Journal of Bioinformatics and Computational Biology最新文献

{"title":"KANBind as a diagnostic probe for DNA-binding protein prediction: A prevalence-calibrated reality check under strict homology control.","authors":"Qipeng Wen, Shaohua Jiang, Yiwen Zhang","doi":"10.1142/S0219720026710022","DOIUrl":"10.1142/S0219720026710022","url":null,"abstract":"<p><p>Deep learning reports over 90% DNA-binding protein (DBP) prediction performance on common benchmarks, but these results are usually obtained on balanced test sets and may not translate to proteome-wide scans with extreme class imbalance. Here, we use KANBind as a diagnostic probe to stress-test sequence-based DBP prediction under strict homology control and realistic prevalence. Evaluated on the homology-controlled HBTD benchmark with prevalence-calibrated reporting, KANBind achieves a calibrated precision of 0.0558 at a realistic bacterial prevalence ([Formula: see text]), implying an expected false discovery rate (FDR) of 94.42%. In a proteome-scale scan, this corresponds to approximately 95 false positives per 100 predicted DBPs. Interpretability analysis indicates that predictions are driven mainly by coarse physicochemical cues such as electrostatics, which may be necessary for DNA binding but are insufficient to determine DBP function. Together, these results suggest that apparent benchmark gains can be dominated by homology leakage and evaluation on balanced sets rather than by generalizable functional rules, motivating stress-test benchmarks with strict homology control and realistic negative backgrounds.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 2","pages":"2671002"},"PeriodicalIF":0.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing of sorafenib analogs to target c-Raf for the management of hepatocellular carcinoma: Molecular dynamics and mmPBSA analysis.","authors":"Saima Ejaz, Rehan Zafar Paracha, Maryum Nisar, Afreenish Amir, Kashif Saleem, Fouzia Parveen Malik","doi":"10.1142/S0219720025500222","DOIUrl":"10.1142/S0219720025500222","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;Introduction:&lt;/b&gt; Sorafenib remains the only approved treatment for advanced hepatocellular carcinoma (HCC), yet its clinical use is hindered by toxicity and the emergence of drug resistance. Sorafenib's anticancer effects are largely attributed to its inhibition of multiple kinases, including c-Raf, a key player in the Ras-Raf-MEK-ERK signaling cascade that promotes cell growth and survival. Given the critical role of c-Raf in tumor progression, targeting this kinase offers a promising strategy for improving therapeutic outcomes. Developing new analogs with stronger c-Raf inhibition, better pharmacokinetics, and reduced side effects could help address the current limitations of sorafenib. &lt;b&gt;Objectives:&lt;/b&gt; This study aimed to design novel sorafenib analogs with enhanced binding affinity and favorable pharmacokinetic profiles, specifically targeting the c-Raf kinase to increase therapeutic efficacy against HCC. By using a fragment replacement approach combined with computational methods, the goal was to identify candidates capable of forming stronger, more stable interactions with c-Raf, potentially overcoming resistance linked to sorafenib treatment. &lt;b&gt;Methods:&lt;/b&gt; A total of 84 sorafenib analogs (A1-A84) were generated by modifying key functional groups, including the 2-picolinamide and substituted phenyl moieties known to influence kinase binding and anticancer activity. These analogs were evaluated through chemoinformatics and pharmacokinetic screening to assess their drug-likeness and safety. Molecular docking was performed to estimate their binding affinity toward c-Raf. Six top-performing analogs (A2, A6, A9, A20, A22, A63) were selected for further analysis. To evaluate their dynamic behavior, 100[Formula: see text]ns all-atom molecular dynamics simulations were conducted, followed by Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations to determine binding free energies. Principal component analysis (PCA) was carried out to explore key motion patterns within the protein-ligand complexes. &lt;b&gt;Results:&lt;/b&gt; Molecular docking showed that the selected analogs exhibited stronger binding affinities (-11.6 to -10.9[Formula: see text]kcal/mol) compared to sorafenib (-9.3[Formula: see text]kcal/mol) and regorafenib (-9.5[Formula: see text]kcal/mol). Molecular dynamics simulations substantiated the docking results. MM-PBSA results revealed that at 100[Formula: see text]ns, the binding free energy for the c-Raf-sorafenib complex was 86.751[Formula: see text]kJ/mol, while the c-Raf complexes with A2, A6, A9, A20, A22, and A63 demonstrated significantly lower free energies of -129.114, -135.637, -136.242, -127.178, -94.25, and -123.176[Formula: see text]kJ/mol, respectively, indicating stronger and more stable binding. PCA further confirmed the stability and favorable dynamic profiles of these analogs trajectory with c-Raf. &lt;b&gt;Discussion:&lt;/b&gt; The improved binding affinities and lower free energies of the top analogs indi","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 2","pages":"2550022"},"PeriodicalIF":0.