Biology subjects

Wingate, D.

Publications and source records attributed to Wingate, D..

2 recordsLinked to original sources

bioRxiv2022

A probabilistic view of protein stability, conformational specificity, and design

Several recently introduced approaches use neural networks as probabilistic models for protein sequence design. These models use various objective functions and optimization schemes. The choice of objective function and optimization scheme comes with trade-offs that are not always well explained. We introduce probabilistic definitions of protein stability and conformational specificity and show how these chemical properties relate to the p(structure| seq) objective used in recent protein design algorithms. This links probabilistic objective functions to experimentally testable outcomes. We present a new sequence decoding algorithm, termed "BayesDesign", that uses Bayes Rule to maximize the p(structure| seq) objective. We evaluate BayesDesign in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif.

bioinformatics↗

bioRxiv2019

ProSPr: Democratized Implementation of Alphafold Protein Distance Prediction Network

Deep neural networks have recently enabled spectacular progress in predicting protein structures, as demonstrated by DeepMins winning entry with Alphalfold at the latest Critical Assessment, of Structure Prediction competition (CASP13). The best protein prediction pipeline leverages intermolecular distance predictions to assemble a final protein model, but this distance prediction network has not been published. Here, we make a trained implementation of this network available to the broader scientific community. We also benchmark its predictive power in the related task of contact prediction against the CASP13 contact prediction winner TripletRes. Access to ProSPr will enable other labs to build on best in class protein distance predictions and to engineer superior protein reconstruction methods.

biophysics↗