PDBench: evaluating computational methods for protein-sequence design

Author:

Castorina Leonardo V1,Petrenas Rokas2,Subr Kartic1,Wood Christopher W2ORCID

Affiliation:

1. School of Informatics, University of Edinburgh , 10 Crichton Street, Newington , Edinburgh EH8 9AB, UK

2. School of Biological Sciences, University of Edinburgh , Roger Land Building , Edinburgh EH9 3FF, UK

Abstract

Abstract Summary Ever increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target. Availability and implementation https://github.com/wells-wood-research/PDBench Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Engineering and Physical Sciences Research Council

UK Research and Innovation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference23 articles.

1. Single-sequence protein structure prediction using language models from deep learning;Chowdhury;bioRxiv,2021

2. Macromolecular modeling with rosetta;Das;Annu. Rev. Biochem,2008

3. Automated structure- and sequence-based design of proteins for high bacterial expression and stability;Goldenzweig;Mol. Cell,2016

4. Amino acid substitution matrices from protein blocks;Henikoff;Proc. Natl. Acad. Sci. USA,1992

5. Convolutional networks with dense connectivity;Huang;IEEE Trans. Pattern Anal. Mach. Intell,2019

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