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting functional co-occurrence probability in PPI networks via multi-level participation expectation.","authors":"Peng Wang","doi":"10.1142/S0219720026500046","DOIUrl":"10.1142/S0219720026500046","url":null,"abstract":"<p><p>This study addresses the problem of protein function annotation and proposes a multi-source biological information-fusion framework called Functional co-Occurrence Probability Estimation (FOPE) for estimating functional co-occurrence probabilities. The framework integrates Protein-Protein Interaction (PPI) network topology and protein domain information, quantifying the functional synergy between protein pairs through bidirectional functional participation modeling. Experiments on four model organisms (A. thaliana, C. elegans, D. melanogaster, and S. cerevisiae) show that FOPE delivers effective predictions for the three main categories of Gene Ontology (GO). Compared to existing representative methods, its macro-Fmax values improved by 25.3%, 19.3%, and 20.9% on average for Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), respectively. Ablation studies further reveal the functional-specific contributions of different information sources: domain information plays a dominant role in MF prediction, while PPI network features are more critical for BP prediction. The effective integration of both is key to achieving comprehensive prediction performance. Robustness tests demonstrate that FOPE maintains strong stability in BP and CC predictions even under significant noise in the PPI network (adding or removing 30% of interactions), verifying the error-tolerance advantages of multi-source information fusion. The FOPE framework proposed in this study provides a feasible information fusion approach for protein function prediction. Preliminary experimental results demonstrate the applicability of the method across different functional categories and multiple model organisms, offering a potential computational pathway for exploring functional synergy relationships among proteins from a system-level perspective.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 2","pages":"2650004"},"PeriodicalIF":0.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NNDock2: A neural network-based scoring function for ranking protein-protein docking models.","authors":"Myong-Ho Chae, Gwang So, Ung-Jin Kim","doi":"10.1142/S0219720026500058","DOIUrl":"https://doi.org/10.1142/S0219720026500058","url":null,"abstract":"<p><p>Protein-protein interactions (PPIs) play crucial roles in diverse cellular functions and biological processes, and structural knowledge of the protein complexes is valuable for the elucidation of those functions and designing new drugs. Due to the limitations of experimental methods, computational modeling approaches capable of producing reliable protein complex models using molecular docking tools are of considerable practical interest. The success of protein docking largely depends on an accurate scoring function that can pick out good protein docking models. In this work, we present a neural network-based scoring function for scoring protein-protein docking models, NNDock2, the updated version of our previous scoring function, NNDock1. To improve NNDock1, we augmented the training decoys by adding a large number of more distant decoys. In addition, instead of interface root mean square deviation (iRMSD) in NNDock1, we used the fraction of native contact ([Formula: see text] as a target function, which shows better correlation with true model quality. We also applied regularization during training to avoid overfitting. We tested NNDock2 on the protein-protein docking benchmark version 5.0 (BM5), DOCKGROUND dataset, and the CAPRI score set and compared the performance of NNDock2 with other state-of-the-art scoring functions. NNDock2 performed comparably to other state-of-the-art scoring functions, despite the simplicity of the method and low computational costs. We envision that NNDock2 could be used as an independent scoring function or as an element or feature of composite or deep learning-based scoring functions for protein complex model quality estimation.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 2","pages":"2650005"},"PeriodicalIF":0.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bond-resolved graph-theoretic modeling of structural invariance and metabolic accessibility in drug molecules.","authors":"K Nalini Devi, G Srinivasa","doi":"10.1142/S0219720026710010","DOIUrl":"10.1142/S0219720026710010","url":null,"abstract":"<p><p>In this paper, we develop a connectivity-exact bond-resolved framework for quantifying structural accessibility and persistence in drug molecules. Representing a molecule as a heavy-atom graph [Formula: see text] each bond is classified uniquely as either a connectivity-critical Entry-point bond ([Formula: see text]) or a connectivity-preserving Fortress bond ([Formula: see text]) yielding the exact identity [Formula: see text] This exhaustive partition separates fragmentation capacity from invariant scaffold structure without adjustable parameters. To refine the invariance coordinate we introduce Total Structural Entrenchment (TSE) a persistence-weighted functional defined over cycle-supported bonds and modulated by local steric wall contributions [Formula: see text]. The resulting two-parameter embedding [Formula: see text] distinguishes superficial cyclic extent from deeply embedded structural reinforcement and resolves degeneracies inherent in raw bond counts. Metabolic progression is formalized as recursive bridge depletion generating a directed migration across the architectural plane toward a bridge-depleted refractory core. Within this framework scaffold persistence is interpreted as a connectivity-driven contraction governed strictly by graph topology. The resulting invariance-variation embedding establishes a mathematically controlled bond-level representation of structural accessibility and cyclic entrenchment.