Bioinformatics Algorithms for Protein Interactions and Applications

蛋白质通过与其他蛋白质相互作用，形成蛋白质复合体，或与其他分子，如核酸和小配体相互作用，推动大多数细胞过程。蛋白质相互作用鉴定的实验技术仍然昂贵、劳动强度大、耗时长，并且存在高水平的假阳性预测。准确预测电子计算机中的这些相互作用是生物信息学中的一个基本问题。我提出的研究计划涉及开发高度准确、可解释和适用的蛋白质相互作用预测的算法和工具。这项工作将影响生物学家确定、理解和使用蛋白质相互作用的能力。我建议建立的模型将广泛提供，并通过Web服务器轻松访问。经过适当培训的用户将能够免费下载和运行该软件，以实现高吞吐量预测。对硅胶预测的高度信心可以极大地减少生物学家的湿实验室工作，因为他们可以将精力集中在最有可能成功的相互作用上。了解这些相互作用将有助于诠释蛋白质的功能，构建PPI网络，并研究疾病机制。所提供的应用可被其他人用于开发新的治疗方法，例如，通过药物筛选、再利用和副作用识别来对抗艾滋病毒、SARS-CoV-2等病毒。

Proteins drive most cellular processes by interacting with other proteins, to form protein complexes, or with other molecules such as nucleic acids and small ligands. Experimental techniques for protein interaction identification remain expensive, labor intensive, time consuming and suffer from high levels of false-positive predictions. Accurately predicting these interactions in silico is a fundamental problem in bioinformatics. My proposed research program concerns developing algorithms and tools for highly accurate, interpretable, and applicable protein interaction prediction. This work will impact the biologists' ability to determine, understand, and use protein interactions. The models I propose to build will be made widely available and easily accessible via web servers. Users with proper training will be able to freely download and run the software for high throughput prediction. High confidence in-silico predictions can dramatically reduce the wet lab work of biologists, as they can focus their efforts on those interactions that are most likely to be successful. Understanding the interactions will help to annotate protein functions, construct PPI networks, and study disease mechanisms. The applications presented can be used by others to develop novel therapeutics, e.g., against viruses like HIV, SARS-CoV-2, through drug screening, repurposing, and side effect identification.