DE-STRESS: a User-friendly Web Application for the Evaluation of Protein Designs

Overview

Journal Protein Eng Des Sel

Publisher Oxford University Press

Specialties Biochemistry
Biotechnology

Date 2021 Dec 15

PMID 34908138

Citations 3

Authors

Michael J Stam

Christopher W Wood

Affiliations

Soon will be listed here.

Abstract

De novo protein design is a rapidly growing field, and there are now many interesting and useful examples of designed proteins in the literature. However, most designs could be classed as failures when characterised in the lab, usually as a result of low expression, misfolding, aggregation or lack of function. This high attrition rate makes protein design unreliable and costly. It is possible that some of these failures could be caught earlier in the design process if it were quick and easy to generate information and a set of high-quality metrics regarding designs, which could be used to make reproducible and data-driven decisions about which designs to characterise experimentally. We present DE-STRESS (DEsigned STRucture Evaluation ServiceS), a web application for evaluating structural models of designed and engineered proteins. DE-STRESS has been designed to be simple, intuitive to use and responsive. It provides a wealth of information regarding designs, as well as tools to help contextualise the results and formally describe the properties that a design requires to be fit for purpose.

Citing Articles

Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity.

Zalewski M, Iglesias V, Barcenas O, Ventura S, Kmiecik S Protein Sci. 2024; 33(10):e5180.

PMID: 39324697 PMC: 11425640. DOI: 10.1002/pro.5180.

Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application.

Engelberger F, Zakary J, Kunze G Front Mol Biosci. 2023; 10:1178035.

PMID: 37228581 PMC: 10204868. DOI: 10.3389/fmolb.2023.1178035.

PDBench: evaluating computational methods for protein-sequence design.

Castorina L, Petrenas R, Subr K, Wood C Bioinformatics. 2023; 39(1).

PMID: 36637198 PMC: 9869650. DOI: 10.1093/bioinformatics/btad027.

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