Selecting cell lines with specific Single Nucleotide Polymorphism (SNP) genotypes is a critical bottleneck in functional genomics, often requiring advanced bioinformatic skills. To address this, we developed CLISGen (Cell LInes SNP Genotypes), a database with a user-friendly web application that simplifies access to SNP genotypes in over 1000 cancer cell lines from the Cancer Cell Line Encyclopedia. CLISGen integrates and harmonizes data from Whole-Genome, Whole-Exome, and RNA sequencing, enriching it with contextual information like copy number alterations and genetic ancestry. The platform allows users to search for specific variants or variants in specific genes or genomic regions and filter results by tissue type or data quality, providing intuitive graphical and tabular outputs. By eliminating a major experimental bottleneck, CLISGen offers researchers a powerful resource to efficiently select suitable cell models for studying the link between genetic variation and cancer. CLISGen is freely available at https://bcglab.cibio.unitn.it/clisgen.
{"title":"CLISGen: A Comprehensive Resource of SNP Genotypes for Human Cell Lines.","authors":"Matteo Marchesin, Davide Dalfovo, Alessandro Romanel","doi":"10.1016/j.jmb.2026.169681","DOIUrl":"10.1016/j.jmb.2026.169681","url":null,"abstract":"<p><p>Selecting cell lines with specific Single Nucleotide Polymorphism (SNP) genotypes is a critical bottleneck in functional genomics, often requiring advanced bioinformatic skills. To address this, we developed CLISGen (Cell LInes SNP Genotypes), a database with a user-friendly web application that simplifies access to SNP genotypes in over 1000 cancer cell lines from the Cancer Cell Line Encyclopedia. CLISGen integrates and harmonizes data from Whole-Genome, Whole-Exome, and RNA sequencing, enriching it with contextual information like copy number alterations and genetic ancestry. The platform allows users to search for specific variants or variants in specific genes or genomic regions and filter results by tissue type or data quality, providing intuitive graphical and tabular outputs. By eliminating a major experimental bottleneck, CLISGen offers researchers a powerful resource to efficiently select suitable cell models for studying the link between genetic variation and cancer. CLISGen is freely available at https://bcglab.cibio.unitn.it/clisgen.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169681"},"PeriodicalIF":4.5,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140727","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}
Pub Date : 2026-02-05DOI: 10.1016/j.jmb.2026.169676
Elisha M Wood-Charlson, Christopher S Henry, Paramvir S Dehal, Gazi Mahmud, Benjamin H Allen, Kathleen Beilsmith, D Dakota Blair, Shane Canon, Mikaela Cashman, Dylan Chivian, Robert Cottingham, Zachary Crockett, Ellen G Dow, Meghan Drake, Janaka N Edirisinghe, José P Faria, Andrew Freiburger, Tianhao Gu, Prachi Gupta, A J Ireland, Sean Jungbluth, Roy Kamimura, Keith Keller, Ahmed Khan, Dileep Kishore, Dan Klos, Filipe Liu, David Lyon, Christopher Neely, Katherine L O'Grady, Gavin Price, Priya Ranjan, William J Riehl, Boris Sadkhin, Sam Seaver, Gwyneth A Terry, Yue Wang, Pamela Weisenhorn, Ziming Yang, Shinjae Yoo, Adam P Arkin
The U.S. Department of Energy's Systems Biology Knowledgebase (KBase; www.kbase.us) is an open, collaborative platform that integrates data, models, and analysis tools to accelerate discovery in microbiology, plant biology, and environmental systems. Recently, KBase expanded as a comprehensive, multi-omics ecosystem. KBase enables representation of scientific samples, long-read sequence analysis, protein structure integration, and scalable modeling of microbial communities across diverse environments. KBase also generates digital notebooks as citable, executable research objects that link data, methods, and interpretation. KBase also supports a global education community focused on training the next generation of scientists to use high-performance computational tools. Together, these advances position KBase as a central hub for open, reproducible systems biology. In turn, this enables us to integrate many of the emerging advances in data federation, semantic interoperability, and agent-assisted analysis, paving the way for KBase to support the next generation of AI-driven discovery tools.
