International Journal of Molecular Sciences

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Open AccessArticle

Network Pharmacology and In Vivo Validation Reveal Berberine-Mediated Regulation of the Liver–Brain Inflammatory Axis in MCD-Induced Steatohepatitis

by Yeon-Joo Yoo, Ji-Han Kim, Seung-Hoon Yoo and Byung-Cheol Lee

Int. J. Mol. Sci. 2026, 27(15), 6967; https://doi.org/10.3390/ijms27156967 - 3 Aug 2026

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Abstract

Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive immunometabolic liver disorder involving lipid dysregulation, inflammation, fibrosis, and extrahepatic immune–neural responses, yet therapies capable of modulating these interconnected processes remain limited. Berberine (BBR), an isoquinoline alkaloid derived from traditional medicinal plants including Coptis chinensis Franch. [...] Read more.

Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive immunometabolic liver disorder involving lipid dysregulation, inflammation, fibrosis, and extrahepatic immune–neural responses, yet therapies capable of modulating these interconnected processes remain limited. Berberine (BBR), an isoquinoline alkaloid derived from traditional medicinal plants including Coptis chinensis Franch. (Coptidis Rhizoma), has shown metabolic and anti-inflammatory activities; however, its effects on hepatic inflammation and the liver–brain inflammatory axis in MASH remain unclear. Here, network pharmacology and molecular docking were used to predict BBR targets and pathways, followed by in vivo validation in a methionine- and choline-deficient diet-induced mouse model. Liver injury and metabolic alterations were assessed using serum biochemistry and lipid profiles, histological changes by hematoxylin and eosin and Sirius Red staining, and hepatic and hypothalamic inflammation by qRT-PCR, flow cytometry, and Iba-1/GFAP immunostaining. SREBF1, AKT1, and TGFB1 were identified as core BBR targets, with pathways linked to lipid metabolism, oxidative stress, inflammation, and fibrogenesis. BBR attenuated liver injury, steatosis, steatohepatitis, and fibrosis, suppressed SREBF1-associated lipogenic signaling and fibrogenic gene expression, remodeled circulating monocyte subsets, reduced Kupffer cell accumulation, and inhibited hypothalamic microglial activation. These findings suggest that BBR alleviates MCD-induced steatohepatitis through multi-target regulation of hepatic metabolic dysfunction, immune remodeling, and hypothalamic neuroinflammation. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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17 pages, 4866 KB

Open AccessArticle

PMconv: How to Compare Proteomes and Metabolomes?

by Anna Kozlova, Anna Kliuchnikova, Arina Gordeeva, Andrey Lisitsa, Elena Ponomarenko and Ekaterina Ilgisonis

Int. J. Mol. Sci. 2026, 27(11), 5086; https://doi.org/10.3390/ijms27115086 - 4 Jun 2026

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Abstract

Integrating proteomic and metabolomic data remains challenging due to the many-to-many relationships between metabolites and proteins and the spatial constraints of cellular compartmentalization. To address this, we developed PMconv, a web-based application for bidirectional, knowledge-based mapping of proteomic and metabolomic datasets. Leveraging curated [...] Read more.

Integrating proteomic and metabolomic data remains challenging due to the many-to-many relationships between metabolites and proteins and the spatial constraints of cellular compartmentalization. To address this, we developed PMconv, a web-based application for bidirectional, knowledge-based mapping of proteomic and metabolomic datasets. Leveraging curated associations from the Human Metabolome Database (HMDB) and protein interaction data from STRING, PMconv infers potential biochemical connections between experimentally detected molecules and pathway-annotated partners. The tool supports interactive network visualization and exports compartment annotations from the Human Protein Atlas to facilitate spatial contextualization of inferred interactions. PMconv is designed as an exploratory resource for hypothesis generation and feature engineering in multi-omics research, with the explicit understanding that knowledge-derived associations require experimental validation for compartment-specific interpretation. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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17 pages, 1786 KB

