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Aggrescan4D：用于 pH 依赖性分析和蛋白质聚集倾向工程的综合工具。

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

机构信息

Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, Poland.

Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain.

出版信息

Protein Sci. 2024 Oct;33(10):e5180. doi: 10.1002/pro.5180.

DOI:10.1002/pro.5180
PMID:39324697
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11425640/
Abstract

Aggrescan4D (A4D) is an advanced computational tool designed for predicting protein aggregation, leveraging structural information and the influence of pH. Building upon its predecessor, Aggrescan3D (A3D), A4D has undergone numerous enhancements aimed at assisting the improvement of protein solubility. This manuscript reviews A4D's updated functionalities and explains the fundamental principles behind its pH-dependent calculations. Additionally, it presents an antibody case study to evaluate its performance in comparison with other structure-based predictors. Notably, A4D integrates advanced protein engineering protocols with pH-dependent calculations, enhancing its utility in advising solubility-enhancing mutations. A4D considers the impact of structural flexibility on aggregation propensities, and includes a large set of precalculated predictions. These capabilities should help to open new avenues for both understanding and managing protein aggregation. A4D is accessible through a dedicated web server at https://biocomp.chem.uw.edu.pl/a4d/.

摘要

Aggrescan4D (A4D) 是一种先进的计算工具，用于预测蛋白质聚集，利用结构信息和 pH 的影响。在其前身 Aggrescan3D (A3D) 的基础上，A4D 进行了多次增强，旨在协助提高蛋白质的溶解度。本文回顾了 A4D 的更新功能，并解释了其 pH 依赖性计算背后的基本原理。此外，还介绍了一个抗体案例研究，以评估其与其他基于结构的预测器相比的性能。值得注意的是，A4D 将先进的蛋白质工程方案与 pH 依赖性计算相结合，提高了其在建议提高溶解度的突变方面的实用性。A4D 考虑了结构灵活性对聚集倾向的影响，并包含了一组大型的预先计算预测。这些功能应该有助于为理解和管理蛋白质聚集开辟新的途径。A4D 可通过专用网络服务器 https://biocomp.chem.uw.edu.pl/a4d/ 访问。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/3e93845047e0/PRO-33-e5180-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/b02081982675/PRO-33-e5180-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/c6aec04ed2fc/PRO-33-e5180-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/6945bda5917b/PRO-33-e5180-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/df7f2675e9ec/PRO-33-e5180-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/2389ae1f6c49/PRO-33-e5180-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/396c43727459/PRO-33-e5180-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/3e93845047e0/PRO-33-e5180-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/b02081982675/PRO-33-e5180-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/c6aec04ed2fc/PRO-33-e5180-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/6945bda5917b/PRO-33-e5180-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/df7f2675e9ec/PRO-33-e5180-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/2389ae1f6c49/PRO-33-e5180-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/396c43727459/PRO-33-e5180-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce2d/11425640/3e93845047e0/PRO-33-e5180-g003.jpg

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