{"title":"KBase: Open-source Platform for Collaborative Biological Data Analysis and Publication.","authors":"Elisha M Wood-Charlson, Christopher S Henry, Paramvir S Dehal, Gazi Mahmud, Benjamin H Allen, Kathleen Beilsmith, D Dakota Blair, Shane Canon, Mikaela Cashman, Dylan Chivian, Robert Cottingham, Zachary Crockett, Ellen G Dow, Meghan Drake, Janaka N Edirisinghe, José P Faria, Andrew Freiburger, Tianhao Gu, Prachi Gupta, A J Ireland, Sean Jungbluth, Roy Kamimura, Keith Keller, Ahmed Khan, Dileep Kishore, Dan Klos, Filipe Liu, David Lyon, Christopher Neely, Katherine L O'Grady, Gavin Price, Priya Ranjan, William J Riehl, Boris Sadkhin, Sam Seaver, Gwyneth A Terry, Yue Wang, Pamela Weisenhorn, Ziming Yang, Shinjae Yoo, Adam P Arkin","doi":"10.1016/j.jmb.2026.169676","DOIUrl":"10.1016/j.jmb.2026.169676","url":null,"abstract":"<p><p>The U.S. Department of Energy's Systems Biology Knowledgebase (KBase; www.kbase.us) is an open, collaborative platform that integrates data, models, and analysis tools to accelerate discovery in microbiology, plant biology, and environmental systems. Recently, KBase expanded as a comprehensive, multi-omics ecosystem. KBase enables representation of scientific samples, long-read sequence analysis, protein structure integration, and scalable modeling of microbial communities across diverse environments. KBase also generates digital notebooks as citable, executable research objects that link data, methods, and interpretation. KBase also supports a global education community focused on training the next generation of scientists to use high-performance computational tools. Together, these advances position KBase as a central hub for open, reproducible systems biology. In turn, this enables us to integrate many of the emerging advances in data federation, semantic interoperability, and agent-assisted analysis, paving the way for KBase to support the next generation of AI-driven discovery tools.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169676"},"PeriodicalIF":4.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130788","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}
Pub Date : 2026-02-05DOI: 10.1016/j.jmb.2026.169675
Hannah Hortman, Ruiling A Zhang, Roy G Hughes, Marco Castillo, Eric Chen, Samia Evans, Jonathan Ortega, Jeremy Bandini, Mark McCahill, Scott C Schmidler, Terrence G Oas
Nascent helicity in polypeptides and unfolded proteins arises from local structure formation and represents one of the earliest events in a protein folding reaction. Nascent helicity may also influence the physical properties of intrinsically disordered regions. For this reason, there has been great interest in statistical mechanical models that describe the coil→helix transitions that lead to nascent helicity. These models, collectively called helix-coil models, have been empirically parameterized using an extensive data set of circular dichroism (CD) measurements of natural and designed peptides that form various degrees of nascent helicity. The purpose of A Bayesian Statistical Engine to Infer HeLicity (ABSEIL) (https://abseil.oit.duke.edu/) is to allow users to submit polypeptide sequences to: (1) predict the overall helicity of the sequence; (2) predict the helicity of each residue; and (3) enumerate the ensemble of helix-coil configurations in order of their relative populations. The tool also allows users to search the database of peptide CD experiments on which the predictive model was trained. The website architecture allows for anonymous usage and enables administrative management. The web application server is managed by the Duke Office of Information Technology (OIT) system administrators and conforms to OIT's security and operational best practices.