Open AccessArticle

The Spermidine Synthase Gene as a Reporter of Transcription Inhibition in Escherichia coli

by Anton R. Izzi, Alisa P. Chernyshova, Mikhail Y. Zhitlov, Alexander Yu. Rudenko, Ratislav M. Ozhiganov, Yury A. Ikhalaynen, Inna A. Volynkina, Lubov V. Dorofeeva, Vadim N. Tashlitsky, Igor A. Rodin, Lyudmila I. Evtushenko, Vera A. Alferova, Petr V. Sergiev, Olga A. Dontsova and Dmitrii A. Lukianov

Int. J. Mol. Sci. 2026, 27(11), 4829; https://doi.org/10.3390/ijms27114829 - 27 May 2026

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Abstract

Antimicrobial resistance is a major threat to modern society and healthcare, as it severely compromises the efficacy of standard antibiotic treatments. To meet the ever-increasing demand for novel antimicrobial drugs, it is crucial to develop new strategies for screening antimicrobial compounds and improve [...] Read more.

Antimicrobial resistance is a major threat to modern society and healthcare, as it severely compromises the efficacy of standard antibiotic treatments. To meet the ever-increasing demand for novel antimicrobial drugs, it is crucial to develop new strategies for screening antimicrobial compounds and improve existing high-throughput techniques. Reporter systems that employ specific genetic markers are powerful tools not only for detecting antimicrobial activity of the substance being studied, but also for identifying the potential mechanism of its action. Among other metabolic pathways, RNA biosynthesis machinery is considered a promising molecular target as it remains underutilized in current antimicrobial therapy and therefore is rarely exposed to drug pressure. However, there is no suitable biomarker for identifying compounds that inhibit the transcription in Gram-negative bacteria. Combining bioinformatic search and RT-qPCR experimental validation, we have established the overexpression of the spermidine synthase gene (speE) as a biomarker associated with impaired transcription in Escherichia coli. Monitoring the expression level of speE in antibiotic-treated cells enables reliable detection of compounds that inhibit bacterial RNA-polymerase, such as rifampicin and fidaxomicin. Moreover, our screening system was successfully applied in practice to analyze chromatography fractions from fermentation broth of antibiotic producers, with compounds of the rifamycin family being identified as hits and isolated. The proposed method has the potential to be used in sequential screening procedures to reveal active antimicrobial compounds that inhibit bacterial transcription process, giving the world novel antimicrobials with minimal risk of resistance development. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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22 pages, 2487 KB

Open AccessArticle

Integrating Molecular Biology and Cryptography: A DNA and RNA-Based Framework for Secure Data Encryption

by Muhammad Naeem Akhtar, Jawad Hussain Awan, Abdul Mateen Shahzaib Asad and Min Young Kim

Int. J. Mol. Sci. 2026, 27(10), 4522; https://doi.org/10.3390/ijms27104522 - 18 May 2026

Cited by 1 | Viewed by 615

Abstract

The rapid growth of digital communication and large-scale data exchange has increased the demand for advanced cryptographic techniques capable of resisting emerging computational threats. Conventional encryption methods primarily rely on mathematical complexity, which may become vulnerable with the advancement of high-performance computing and [...] Read more.

The rapid growth of digital communication and large-scale data exchange has increased the demand for advanced cryptographic techniques capable of resisting emerging computational threats. Conventional encryption methods primarily rely on mathematical complexity, which may become vulnerable with the advancement of high-performance computing and future quantum technologies. Biological molecules such as deoxyribonucleic acid (DNA) and RiboNucleic Acid (RNA) provide unique properties, including extremely high storage density, massive parallelism, and complex nucleotide structures that can inspire novel cryptographic mechanisms. This study proposes a bio-inspired cryptographic framework that integrates DNA encoding and RNA-based transformations to enhance data security. In the proposed framework, digital information is first converted into binary format and mapped to nucleotide sequences using a predefined encoding scheme. The encryption process incorporates multiple molecular transformations, including complementary base pairing, sequence permutation, and transcription-inspired DNA-to-RNA conversion to generate a highly randomized ciphertext. Decryption reverses these transformations to reconstruct the original plaintext. Security evaluation demonstrates that the proposed framework produces high entropy outputs, a substantially large key space, and enhanced resistance to statistical and brute-force attacks. The results indicate that DNA and RNA-inspired cryptographic systems can substantially enhance encryption complexity while maintaining reliable data recovery. This research highlights the potential of molecular cryptography as a promising interdisciplinary approach for future secure communication and biological data storage systems. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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13 pages, 22731 KB

