Biology subjects

Vani, B.

Publications and source records attributed to Vani, B..

3 recordsLinked to original sources

bioRxiv2026

Rapid generation of antibody conformational ensembles using machine learning

We introduce Cadense, a generative framework for antibody ensemble prediction that produces diverse Fv structures from sequence, informed by conformational dynamics. While a valuable resource, static structure prediction provides only a single snapshot of dynamic proteins, whose motions influence their binding and other properties. This is particularly relevant for antibodies, an important class of molecules for drug discovery, where flexible complementarity determining region (CDR) loops are difficult to model with static approaches. Cadense addresses this gap by generating ensembles that match conformational and biophysical property distributions from molecular dynamics (MD), while reducing the computational burden from days of simulation to seconds of generation. We show Cadense ensembles can recover both apo and holo conformations of the same antibody and serve as effective input to physics-based docking, capturing the conformational flexibility important to properly model antibody antigen interactions. Together, these results establish Cadense as a useful tool for antibody conformational ensemble generation with the promise to enable faster, dynamics-aware antibody design.

bioinformatics↗

bioRxiv2023

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery but remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from dataset preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.

biophysics↗

bioRxiv2020

Insulin dissociates by diverse mechanisms of coupled unfolding and unbinding

The protein hormone insulin exists in various oligomeric forms, and a key step in binding its cellular receptor is dissociation of the dimer. This dissociation process and its corresponding association process have come to serve as a paradigms of coupled (un)folding and (un)binding more generally. Despite its fundamental and practical importance, the mechanism of insulin dimer dissociation remains poorly understood. Here, we use molecular dynamics simulations, leveraging recent developments in umbrella sampling, to characterize the energetic and structural features of dissociation in unprecedented detail. We find that the dissociation is inherently multipathway with limiting behaviors corresponding to conformational selection and induced fit, the two prototypical mechanisms of coupled folding and binding. Along one limiting path, the dissociation leads to detachment of the C-terminal segment of the insulin B chain from the protein core, a feature believed to be essential for receptor binding. We simulate IR spectroscopy experiments to aid in interpreting current experiments and identify sites where isotopic labeling can be most effective for distinguishing the contributions of the limiting mechanisms.

biophysics↗