Computational and structural biotechnology journal最新文献

{"title":"Knowledge Discovery and Drug-Repurposing Framework for Pancreatic Ductal Adenocarcinoma: Molecular Networking and Computational Docking.","authors":"Tarik Corbo, Elisabeth Pimpisa Graarud, Mathilde Resell, Abdurahim Kalajdzic, Naris Pojskic, Duan Chen, Björn I Gustafsson, Chun-Mei Zhao","doi":"10.34133/csbj.0067","DOIUrl":"https://doi.org/10.34133/csbj.0067","url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, driven by profound molecular heterogeneity and resistance to current therapy. To support systematic target identification, we established a proteomics-anchored knowledge discovery framework integrating cross-model proteomics harmonization, network topology, high-confidence structural modeling, and large-scale in silico docking. From 1,975 proteins consistently detected across murine and human PDAC models, 32 immunohistochemically confirmed candidates were prioritized for structure-based screening against 7,509 clinically characterized compounds. Blind docking, refined pose sampling, ligand-efficiency scoring, and ADME filtering identified EIF2A, STAM, ANXA2, and AHNAK2 as robustly druggable targets. These proteins exhibited high-affinity interactions with zavegepant (a clinically approved CGRP receptor antagonist), omilancor, bemcentinib, conivaptan, and APTO-253. Docking validation (RMSD 1.98 to 2.56 Å) confirmed methodological reliability, and network analyses placed the 4 proteins within modules linked to endosomal/membrane trafficking and invasive phenotypes. Survival analyses in 176 PDAC patients further supported their clinical relevance. Thus, we suggest a systems-level platform for nominating ligandable PDAC targets and clinically actionable compounds. The framework highlights opportunities for rational drug repurposing and motivates future mechanistic studies at the intersection of proteomics and structure-based screening for targets to PDAC.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0067"},"PeriodicalIF":4.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13139727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834685","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}

{"title":"TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data.","authors":"Yufeng Wu, Yanlei Kang, Jiali Gu, Hancan Zhu, Zhong Li","doi":"10.34133/csbj.0080","DOIUrl":"https://doi.org/10.34133/csbj.0080","url":null,"abstract":"<p><p>The emergence of scRNA-seq has enabled high-resolution gene expression analysis at the single-cell level, providing important opportunities for inferring gene regulatory networks (GRNs) within individual cells. This study proposes a novel method, termed Topological Data Analysis-guided Gene Network Embedding (TDAGENE), which introduces the topological data analysis (TDA) to enhance the GRN inference. It integrates the global topological feature with local graph representation and, therefore, improves its ability to model the gene expression by capturing the topological structure of GRN and facilitate the identification of gene interaction relationships. Various experiments demonstrate that TDAGENE outperforms existing methods in GRN inference tasks. It achieves optimal predictions on 90% of datasets in terms of area under the precision-recall curve (AUPRC) and optimal performance on 66.7% of datasets in terms of area under the receiver operating characteristic curve (AUROC). Compared to the latest methods, it shows an average improvement of 17.66% in AUPRC and 3.08% in AUROC. Additionally, we apply TDAGENE to analyze 3 key regulators (NANOG, SOX2, and POU5F1), revealing that incorporating topological information effectively captures critical features during cell fate specification. These findings highlight the potential of TDAGENE in inferring GRNs.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0080"},"PeriodicalIF":4.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13139726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834680","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}

