New Computational Methods for Data-driven Protein Structure Prediction

蛋白质在所有生物过程中起着重要作用。蛋白质的准确描述 结构是理解生物生命的重要一步， 治疗和药物的发展。虽然实验结构确定 虽然已经有了很大的改善，但可用蛋白质的数量仍然有很大的差距。 序列和解决的蛋白质结构，这只能通过计算填充 预测.这个项目的长期目标是应用机器学习和优化 算法来理解蛋白质序列-结构-功能关系，通过分析 序列、结构和功能数据，并开发数据驱动的计算方法， 结构和功能预测的工具。我们相信，通过开发先进的 算法从不断增加的序列和结构数据中提取知识，我们可以建模 蛋白质序列-结构关系非常准确，改善结构和功能 大大的预测。这个项目已经产生了一些CASP获奖的，广泛使用的数据- 驱动算法和网络服务器（http：raptorx.uchicago.edu），用于蛋白质结构建模。 此次更新将进一步开发机器学习（尤其是深度学习）算法， 蛋白质结构建模没有好的模板。具体目标是：（1）深化 学习（DL）算法的预测蛋白质的接触和距离矩阵;（2）发展 基于距离的算法，用于快速和准确的从头折叠蛋白质没有模板;（3） 开发DL算法，用于仅使用弱相似模板的基于模板的建模。这 更新将导致对蛋白质序列结构的进一步理解和新的模型 关系和收益公开可用的资源，用于自动化，准确，定量分析 一系列蛋白质。其影响将在全世界范围内成倍增加 用户使用我们的网络服务器来研究与基本生物学相关的各种蛋白质。 研究和人类疾病，在低通量和高通量实验。

Proteins play fundamental roles in all biological processes. Accurate description of protein structure is an important step towards understanding of biological life and highly relevant in the development of therapeutics and drugs. Although experimental structure determination has been greatly improved, there is still a very large gap between the number of available protein sequences and that of solved protein structures, which can only be filled by computational prediction. The long-term goal of this project is to apply machine learning and optimization algorithms to understand protein sequence-structure-function relationship by analyzing sequence, structure and functional data and to develop data-driven computational methods and tools for structure and functional prediction. We believe that by developing sophisticated algorithms to extract knowledge from the increasing sequence and structure data, we can model protein sequence-structure relationship very accurately and improve structure and functional prediction greatly. This project has already produced a few CASP-winning, widely-used data- driven algorithms and web servers (http://raptorx.uchicago.edu) for protein structure modeling. This renewal will further develop machine learning (especially deep learning) algorithms for protein structure modeling without good templates. The specific aims are: (1) developing deep learning (DL) algorithms for the prediction of protein contact and distance matrix; (2) developing distance-based algorithms for fast and accurate ab initio folding of proteins without templates; (3) developing DL algorithms for template-based modeling with only weakly similar templates. This renewal will lead to further understanding and new models of protein sequence-structure relationship and yield publicly available resources for automated, accurate, quantitative analysis for a wide range of proteins. The impact will be multiplied by tens of thousands of worldwide users employing our web servers to study a wide variety of proteins relevant to basic biological research and human diseases, in both low- and high-throughput experiments.