{"title":"ABSEIL: A polypeptide helicity and ensemble prediction tool.","authors":"Hannah Hortman, Ruiling A Zhang, Roy G Hughes, Marco Castillo, Eric Chen, Samia Evans, Jonathan Ortega, Jeremy Bandini, Mark McCahill, Scott C Schmidler, Terrence G Oas","doi":"10.1016/j.jmb.2026.169675","DOIUrl":"10.1016/j.jmb.2026.169675","url":null,"abstract":"<p><p>Nascent helicity in polypeptides and unfolded proteins arises from local structure formation and represents one of the earliest events in a protein folding reaction. Nascent helicity may also influence the physical properties of intrinsically disordered regions. For this reason, there has been great interest in statistical mechanical models that describe the coil→helix transitions that lead to nascent helicity. These models, collectively called helix-coil models, have been empirically parameterized using an extensive data set of circular dichroism (CD) measurements of natural and designed peptides that form various degrees of nascent helicity. The purpose of A Bayesian Statistical Engine to Infer HeLicity (ABSEIL) (https://abseil.oit.duke.edu/) is to allow users to submit polypeptide sequences to: (1) predict the overall helicity of the sequence; (2) predict the helicity of each residue; and (3) enumerate the ensemble of helix-coil configurations in order of their relative populations. The tool also allows users to search the database of peptide CD experiments on which the predictive model was trained. The website architecture allows for anonymous usage and enables administrative management. The web application server is managed by the Duke Office of Information Technology (OIT) system administrators and conforms to OIT's security and operational best practices.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169675"},"PeriodicalIF":4.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13003202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137075","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}
Pub Date : 2026-02-05DOI: 10.1016/j.jmb.2026.169678
Akshita Kumar, Ashar J Malik, David B Ascher
Metal ions play critical structural, regulatory, and enzymatic roles in proteins, making their binding essential for biological processes. Experimental identification of metal-binding sites is resource-intensive and limited in scalability. Recent advances in protein language models have transformed computational predictions, yet current tools do not address how residue-level metal-binding probabilities change upon mutation. To fill this gap, mCSM-metal leverages embeddings from ESMBind with our graph-based structural signatures to accurately predict the effects of single or multiple point mutations on the binding of seven essential ions (Zn2+, Ca2+, Mg2+, Mn2+, Fe3+, Co2+, Cu2+). Our model achieves accuracies, F1-scores, and Matthews Correlation Coefficient values up to 0.97, 0.97, and 0.95, outperforming other approaches. The webserver provides an interactive platform to assess and visualise local and long-range impacts of mutations on metal-ion binding, offering new avenues for applications in structural biology, disease modelling, and protein engineering. The web application is freely available at: https://biosig.lab.uq.edu.au/mcsm_metal/.
{"title":"mCSM-metal: A Deep Learning Resource to Predict Effect of Mutations on Metal Ion Binding.","authors":"Akshita Kumar, Ashar J Malik, David B Ascher","doi":"10.1016/j.jmb.2026.169678","DOIUrl":"10.1016/j.jmb.2026.169678","url":null,"abstract":"<p><p>Metal ions play critical structural, regulatory, and enzymatic roles in proteins, making their binding essential for biological processes. Experimental identification of metal-binding sites is resource-intensive and limited in scalability. Recent advances in protein language models have transformed computational predictions, yet current tools do not address how residue-level metal-binding probabilities change upon mutation. To fill this gap, mCSM-metal leverages embeddings from ESMBind with our graph-based structural signatures to accurately predict the effects of single or multiple point mutations on the binding of seven essential ions (Zn<sup>2+</sup>, Ca<sup>2+</sup>, Mg<sup>2+</sup>, Mn<sup>2+</sup>, Fe<sup>3+</sup>, Co<sup>2+</sup>, Cu<sup>2+</sup>). Our model achieves accuracies, F1-scores, and Matthews Correlation Coefficient values up to 0.97, 0.97, and 0.95, outperforming other approaches. The webserver provides an interactive platform to assess and visualise local and long-range impacts of mutations on metal-ion binding, offering new avenues for applications in structural biology, disease modelling, and protein engineering. The web application is freely available at: https://biosig.lab.uq.edu.au/mcsm_metal/.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169678"},"PeriodicalIF":4.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130753","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}
Pub Date : 2026-02-01Epub Date: 2025-12-17DOI: 10.1016/j.jmb.2025.169597
Jeanine F. Amacher , Patrick R. Cushing , Lars Vouilleme , Sierra N. Cullati , Bin Deng , Scott A. Gerber , Prisca Boisguerin , Dean R. Madden
