apparently causing liquid droplet formation in SAA in solution. Structural and binding studies by spectroscopy and ELISA reveal previously unknown synergy between amyloid formation and heparin/HS binding by SAA. We propose that this synergy potentially extends to other protein amyloids and stems from longitudinal binding of HS polyanions to basic residue arrays on amyloid oligomers or fibrils. This binding mode explains our finding that a minimal heparin chain length exceeding 20 monosaccharides is necessary to compete with HS for binding to amyloid oligomers. The results help explain prior failure of a small-molecule drug in targeting of SAA-HS interactions and consider alternative HS-targeting approaches for AA and, potentially, other amyloid diseases.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"437 8","pages":"Article 169007"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424635","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

A combined in silico approach to design peptide ligands with increased receptor-subtype selectivity.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-13 DOI: 10.1016/j.jmb.2025.169006

Adam Zech, Victoria Most, Anna Mutti, Passainte Ibrahim, Regine Heilbronn, Christoph Schwarzer, Peter W Hildebrand, René Staritzbichler

{"title":"A combined in silico approach to design peptide ligands with increased receptor-subtype selectivity.","authors":"Adam Zech, Victoria Most, Anna Mutti, Passainte Ibrahim, Regine Heilbronn, Christoph Schwarzer, Peter W Hildebrand, René Staritzbichler","doi":"10.1016/j.jmb.2025.169006","DOIUrl":"10.1016/j.jmb.2025.169006","url":null,"abstract":"<p><p>G-protein coupled receptors are major drug targets that change their conformation upon binding of ligands to their extracellular binding pocket to transduce the signal to intracellular G-proteins or arrestins. In drug screening campaigns, computational methods are frequently used to predict binding affinities for chemical compounds in silico before experimental testing. Some of these methods take into consideration the inherent flexibility of the ligand and to some extent also of the receptor. Due to high structural flexibility, peptide ligands are exceptionally difficult to handle and approaches to effectively sample in silico receptor-peptide ligand interactions are limited. Here we describe a pipeline starting from microseconds molecular dynamics simulations of receptor and receptor ligand complexes to find reasonable starting conformations and derive constraints for subsequent flexible docking of peptide ligands, using Rosetta's FlexPepDock approach. We applied this approach to predict binding affinities for dynorphin and its variants to members of the opioid receptor family. Using an ensemble of docking poses, Rosetta's fixbb protein design method explored the sequence space at defined positions, to enhance binding affinities, aiming to increase subtype selectivity towards κ-opioid receptor while decreasing it towards μ-opioid receptor. The results of our computations were validated experimentally in a related study (Zangrandi et al., 2024<sup>1</sup>). Four out of six proposed variants lead to a significant increase in subtype selectivity in favor of κ-opioid receptor, highlighting the potential of our approach to design subtype selective peptide variants. The established workflow may also apply for other receptor types activated by peptide ligands.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169006"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424575","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

InSty: A ProDy Module for Evaluating Protein Interactions and Stability.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-13 DOI: 10.1016/j.jmb.2025.169009

Karolina Mikulska-Ruminska, James M Krieger, Anupam Banerjee, Xin Cao, Gary Wu, Anthony T Bogetti, Feng Zhang, Carlos Simmerling, Evangelos A Coutsias, Ivet Bahar

{"title":"InSty: A ProDy Module for Evaluating Protein Interactions and Stability.","authors":"Karolina Mikulska-Ruminska, James M Krieger, Anupam Banerjee, Xin Cao, Gary Wu, Anthony T Bogetti, Feng Zhang, Carlos Simmerling, Evangelos A Coutsias, Ivet Bahar","doi":"10.1016/j.jmb.2025.169009","DOIUrl":"10.1016/j.jmb.2025.169009","url":null,"abstract":"<p><p>ProDy is a widely used application programming interface for analyzing the collective dynamics of proteins and their complexes, offering enhanced capabilities to address the growing needs of the computational biology community to bridge structure and function. Here, we introduce InSty, a new module integrated into ProDy to identify and quantify intra- and intermolecular interactions critical to protein stability and structural dynamics. InSty analyzes the non-covalent interactions using conformational ensemble data from both experiments and computational predictions, assesses their time evolution and persistence during molecular dynamics simulations as well as their conservation across homologs. It provides insights into the significance of these interactions in achieving function and/or supporting stability. InSty outputs lend themselves to statistical evaluation, visualization, and automated ensemble analysis for interpreting the significance of the interactions in the context of protein dynamics, sequence evolution, and allostery. Consolidation of InSty with various ProDy modules enables its efficient usage as a versatile tool that supports mutagenesis studies and identifies critical spots for functional interactions. The InSty module is available as part of the ProDy package at https://github.com/prody/ProDy.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169009"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424632","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

AFflecto: A web server to generate conformational ensembles of flexible proteins from AlphaFold models.

IF 4.7 2区生物学

Journal of Molecular Biology Pub Date : 2025-02-13 DOI: 10.1016/j.jmb.2025.169003

Mátyás Pajkos, Ilinka Clerc, Christophe Zanon, Pau Bernadó, Juan Cortés

{"title":"AFflecto: A web server to generate conformational ensembles of flexible proteins from AlphaFold models.","authors":"Mátyás Pajkos, Ilinka Clerc, Christophe Zanon, Pau Bernadó, Juan Cortés","doi":"10.1016/j.jmb.2025.169003","DOIUrl":"https://doi.org/10.1016/j.jmb.2025.169003","url":null,"abstract":"<p><p>Intrinsically disordered proteins and regions (IDPs/IDRs) leverage their structural flexibility to fulfill essential cellular functions, with dysfunctions often linked to severe diseases. However, the relationships between their sequences, structural dynamics and functional roles remain poorly understood. Understanding these complex relationships is crucial for therapeutic development, highlighting the need for methods to generate plausible IDP/IDR conformational ensembles. While AlphaFold (AF) excels at modeling structured domains, it fails to accurately represent disordered regions, leaving a significant portion of proteomes inaccurately modeled. We present AFflecto, a user-friendly web server for generating large conformational ensembles of proteins that include both structured domains and IDRs from AF structural models. AFflecto identifies IDRs as tails, linkers or loops by analyzing their structural context. Additionally, it incorporates a method to identify conditionally folded IDRs that AF may incorrectly predict as natively folded elements. The conformational space is globally explored using efficient stochastic sampling algorithms. AFflecto's web interface allows users to customize the modeling, by modifying boundaries between ordered and disordered regions, and selecting among several sampling strategies. The web server is freely available at https://moma.laas.fr/applications/AFflecto/.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169003"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708010","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