Douglas E. V. Pires

BENDER DB compiles predicted protein binding sites from neglected disease pathogens using AlphaFold structures and multiple predictors, offering a consensus and visualization tools to aid drug discovery.

The paper uses topology to analyze the protein universe, identifying structural and functional features and their implications for mutations and evolution.

PERSEUcpp predicts cell-penetrating peptides and their uptake efficiency using machine learning, achieving high accuracy and interpretability.

This paper presents a computational pipeline for designing therapeutic antibodies using AI and physics-based methods, showing improved binding and developability across SARS-CoV-2 variants.

PRIMITI predicts miRNA-target mRNA interactions using machine learning with CLIP-seq and expression data, achieving high AUC scores and outperforming existing methods.

epitope1D predicts linear B-cell epitopes using explainable machine learning with graph-based descriptors and organism ontology, achieving high accuracy on cross-validation and blind tests.

The paper identifies a recurrent de novo splice site variant in DNM1 exon 10a causing severe DEE through a dominant-negative mechanism, highlighting the importance of considering alternative isoforms in disease causation.

The paper develops an online tool, cropCSM, using predictive models to identify safe and potent herbicides.

The paper presents a web resource, COVID-3D, that visualizes SARS-CoV-2 genetic variation in 3D structures to aid in understanding its impact on infection, immunity, and drug development.

TAG.ME is a new alignment-independent method for taxonomic assignment of genetic markers, outperforming existing approaches in classifying amplicon sequences from gut and soil samples.