{"title":"Network-Based Prediction of Oligodendroglioma Driver Gene Candidates within the Region of the 1p/19q Co-deletion Utilizing Single-Cell Transcriptomes.","authors":"Michael Seifert","doi":"10.34133/csbj.0059","DOIUrl":"https://doi.org/10.34133/csbj.0059","url":null,"abstract":"<p><p>All oligodendrogliomas have a characteristic 1p/19q co-deletion that alters the expression of hundreds of genes on both affected chromosomal arms. The search for genes on 1p and 19q that drive oligodendroglioma development has only made little progress over the last years. Therefore, a computational network-based approach for the analysis of single-cell oligodendroglioma transcriptomes is developed to predict potential driver gene candidates within the region of the 1p/19q co-deletion purely based on tumor cells. Nine genes with strong impact on signaling pathways (<i>ATP6V0B</i>, <i>F3</i>, <i>FUCA1</i>, <i>FTL</i>, <i>HNRNPR</i>, <i>ID3</i>, <i>JUN</i>, <i>MIIP</i>, and <i>PGM1</i>) and 6 partially overlapping genes with strong impact on immune pathways (<i>F3</i>, <i>FTL</i>, <i>FOSB</i>, <i>IFI6</i>, <i>ISG15</i>, and <i>SPINT2</i>) were consistently predicted in at least 2 of the 3 analyzed oligodendrogliomas. Almost all of these genes are known to play important roles in growth, proliferation, and stem cells of closely related gliomas, but also roles in migration or reprogramming of the microenvironment had been reported in experimental glioma studies. Comparisons to a previous network-based bulk oligodendroglioma analysis and additional evaluations of the expression behavior of candidate genes in related normal brain cells further strengthen the study. Additional validations based on 2 independent oligodendrogliomas support the candidate genes. Robustness of the predictions is shown for imputed and nonimputed data. Strengths of the network-based approach are demonstrated by comparisons to related approaches. All findings clearly suggest that the developed network-based approach for the analysis of single-cell tumor transcriptomes is able to predict novel potential driver gene candidates for oligodendrogliomas. These are very valuable information for future experimental studies. The computational network-based approach can also be transferred to the analysis of single-cell transcriptomes of other types of cancer.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0059"},"PeriodicalIF":4.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834653","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}

{"title":"Shaping Cell Identity: Global Transcriptome and Pathway Shifts during Mouse Mammary Epithelial Cell Differentiation.","authors":"Waqar Ahmad, Neena Gopinathan Panicker, Tahir A Rizvi, Farah Mustafa","doi":"10.34133/csbj.0055","DOIUrl":"https://doi.org/10.34133/csbj.0055","url":null,"abstract":"<p><p>Mouse mammary epithelial cells possess a remarkable ability to regenerate the entire mammary gland through precisely regulated differentiation, involving complex molecular, morphological, and functional changes. Here, we performed comprehensive transcriptomic profiling of HC11 mouse mammary epithelial cells undergoing lactogenic differentiation using RNA sequencing and integrative bioinformatics. We identified 566 differentially expressed genes, reflecting extensive transcriptional reprogramming and activation of biosynthetic, metabolic, and secretory programs. Strong up-regulation of terminal and lactogenic differentiation markers, including Wap, Csn2, Lpl, Cd36, Lalba, Btn1a1, Xdh, Gata3, and Cebpb, signified maturation into a secretory phenotype. Functional evaluation via gene set enrichment analysis revealed transcriptional enrichment of mTOR, prolactin, insulin, ErbB, and autophagy-associated pathways, consistent with anabolic readiness and terminal differentiation. Conversely, p53, Wnt, and FoxO pathways were down-regulated, marking a transition from proliferation to differentiation. Transcription factors (FoxO1, Zbtb16, and Srebf1) and epigenetic regulators (Gadd45a and Hist1h1e) exhibited dynamic changes, underscoring coordinated transcriptional and chromatin remodeling. Gene set enrichment and protein-protein interaction analyses identified 10 hub genes, Agt, Ccnd1, Igf1, Mki67, Myc, Calm4, Rasgrp1, Cd69, Il6, and Pecam1, as central drivers of differentiation. Clustering of uniquely regulated genes further implicated roles in milk synthesis, protease activity, and lineage stabilization. Together, these findings define a transcriptional framework for lactogenic differentiation in the HC11 cell line model and provide a basis for future mechanistic studies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0055"},"PeriodicalIF":4.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834721","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}

