Biology subjects

Gaudreault, F.

Publications and source records attributed to Gaudreault, F..

3 recordsLinked to original sources

bioRxiv2024

AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens. We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental and that prioritization of models is essential for achieving good performance. In this study, we also compare docking performance with AlphaFold, the new benchmark in the field. We distinguish between two types of success tailored to specific downstream applications: 1) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and 2) criteria for producing higher-quality models suitable for engineering purposes. Our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking performance is noticeably lower than the one of AlphaFold3 in both epitope mapping and antibody design. While we observe a strong dependence on CDR-H3 length for physics-based tools on their ability to successfully predict, this helps define an applicability range where physics-based docking can be competitive to AlphaFold3.

bioinformatics↗

bioRxiv2024

Microvascular structure variability explains variance in fMRI functional connectivity

The influence of regional brain vasculature on resting-state fMRI BOLD signals is well documented. However, the role of brain vasculature is often overlooked in functional connectivity research. In the present report, utilizing publicly available whole-brain vasculature data in the mouse, we investigate the relationship between functional connectivity and brain vasculature. This is done by assessing interregional variations in vasculature through a novel metric termed vascular similarity. First, we identify features to describe the regional vasculature. Then, we employ multiple linear regression models to predict functional connectivity, incorporating vascular similarity alongside metrics from structural connectivity and spatial topology. Our findings reveal a significant correlation between functional connectivity strength and regional vasculature similarity, especially in anesthetized mice. We also show that multiple linear regression models of functional connectivity using standard predictors are improved by including vascular similarity. We perform this analysis at the cerebrum and whole-brain levels using data from both male and female mice. Our findings regarding the relation between functional connectivity and the underlying vascular anatomy may enhance our understanding of functional connectivity based on fMRI and provide insights into its disruption in neurological disorders.

neuroscience↗

bioRxiv2022

Enhanced antibody-antigen structure predictionfrom molecular docking using AlphaFold2

Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success for the medically relevant class of antibody-antigen complexes due to the lack of co-evolutionary constraints. Some research groups have demonstrated the usefulness of the AF2 confidence metrics for assessing the plausibility of protein folding models. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on the pLDDT and pTMscore metrics. We show benefits of the AF2 composite score for rescoring docking poses in two scenarios: (1) a more trivial experiment based on the bound conformations of the antibody and antigen backbone structures, and (2) a more realistic test employing the unbound backbone conformations of the binding partners. Docking success rates improved after AF2 rescoring with particular emphasis on early enrichment of positives at the very top of the re-ranked list of decoys. The AF2 rescoring markedly improved classification of positives and negatives in most systems. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.

bioinformatics↗