PDZ interaction networks are finely-tuned products of evolution. These widespread binding domains recognize short linear motifs (SLiMs), usually at the C-terminus of their interacting partners, and are involved in trafficking and signaling pathways, the formation of tight junctions, and scaffolding of the post-synaptic density of neurons, amongst other roles. Typically, a single PDZ domain binds multiple targets; conversely, each PDZ-binding protein engages several PDZ domains, dependent on cellular conditions. Historical PDZ binding motifs rely on two key positions for binding. However, previous insights on modulator, or non-motif, selectivity preferences reveal that these limited motifs are insufficient to describe PDZ-mediated interactomes, consistent with the observation that the degree of promiscuity is much more limited than predicted by defined binding classes. Here, we use these principles to engineer and test a peptide-based inhibitor capable of interacting with a single PDZ domain-containing protein in a disease-relevant cellular system. We first interrogate a previously developed sequence selective for cystic fibrosis transmembrane conductance regulator (CFTR)-Associated Ligand (CAL), one of five PDZ domains known to bind the CFTR C-terminus, probing for off-target PDZ partners. Once identified, we use parallel biochemical and structural refinement to eliminate these interactions and introduce a CAL PDZ inhibitor with unprecedented PDZ domain selectivity. We test and verify specificity using relevant cellular PDZ target networks in a mass spectrometry-based approach. Our resultant selective inhibitor enhances chloride efflux when applied to polarized patient bronchial epithelial cells, as well as confirms that engineering an effectively single-PDZ peptide is possible when modulator preferences are applied.
{"title":"Sequence Engineering at Non-motif Modulator Residues Yields a Peptide That Effectively Targets a Single PDZ Protein in a Disease-relevant Cellular Context","authors":"Jeanine F. Amacher , Patrick R. Cushing , Lars Vouilleme , Sierra N. Cullati , Bin Deng , Scott A. Gerber , Prisca Boisguerin , Dean R. Madden","doi":"10.1016/j.jmb.2025.169597","DOIUrl":"10.1016/j.jmb.2025.169597","url":null,"abstract":"<div><div>PDZ interaction networks are finely-tuned products of evolution. These widespread binding domains recognize short linear motifs (SLiMs), usually at the C-terminus of their interacting partners, and are involved in trafficking and signaling pathways, the formation of tight junctions, and scaffolding of the post-synaptic density of neurons, amongst other roles. Typically, a single PDZ domain binds multiple targets; conversely, each PDZ-binding protein engages several PDZ domains, dependent on cellular conditions. Historical PDZ binding motifs rely on two key positions for binding. However, previous insights on modulator, or non-motif, selectivity preferences reveal that these limited motifs are insufficient to describe PDZ-mediated interactomes, consistent with the observation that the degree of promiscuity is much more limited than predicted by defined binding classes. Here, we use these principles to engineer and test a peptide-based inhibitor capable of interacting with a single PDZ domain-containing protein in a disease-relevant cellular system. We first interrogate a previously developed sequence selective for cystic fibrosis transmembrane conductance regulator (CFTR)-Associated Ligand (CAL), one of five PDZ domains known to bind the CFTR C-terminus, probing for off-target PDZ partners. Once identified, we use parallel biochemical and structural refinement to eliminate these interactions and introduce a CAL PDZ inhibitor with unprecedented PDZ domain selectivity. We test and verify specificity using relevant cellular PDZ target networks in a mass spectrometry-based approach. Our resultant selective inhibitor enhances chloride efflux when applied to polarized patient bronchial epithelial cells, as well as confirms that engineering an effectively single-PDZ peptide is possible when modulator preferences are applied.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 3","pages":"Article 169597"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792920","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}
Pub Date : 2026-02-01Epub Date: 2025-12-17DOI: 10.1016/j.jmb.2025.169596
Joaquin Atalah , Hind Basbous , Gregory Effantin , Sylvie Kieffer-Jaquinod , Guy Schoehn , Eric Girard , Bruno Franzetti
TET peptidases of the M42 family are ∼500 kDa hollow dodecameric complexes ubiquitous in prokaryotes. These enzymes act as strict aminopeptidases, catalyzing the removal of N-terminal amino acids from peptides. A common feature of M42 TET aminopeptidases characterized to date is their marked substrate preference for a limited subset of amino acids. Unlike other hyperthermophilic archaea studied so far, the autotrophic archaeon Methanocaldococcus jannaschii possesses only a single gene encoding an M42 peptidase. This enzyme, named MjTET, is the first reported M42 peptidase to exhibit broad amino acid specificity, including activity on aromatic residues. To assess their peptide degradation efficiencies, the catalytic constants of MjTET were compared to those of its close analogs from Pyrococcus horikoshii. The specialized TETs from P. horikoshii displayed higher catalytic efficiencies than the generalist MjTET, likely reflecting the reliance of Thermococcales on peptide fermentation for energy. Additionally, the structure of MjTET was resolved to 3 Å using cryo-EM and compared with the available models of the four P. horikoshii TETs to identify features underlying substrate specificity. This analysis, combined with mutagenesis studies, revealed a previously uncharacterized loop in the catalytic domain that contributes to substrate discrimination. Collectively, these findings show that substrate specificity in TET enzymes arises from a complex interplay of tertiary structure, oligomeric assembly, and electrostatic surface potential.
