Prediction of protein-protein interaction sites in intrinsically disordered proteins.
Prediction of protein-protein interaction sites in intrinsically disordered proteins.
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DOI：
10.3389/fmolb.2022.985022
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发表时间：
2022
影响因子：
5
通讯作者：
中科院分区：
文献类型：
--
作者：
内源性无序蛋白（IDP）通过与其他蛋白质相互作用参与许多生物学过程，包括转录、翻译和细胞周期的调节。随着可用的无序序列数据的增加，识别IDP结合位点以用于这些蛋白质的功能注释是至关重要的。在过去的几十年中，已经开发了许多基于蛋白质序列信息的计算方法来预测IDP的蛋白质-蛋白质结合位点（IDP-PPIS）。此外，随着人工智能的快速发展，每年都会开发新的IDP-PPIS预测器。因此，有必要对这一领域的这些方法提供最新的概述。本文收集了近期发表的30个有代表性的预测器，并对它们的数据库、特点和算法进行了总结。我们描述了如何基于公共数据生成特征并用于IDP-PPIS预测的过程，沿着生成特征表示的方法。所有的预测因子都被分为三类：评分函数、基于机器学习的预测和共识方法。对于每个类别，我们描述了算法的细节和它们的性能。希望本文不仅能对IDP结合预测的现状提供一个全面的了解，也能为选择不同的方法提供一个指导。更重要的是，它将揭示未来发展趋势和原则的启示。
Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.
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影响因子：
3.7
作者：
Minneci F;Piovesan D;Cozzetto D;Jones DT
通讯作者：
Jones DT
影响因子：
16.6
作者：
Bryant P;Pozzati G;Elofsson A
通讯作者：
Elofsson A
影响因子：
3.7
作者：
Chandra S;Chattopadhyay G;Varadarajan R
通讯作者：
Varadarajan R
影响因子：
14.9
作者：
wwPDB consortium
通讯作者：
wwPDB consortium
DOI：
10.1016/j.bbapap.2018.03.002
发表时间：
2018-05-01
影响因子：
3.2
作者：
Basu, Sankar;Biswas, Parbati
通讯作者：
Biswas, Parbati