Open AccessArticle

Insulin Receptor-Related Receptor Activation by Artificial Double-ER Mutations in the Transmembrane Domain

by Oxana V. Serova, Alina A. Gavrilenkova, Andrey S. Kuznetsov, Alexander S. Goryashchenko, Alexandra R. Agisheva, Yaroslav V. Bershatsky, Vladislav A. Lushpa, Olga T. Zangieva, Mikhail S. Karbyshev, Andrei S. Gerasimov, Ivan S. Okhrimenko, Roman G. Efremov, Igor E. Deyev and Eduard V. Bocharov

Int. J. Mol. Sci. 2026, 27(10), 4364; https://doi.org/10.3390/ijms27104364 - 14 May 2026

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Abstract

The orphan insulin receptor-related receptor (IRR), in contrast to its homologs from the insulin receptor family, is activated by a mildly alkaline extracellular medium. We have previously demonstrated that IRR activation is defined by two synergistic sites located in the dimeric extracellular domain. [...] Read more.

The orphan insulin receptor-related receptor (IRR), in contrast to its homologs from the insulin receptor family, is activated by a mildly alkaline extracellular medium. We have previously demonstrated that IRR activation is defined by two synergistic sites located in the dimeric extracellular domain. Here, we describe artificial mutations in the IRR transmembrane domain that promote receptor activation. First, using molecular modeling based on the NMR-derived structure, we proposed amino acid substitutions that could enhance non-covalent interactions between the transmembrane segments of the IRR dimer. These mutations were subsequently tested for effects on pH sensing by IRR. We showed that double-mutant A938E-A939R was highly phosphorylated at neutral pH and still sensitive to alkaline pH. Remarkably, the double substitution of V929E-G930R resulted in strong basal phosphorylation of the receptor over the pH titration range. Through site-directed mutagenesis, we demonstrated that the transmembrane domain plays a critical role in IRR activation, allowing for targeted control of functioning of the receptor, including its pH sensitivity. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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12 pages, 1977 KB

Open AccessArticle

Population-Scale Plasma Proteomic Profiles Associated with Chronic Periodontitis in the UK Biobank

by Su Kang Kim, Min Kyoung Kim, Sang Wook Kang and Ju Yeon Ban

Int. J. Mol. Sci. 2026, 27(5), 2514; https://doi.org/10.3390/ijms27052514 - 9 Mar 2026

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Abstract

Periodontitis is a chronic infectious disease characterized by the destruction of the tooth-supporting tissues, including the gingiva, periodontal ligament, and alveolar bone, which may ultimately lead to tooth loss. However, blood-based biomarkers reflecting systemic inflammation in periodontitis remain poorly defined. We analyzed plasma [...] Read more.

Periodontitis is a chronic infectious disease characterized by the destruction of the tooth-supporting tissues, including the gingiva, periodontal ligament, and alveolar bone, which may ultimately lead to tooth loss. However, blood-based biomarkers reflecting systemic inflammation in periodontitis remain poorly defined. We analyzed plasma proteomic data from the UK Biobank using Olink Explore proteomics to identify systemic protein signatures distinguishing chronic periodontitis patients (n = 90) from healthy controls (n = 2234). Among 2151 proteins passing quality control, 29 proteins showed significant differential expression (FDR < 1.0 × 10⁻⁵). Growth differentiation factor 15 (GDF15) exhibited the strongest upregulation (mean NPX: −0.183 to 0.157, effect size = 0.337, FDR = 2.82 × 10⁻¹²), followed by N-terminal pro-B-type natriuretic peptide (NT-proBNP) (effect size = 0.594), Interleukin-6 (IL-6) (effect size = 0.450), and Insulin-like growth factor binding protein-(4IGFBP4) (effect size = 0.269). Multiple TNF receptor superfamily members (TNFRSF1A/1B, TNFRSF10A/10B) and proteins involved in extracellular matrix remodeling (COL6A3, ADAM12) and vascular stress (ADM) were significantly elevated. In contrast, EGFR and DNER showed decreased expression. Protein–protein interaction network analysis revealed IL-6 as a central hub protein forming a tightly interconnected cluster with TNF receptor family members. These findings indicate systemic plasma protein profiles associated with chronic periodontitis within this population-based cohort. The identified proteins may provide a basis for future evaluation of blood-based biomarkers for chronic periodontitis, pending further validation. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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23 pages, 4932 KB

