Biology subjects

Khade, P. M.

Publications and source records attributed to Khade, P. M..

3 recordsLinked to original sources

bioRxiv2025

BRIDGE: A Coarse-Grained Architecture to Embed Protein-Protein Interactions for Therapeutic Applications

In this work, we present Biophysical Representation of Interfaces via Delaunay-based Graph Embeddings (BRIDGE), a coarse-grained graph neural network that captures embeddings containing meaningful information about protein-protein interactions. The BRIDGE model takes as input graphs defined by the Delaunay tesselation of the individual chains and is pre-trained to predict the Delaunay adjacency at the protein-protein interface. The model achieves state-of-the-art performance in this task. The biophysical information captured by the BRIDGE embedding layer due to this pre-training task can further be used for downstream tasks, including for therapeutically relevant property prediction. We demonstrate the use of these embeddings by training models to predict antibody-antigen (Ab-Ag) affinity and antibody viscosity. These predictors, which require only the structures of the individual molecules in isolation, are especially well suited to large molecule therapeutic applications where complex structures are rarely determined until the very latest stages of a project.

bioinformatics↗

bioRxiv2023

Mixed structure- and sequence-based approach for protein graph neural networks with application to antibody developability prediction

There are hundreds of thousands of known proteins but significantly fewer unique protein folds. Furthermore, proteins often have conserved and even repeating geometric patterns, which can be captured by models of protein structure and function. In this work, we use Delaunay tessellations and -shapes, which capture these conserved geometric patterns, to define graph adjacency in Graph Convolutional Networks (GCN). We demonstrate the utility of the resulting GCN model on antibody developability prediction. Compared to the frequently used graph adjacencies based on k-nearest-neighbors or a fixed cutoff radius, the Delaunay tessellation and -shapes better capture residue-specific interactions at a lower computational cost for a given system size. The resulting models achieve state-of-the-art performance on an antibody developability prediction task. Finally, we propose an extension of the model which does not require known or predicted structures but uses an "archetypical" antibody structure to infer likely contacts.

biophysics↗

bioRxiv2022

GOThresher: a program to remove annotation biases from protein function annotation datasets

MotivationAdvances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets. ResultsWe introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases. Implementation and AvailabilityGOThresher is written in Python and released via PyPI https://pypi.org/project/gothresher/ and on the Bioconda Anaconda channel https://anaconda.org/bioconda/gothresher. The source code is hosted on GitHub https://github.com/FriedbergLab/GOThresher and distributed under the GPL 3.0 license. Contact{idoerg@iastate.edu | parnal@iastate.edu}

bioinformatics↗