{"title":"A Hybrid Modeling Framework for Predictive Digital Twins of CHO Cell Culture.","authors":"Anne Richelle, David Andersson, Athanasios Antonakoudis, Jesper Jakobsson, Shanti Pijeaud, Anton Vernersson, Johan Trygg","doi":"10.34133/csbj.0078","DOIUrl":"https://doi.org/10.34133/csbj.0078","url":null,"abstract":"<p><p>Digital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is challenged by the nonlinear dynamics and metabolic complexity of these systems. We present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct predictive digital twins for Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies. The framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates. Applied to 23 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data. Cross-validation analyses demonstrated strong generalization across process variations, highlighting the framework's capacity to capture both biochemical constraints and adaptive cellular behavior. This hybrid modeling approach provides a mechanistically interpretable yet data-adaptive foundation for constructing bioprocess digital twins. By bridging statistical, mechanistic, and machine learning methodologies, it advances the computational representation of CHO cell culture systems and offers a generalizable strategy for predictive modeling in complex biological production processes.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0078"},"PeriodicalIF":4.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834692","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}

{"title":"Rational Targeting and gRNA Design for Enhancing Quorum Quenching in <i>Pseudomonas aeruginosa</i> PAO1.","authors":"Javier Alejandro Delgado-Nungaray, Luis Joel Figueroa-Yáñez, Eire Reynaga-Delgado, Mario Alberto García-Ramírez, Orfil Gonzalez-Reynoso","doi":"10.34133/csbj.0089","DOIUrl":"https://doi.org/10.34133/csbj.0089","url":null,"abstract":"<p><p>Quorum quenching enzymes (QQEs) are a promising antivirulence strategy by disrupting quorum sensing (QS), a mechanism that regulates biofilm formation in <i>Pseudomonas aeruginosa</i>, a key factor in adaptive antibiotic resistance. In this study, a systems biology approach based on the genome-scale metabolic model iJD1249 and flux balance analysis simulating growth in Luria-Bertani medium and QS-activating conditions was used to identify gene targets associated with enhanced endogenous PvdQ production, the most representative QQE. Following gene-protein-reaction filtering of nonessential genes involved in QS-related pathways, a rational CRISPR-Cas9 guide RNA (gRNA) design strategy was implemented to support future genome editing validation. gRNAs were first generated using CHOPCHOP, considering on-target efficiency, mismatch number, and self-complementarity. A semiquantitative scoring system based on gRNA efficiency parameters was applied to prioritize top gRNAs, followed by secondary structure prediction using RNAfold. Simulations identified 10 genes associated with PvdQ maximization. Among them, <i>fabI</i>, involved in palmitate biosynthesis II, emerged as the most promising target. Its knockout is predicted to limit acyl-acyl carrier protein intermediate availability required for QS signal biosynthesis, potentially influencing <i>pvdQ</i> expression through metabolic redistribution. To avoid unintended pyoverdine enhancement, which is directly influenced by PvdQ, gRNAs were also designed to target <i>pvdH</i>. From an initial set of 78 and 146 sequences for <i>fabI</i> and <i>pvdH</i>, respectively, gRNA No. 12 (<i>fabI</i>) and gRNA No. 16 (<i>pvdH</i>) were identified as the most efficient gRNA hits for gene knockout. Experimental validation is required to confirm the predicted metabolic effects and provide deeper insights for QQ-based strategies against <i>P. aeruginosa</i>.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0089"},"PeriodicalIF":4.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147834730","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}