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 2","pages":"2671001"},"PeriodicalIF":0.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for the identification of single nucleotide polymorphisms.","authors":"Neelofar Sohi, Shaheena Sohi","doi":"10.1142/S0219720026500034","DOIUrl":"https://doi.org/10.1142/S0219720026500034","url":null,"abstract":"<p><p>Human Genome Project (HGP), Genome Wide Association Studies (GWAS) and The Cancer Genome Atlas (TCGA) are some of the remarkable research endeavors that generated massive amounts of information about Single Nucleotide Polymorphisms (SNPs) and other genetic variations, providing valuable insights for understanding the association of SNPs with diseases. It enables early diagnosis, prevention, and treatment planning for diseases. In this study, a novel approach is proposed for the identification of SNPs. This approach consists of two techniques: technique I introduces a modified matching strategy for chosen matching algorithms and technique II combines the Divide-and-Conquer technique with technique 1. Performance evaluation of the proposed techniques is performed using performance metrics such as Precision, Recall, F-measure, Execution Time, and Resource Utilization (including CPU Utilization and RAM Usage). The proposed techniques overcome most of the research gaps and shortcomings of the existing techniques.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 2","pages":"2650003"},"PeriodicalIF":0.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the regulation of model parameters on delay time in the p53 dynamical response to single-stranded breaks.","authors":"Aiqing Ma, Xiyan Yang","doi":"10.1142/S0219720026510017","DOIUrl":"https://doi.org/10.1142/S0219720026510017","url":null,"abstract":"<p><p>The dynamic p53 response is a known determinant of cell fate. However, its temporal control, specifically the mechanisms regulating the delay time ([Formula: see text]) of the graded p53 pulse following single-stranded breaks (SSBs), remains poorly understood. To systematically dissect this timing mechanism, we developed and analyzed a mechanistic ordinary differential equation (ODE) model of the p53-Mdm2-ATR network. We first established that increasing damage intensity reliably shortens the delay time, accelerating the cellular decision-making process. Our analysis revealed a critical finding: the delay time is most acutely sensitive to the p53-dependent Mdm2 production rate ([Formula: see text]), highlighting the dominant role of the negative feedback loop in setting the pace. We further classified the model parameters into functional roles: accelerators (e.g. Ataxia-Telangiectasia and RAD3-related (ATR) production rate dependent on damage ([Formula: see text]), p53 activation rate dependent on ATR ([Formula: see text]), p53-dependent Mdm2 production rate ([Formula: see text]), p53-dependent Wip1 production rate ([Formula: see text], ATR degradation rate ([Formula: see text]) and Mdm2-dependent p53 degradation rate ([Formula: see text], which shorten the delay time ([Formula: see text]), and brakes (e.g. ATR-dependent Mdm2 degradation rate ([Formula: see text]), self-degradation rate of Mdm2 ([Formula: see text]) and self-degradation rate of Wip1 ([Formula: see text]), which prolong it. Sensitivity analysis showed that as parameter values increase, [Formula: see text] becomes less sensitive to [Formula: see text]. The sensitivity to [Formula: see text] exhibited an initial increase followed by a decrease, whereas the opposite trend was observed for [Formula: see text]. The remaining parameters ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) all showed a monotonic decrease in sensitivity. This work provides a quantitative blueprint for therapeutic interventions, suggesting that targeting the p53-Mdm2 feedback strength is the most effective strategy to sensitize cancer cells and shorten the critical delay to cell death.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 1","pages":"2651001"},"PeriodicalIF":0.