Importance
This study first reported a novel TET peptidase from Methanogenic hyperthermophilic archaea. Its enzymatic properties compared to the specialized TET enzyme characterized so far from heterotrophic archaea suggest a link with autotrophy. It also represents an important step in explaining the structural features guiding substrate specificity.
{"title":"Structural and Biochemical Insights into the Broad-Spectrum TET Enzyme From Methanocaldococcus jannaschii Reveal the Basis of Substrate Specificity in M42 Aminopeptidases","authors":"Joaquin Atalah , Hind Basbous , Gregory Effantin , Sylvie Kieffer-Jaquinod , Guy Schoehn , Eric Girard , Bruno Franzetti","doi":"10.1016/j.jmb.2025.169596","DOIUrl":"10.1016/j.jmb.2025.169596","url":null,"abstract":"<div><div>TET peptidases of the M42 family are ∼500 kDa hollow dodecameric complexes ubiquitous in prokaryotes. These enzymes act as strict aminopeptidases, catalyzing the removal of N-terminal amino acids from peptides. A common feature of M42 TET aminopeptidases characterized to date is their marked substrate preference for a limited subset of amino acids. Unlike other hyperthermophilic archaea studied so far, the autotrophic archaeon <em>Methanocaldococcus jannaschii</em> possesses only a single gene encoding an M42 peptidase. This enzyme, named MjTET, is the first reported M42 peptidase to exhibit broad amino acid specificity, including activity on aromatic residues. To assess their peptide degradation efficiencies, the catalytic constants of MjTET were compared to those of its close analogs from <em>Pyrococcus horikoshii</em>. The specialized TETs from <em>P. horikoshii</em> displayed higher catalytic efficiencies than the generalist MjTET, likely reflecting the reliance of Thermococcales on peptide fermentation for energy. Additionally, the structure of MjTET was resolved to 3 Å using cryo-EM and compared with the available models of the four <em>P. horikoshii</em> TETs to identify features underlying substrate specificity. This analysis, combined with mutagenesis studies, revealed a previously uncharacterized loop in the catalytic domain that contributes to substrate discrimination. Collectively, these findings show that substrate specificity in TET enzymes arises from a complex interplay of tertiary structure, oligomeric assembly, and electrostatic surface potential.</div></div><div><h3>Importance</h3><div>This study first reported a novel TET peptidase from Methanogenic hyperthermophilic archaea. Its enzymatic properties compared to the specialized TET enzyme characterized so far from heterotrophic archaea suggest a link with autotrophy. It also represents an important step in explaining the structural features guiding substrate specificity.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 3","pages":"Article 169596"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792970","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}
Pub Date : 2026-02-01Epub Date: 2025-12-05DOI: 10.1016/j.jmb.2025.169576
Tristan Alexander Mauck, Martin Zacharias
Cellular metabolic systems but also the extracellular environment can generate reactive oxygen species that lead to oxidation of methionine (MET) and interfere with protein folding and protein–protein association. The molecular mechanism of how MET oxidation (MEO) influences conformational stability and binding is not well understood. We employ alchemical free energy simulations to systematically study the influence of MET oxidation on protein–protein binding using the tetramerization domain of the tumor suppression protein p53 as a model system. A single MEO in one tetramerisation domain destabilizes the tetramer by ≈1.1–1.8 kcal/mol depending slightly on the MEO diastereomer. The simulations on double and triple oxidations reveal increased destabilization (≈3–7 kcal/mol) and significant cooperative effects depending on the relative position of the oxidized residues. The MET oxidation effects are of similar magnitude for the change in stability of the human prion protein (HPP) that served as a second model system and also agreed with available experimental data. The calculations predict a significant dependence of stability changes on the position of the MEO and also indicate non-additive effects of multiple oxidations which may play a role to protect proteins from oxidative damage and stress. Analysis of the Molecular Dynamics trajectories allowed us to interpret the oxidation effects in molecular detail. The simulation methodology could also serve as a general protocol to analyze single and multiple MET oxidations in other systems and its influence on protein binding and stability.
