Workshop in Biostatistics

DATE: May 12, 2016
TIME: 1:30 - 3:00 pm
LOCATION: Medical School Office Building, Rm x303
TITLE: Methods for representing and computing about protein structure pockets

Russ Altman
Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline) and, by courtesy, of Computer Science


Small molecule drugs exert their influence by binding protein structures and modifying their function.   While there are many methods for simulating the physical interactions between drugs and proteins, we are interested in knowledge-based methods that take advantage of existing knowledge about protein-drug interactions.  We have created the FEATURE system that represents small volumes of protein structure as a vector of properties.  FEATURE is useful for recognizing volumes that are similar, and we have developed an algorithm called PocketFEATURE which compares the similarity of two protein pockets based on the degree to which they contain similar FEATURE volumes.  We have also developed an algorithm called DrugFEATURE for assessing the likelihood that a protein pocket will bind any small molecule at all. A third algorithm, FragFEATURE looks for small molecule fragments that are likely to bind within a protein pocket—and can in principal be used as a first step in drug design.   I will discuss the basic FEATURE representation and how we have used it in these and other algorithms focused on understanding drug binding and drug effects.  Much of this work was done with Sr. Research Scientist, Dr. Tianyun Liu.

Suggested readings:

Liu, T., & Altman, R. B. (2011). Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding. PLoS Computational Biology, 7(12), e1002326.

Liu, T., & Altman, R. B. (2014). Identifying Druggable Targets by Protein Microenvironments Matching: Application to Transcription Factors. CPT: Pharmacometrics & Systems Pharmacology, 3(1), e93.

Liu, T., & Altman, R. B. (2015). Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach. Journal of Chemical Information and Modeling, 55(7), 1483–1494.

Tang, G. W., Tang, G. W., Altman, R. B., & Altman, R. B. (2014). Knowledge-based Fragment Binding Prediction. PLoS Computational Biology, 10(4), e1003589.