Computational and structural biotechnology journal最新文献_第5页

Characterization of the KRas G12D-inhibitor interactions by differential HDX-MS and molecular dynamics simulations. 用差分HDX-MS和分子动力学模拟表征KRas g12d抑制剂相互作用。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-08 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.08.008

Evgeniy V Petrotchenko, Brandon Novy, Edith Nagy, Konstantin I Popov, Jason B Cross, Roopa Thapar, Christoph H Borchers

{"title":"Characterization of the KRas G12D-inhibitor interactions by differential HDX-MS and molecular dynamics simulations.","authors":"Evgeniy V Petrotchenko, Brandon Novy, Edith Nagy, Konstantin I Popov, Jason B Cross, Roopa Thapar, Christoph H Borchers","doi":"10.1016/j.csbj.2025.08.008","DOIUrl":"10.1016/j.csbj.2025.08.008","url":null,"abstract":"Hydrogen-deuterium exchange (HDX) combined with mass spectrometry (MS) is a powerful technique for studying changes in protein structure and dynamics upon ligand binding. Protein-ligand complexes can result in increased protection of peptide-bond amides in HDX indicating protein structure stabilization. We have characterized the interaction of small-molecule inhibitors towards the KRas G12D oncoprotein by intact-protein and bottom-up HDX-MS, in combination with molecular dynamics (MD) simulations. Significant differences in HDX protection were detected upon inhibitor binding in the flexible switch-II pocket of the protein. MD simulations of the free and inhibitor-bound KRas G12D proteins also revealed changes in the hydrogen bond network of backbone amides in the switch-II region upon inhibitor binding, explaining the observed HDX protection changes. We have proposed simple semi-empirical metrics which relate changes in HDX-MS experimental values and observed in MD simulations changes in individual backbone hydrogen-bonds between free- and ligand-bound protein states. This combined HDX-MS and MD approach provides an atomistic picture of changes in the KRas G12D secondary structure upon ligand binding and may be a useful tool for future drug design efforts.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3618-3624"},"PeriodicalIF":4.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring novel furochochicine derivatives as promising JAK2 inhibitors in HeLa cells: Integrating docking, QSAR-ML, MD simulations, and experiments. 探索新型呋喃水仙碱衍生物作为HeLa细胞中有希望的JAK2抑制剂：整合对接，QSAR-ML， MD模拟和实验。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-08 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.08.007

Duangjai Todsaporn, Kamonpan Sanachai, Chanat Aonbangkhen, Athina Geronikaki, Victor Kartsev, Boris Lichitsky, Andrey Komogortsev, Phornphimon Maitarad, Thanyada Rungrotmongkol

{"title":"Exploring novel furochochicine derivatives as promising JAK2 inhibitors in HeLa cells: Integrating docking, QSAR-ML, MD simulations, and experiments.","authors":"Duangjai Todsaporn, Kamonpan Sanachai, Chanat Aonbangkhen, Athina Geronikaki, Victor Kartsev, Boris Lichitsky, Andrey Komogortsev, Phornphimon Maitarad, Thanyada Rungrotmongkol","doi":"10.1016/j.csbj.2025.08.007","DOIUrl":"10.1016/j.csbj.2025.08.007","url":null,"abstract":"Cervical cancer, largely driven by high-risk human papillomavirus (HPV), remains a global health challenge. Janus tyrosine kinase 2 (JAK2) has emerged as a promising therapeutic target for HPV-induced malignancies. This study employed both in silico and in vitro approaches to discover novel JAK2 inhibitors from a library of 76 furochochicine (FCC) derivatives. Twenty-nine compounds were selected via virtual screening, synthesized, and tested for cytotoxicity against HeLa cells. Four FCCs showed potent cytotoxicity with selectivity indices (SI) greater than 3. These cytotoxicity data were used to construct QSAR models with machine learning; eXtreme Gradient Boosting (XGB) yielded the best performance (RMSE = 0.177, R² = 0.831, MAPE = 2.93 %) and was used to predict additional FCC derivatives. FCC90 emerged as a lead compound with strong predictive accuracy (MAPE = 1.43 %) and selectivity (SI = 3.25). JAK2 kinase assays revealed strong inhibition by FCC6, FCC27, and FCC90 (IC₅₀ = 9.10-27.34 nM), with FCC6 and FCC27 surpassing ruxolitinib. Flow cytometry confirmed apoptosis and sub-G1 cell cycle arrest. Molecular dynamics simulations supported the stability of FCC-JAK2 complexes. Furthermore, all active compounds met extended Rule of Five (eRo5) criteria. These findings highlight the potential of FCC derivatives as JAK2 inhibitors for cervical cancer therapy.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3625-3639"},"PeriodicalIF":4.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Repurposing of the nucleoside analogs for influenza. 核苷类似物对流感的重新利用。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-08 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.08.006

