DE-STRESS:用于评估蛋白质设计的用户友好型网络应用程序。

IF 2.6 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Protein Engineering Design & Selection Pub Date : 2021-02-15 DOI:10.1093/protein/gzab029
Michael J Stam, Christopher W Wood
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引用次数: 0

摘要

全新蛋白质设计是一个快速发展的领域,目前文献中有许多有趣而有用的设计蛋白质实例。然而,大多数设计在实验室表征时都会归类为失败,通常是由于低表达、错误折叠、聚集或缺乏功能。这种高损耗率使蛋白质设计变得不可靠且成本高昂。如果能快速、简便地生成有关设计的信息和一套高质量的衡量标准,就有可能在设计过程中更早地发现其中的一些失败。我们介绍的 DE-STRESS(DEsigned STRucture Evaluation ServiceS)是一个用于评估设计和工程蛋白质结构模型的网络应用程序。DE-STRESS 设计简单,使用直观,反应灵敏。它提供了大量与设计相关的信息,并提供了一些工具来帮助确定结果的来龙去脉,以及正式描述设计所需的特性以满足目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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DE-STRESS: a user-friendly web application for the evaluation of protein designs.

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.

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来源期刊
Protein Engineering Design & Selection
Protein Engineering Design & Selection 生物-生化与分子生物学
CiteScore
3.30
自引率
4.20%
发文量
14
审稿时长
6-12 weeks
期刊介绍: Protein Engineering, Design and Selection (PEDS) publishes high-quality research papers and review articles relevant to the engineering, design and selection of proteins for use in biotechnology and therapy, and for understanding the fundamental link between protein sequence, structure, dynamics, function, and evolution.
期刊最新文献
TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks. Correction to: De novo design of a polycarbonate hydrolase. Interactive computational and experimental approaches improve the sensitivity of periplasmic binding protein-based nicotine biosensors for measurements in biofluids. Design of functional intrinsically disordered proteins. The shortest path method (SPM) webserver for computational enzyme design.
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