{"title":"Influence of Methionine Oxidation on Protein Stability and Association Studied by Free Energy Simulations","authors":"Tristan Alexander Mauck, Martin Zacharias","doi":"10.1016/j.jmb.2025.169576","DOIUrl":"10.1016/j.jmb.2025.169576","url":null,"abstract":"<div><div>Cellular metabolic systems but also the extracellular environment can generate reactive oxygen species that lead to oxidation of methionine (MET) and interfere with protein folding and protein–protein association. The molecular mechanism of how MET oxidation (MEO) influences conformational stability and binding is not well understood. We employ alchemical free energy simulations to systematically study the influence of MET oxidation on protein–protein binding using the tetramerization domain of the tumor suppression protein p53 as a model system. A single MEO in one tetramerisation domain destabilizes the tetramer by ≈1<em>.</em>1–1<em>.</em>8 kcal/mol depending slightly on the MEO diastereomer. The simulations on double and triple oxidations reveal increased destabilization (≈3–7 kcal/mol) and significant cooperative effects depending on the relative position of the oxidized residues. The MET oxidation effects are of similar magnitude for the change in stability of the human prion protein (HPP) that served as a second model system and also agreed with available experimental data. The calculations predict a significant dependence of stability changes on the position of the MEO and also indicate non-additive effects of multiple oxidations which may play a role to protect proteins from oxidative damage and stress. Analysis of the Molecular Dynamics trajectories allowed us to interpret the oxidation effects in molecular detail. The simulation methodology could also serve as a general protocol to analyze single and multiple MET oxidations in other systems and its influence on protein binding and stability.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 3","pages":"Article 169576"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145699509","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}
Pub Date : 2026-02-01Epub Date: 2025-12-08DOI: 10.1016/j.jmb.2025.169590
Yang Tong , Yuting Wang , Gerui Liu , Yihu Wei , Xiaoxiao Yang , Jiapei Yuan , Yang Yang , Qiang Zhang
The steady-state abundance of mRNA is governed by the interplay between transcription and degradation, yet the contribution of RNA stability to cancer biology remains incompletely understood. Here, we systematically investigate RNA decay dynamics across 22 cancer types using RNA-seq data from the Cancer Cell Line Encyclopedia. By inferring transcriptome-wide RNA stability profiles, we identify distinct molecular subtypes defined by post-transcriptional regulation. Integrative analyses reveal that RNA-binding proteins (RBPs) and microRNAs (miRNAs), including SNRPA and RBMX, act as key modulators of RNA stability and are essential for cancer cell proliferation and survival. Somatic mutations, particularly those affecting miRNA binding sites, were found to significantly perturb RNA decay, implicating dysregulation of pathways such as nonsense-mediated decay. Furthermore, machine learning models demonstrate that RNA stability profiles predict sensitivity to 24 anticancer drugs, nominating specific RBPs as candidate biomarkers for therapeutic response. Collectively, our findings establish RNA stability as a pivotal layer of gene regulation in cancer, with broad implications for molecular stratification and precision oncology.