Open AccessArticle

Library Preparation Biases Plant Virome Detection: Poly(A) mRNA Enrichment vs. rRNA Depletion in Pepper and Garlic

by Hoseong Choi, Dong Woo Kang, Yeonhwa Jo, Jisoo Park, Dongjoo Min, Gyeong Geun Min, Jisu Kim, Chaemin Shin, Jin-Sung Hong and Won Kyong Cho

Int. J. Mol. Sci. 2026, 27(5), 2300; https://doi.org/10.3390/ijms27052300 - 28 Feb 2026

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Abstract

High-throughput RNA sequencing reveals plant viromes, but library preparation methods may bias viral detection. Here, we compared rRNA-depleted total RNA-seq and poly(A)-selected mRNA-seq using field-collected pepper leaves (Anseong and Jincheon) and garlic cloves (Hoengseong) from Korean commercial fields. rRNA-depleted total RNA-seq consistently recovered [...] Read more.

High-throughput RNA sequencing reveals plant viromes, but library preparation methods may bias viral detection. Here, we compared rRNA-depleted total RNA-seq and poly(A)-selected mRNA-seq using field-collected pepper leaves (Anseong and Jincheon) and garlic cloves (Hoengseong) from Korean commercial fields. rRNA-depleted total RNA-seq consistently recovered more viruses, longer contigs, and complete multipartite DNA virus genomes (e.g., milk vetch dwarf virus components, tomato spotted wilt virus segments), while mRNA-seq was dominated by highly expressed polyadenylated viruses like broad bean wilt virus 2. In Jincheon pepper, mRNA-seq missed hot pepper endornavirus, pepper cryptic virus 2, and multiple milk vetch dwarf virus segments revealed by total RNA-seq. Garlic libraries showed similar patterns, with total RNA-seq additionally detecting low-titer RNA viruses likely representing contamination. rRNA-depleted total RNA-seq provides a more complete, less biased view of plant viromes and is recommended for comprehensive virus discovery and genome reconstruction, while mRNA-seq remains useful for polyadenylated virus quantification and host gene expression analysis alongside virome profiling. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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24 pages, 1821 KB

Open AccessArticle

PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein–Peptide Docking

by Andrea Giuseppe Cavalli, Giulio Vistoli, Alessandro Pedretti, Laura Fumagalli and Angelica Mazzolari

Int. J. Mol. Sci. 2026, 27(2), 870; https://doi.org/10.3390/ijms27020870 - 15 Jan 2026

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Abstract

Over the past two decades, pharmaceutical peptides have emerged as a powerful alternative to traditional small molecules, offering high potency, specificity, and low toxicity. However, most computational drug discovery tools remain optimized for small molecules and need to be entirely adapted to peptide-based [...] Read more.

Over the past two decades, pharmaceutical peptides have emerged as a powerful alternative to traditional small molecules, offering high potency, specificity, and low toxicity. However, most computational drug discovery tools remain optimized for small molecules and need to be entirely adapted to peptide-based compounds. Molecular docking algorithms, commonly employed to rank drug candidates in early-stage drug discovery, often fail to accurately predict peptide binding poses due to their high conformational flexibility and scoring functions not being tailored to peptides. To address these limitations, we present PepScorer::RMSD, a novel machine learning-based scoring function specifically designed for pose selection and enhancement of docking power (DP) in virtual screening campaigns targeting peptide libraries. The model predicts the root-mean-squared deviation (RMSD) of a peptide pose relative to its native conformation using a curated dataset of protein–peptide complexes (3–10 amino acids). PepScorer::RMSD outperformed conventional, ML-based, and peptide-specific scoring functions, achieving a Pearson correlation of 0.70, a mean absolute error of 1.77 Å, and top-1 DP values of 92% on the evaluation set and 81% on an external test set. Our PLANTS-based workflow was benchmarked against AlphaFold-Multimer predictions, confirming its robustness for virtual screening. PepScorer::RMSD and the curated dataset are freely available in Zenodo Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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19 pages, 2897 KB

