Biomedical Informatics Commons^™

Computational Discovery Of Sars-Cov-2 Viral Entry Inhibitory Peptides From Androctonus Mauretanicus Scorpion Venom: Molecular Docking And Molecular Dynamics Simulations Targeting The Spike Protein, Reda Chahir, Salaheddine Redouane, Jacob Galan, Hicham Hboub, Lahoussaine Aserrar, Salma Chakir, Ahmed Salim Lahlou, Hinde Aassila, Rachid El Fatimy, Naoual Oukkache

School of Medicine Publications

Background and Objective: 

While vaccination remains central to controlling the COVID-19 pandemic, the emergence of SARS-CoV-2 variants with partial resistance to immune responses has highlighted the need for complementary therapeutic strategies. Among these, antiviral agents that inhibit viral entry mechanisms are of particular interest. Animal venoms, especially scorpion venoms, are a rich source of bioactive peptides with potential antiviral properties. This study aimed to evaluate peptides derived from the Moroccan scorpion Androctonus mauretanicus as inhibitors of SARS-CoV-2 spike glycoprotein, which mediates virus entry into host cells via ACE2 receptor binding.

Material and Methodology: 

Six peptides from the venom of the …

A Molecular Renaissance In Alzheimer's Disease Research: The Rise Of Systems Biology And Spatial Omics, Lucia M. Perez-Navarro, Juan Lopez-Alvarenga

School of Medicine Publications

Recent advances in multi-omics and spatial proteomics are reshaping our understanding of Alzheimer's disease. Guo et al.1 applied integrative multi-omics to stratify mild cognitive impairment into biologically distinct subtypes with divergent progression trajectories: one metabolically impaired and slow-progressing, the other immune-activated and rapidly declining. Current techniques such as STC-DESI, LCM-MS, and machine learning enhance regional proteomic resolution, supporting biomarker discovery and spatially targeted interventions. This work exemplifies a broader shift toward precision medicine and a systems-level molecular framework in neurodegenerative disease research.

Bioinformatics-Guided Identification And Quantification Of Biomarkers Of Crotalus Atrox Envenoming And Its Neutralization By Antivenom, Auwal A. Bala, Anas Bedraoui, Salim El Mejjad, Nicholas K. Willard, Joseph D. Hatcher, Anton Iliuk, Joanne E. Curran, Elda E. Sanchez, Montamas Suntravat, Emelyn Salazar, Rachid El Fatimy, Tariq Daouda, Jacob Galan

School of Medicine Publications

Quantitative mass spectrometry-based proteomics of extracellular vesicles (EVs) provides systems-level exploration for the analysis of snakebite envenoming (SBE) as the venom progresses, causing injuries such as hemorrhage, trauma, and death. Predicting EV biomarkers has become an essential aspect of this process, offering an avenue to explore the specific pathophysiological changes that occur after envenoming. As new omics approaches emerge to advance our understanding of SBE, further bioinformatics analyses are warranted to incorporate the use of antivenom or other therapeutics to observe their global impact on various biological processes. Herein, we used an in vivo BALB/c mouse model and proteomics approach …

Uncovering The Complex Genetic Architecture Of Human Plasma Lipidome Using Machine Learning Methods, Miikael Lehtimäki, Binisha H. Mishra, Coral Del-Val, Leo-Pekka Lyytikäinen, Mika Kähönen, C. Robert Cloninger, Olli T. Raitakari, Reijo Laaksonen, Igor Zwir, Terho Lehtimäki, Pashupati P. Mishra

School of Medicine Publications

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30–45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.