{"title":"Using Steady-State Visual Evoked Potentials to Characterize Wide-Ranging Retinopathy Linked to <i>CRB1</i>: Implications for Clinical Trials.","authors":"Kim Eliane Stäubli, Marc Pabst, Roni O Maimon-Mor, Ana Catalina Rodriguez-Martinez, H Steven Scholte, Mariya Moosajee, Tessa Marlijn Dekker","doi":"10.34133/csbj.0042","DOIUrl":"https://doi.org/10.34133/csbj.0042","url":null,"abstract":"<p><p>Rapid advances in gene therapy are moving sight rescue treatments for inherited retinal diseases (IRDs) within reach, creating an urgent need to understand how these conditions affect neural signaling from the eye to the brain. However, capturing functional change across the diverse IRD sight-loss spectrum using a unified testing framework is challenging. Computational neuroimaging may help address this gap by exploiting known principles of visual-system tuning to derive more sensitive and computationally meaningful markers of visual function. This is particularly important in <i>CRB1</i> retinopathy, an IRD with a strikingly wide phenotype range, where neural impacts across the full disease spectrum are not yet characterized. To investigate the functional impact of <i>CRB1</i> retinopathy, we recorded steady-state visual evoked potentials (ssVEPs) in 72 eyes from 18 patients and 18 sighted controls using a patient-friendly, large-field protocol embedding phase-reversing sinusoidal gratings and full-screen flashes into age-appropriate videos. Fitting these data with neural tuning functions revealed significant ssVEP attenuation in patients, with the greatest reductions in those with generalized retinal degeneration, alongside shifts in spatial-frequency tuning toward lower frequencies. Our results show that with appropriate stimulus selection, ssVEPs offer a reliable response-free measure of visual function that correlates well with behavioral vision assessments and discriminates between <i>CRB1</i> subgroups, with the optimally sensitive stimulus for detecting functional variation varying with the level of vision. Computational electroencephalogram-based neuroimaging thus provides quantitative insights across the wide spectrum of <i>CRB1</i> pathology and represents a valuable complementary tool for evaluating disease progression and treatment outcomes in <i>CRB1</i> retinopathy and related IRDs.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0042"},"PeriodicalIF":4.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147812097","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}

{"title":"Air Bacterial Microbiomes in Hospitals: Case Studies from a Metropolis and a Small City of Thailand.","authors":"Piraya Chatthanathon, Doonyapong Wongsawaeng, Tassanee Chetwittayachan, Thanya Cheibchalard, Asada Leelahavanichkul, Ekasit Kowitdamrong, Jarun Sayasathid, Naraporn Somboonna","doi":"10.34133/csbj.0068","DOIUrl":"https://doi.org/10.34133/csbj.0068","url":null,"abstract":"<p><p><b>Experimental objective:</b> Hospital air can act as a reservoir of opportunistic and antimicrobial-resistant microorganisms, which may contribute to hospital-acquired infections. However, the composition of airborne bacterial communities and the factors shaping them within hospital environments remain insufficiently characterized. This study investigated airborne bacterial microbiomes across hospital areas and sampling approaches and compared hospitals located in a metropolis versus a smaller city in Thailand. <b>Methods:</b> Air samples were collected from various hospital zones using active air-pump sampling and passive air-grille or high-efficiency particulate air-filter swab approaches at King Chulalongkorn Memorial Hospital in Bangkok and Naresuan University Hospital in Phitsanulok. Microbiota were analyzed using 16<i>S</i> ribosomal RNA gene sequencing, followed by bioinformatic analyses. <b>Results:</b> Bacterial community compositions and alpha-diversity varied significantly along sampling method, hospital area, and geographic location. Passive air-grille swabs captured higher microbial biomass and diversity, consistent with accumulated microbiome deposition over time. Areas with open and semiopen ventilation (e.g., restaurant and outpatient departments) exhibited higher bacterial diversity than filtered areas (e.g., operating rooms). The metropolitan hospital showed higher abundances of <i>Cutibacterium</i>, <i>Acinetobacter</i>, <i>Curtobacterium</i>, and members of Comamonadaceae, whereas the hospital in the smaller city displayed greater overall diversity. High-efficiency particulate air-filter samples showed reduced diversity but enriched in spore-forming taxa. Predicted functional profiles also differed between sampling approaches and hospital locations, including pathways that might be related with human diseases. <b>Conclusion:</b> Hospital air microbiomes were heterogeneous and influenced by environmental conditions and sampling strategy. These findings provide insights for factor correlations and may inform improved air-quality management strategies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0068"},"PeriodicalIF":4.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147812079","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}