Open AccessArticle

Functional Analysis of Hyaluronidase-like Genes in Ovarian Development of Macrobrachium nipponense and Comparative Evaluation with Other Key Regulatory Genes

by Zhiming Wang, Hao Dong, Hui Qiao, Wenyi Zhang, Shubo Jin, Yiwei Xiong, Zhenghao Ye, Yan Gong, Sufei Jiang and Hongtuo Fu

Int. J. Mol. Sci. 2025, 26(21), 10748; https://doi.org/10.3390/ijms262110748 - 5 Nov 2025

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Abstract

This study conducted a bioinformatic analysis of two Hyaluronidase-like isoforms (Mn-HyaL1 and Mn-HyaL2) in Macrobrachium nipponense and investigated their phylogenetic relationships. The open reading frames of Mn-HyaL1 and Mn-HyaL2 were 1101 bp (encoding 366 amino acids) and 1164 bp (encoding 387 [...] Read more.

This study conducted a bioinformatic analysis of two Hyaluronidase-like isoforms (Mn-HyaL1 and Mn-HyaL2) in Macrobrachium nipponense and investigated their phylogenetic relationships. The open reading frames of Mn-HyaL1 and Mn-HyaL2 were 1101 bp (encoding 366 amino acids) and 1164 bp (encoding 387 amino acids), respectively. Both isoforms exhibited similar conserved domains, with an amino acid sequence similarity of 60.21%. Quantitative PCR analysis revealed that the expression levels of Mn-HyaL1 and Mn-HyaL2 increased during the mid-to-late phase of each developmental stage, were higher during the reproductive season than in the non-reproductive season, and were more abundant in the hepatopancreas than in other tissues. RNA interference experiments targeting both genes simultaneously demonstrated that knockdown of Mn-HyaL2 significantly accelerated ovarian development in M. nipponense, indicating that Mn-HyaL genes function as negative regulators of ovarian maturation. A comparative analysis of multiple genes revealed the following descending order of potency in promoting ovarian development in M. nipponense: Mn-Cholesterol 7-desaturase > Mn-Cathepsin L1. The order of potency in inhibiting ovarian development in M. nipponense, from strongest to weakest, was determined to be Mn-Gonad-inhibiting hormone > Mn-HyaL2. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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13 pages, 1868 KB

Open AccessArticle

Deep Sequencing Analysis of Hepatitis C Virus Subtypes and Resistance-Associated Substitutions in Genotype 4 Patients Resistant to Direct-Acting Antiviral (DAA) Treatment in Egypt

by Damir Garcia-Cehic, Asmaa Mosbeh, Heba A. Gad, Asmaa Ibrahim Gomaa, Marta Ibañez Lligoña, Josep Gregori, Sergi Colomer-Castell, Carolina Campos, Francisco Rodriguez-Frias, Juan Ignacio Esteban, Mohamed S. Kohla, Mohamed Helmy Abdel-Rahman and Josep Quer

Int. J. Mol. Sci. 2025, 26(21), 10649; https://doi.org/10.3390/ijms262110649 - 31 Oct 2025

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Abstract

Egypt has the highest global prevalence of hepatitis C virus (HCV), with genotype 4 (G4) in over 94% of cases. Direct-acting antivirals (DAAs) yield sustained virologic response (SVR) rates above 95%. Second-generation DAAs are recommended for patients with virological failure, achieving over 90% [...] Read more.

