意大利帕多瓦大学Silvio C. E. Tosatto团队在研究中取得进展。他们研究评估了蛋白质内在无序序列预测的方法。2021年4月19日，国际学术期刊《自然-方法学》发表了这一成果。

研究人员利用DisProt数据库中的646种蛋白质数据集对43种方法进行了评估。研究发现最好的预测方法使用深度学习技术，该方法尤其比物理化学方法更具有优势。对于整个数据集而言，最优的无序预测方法具有Fmax = 0.483，在去除内在结构化区域之后其Fmax 为0.792。仍然难以预测无序序列的结合区域，Fmax = 0.231。有趣的是，不同方法之间的计算时间可以相差四个数量级。

据悉，内在无序蛋白打破了传统的蛋白结构-功能模式，对实验研究具有一定挑战。因为大部分分析都取决于计算预测，所以其高准确性对于实验至关重要。蛋白质内在无序预测（CAID）实验的关键是基于群体的盲法测试，这是预测内在无序区域和结合所涉及残基子集的方法。

附：英文原文

Title: Critical assessment of protein intrinsic disorder prediction

Author: Marco Necci, Damiano Piovesan, Silvio C. E. Tosatto

Issue&Volume: 2021-04-19

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43methods were evaluated on a dataset of 646proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax=0.483 on the full dataset and Fmax=0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax=0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.

DOI: 10.1038/s41592-021-01117-3

Source: https://www.nature.com/articles/s41592-021-01117-3