Geometric Hashing: An Overview
IEEE Computational Science & Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Populating Local Minima in the Protein Conformational Space
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Refinement of docked protein complex structures using evolutionary traces
BIBMW '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops
Protein docking with information on evolutionary conserved interfaces
BIBMW '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops
Efficient basin hopping in the protein energy surface
BIBM '12 Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
A basin hopping algorithm for protein-protein docking
BIBM '12 Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Refining multimeric protein complexes using conservation, electrostatics and probabilistic selection
BIBMW '12 Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)
Advances in Artificial Intelligence - Special issue on Artificial Intelligence Applications in Biomedicine
Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Protein-protein Docking Using Information from Native Interaction Interfaces
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Predicting the structure of protein assemblies is fundamental to our ability to understand the molecular basis of biological function. The basic protein-protein docking problem involving two protein units docking onto each-other remains challenging. One direction of research is exploring probabilistic search algorithms with high exploration capability, but these algorithms are limited by errors in current energy functions. A complementary direction is choosing to understand what constitutes true interaction interfaces. In this paper we present a method that combines the two directions and advances research into computationally-efficient yet high-accuracy docking. We present an informatics-driven probabilistic search algorithm for rigid protein-protein docking. The algorithm builds upon the powerful basin hopping framework, which we have shown in many settings in molecular modeling to have high exploration capability. Rather than operate de novo, the algorithm employs information on what constitutes a native interaction interface. A predictive machine learning model is built and trained a priori on known dimeric structures to learn features correlated with a true interface. The model is fast, accurate, and replaces expensive physics-based energy functions in scoring sampled configurations. A sophisticated energy function is used to refine only high-scoring configurations. The result is an ensemble of high-quality decoy configurations that we show here to approach the known native dimeric structure better than other state-of-the-art docking methods. We believe the proposed method advances computationally-efficient high-accuracy docking.