Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

本质上无序的蛋白质和蛋白质区域构成了许多蛋白质组的很大一部分，它们在其中发挥着多种重要作用。了解无序蛋白质区域在生物功能中的作用的关键的第一步是正确识别这些无序区域。用于疾病预测的计算方法已成为指导实验、解释结果和提出假设的核心工具集。鉴于可用的多种不同预测因素，共识评分已成为减轻任何单一方法的偏差或局限性的流行方法。共识分数整合了多个独立的紊乱预测因子的结果，并提供了每个残基的值，该值反映了预测残基紊乱的工具的数量。尽管共识分数有助于缓解使用任何单一疾病预测因子的固有问题，但生成它们的计算成本很高。它们还需要安装多种不同的软件工具，这可能非常困难。为了应对这一挑战，我们开发了一种基于深度学习的共识障碍评分预测器。我们的预测器 Metapredict 使用双向循环神经网络，该网络根据 12 个蛋白质组的共识紊乱评分进行训练。通过使用两种正交方法对元预测进行基准测试，我们发现元预测是目前可用的最准确的疾病预测器之一。 Metapredict 的速度也非常快，可以在几分钟内实现蛋白质组规模的紊乱预测。重要的是，metapredict 是完全开源的，并作为 Python 包、命令行工具集合和 Web 服务器进行分发，从而最大限度地提高了预测器的潜在实用性。 我们相信元预测为单一蛋白质和蛋白质组提供了一种方便、可访问、准确且高性能的预测器。