Egypt has the highest global prevalence of hepatitis C virus (HCV), with genotype 4 (G4) in over 94% of cases. Direct-acting antivirals (DAAs) yield sustained virologic response (SVR) rates above 95%. Second-generation DAAs are recommended for patients with virological failure, achieving over 90% eradication. This study aimed to classify and evaluate the pattern of HCV resistance-associated substitutions (RASs) in patients who failed DAA treatment in Egypt. A total of 1778 chronically infected HCV patients from Egypt’s Nile Delta were enrolled (2016–2018). Among them, 37 relapsed, and high-quality serum samples from 22 patients were available, including 6 cases with pre- and post-treatment samples. Next-generation sequencing (NGS) was performed for HCV subtyping and RAS identification. Among the 22 analyzed cases, 21 (95.4%) were G4: 11 were classified as subtype G4a, seven G4o, and three G4m. One patient (4.5%) was identified as G1g. One case shifted from G4a pre- to G4o post-treatment, suggesting reinfection. The RAS pattern in rare G4 subtypes (G4m/G4o) differs from the G4a subtype. The combination of L28M/L30S mutations was detected in 8/11 G4a samples; in contrast, RASs in G4o were characterized by T30S or Y93C/H/N/S substitutions. Notably, some substitutions identified as RASs may represent fixed polymorphisms in regional viral populations, such as those in Egypt’s Nile Delta. HCV subtypes significantly influence the RAS pattern, particularly within the NS5A region, after DAA-treatment failure. The RAS pattern differs among G4 subtypes, particularly in rare ones, predisposing patients to resistance and underscoring the importance of NGS in regional populations to optimize treatment strategies. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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23 pages, 4383 KB

Open AccessArticle

Gaussian Accelerated Molecular Dynamics Simulations Combined with NRIMD to Explore the Mechanism of Substrate Selectivity of Cid1 Polymerase for Different Nucleoside Triphosphates

by Hanwen Liu, Xue Zhou, Haohao Wang, Fuyan Cao and Weiwei Han

Int. J. Mol. Sci. 2025, 26(19), 9325; https://doi.org/10.3390/ijms26199325 - 24 Sep 2025

Cited by 1 | Viewed by 1420

Abstract

Cid1 protein is a crucial component in the RNA interference pathway and abnormal nuclear RNA turnover processes, primarily responsible for adding uridine to the 3′ end of RNA. Cid1 exhibits selective polymerization of UTP over other nucleoside triphosphates. To explore the mechanism of [...] Read more.

Cid1 protein is a crucial component in the RNA interference pathway and abnormal nuclear RNA turnover processes, primarily responsible for adding uridine to the 3′ end of RNA. Cid1 exhibits selective polymerization of UTP over other nucleoside triphosphates. To explore the mechanism of this selectivity, five systems: free-Cid1, Cid1-ATP, Cid1-UTP, Cid1-CTP, and Cid1-GTP with 500 ns Gaussian accelerated molecular dynamics (GaMD) simulations were performed to investigate conformational changes and binding affinities between substrates and Cid1. The results showed that UTP formed stronger and more numerous non-covalent interactions with Cid1 compared to the other three substrates. The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding energy analysis revealed a substrate preference for Cid1 polymerase in the order of UTP, followed by ATP, CTP, and GTP. These findings provide theoretical insights into the substrate selectivity mechanism of Cid1 and provide theoretical clues for the design and modification of Cid1 polymerase. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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Review

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36 pages, 10206 KB

Open AccessReview

Machine Learning and Deep Learning Frameworks for Human–Virus Protein–Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges

by Subhadeep Basu, Dipanwita Adhikary, Kuntal Ghosh, Swarup Chattopadhyay, Shramana Deb, Ritwick Mondal, Jayanta Roy, Anjan Chowdhury and Julián Benito-León

Int. J. Mol. Sci. 2026, 27(13), 6034; https://doi.org/10.3390/ijms27136034 - 5 Jul 2026

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Abstract

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, [...] Read more.

