Biology subjects

Moffat, L.

Publications and source records attributed to Moffat, L..

3 recordsLinked to original sources

bioRxiv2022

Design in the DARK: Learning Deep Generative Models for De Novo Protein Design

AO_SCPLOWBSTRACTC_SCPLOWThe design of novel protein sequences is providing paths towards the development of novel therapeutics and materials. At the forefront is the challenging field of de novo protein design, which looks to design protein sequences unlike those found in nature using general design methodologies. In this work, we develop a tool for de novo design, based on a deep generative sequence model, that rapidly samples novel protein sequences with diverse and ordered structures. To build this tool we develop a framework, called DARK, that trains the underlying generative model on an iteratively expanding set of synthetic sequences. The resulting model generalizes where models trained on natural sequences struggle and greatly improves on the efficiency of comparable sampling-based approaches. We further show how it can generate high quality candidates for de novo design problems and aid in the development of further novel design methods, in all, providing another step, amongst others, towards truly automated and intelligent protein design.

bioinformatics↗

bioRxiv2021

Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design

AO_SCPLOWBSTRACTC_SCPLOWThe prediction of protein structure and the design of novel protein sequences and structures have long been intertwined. The recently released AlphaFold has heralded a new generation of accurate protein structure prediction, but the extent to which this affects protein design stands yet unexplored. Here we develop a rapid and effective approach for fixed backbone computational protein design, leveraging the predictive power of AlphaFold. For several designs we demonstrate that not only are the AlphaFold predicted structures in agreement with the desired backbones, but they are also supported by the structure predictions of other supervised methods as well as ab initio folding. These results suggest that AlphaFold, and methods like it, are able to facilitate the development of a new range of novel and accurate protein design methodologies.

bioinformatics↗

bioRxiv2020

Semi-Supervised Learning of Protein Secondary Structure from Single Sequences

Accurate modelling of a single orphan protein sequence in the absence of homology information has remained a challenge for several decades. Although not as performant as their homology-based counterparts, single-sequence bioinformatic methods are not constrained by the requirement of evolutionary information and so have a swathe of applications and uses. By taking a bioinformatics approach to semi-supervised machine learning we develop Profile Augmentation of Single Sequences (PASS), a simple but powerful framework for developing accurate single-sequence methods. To demonstrate the effectiveness of PASS we apply it to the mature field of secondary structure prediction. In doing so we develop S4PRED, the successor to the open-source PSIPRED-Single method, which achieves an unprecedented Q3 score of 75.3% on the standard CB513 test. PASS provides a blueprint for the development of a new generation of predictive methods, advancing our ability to model individual protein sequences.

bioinformatics↗