{"title":"Transcriptome-wide RNA Stability Across Cancers Reveals Therapeutic Vulnerabilities","authors":"Yang Tong , Yuting Wang , Gerui Liu , Yihu Wei , Xiaoxiao Yang , Jiapei Yuan , Yang Yang , Qiang Zhang","doi":"10.1016/j.jmb.2025.169590","DOIUrl":"10.1016/j.jmb.2025.169590","url":null,"abstract":"<div><div>The steady-state abundance of mRNA is governed by the interplay between transcription and degradation, yet the contribution of RNA stability to cancer biology remains incompletely understood. Here, we systematically investigate RNA decay dynamics across 22 cancer types using RNA-seq data from the Cancer Cell Line Encyclopedia. By inferring transcriptome-wide RNA stability profiles, we identify distinct molecular subtypes defined by post-transcriptional regulation. Integrative analyses reveal that RNA-binding proteins (RBPs) and microRNAs (miRNAs), including SNRPA and RBMX, act as key modulators of RNA stability and are essential for cancer cell proliferation and survival. Somatic mutations, particularly those affecting miRNA binding sites, were found to significantly perturb RNA decay, implicating dysregulation of pathways such as nonsense-mediated decay. Furthermore, machine learning models demonstrate that RNA stability profiles predict sensitivity to 24 anticancer drugs, nominating specific RBPs as candidate biomarkers for therapeutic response. Collectively, our findings establish RNA stability as a pivotal layer of gene regulation in cancer, with broad implications for molecular stratification and precision oncology.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 3","pages":"Article 169590"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145720205","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}
Pub Date : 2026-02-01Epub Date: 2025-12-12DOI: 10.1016/j.jmb.2025.169592
Jijie Chai
Jijie Chai earned his Ph.D. in analytical chemistry from the Institute of Materia Medica, Chinese Academy of Medical Sciences, and conducted postdoctoral research at Princeton University. In 2004, he established his independent research group at National Institute of Biological Science (China), focusing on plant immune receptors, particularly nucleotide-binding leucine-rich repeat (NLR) proteins. NLRs are the largest family immune receptors that enable plants to detect a wide array of pathogen effectors and activate effector-triggered immunity (ETI). Despite their recognition diversity, they elicit remarkably conserved immune responses, a mechanistic puzzle for many years. Chai’s team achieved a landmark breakthrough by reconstituting and solving the cryo-EM structure of activated Arabidopsis coiled-coil NLR (CNL) ZAR1, revealing its assembly into a pentameric resistosome. The N-terminal helices of ZAR1 in the resistosome form a funnel-like structure, which was later confirmed as a Ca2+-permeable ion channel. These discoveries directly link NLR activation to Ca2+ influx, a central immune signaling event. Chai’s findings further showed that CNLs form structurally conserved resistosomes with intrinsic Ca2+-permeable channel activity. Notably, Chai discovered that TIR-domain NLRs (TNLs) also form resistosomes but function as NADases, producing small-molecule signals that activate downstream helper NLRs. These helper NLRs also assemble into Ca2+-conducting resistosomes. Together, these findings establish a unified mechanism in plant immunity, with diverse NLRs converge on resistosome formation that ultimately drives Ca2+ influx as a central hub of defense activation. This paradigm shift underscores the functional conservation of NLR-mediated immunity across plant species and has fundamentally reshaped the field of plant innate immunity.
