Research Project
10246779
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.
