Evolutionary Bioinformatics

Prime mass amino acids residues, assigned based upon the nominal mass of their repeating structure in peptides and proteins, have been found to occur more often than by chance. These 9 such residues are predominantly hydrophobic in character and thus play an important role in protein stability and folding. This study investigates their prevalence in human proteins and the function of such proteins.

To identify human proteins rich in the prime mass residues and assess their functions.

Prime-rich proteins are found to proliferate among those important to transcriptional events, cell adhesion, and the formation of cell surface membranes. Eighteen prime-rich proteins are identified that are abundant in 3 or 4 prime residues, the latter notably contain alanine, cysteine, histidine or threonine, and proline. The percentage of prime residues in these prime-rich proteins exceeds one-half (>50%) of all residues in many cases, and these residues are found to be concentrated in particular regions of the protein.

Proteins rich in prime residues are found to play an important role in biological events important to cellular life. Such prime residues represent those associated with the highest hydrophobicity, and those essential to protein folding and function. Many prime residues were recruited early into the genetic code according to the composition of the last universal common ancestor (LUCA) proteins. Consideration is given to whether the formation of evolution of these proteins is predicated by the primality of the residues or their physicochemical and structural properties. This remains an open question and awaits further inquiry and debate.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered a global health crisis, emphasizing the urgent need for accurate and rapid diagnostic tools. Modern molecular biology technologies, including CRISPR-Cas systems, provide highly efficient strategies for viral detection. Bioinformatic pipelines are essential for identifying conserved genomic regions and enabling rational single-guide RNA (sgRNA) design.

This study aimed to design specific sgRNAs targeting the spike gene of SARS-CoV-2 isolates from Iranian patients using the SHERLOCK diagnostic platform. Complete genomes of the RefSeq virus and 470 SARS-CoV-2 isolates, representing all variants of concern (VOCs) detected in Iran, were retrieved from the NCBI and GISAID databases. Multiple sequence alignment with ClustalW identified conserved sequences within the receptor-binding domain (RBD) that differ from the RBD of SARS-CoV and MERS-CoV RefSeq genomes. Based on these regions, sgRNAs and isothermal amplification primers were designed using ADAPT, OLIGO7, and the UCSC Genome Browser to maximize diagnostic sensitivity and specificity. Secondary and tertiary structures of sgRNA-target complexes were analyzed via RNAfold and RNAup to select the most efficient sgRNA–amplicon combination.

Twenty-two–nucleotide sgRNA candidates were initially selected based on sequence alignment, showing high similarity to the SARS-CoV-2 RefSeq and low homology to SARS-CoV and MERS-CoV genomes. Analyses of secondary structures, RNA–RNA interactions, and free energy identified 6 sgRNAs with favorable 2-dimensional conformations and strong interaction profiles. Among these, the sgRNA1–Amplicon2 sequence exhibited the most stable 3-dimensional structure and a molecular docking score of −309.67, indicating high sensitivity and specificity for viral detection.

This study successfully designed an sgRNA with high sensitivity and specificity for rapid SARS-CoV-2 detection using the CRISPR-Cas13a system, informed by genomic analysis of Iranian isolates. The proposed approach provides an efficient framework for the rapid design and deployment of CRISPR-based diagnostic tools applicable to diverse viral pathogens.

Mycobacterium leprae causes leprosy, an infectious disease that has persisted over centuries and is still an issue in public health in many nations. Diverse bioinformatics techniques have been effectively employed to annotate the functions of hypothetical proteins (HPs) originating from different pathogenic bacteria. The objective of the current research was to elucidate the functions of an HP obtained from M. leprae.

A variety of in silico tools were utilized to make predictions regarding the structure and function of this protein. To identify homologous proteins, the BLASTp program was used to search for sequence similarity across the available biological databases. Additionally, using the proper bioinformatics methods, a number of properties were determined, including physicochemical characteristics, subcellular localization, phylogenetic analysis, functional annotation, pathway analysis, protein-protein interaction, secondary and tertiary structure determination, active site detection, quality assessment analysis, molecular docking, pharmacokinetic and toxicity profiling, and further molecular dynamics simulations.

The HP exhibited putative biological activity associated with a conserved functional domain, the CT_C_D superfamily domain. The allophanate hydrolase activity of the chosen HP was predicted by the functional annotation. Pathway analysis demonstrated the protein’s involvement in cellular and metabolic processes. Numerous functional partners that play a crucial role in bacterial survival were identified through the chosen HP’s protein-protein interactions. Furthermore, active site prediction and molecular docking analysis of the HP with ligands indicated that it could be a therapeutic target for M. leprae. ADMET analysis indicated that the selected compound has favorable bioavailability, drug-likeness, and safety. The stability of these complexes was verified by molecular dynamics simulations, which suggests they have therapeutic potential.

This study emphasizes the effectiveness of in silico methods in predicting the biological functions of HP and generating hypotheses for potential therapeutic targets.

Species of glossiphoniid leech provide model systems for exploring fundamental questions in evolutionary and developmental biology. In this study, we took advantage of large embryonic cells and stereotypical cleavage patterns in the leech, Helobdella austinensis, to identify genes associated with the births of teloblasts and their lineage specifications.

Using staged embryos and pools of dissected precursor cells and teloblasts, a systematic computational comparison of staged and cell type-specific transcriptomic data was employed to identify unique gene sets associated with mesodermal (M), neuroectodermal (N) and teloblast (M + N) cell formation.

Our predicted candidate genes comprised sets of nucleic acid binding factors as anticipated but displayed little similarity with differentiation factors in other metazoans (eg, mammals, cnidarians), suggesting that stem cell/lineage induction processes have diverged across the Animalia and may reflect fundamentally different modes of embryonic development.

Severe dengue (SD) represents a life-threatening progression of dengue virus infection. Early identification of patients at risk of transitioning from dengue fever (DF) to SD remains a major clinical challenge. Unraveling the transcriptomic changes underlying this progression may aid in developing timely therapeutic interventions.

RNA-seq datasets comprising 103 samples (62 SD and 41 DF) were retrieved from the GEO repository. Following normalization using DESeq2, differentially expressed genes (DEGs) were identified between the 2 disease stages. Functional enrichment analysis was performed to uncover dysregulated biological processes. A hybrid computational framework combining classical machine learning (Logistic Regression, Support Vector Machine, and Random Forest) and deep learning models (Artificial Neural Network, Convolutional Neural Network, and Transformer-based architectures) were applied to classify SD and DF samples. Model performance was evaluated using ROC-AUC and balanced accuracy metrics.

Differential expression analysis identified 55 significantly dysregulated genes distinguishing severe dengue from dengue fever. These genes were enriched in pathways related to metal ion homeostasis, platelet signaling, ferroptosis, and oxidative stress. Among multiple machine learning and deep learning models, the Transformer-CNN achieved the best performance (test AUC = 0.85; balanced accuracy = 0.89). SHAP-based interpretation highlighted ILDR2, TCP1, HNRNPUL1, SEC14L5, ATP2C2, LOXL3, ACVRL1, STEAP3, and ST8SSIA5 as key discriminative features. Integrated network analyses further implicated coordinated regulation of iron metabolism, calcium signaling, and platelet dysfunction in severe dengue.

This study integrates RNA-seq and hybrid Artificial Intelligence modeling to identify transcriptomic signatures associated with dengue severity. The study highlights candidate genes and pathways that provide a hypothesis-generating foundation; further increasing the sample size and experimental validation will support early risk stratification in severe dengue.