{"title":"Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization.","authors":"Zanyu Shi, Yang Wang, Pathum M Weerawarna, Timothy I Richardson, Jie Zhang, Yijie Wang, Kun Huang","doi":"10.34133/csbj.0012","DOIUrl":"https://doi.org/10.34133/csbj.0012","url":null,"abstract":"<p><p>Explainable artificial intelligence approaches accelerate drug discovery by improving molecular representation learning, identifying key molecular structures, and rationalizing drug property prediction. However, developing end-to-end explainable models for structure-activity relationship modeling in target-specific compound property prediction remains challenging due to the limited availability of compound-protein interaction data for individual targets and the fact that small changes in chemical substituents or local structural motifs can lead to large differences in molecular properties. Thus, optimally leveraging structural and property information and identifying key moieties related to compound-protein affinity for specific targets is essential. We propose a framework implementing graph neural networks (GNNs) to leverage property and structure information from pairs of molecules with activity cliffs targeting specific proteins to predict compound-protein affinity (i.e., half-maximal inhibitory concentration, IC₅₀) and explain property differences. To enhance model explainability, we trained GNNs with structure-aware loss functions using group lasso and sparse group lasso regularizations, which prune and highlight molecular subgraphs relevant to activity differences. We applied this framework to the activity cliff data of molecules targeting 6 tyrosine-protein kinases across Src, Abl, and Tec families, as well as anaplastic lymphoma kinase. Integrating common- and uncommon-node information with sparse group lasso improves molecular property prediction for specific protein targets, as evidenced by lower root mean square errors and higher Pearson's correlation coefficients. Applying regularizations also enhances feature attribution for GNNs by boosting graph-level global direction scores and improving atom-level coloring accuracy. These advances strengthen model interpretability in drug discovery pipelines, particularly in identifying critical molecular substructures in lead optimization.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0012"},"PeriodicalIF":4.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764885","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}

{"title":"intDesc-AbMut: A Tool for Describing and Understanding How Antibody Mutations Impact Their Environmental Interactions.","authors":"Shuntaro Chiba, Masateru Ohta, Tsutomu Yamane, Yasushi Okuno, Mitsunori Ikeguchi","doi":"10.34133/csbj.0027","DOIUrl":"https://doi.org/10.34133/csbj.0027","url":null,"abstract":"<p><p>Structure-based antibody engineering requires systematic and reproducible characterization of residue-level interactions within antigen-antibody complexes. In our previous double-point mutation strategy, mutant selection was guided by preservation or enhancement of favorable local interactions; however, identifying and comparing these interactions depended on labor-intensive manual structural inspection. Here, we present intDesc-AbMut, a residue-centric computational tool that automates the extraction, classification, visualization, and descriptor generation of interactions involving designated residues in antigen-antibody complexes. The software explicitly defines 36 interaction types, encompassing conventional hydrogen bonds and van der Waals contacts as well as weak hydrogen bonds (e.g., CH···O and CH···π), sulfur-related contacts, bond dipole interactions, and orthogonal multipolar interactions. This fine-grained interaction taxonomy enables quantitative representation of local packing, weak hydrogen bonding, and directional electrostatic features surrounding mutated residues. As a proof of concept, we incorporate these interaction descriptors into a machine learning framework to evaluate whether they encode structural characteristics consistent with experimentally observed side-chain conformations. The results demonstrate that the descriptors capture meaningful structural signals underlying crystal-structure-like conformations. This analysis assesses descriptor informativeness rather than predicting binding affinity. In summary, intDesc-AbMut provides a systematic and extendable framework for mutation-focused interaction analysis in antibody structural studies and related protein design applications.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0027"},"PeriodicalIF":4.1,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764904","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}