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting distant cancer metastasis using a weighted gene interaction network and sample-specific differential correlations.","authors":"Jiahui Kang, Kyungsook Han","doi":"10.1142/S0219720025500246","DOIUrl":"https://doi.org/10.1142/S0219720025500246","url":null,"abstract":"<p><p>Predicting metastasis in early stages of cancer plays a crucial role in effectively controlling cancer progression and thereby improving patient survival outcomes. Although several computational methods have been developed to predict cancer metastasis, most focus on lymph node metastasis. Distant metastasis is more difficult to detect or predict than lymph node metastasis. In this study, we developed a multilayer perceptron (MLP) model to predict distant cancer metastases. We constructed a weighted gene interaction network and computed sample-specific differential gene correlations for individual cancer samples. The MLP model was trained on sample-specific differential gene correlations and tested on independent datasets of differential gene correlations from samples that were not used in training the model. The MLP model is capable of predicting whether or not distant metastasis will occur and potential distant metastatic sites. In independent testing, it predicted distant metastasis with a high performance (AUC of 0.95) and predicted metastatic sites with an average AUC of 0.97. In comparison of our model with other state-of-the-art methods using the same data set, our model showed better performance than the others. The prediction model developed in this study may help clinicians determine site-specific testing and treatment options.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 1","pages":"2550024"},"PeriodicalIF":0.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of evolutionary computation and ensemble learning in bioinformatics (Case Study: Protein-Peptide Interaction Prediction).","authors":"Shima Shafiee, Abdolhossein Fathi, Ghazaleh Taherzadeh","doi":"10.1142/S0219720026500022","DOIUrl":"https://doi.org/10.1142/S0219720026500022","url":null,"abstract":"<p><p>The performance of classifiers can decline due to irrelevant or non-informative features. Therefore, extracting meaningful high-level features from raw data through effective feature selection and construction is vital. This challenge is particularly significant in bioinformatics, especially for predicting protein-peptide interactions. To address this, we propose IntPPPred, a computational method designed to enhance residue-level prediction of such interactions. IntPPPred leverages evolutionary computation and ensemble learning to build high-level features from selected informative ones, reducing computational complexity and feature space. It first identifies unique and effective features before applying multiple feature constructions using a gravitational search algorithm. This enhances the prediction capability of a stacking-based ensemble classifier, particularly on imbalanced datasets. Experimental results show that IntPPPred improves Matthews correlation coefficient (MCC), F-measure, and precision by at least 1.2%, 19.6%, and 3.9%, respectively, over existing methods. When evaluated on a second dataset, further gains of 5.2%, 24%, 16.7%, and 1.1% were observed in precision, sensitivity, F-measure, and MCC. Additionally, performance consistency between cross-validation and independent test sets confirms the method's robustness. Overall, IntPPPred serves as an effective computational tool that supports experimental research and enhances machine learning performance in protein-peptide interaction prediction.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 1","pages":"2650002"},"PeriodicalIF":0.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep subspace fusion based on integrated self-supervision for cancer subtype identification.","authors":"Min Li, Mingzhuang Zhang, Mingzh Lou, Shaobo Deng, Guangda Hou, Licao Wanzhang, Wengan Xu","doi":"10.1142/S0219720026500010","DOIUrl":"https://doi.org/10.1142/S0219720026500010","url":null,"abstract":"<p><p>Given the rapid advancements in high-throughput technology, multi-omics data have become essential for identifying cancer subtypes and providing accurate medical treatments for patients. However, integrating multi-omics data and collecting patient information pose complex and challenging tasks. Although numerous integration techniques have emerged in recent years to address the challenges posed by heterogeneity and noise in omics data, most of these algorithms are based on unsupervised methods due to the lack of labeled data. This indicates there is still potential for enhancing the extraction of valuable information from omics data. This study introduces a novel framework, namely Deep Subspace Fusion based on Integrated Self-supervision (DSFIS), for the recognition of cancer subtypes. DSFIS is built on the autoencoder with a self-representation layer and guides the autoencoder to generate the most representative sample subspace structure by integrating self-supervision. This framework can not only create a comprehensive representation of the differences and similarities among patients but also more fully uncover the potential information from omics data. The DSFIS was compared to eight cutting-edge approaches for integrating multi-omics data. The experimental findings demonstrated that DSFIS effectively identified cancer subtypes according to the omics data. It achieved significant results superior to other algorithms in survival prognosis analysis and clinical correlation analysis, demonstrating that DSFIS has great potential in identifying cancer subtypes through multi-omics data.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"24 1","pages":"2650001"},"PeriodicalIF":0.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}