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A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5373

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Prof. Dr. Haijun Gong

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Department of Mathematics & Statistics, Saint Louis University, St. Louis, MO 63103, USA
Interests: statistics; machine learning; deep learning; model checking; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue titled “Emerging Trends in Bioinformatics and Computational Biology”. This issue will showcase cutting-edge research and innovative methodologies that are shaping the future of bioinformatics and computational biology, with a strong emphasis on molecular-level biological applications.

We welcome high-quality original research papers, reviews, and perspective articles that explore current challenges and breakthroughs in the computational analysis of molecular data. Topics of interest include, but are not limited to, the following:

Novel algorithms and computational methods for molecular data analysis;
Advances in genomics, transcriptomics, proteomics, and metabolomics;
Multi-omics data integration and systems-level insights;
Computational modeling of molecular mechanisms and regulatory network inference;
Applications of machine learning, deep learning, and AI in bioinformatics;
Missing value imputation and change-point detection;
Big data approaches for molecular biology research;
Novel methods for molecular diagnostics, precision medicine, and therapeutics;
Data visualization and mining techniques for high-dimensional molecular data.

This Special Issue aims to bring together contributions from worldwide researchers in the fields of bioinformatics and computational biology. Submissions should provide new insights, introduce transformative methods, or present impactful applications that deepen our understanding of molecular biology through computational innovation.

Notably, we also want to thank the journal’s Topical Advisory Panel Member, Dr. Tong Si, for her contribution and support to the Special Issue operation, promotion and development of this Special Issue.

Prof. Dr. Haijun Gong
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Current Issues in Molecular Biology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

bioinformatics
computational biology
genomics
proteomics
metabolomics
multi-omics
machine learning
algorithms
big data
molecular diagnostics
personalized medicine
data visualization

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Published Papers (6 papers)

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Research

21 pages, 4399 KB

Open AccessArticle

Computational Chemistry and Toxicology of Phosphonate Esters of Alkyl Acetoacetates, an Unexplored Class of V-Agents

by Georgios Pampalakis and Eleni Pontiki

Curr. Issues Mol. Biol. 2026, 48(8), 779; https://doi.org/10.3390/cimb48080779 - 30 Jul 2026

Viewed by 126

Abstract

V-agents are exceedingly toxic oily substances, among which phosphonothiolates VX and VR have been extensively studied. Nevertheless, V-agents encompass a large family of nerve agents with diverse structures, including the phosphonate esters of alkyl acetoacetates or 2-alkoxycarbonyl-1-methylvinyl cycloalkyl methylphosphonates. These agents exist in two geometric isomers, and their properties remain largely unknown. Due to continuous concerns about chemical terrorism and safety, it is necessary to study their properties in order to develop effective countermeasures. Here, we applied computational tools to predict their ADME profile, chemical properties, and structure-related toxicity. These agents exhibited optimal drug-like properties, and it was predicted that they can penetrate the skin, acting as percutaneous hazards, and further penetrate the gastrointestinal tract and the blood–brain barrier. Certain CYP450 enzymes could differentially recognize the E- and Z-isomers, and this may explain the observation that E-isomers are significantly more toxic than their Z-counterparts. Analyses of the E- and Z-isomer stabilities also offered evidence for the reported lability of the Z-isomers. In conclusion, this study provides the first detailed in silico description of these V-agents, requiring future targeted experimental validation. Full article

(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)

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13 pages, 745 KB

Open AccessArticle

Integration of Machine Learning-Based Pathogenicity Prediction and Phenotype Matching Improves Variant Prioritization in Rare Clinical Testing

by Jiri Ruzicka, Jean-Marie Ravel, Jérôme Audoux, Alexandre Boulat, Julien Thévenon, Kévin Yauy, Marine Dancer, Laure Raymond, Yannis Lombardi, Nicolas Philippe, Michael GB Blum, Nicolas Duforet-Frebourg and Laurent Mesnard

Curr. Issues Mol. Biol. 2026, 48(7), 706; https://doi.org/10.3390/cimb48070706 - 11 Jul 2026