{"title":"Rising Star: A Unified Mechanism of Plant NLR Immune Signaling","authors":"Jijie Chai","doi":"10.1016/j.jmb.2025.169592","DOIUrl":"10.1016/j.jmb.2025.169592","url":null,"abstract":"<div><div>Jijie Chai earned his Ph.D. in analytical chemistry from the Institute of Materia Medica, Chinese Academy of Medical Sciences, and conducted postdoctoral research at Princeton University. In 2004, he established his independent research group at National Institute of Biological Science (China), focusing on plant immune receptors, particularly nucleotide-binding leucine-rich repeat (NLR) proteins. NLRs are the largest family immune receptors that enable plants to detect a wide array of pathogen effectors and activate effector-triggered immunity (ETI). Despite their recognition diversity, they elicit remarkably conserved immune responses, a mechanistic puzzle for many years. Chai’s team achieved a landmark breakthrough by reconstituting and solving the cryo-EM structure of activated Arabidopsis coiled-coil NLR (CNL) ZAR1, revealing its assembly into a pentameric resistosome. The N-terminal helices of ZAR1 in the resistosome form a funnel-like structure, which was later confirmed as a Ca<sup>2+</sup>-permeable ion channel. These discoveries directly link NLR activation to Ca<sup>2+</sup> influx, a central immune signaling event. Chai’s findings further showed that CNLs form structurally conserved resistosomes with intrinsic Ca2<sup>+</sup>-permeable channel activity. Notably, Chai discovered that TIR-domain NLRs (TNLs) also form resistosomes but function as NADases, producing small-molecule signals that activate downstream helper NLRs. These helper NLRs also assemble into Ca<sup>2+</sup>-conducting resistosomes. Together, these findings establish a unified mechanism in plant immunity, with diverse NLRs converge on resistosome formation that ultimately drives Ca<sup>2+</sup> influx as a central hub of defense activation. This paradigm shift underscores the functional conservation of NLR-mediated immunity across plant species and has fundamentally reshaped the field of plant innate immunity.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 3","pages":"Article 169592"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754859","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}
Pub Date : 2026-02-01Epub Date: 2025-12-16DOI: 10.1016/j.jmb.2025.169594
Dongyue Hou , Yuchen Liu , Hanbo Lin , Jiajie Gu , Juncheng Qian , Shaofei Wang , Yuzong Chen , Dianwen Ju , Xian Zeng
Extensive interactions between microbiota and active substances are health- and disease-relevant. Mechanistic understanding from genomic perspective of these interactions and potential impacts is important for biomedical and pharmaceutical research. However, current data repositories often lack systematic integration from a genomic perspective. Here we describe an update of the MASI microbiota-active substance interactions database. This update includes new data of (1) genomic-derived 166,766 microbiota-drug interactions and 205,505 microbiota-food interactions linked by 415 biosynthetic gene clusters (BGCs), 59 metabolic gene clusters (MGCs), and 7250 genome-scale metabolic network models (GEMs) of ∼1200 microbiota species, and (2) 1848 microbiota-microbiota interaction records mediated by 39 quorum sensing languages, and (3) 46,717 microbiota-disease associations between 640 species and 59 diseases. Overall, this update provides 44,643 interasctions derived from ∼2000 publications and 380,571 genome-derived interactions, covering 1867 microbe species, 1576 therapeutic substances, 357 dietary substances, which is freely accessible at https://www.aiddlab.com/MASI2025/index.html.
{"title":"MASI2.0: Insights of Microbiota Metabolic Potential from Incorporating Genomic Information on Microbiota-Active Substance Interactions","authors":"Dongyue Hou , Yuchen Liu , Hanbo Lin , Jiajie Gu , Juncheng Qian , Shaofei Wang , Yuzong Chen , Dianwen Ju , Xian Zeng","doi":"10.1016/j.jmb.2025.169594","DOIUrl":"10.1016/j.jmb.2025.169594","url":null,"abstract":"<div><div>Extensive interactions between microbiota and active substances are health- and disease-relevant. Mechanistic understanding from genomic perspective of these interactions and potential impacts is important for biomedical and pharmaceutical research. However, current data repositories often lack systematic integration from a genomic perspective. Here we describe an update of the MASI microbiota-active substance interactions database. This update includes new data of (1) genomic-derived 166,766 microbiota-drug interactions and 205,505 microbiota-food interactions linked by 415 biosynthetic gene clusters (BGCs), 59 metabolic gene clusters (MGCs), and 7250 genome-scale metabolic network models (GEMs) of ∼1200 microbiota species, and (2) 1848 microbiota-microbiota interaction records mediated by 39 quorum sensing languages, and (3) 46,717 microbiota-disease associations between 640 species and 59 diseases. Overall, this update provides 44,643 interasctions derived from ∼2000 publications and 380,571 genome-derived interactions, covering 1867 microbe species, 1576 therapeutic substances, 357 dietary substances, which is freely accessible at <span><span>https://www.aiddlab.com/MASI2025/index.html</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 3","pages":"Article 169594"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145779655","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}
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