Collaborative Research: Identification and Structural Modeling of Intrinsically Disordered Protein-Protein and Protein-Nucleic Acids Interactions

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Many key cellular processes rely on the protein-protein and protein-nucleic acid interactions. A large and functionally important portion of these interactions is carried out by intrinsically disordered regions (IDRs) in proteins. Proteins with IDRs are involved in the pathogenesis of numerous human diseases and are considered as attractive and potent drug targets. IDRs lack a stable structure under physiological conditions and as such are particularly challenging to analyze and work with. This project addresses this challenge by developing a full suite of advanced computational tools and databases for predicting and modeling functions and structures IDR-protein and IDR-nucleic acids interactions. The knowledge of the interacting IDRs, their binding partners, and modeled 3D structures of interactions will guide building hypotheses for experiment design and interpretation of experimental data. The project will train Ph.D. students of different backgrounds through interdisciplinary coursework and mentoring at Purdue University and Virginia Commonwealth University (VCU). High school students will be recruited through outreach activities and programs that the PIs are involved in. Altogether, this project focuses on the interdisciplinary computational life science education and research efforts at Purdue and VCU.Three interlocked computational methods will be developed for studying molecular interactions of IDRs at the 1 dimensional (1D), 2D, and 3D levels, significantly advancing over the conventional solutions that are limited to 1D/sequence predictions. The corresponding aims are: (1) high-accuracy prediction of protein and nucleotide binding regions within IDR sequences using cutting-edge multi-task deep learning models (1D level); (2) integrative identification of the partner molecules (proteins and nucleic acids) for these binding regions (2D level); and (3) structure modeling by innovative docking between IDRs and the partner proteins and nucleotides (3D level). The developed tools and results will be provided to the research community through a web-based database and open source repositories. Overall, this work significantly advances structural bioinformatics field by developing modern computational tools and a database for understanding, predicting, and modeling tertiary structures of interactions of IDRs with proteins and nucleotides. The resulting new deep learning technologies will be transferrable to other bioinformatics areas that rely on the prediction and analysis from protein sequences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.