Viewed by 472

Abstract

Genome and exome sequencing have become central to diagnosing rare hereditary diseases, but each test returns thousands of variants that a clinical scientist must review by hand to find the one responsible for the patient’s condition. This manual interpretation is the main bottleneck in clinical genomics. To reduce it, we developed DiagAI, a machine-learning system that ranks the variants found in a patient and returns a short list of the most likely causal candidates. DiagAI combines three sources of evidence: a pathogenicity score from the Universal Pathogenicity Predictor (UP2), a model we trained to estimate how damaging a variant is on the five-tier scale of the American College of Medical Genetics and Genomics (ACMG); a phenotype-matching score from PhenoGenius, which weighs how well a gene’s known clinical features match the patient’s symptoms (encoded as Human Phenotype Ontology, or HPO, terms); and expert rules covering inheritance pattern and sequencing quality. We evaluated DiagAI on 966 exomes from adults investigated for kidney disease of unknown cause, of which 196 had a confirmed genetic diagnosis. We first tested UP2 on its own by ranking 62 confirmed disease-causing missense variants that were absent from its training data: UP2 placed the causal variant within the top 100 candidates in 87% of cases, compared with 61% for the widely used tool REVEL. Across the 196 diagnosed exomes, the full DiagAI shortlist contained the causal variant in 94.9% of cases when the patient’s symptoms were provided and in 90.8% when they were not, with a typical shortlist of about 10 variants. When symptoms were provided, the single top-ranked variant was the correct diagnosis in 74% of cases, versus 42% without symptoms, exceeding the performance of the established tools Exomiser and AI-MARRVEL on the same cohort. DiagAI produces compact, accurate shortlists that can reduce the manual interpretation workload as diagnostic sequencing volumes continue to grow. Full article

(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)

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27 pages, 6328 KB

Open AccessArticle

Screening of Natural Product-Derived USP7 Inhibitors for Cancer Therapy via Integrated Machine Learning and Molecular Simulations

by Faris Alrumaihi

Curr. Issues Mol. Biol. 2026, 48(6), 621; https://doi.org/10.3390/cimb48060621 - 16 Jun 2026

Viewed by 458

Abstract

Ubiquitination, a crucial cellular protein regulation process, is linked to various diseases, including cancer. Deubiquitinases (DUBs) can reverse ubiquitination, offering a therapeutic strategy. USP7, a DUB, is a key target in oncology due to its role in destabilizing p53, and small-molecule inhibitors could restore p53 activity and combat tumor growth. In this study, we integrated a machine learning (ML)-based screening approach with molecular docking and molecular dynamics (MD) simulations in order to identify potential small-molecule inhibitors of USP7. ML-based screening identified 22 active molecules from a library of 2301 natural compounds. Among the 22 active compounds, only fifteen compounds fulfilled the drug-likeness criteria. Subsequently, molecular docking found three compounds, PubChem 162957515, 114917, and 442879 as potential inhibitors based on binding affinity and interactions. Further, MD simulations and MM-PBSA analyses were performed to evaluate the stability and dynamic behavior of the complexes. Binding energy calculations Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) revealed that compounds PubChem 114917 and 162957515 exhibited strong binding affinities of −20.98 kcal/mol and −18.68 kcal/mol, respectively, implying that these compounds could serve as promising inhibitors for the development of anticancer therapeutics. Full article

(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)

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19 pages, 11132 KB

Open AccessArticle

phyloPipeR: An R Package for End-to-End Phylogenetic Reconstruction and Tree Comparison

by Feifei Li, Yue Zou, Tong Li, Lingling Xie, Dandan Liu, Kunhong Song, Yanting Luo, Dan Qin, Youjin Hao and Bo Li

Curr. Issues Mol. Biol. 2026, 48(6), 600; https://doi.org/10.3390/cimb48060600 - 5 Jun 2026

Viewed by 479

Abstract

Phylogenetic reconstruction is a multi-step process that typically involves sequence retrieval, alignment, trimming, and tree inference, often requiring the integration of multiple independent tools. This fragmented workflow increases technical complexity and limits reproducibility, particularly in large-scale analyses. Here, we present phyloPipeR, an R package that provides an integrated and automated framework for end-to-end phylogenetic analysis and tree comparison within a unified environment. The phyloPipeR enables complete workflows from ortholog retrieval to tree inference and quantitative comparison, while also supporting modular execution of individual steps. The package implements multiple phylogenetic inference methods and supports both concatenation and coalescent strategies for multi-gene analyses. By integrating tree reconstruction and quantitative comparison within a single framework, phyloPipeR improves reproducibility, reduces technical barriers, and provides a scalable solution for systematic and integrative evolutionary studies. Full article

