Predicting Protein Structure with Guided Conformation Space Search

Protein structure prediction is one of the great challenges in structural biology. The ability to accurately predict the three-dimensional structure of proteins would bring about significant scientific advances and would facilitate finding cures and treatments for many diseases. We propose a novel computational framework for protein structure prediction. The novelty of the framework lies in its approach to conformation space search. Conformation space search is considered to be the primary bottleneck towards consistent, high-resolution prediction. The proposed approach to conformation space search represents a major conceptual shift in protein structure prediction, made possible by combining insights and algorithms from robotics and machine learning with techniques from molecular biology in an innovative manner. The key innovation comes from the insight that target-specific information can effectively guide conformation space search towards biologically relevant regions. We propose a framework for protein structure prediction that achieves biological accuracy and computational efficiency by guiding conformation space search using target-specific information. The proposed framework exploits information about the characteristics of the target’s energy landscape acquired continuously during search. As search progresses, the continuous integration of these sources of information will tailor conformation space search to the particular characteristics of the target. This tailored conformation space exploration can overcome the current bottleneck, yielding highly accurate and efficient structure prediction.