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Abstract / Description of output
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.
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.
Original language | English |
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Article number | gzab029 |
Number of pages | 6 |
Journal | Protein Engineering, Design & Selection (PEDS) |
Volume | 34 |
DOIs | |
Publication status | Published - 15 Dec 2021 |
Keywords / Materials (for Non-textual outputs)
- protein design
- protein engineering
- structural bioinformatics
- web application
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Development and experimental validation of a deep-learning based pipeline for user-centric protein design.
3/12/18 → 2/03/22
Project: Research