(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)

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31 pages, 9123 KB

Open AccessArticle

Exploring the Biological Potency of Carotenoids Against Alzheimer’s Disease: An Integrated Approach of Molecular Docking and Molecular Dynamics

by Meriem Khedraoui, El Mehdi Karim, Imane Yamari, Abdelkbir Errougui, Doni Dermawan, Nasser Alotaiq and Samir Chtita

Curr. Issues Mol. Biol. 2026, 48(4), 407; https://doi.org/10.3390/cimb48040407 - 16 Apr 2026

Viewed by 998

Abstract

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction, amyloid-β aggregation, mitochondrial stress, and aberrant kinase activity. Carotenoids, naturally occurring pigments with antioxidant and neuroprotective properties, have emerged as promising candidates for AD intervention. In this study, we performed a systematic stepwise computational screening of a large carotenoid library (n = 1191) to identify multitarget candidates against AD–related proteins. The workflow consisted of predefined ADMET filtering (oral absorption > 90%, Caco-2 > 0.9, logBB > −1, and absence of major CYP inhibition and toxicity alerts), reducing the dataset to 61 compounds, followed by multi-target molecular docking against AChE, BChE, BACE-1, MAO-B, and GSK3-β. Compounds were ranked using an aggregated mean docking score across all five targets, and the top-performing candidate was subjected to detailed mechanistic analyses. Hopkinsiaxanthin emerged as the highest-ranked multitarget carotenoid and was further evaluated using frontier molecular orbital (FMO) analysis, pharmacophore modeling, 100 ns molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and per-residue decomposition. Docking predicted favorable estimated binding affinities toward all targets. MD simulations confirmed stable receptor–ligand complexes with low RMSD values (0.278–0.285 nm). MM/PBSA analysis indicated favorable binding free energies, particularly for GSK3-β (−22.73 kcal/mol) and AChE (−21.50 kcal/mol). Per-residue decomposition identified key hotspot residues driving stabilization. Overall, this structured computational framework identifies Hopkinsiaxanthin as a promising multitarget scaffold and supports its prioritization for experimental validation in AD models. Full article

(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)

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20 pages, 16568 KB

Open AccessArticle

Scissor–CIBERSORTx Deconvolution Reveals Functional Heterogeneity of CTAL/aTAL Cells and Associated Biomarkers in Renal Fibrosis

by Hengping Wang, Yuan Zhang, Jiale Li, Ying Fu and Huiyan Wang

Curr. Issues Mol. Biol. 2026, 48(2), 215; https://doi.org/10.3390/cimb48020215 - 16 Feb 2026

Viewed by 994

Abstract

Renal fibrosis (RF) represents a major pathological outcome of chronic kidney disease, currently accompanied by extremely limited therapeutic strategies. To decipher key cellular and molecular drivers, we integrated single-cell and bulk transcriptomic profiles for comprehensive analysis. Based on the RF-related single-cell and bulk transcriptomic data, key cell subtypes were identified through Scissor analysis, custom signature matrix construction via CIBERSORTx, and Weighted Gene Co-Expression Network Analysis (WGCNA). Subsequently, key subtype-related biomarkers were identified through the expression analysis, and functional enrichment analysis for biomarkers was conducted to elucidate the potential mechanisms by which biomarkers regulate RF. Through comprehensive profiling, thick ascending limb (TAL) cells were predominant and displayed marked heterogeneity in renal fibrosis (RF), with cortical TAL (CTAL) and adaptive TAL (aTAL) identified as principal subtypes. A set of candidate biomarkers was identified. Quantitative polymerase chain reaction (qPCR) validation in mouse models confirmed aberrant expression of these biomarkers, with STAT1 and PARP8 upregulated and HS6ST2, PTGER3, and TMEM207 downregulated in RF. Furthermore, functional enrichment analyses indicated that these biomarkers were associated with pathways underlying metabolic reprogramming and immune perturbation. Our study implicates CTAL and aTAL as central cellular players in RF and identifies their associated biomarkers. These experimentally validated biomarkers provide novel targets and repurposing opportunities for RF therapeutic intervention. Full article

(This article belongs to the Special Issue Emerging Trends in Bioinformatics and Computational Biology)

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