Pitchayathida Mee-Udorn, Jaraspim Narkpuk, Peera Jaru-Ampornpan, Suradej Hongeng, Tanaporn Uengwetwanit, Nitipol Srimongkolpithak

{"title":"Repurposing of the nucleoside analogs for influenza.","authors":"Pitchayathida Mee-Udorn, Jaraspim Narkpuk, Peera Jaru-Ampornpan, Suradej Hongeng, Tanaporn Uengwetwanit, Nitipol Srimongkolpithak","doi":"10.1016/j.csbj.2025.08.006","DOIUrl":"10.1016/j.csbj.2025.08.006","url":null,"abstract":"Influenza viruses remain a global health concern prompting the search for new antivirals. Drug repurposing offers an efficient approach to identify potential therapeutics. This study repurposed 35 FDA-approved nucleoside analogs, screening them against influenza H1N1. Seven compounds exhibited significant antiviral activity, with cytidine analogs Gemcitabine (IC₅₀ = 0.64 ± 0.21 µM) and 5-Azacytidine (IC₅₀ = 3.42 ± 0.38 µM) showing the strongest inhibition. Molecular dynamics simulations showed that key binding site residues (Arg45, Lys229, Arg239, Lys308, Lys480) and a magnesium ion are crucial for drug binding. Stable hydrogen bonds between active analogs and specific residues (Arg239, Thr307, Asn310), along with significant interactions with RNA complementary bases, are associated with antiviral activity. These findings offer structural insights into polymerase inhibition and provide a foundation for future drug design and monitoring of resistance development.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3762-3769"},"PeriodicalIF":4.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational modeling of drug-eluting balloons in peripheral artery disease: Mechanisms, optimization, and translational insights. 外周动脉疾病中药物洗脱气球的计算建模：机制、优化和转化见解。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-07 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.08.005

Mohammed A AboArab, Vassiliki T Potsika, Dimitrios S Pleouras, Dimitrios I Fotiadis

{"title":"Computational modeling of drug-eluting balloons in peripheral artery disease: Mechanisms, optimization, and translational insights.","authors":"Mohammed A AboArab, Vassiliki T Potsika, Dimitrios S Pleouras, Dimitrios I Fotiadis","doi":"10.1016/j.csbj.2025.08.005","DOIUrl":"10.1016/j.csbj.2025.08.005","url":null,"abstract":"Drug-eluting balloons (DEBs) represent a promising alternative to stent-based interventions for peripheral artery disease (PAD), and their therapeutic efficacy is dependent on optimizing drug transfer, mechanical deployment, and vessel-wall interactions. This review synthesizes current advancements in computational modeling; systematically analyzes studies identified through comprehensive ScienceDirect, Scopus, and PubMed (2015-2025) searches; and selects them according to PRISMA guidelines. Key strategies, including computational fluid dynamics (CFD), finite element analysis (FEA), fluid-structure interaction (FSI), and machine learning (ML), are critically examined to elucidate how drug kinetics, coating stability, and mechanical stress govern therapeutic outcomes. CFD-based mass transfer models capture flow-driven drug dispersion and washout dynamics, whereas FEA links balloon mechanics, plaque morphology, and drug penetration efficiency. FSI frameworks provide insight into the coupled effects of wall deformation and hemodynamics, identifying high-risk regions of drug underdelivery. ML-driven surrogates and physics-informed neural networks (PINNs) enable real-time, patient-specific predictions with computational accelerations exceeding 600 × while maintaining less than 2 % deviation from high-fidelity solvers. Persistent challenges include anatomical simplifications, limited in-vivo validation, and insufficient integration of biological remodeling. Future directions emphasize hybrid in-silico pipelines integrating imaging-derived 3D geometries, multiscale simulations, and AI-driven pharmacokinetic modeling to establish clinically translatable digital twins for precision-guided DEB therapies in PAD.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3640-3653"},"PeriodicalIF":4.1,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling. 一个代谢信息神经网络，用于将组学数据整合到基因组尺度的代谢模型中。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-07 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.08.004