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, and delta genera, with SARS-CoV-2 belonging to the beta-coronavirus family. The virus exhibits high transmissibility and causes a wide spectrum of clinical manifestations ranging from mild respiratory symptoms to severe complications such as acute respiratory distress syndrome, multi-organ failure, and death, particularly among elderly and immunocompromised individuals. Structurally, SARS-CoV-2 possesses a large single-stranded RNA genome encoding major structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, which play critical roles in host-cell recognition and viral infection. Understanding the molecular mechanisms of virus–host interactions, especially protein–protein interactions (PPIs), is essential for uncovering viral pathogenesis and identifying potential therapeutic targets. Traditional experimental techniques for PPI detection, such as yeast two-hybrid and affinity purification methods, are often expensive, labor-intensive, and prone to inaccuracies. Consequently, computational approaches based on machine learning (ML) and deep learning (DL) have gained significant attention for efficient and scalable PPI prediction. These methods use diverse biological information, including protein sequences, structural features, genomic data, Gene Ontology annotations, and interaction networks, to model complex biological relationships. This survey reviews computational approaches to PPI prediction, highlighting ML- and DL-based techniques, methodological advances, performance evaluation practices, and limitations that affect benchmark comparability. It also discusses biological databases and data sources commonly used in PPI studies and explicitly considers how models trained in coronavirus-centered settings may generalize to other viral families with different mechanisms of host interaction. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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27 pages, 1090 KB

Open AccessReview

Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning

by Jakub Banaszek, Dawid Bąk, Kinga Barańska, Alicja Czajka, Dominika Ciesielska, Jakub Kleinrok, Weronika Pająk, Agnieszka Korolczuk, Maciej Mazur and Kamil Rusztyn

Int. J. Mol. Sci. 2026, 27(12), 5244; https://doi.org/10.3390/ijms27125244 - 10 Jun 2026

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Abstract

The introduction of immune checkpoint inhibitors (ICIs) into the treatment of melanoma has significantly reduced mortality over the past decade. However, therapeutic benefit is not observed in all patients, and treatment may be associated with severe adverse events. Therefore, identifying patients who are [...] Read more.

The introduction of immune checkpoint inhibitors (ICIs) into the treatment of melanoma has significantly reduced mortality over the past decade. However, therapeutic benefit is not observed in all patients, and treatment may be associated with severe adverse events. Therefore, identifying patients who are most likely to benefit from immunotherapy remains of critical importance. Currently used biomarkers, such as programmed death-ligand 1 (PD-L1) expression and manual assessment of tumour-infiltrating lymphocytes (TILs), have limited predictive value. This narrative review provides a critical appraisal of studies employing digital pathology tools, multiplex and spatial techniques (including multiplex immunofluorescence, imaging mass cytometry, and digital spatial profiling), as well as machine learning algorithms for predicting response to ICIs in patients with melanoma. Available evidence suggests that the highest predictive value may be achieved by approaches integrating quantitative assessment of immune infiltration with information on its spatial distribution, functional state, and interactions within the tumour microenvironment. Particular relevance may be attributed to features associated with the “immune-inflamed”, “immune-excluded”, and “immune-desert” phenotypes, the presence of tertiary lymphoid structures, and the organisation of local immune niches. In addition, this review highlights key limitations in the interpretation of current data, including lack of methodological standardisation, data heterogeneity, and insufficient validation. Directions for future research necessary for the implementation of these approaches into routine clinical practice are also discussed. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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32 pages, 1215 KB

Open AccessReview

Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine

by Nikita Golushko and Anton Buzdin

Int. J. Mol. Sci. 2026, 27(7), 3334; https://doi.org/10.3390/ijms27073334 - 7 Apr 2026

Cited by 2 | Viewed by 2767

Abstract

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome [...] Read more.

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome coverage. However, bulk RNAseq inherently averages gene expression signals across heterogeneous cell populations, thereby masking cellular diversity and obscuring rare cell types. In contrast, single-cell RNA sequencing (scRNAseq) enables a high-resolution analysis of cellular heterogeneity, allowing the identification of distinct cell types, transitional states, and developmental trajectories. Nevertheless, scRNAseq is associated with higher cost, limited scalability, increased technical noise, sparse expression matrices, and protocol-dependent biases introduced during tissue dissociation or nuclear isolation. In this review, we summarize the conceptual and methodological foundations of integrating bulk RNAseq and scRNAseq data, emphasizing their complementary strengths and limitations. We discuss how scRNAseq-derived cell-type atlases can serve as reference matrices for computational reconstruction (deconvolution) of bulk RNAseq profiles and examine key sources of technical and biological variability. Furthermore, we outline major integration strategies, including reference-based deconvolution, pseudobulk aggregation, and Bayesian joint modeling to provide an overview of widely used analytical tools and essential components of scRNAseq data processing workflows. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Informatics)

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