Biology subjects

McWhirter, J. L.

Publications and source records attributed to McWhirter, J. L..

2 recordsLinked to original sources

bioRxiv2023

ZymePackNet: rotamer-sampling free graph neural network method for protein sidechain prediction

Protein sidechain conformation prediction, or packing, is a key step in many in silico protein modeling and design tasks. Popular protein packing methods typically rely on approximated energy functions and complex algorithms to search dense rotamer libraries. Inspired by the recent success of deep learning in protein modeling tasks, we present ZymePackNet, a graph neural network based protein packing tool that does not require a rotamer library, scoring functions or a search algorithm. We train regression models using protein crystal structures represented as graphs, which are employed sequentially to "germinate" the sidechain starting from atoms anchoring the protein backbone to the sidechains termini, followed by an iterative refinement stage. ZymePackNet is fast and accurate compared to state-of-the-art protein packing methods. We validate our model on three native backbone datasets achieving a mean average error of 16.6{degrees}, 24.1{degrees}, 42.1{degrees}, and 53.0{degrees} for sidechain dihedral angles ({chi}1 to{chi} 4). ZymePackNet captures complex physical interactions such as{pi} stacking without explicitly accounting for it in the model; such effects are currently lacking in the energy terms used in traditional packing tools. Contactabmukho@vt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

biochemistry↗

bioRxiv2023

Automated Protein Affinity Optimization using a 1D-CNN Deep Learning Model

Functional biologics design is a multi-objective optimization problem often with competing design objectives. We report on a novel deep learning based protein sequence prediction framework, ZymeSwapNet, that can be customized to handle a wide range of quantifiable design objectives, a current limitation of traditional protein design methods. We train a simple convolutional neural network (1D-CNN) on nonredundant curated protein crystal structures, using a set of geometric and topological features that describes a local protein environment, to predict the likelihood of each amino acid type for residue sites in the design region. While the model can be directly used to rank templates derived from mutagenesis campaigns, we extend the scope by developing a sequence/mutation generator that optimizes the desired multivariate distribution using a Monte-Carlo sampling. Using a case study - the design of a stable heterodimeric Fc (HetFc) antibody domain - we show that we can further include a Metropolis criterion to bias the sampling to enhance features such as the heterodimeric binding specificity, in addition to original sampling objective of enhancing stability. We demonstrate that ZymeSwapNet can generate stable HetFc designs, within minutes that had taken several rounds of rational structure and physical force-field based modeling attempts.

bioengineering↗