Gabriele Tazza, Francesco Moro, Dario Ruggeri, Bas Teusink, László Vidács

{"title":"MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.","authors":"Gabriele Tazza, Francesco Moro, Dario Ruggeri, Bas Teusink, László Vidács","doi":"10.1016/j.csbj.2025.08.004","DOIUrl":"10.1016/j.csbj.2025.08.004","url":null,"abstract":"The understanding of cellular behavior relies on the integration of metabolism and its regulation. Multi-omics data provide a detailed snapshot of the molecular processes underpinning cellular functions and their regulation, describing the current state of the cell. While Machine Learning (ML) models can uncover complex patterns and relationships within these data, they require large datasets for training and often lack interpretability. On the other hand, mathematical models, such as Genome-Scale Metabolic Models (GEMs), offer a structured framework for analyzing the organization and dynamics of specific cellular mechanisms. At the same time, they don't allow for seamless integration of omics information. Recently, a new framework to embed GEMs in a neural network has been introduced: these hybrid models combine the strengths of mechanistic and data-driven approaches, offering a promising platform for integrating different data sources with mechanistic knowledge. In this study, we present a Metabolic-Informed Neural Network (MINN) that utilizes multi-omics data to predict metabolic fluxes in Escherichia coli, under different growth rates and gene knockouts. We test its performances against pure ML and parsimonious Flux Balance Analysis (pFBA), demonstrating its efficacy in improving prediction performances. We also highlight how conflicts can emerge between the data-driven and the mechanistic objectives, and we propose different solutions to mitigate them. Finally, we illustrate a strategy to couple the MINN with pFBA, enhancing the interpretability of the solution.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3609-3617"},"PeriodicalIF":4.1,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12359237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small, open-source text-embedding models as substitutes to OpenAI models for gene analysis. 小型的、开源的文本嵌入模型作为OpenAI基因分析模型的替代品。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-06 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.07.053

Dailin Gan, Jun Li

引用次数: 0

The influence of HIV sense and antisense transcripts on stochastic HIV transcription and reactivation. HIV正义和反义转录物对HIV随机转录和再激活的影响。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-06 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.08.003

Kamil Więcek, Janusz Wiśniewski, Heng-Chang Chen

{"title":"The influence of HIV sense and antisense transcripts on stochastic HIV transcription and reactivation.","authors":"Kamil Więcek, Janusz Wiśniewski, Heng-Chang Chen","doi":"10.1016/j.csbj.2025.08.003","DOIUrl":"10.1016/j.csbj.2025.08.003","url":null,"abstract":"In this study, we established dozens of single provirus-infected cellular clones offering various transcriptional phenotypes of HIV. We proposed that stochastic fluctuations in HIV transcription can appear at, at least, two levels: (1) the chromosomal landscape and (2) the in situ HIV integration site. In the former case, proviruses integrating at different genomic locations demonstrated a variety of transcriptional bursting and can be classified in noise space constructed based on the parameters associated with the coefficient of variation and using a mathematical model fitting a curve of exponential decay. In the latter case, stochastic HIV transcription can be unveiled through its phenotypic bifurcation and tended to be a pure epigenetic phenomenon: the identical provirus demonstrated fluctuations in its transcription with an elevated frequency. We observed similar expression patterns between sense and antisense RNA transcripts. Notably, both HIV long terminal repeats reacted to drug stimulation and may reveal distinct behaviors. Overall, our data suggest that HIV antisense transcripts could be involved in the stochastic nature of HIV transcription.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3528-3546"},"PeriodicalIF":4.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ProT-GFDM: A generative fractional diffusion model for protein generation. ProT-GFDM：蛋白质生成的生成分数扩散模型。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-05 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.07.045

