Our long-term goal is protein-nucleic acid (NA, namely RNA and DNA) structure prediction, for the ultimate goal of rational drug design. Protein-RNA interactions play critical roles in RNA processing, gene expression and viral replication. Screen of compounds that inhibit or promote specific protein-RNA binding and design of short RNA sequences (RNA aptamers) that bind to cancer cell-surface antigens or other disease-associated protein targets have profound applications in drug development. Protein-DNA interactions are essential for transcription, DNA damage repair and apoptosis. Reliable predictive model for protein-NA structures will thus have a far-reaching impact on understanding the fundamental biological processes and on rational design of therapeutic interventions. However, despite the widespread biomedical significance of the problem, computational studies on protein-RNA structure prediction remain very limited. One of the key bottlenecks is lack of large training and testing data sets of experimentally determined protein-RNA complex structures. In this project, we propose to establish a platform service for the protein-RNA structure prediction community by constructing rigorous benchmarking data sets and for the first time, flexible decoys for algorithm development (including parameter training), assessment and systematic improvement. We also propose to develop a new statistical scoring framework for predicting protein-RNA structures by extracting the molecular interaction information from the benchmarks and by accounting for molecular flexibility. The datasets including flexible decoys and the software will be freely distributed to the academic community. The methods will be generalized to protein-DNA structure predictions. We will also test the predictive power of our algorithms by predicting RNA aptamer binding to prostate-specific membrane antigen (PSMA). Our prediction and rational design of PSMA-inhibiting RNA aptamers will be tested thoroughly through experimental assays. Our ability to predict and design RNA aptamers that bind to cancer cell-surface antigens such as PSMA with high affinity and specificity will have great potential for targeted cancer diagnostics and therapy.
The ability of predicting protein-nucleic acid (namely, RNA and DNA) complex structures is vitally important for understanding gene regulation and for rational drug design. The computationally designed short RNA molecules and rational design strategy proposed in this project have great potential for targeted cancer diagnostics and therapy.
