CoCoNat:基于深度学习的蛋白质序列盘绕线圈域预测工具

Matteo Manfredi, Castrense Savojardo, P. Martelli, R. Casadio
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摘要

盘绕结构域(CCD)是所有生物体蛋白质中都能观察到的结构基元,它能发挥多种关键功能。通过计算识别蛋白质序列中的 CCD 片段对其功能表征非常重要。这项任务基本上可分为三个独立步骤:检测片段边界、沿片段标注七联重复模式以及对其寡聚状态进行分类。多年来,针对其中一个或多个预测步骤提出了多种方法。在本方案中,我们将说明如何利用基于蛋白质语言模型的新方法 CoCoNat 来描述 CCD。CoCoNat发布之时(2023年8月),是CCD检测领域最先进的技术。网络服务器允许用户提交输入的蛋白质序列,几分钟后就能看到预测的结构域。用户还可以选择向模型提供预先计算的片段,模型将预测每个片段的寡聚状态。通过下载单机版,CoCoNat 可以很容易地集成到生物流水线中,单机版提供了一个可执行脚本来生成输出结果。主要特点 - 从蛋白质序列预测盘绕线圈片段的网络服务器。- 单个工具可进行三种不同的预测(片段位置、七和弦重复注释、寡聚状态)。- 可在线可视化结果,或下载不同格式的预测结果进行进一步处理。- 可与本地版本的工具轻松集成到自动化流水线中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CoCoNat: A Deep Learning–Based Tool for the Prediction of Coiled-coil Domains in Protein Sequences
Coiled-coil domains (CCDs) are structural motifs observed in proteins in all organisms that perform several crucial functions. The computational identification of CCD segments over a protein sequence is of great importance for its functional characterization. This task can essentially be divided into three separate steps: the detection of segment boundaries, the annotation of the heptad repeat pattern along the segment, and the classification of its oligomerization state. Several methods have been proposed over the years addressing one or more of these predictive steps. In this protocol, we illustrate how to make use of CoCoNat, a novel approach based on protein language models, to characterize CCDs. CoCoNat is, at its release (August 2023), the state of the art for CCD detection. The web server allows users to submit input protein sequences and visualize the predicted domains after a few minutes. Optionally, precomputed segments can be provided to the model, which will predict the oligomerization state for each of them. CoCoNat can be easily integrated into biological pipelines by downloading the standalone version, which provides a single executable script to produce the output. Key features • Web server for the prediction of coiled-coil segments from a protein sequence. • Three different predictions from a single tool (segment position, heptad repeat annotation, oligomerization state). • Possibility to visualize the results online or to download the predictions in different formats for further processing. • Easy integration in automated pipelines with the local version of the tool.
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