Xiao Liang, Wentao Ma, Eric Paquet, Herna Viktor, Wojtek Michalowski

引用次数: 0

gSELECT: A novel pre-analysis machine-learning library enabling early hypothesis testing and predictive gene selection in single-cell data. gSELECT：一个新的预分析机器学习库，可以在单细胞数据中进行早期假设检验和预测基因选择。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-05 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.07.047

Deniz Caliskan, Aylin Caliskan, Thomas Dandekar, Tim Breitenbach

{"title":"gSELECT: A novel pre-analysis machine-learning library enabling early hypothesis testing and predictive gene selection in single-cell data.","authors":"Deniz Caliskan, Aylin Caliskan, Thomas Dandekar, Tim Breitenbach","doi":"10.1016/j.csbj.2025.07.047","DOIUrl":"10.1016/j.csbj.2025.07.047","url":null,"abstract":"Identifying biologically meaningful gene sets and evaluating their ability to separate conditions based on gene expression is an important step in many transcriptomic analyses. While most workflows support data-driven feature selection, few allow direct evaluation of predefined gene sets in a classification context. This limits the ability to assess literature-derived panels or biologically motivated hypotheses prior to downstream analysis. For this, we developed gSELECT, a Python library for evaluating the classification performance of both automatically ranked and user-defined gene sets. It operates on .csv or .h5ad expression matrices with group labels and can be easily integrated into existing analysis pipelines. Gene selection can be based on mutual information ranking, random sampling, or custom input. This supports hypothesis-driven testing without data-derived selection bias and allows direct evaluation of known or candidate markers. Classification is performed using multilayer perceptrons with Monte Carlo cross-validation, either on the full dataset or with a user-defined train/test split. Exhaustive and greedy strategies are available to explore combinatorial effects among genes to identify minimal gene combinations with high predictive power. gSELECT is intended as a pre-analysis tool to evaluate dataset separability and to support early assessment of candidate genes before committing to resource-intensive downstream analyses.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3510-3527"},"PeriodicalIF":4.1,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12354962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A subset of Polycomb-targeted transcription factor genes become hypermethylated yet upregulated in colorectal cancer. polycomb靶向转录因子基因的一个子集在结直肠癌中变得高甲基化但上调。

IF 4.1 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-08-05 eCollection Date: 2025-01-01 DOI: 10.1016/j.csbj.2025.07.057

Min-Kyeong Kwon, Goeun Park, Dayoung Go, Donghyun Park, Sridhar Hannenhalli, Sun Shim Choi

{"title":"A subset of Polycomb-targeted transcription factor genes become hypermethylated yet upregulated in colorectal cancer.","authors":"Min-Kyeong Kwon, Goeun Park, Dayoung Go, Donghyun Park, Sridhar Hannenhalli, Sun Shim Choi","doi":"10.1016/j.csbj.2025.07.057","DOIUrl":"10.1016/j.csbj.2025.07.057","url":null,"abstract":"DNA methylation is a key epigenetic regulator often disrupted in cancer, yet how promoter methylation dynamics translate into transcriptional changes during cancer progression remains incompletely understood. Here, we employed targeted bisulfite sequencing and RNA-seq on paired tumor and non-tumor tissues from 80 Korean colorectal cancer (CRC) patients to map promoter methylation and gene expression dynamics. Promoters with high baseline methylation in non-tumor tissues tended to become hypomethylated in tumors, while those with low baseline methylation underwent partial hypermethylation. However, these changes did not consistently correlate with gene silencing or activation. Strikingly, promoters marked by Polycomb (PcG⁺) in non-tumor tissue were prone to hypermethylation yet often remained transcriptionally active in tumors, a paradox most prominent in transcription factor (TF) genes. In contrast, hypermethylation in PcG⁻ promoters was more consistently associated with transcriptional repression. Our findings suggest that epigenetic plasticity at PcG⁺ TF gene promoters can override the typically repressive effects of DNA methylation, potentially enabling tumors to maintain or enhance the expression of key regulatory genes. This highlights the importance of PcG occupancy in shaping the functional consequences of methylation changes during colorectal tumorigenesis, warranting deeper investigation into how these epigenetic adaptations drive cancer progression.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3556-3564"},"PeriodicalIF":4.1,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0