Douglas L. Brutlag
Web: http://motif.stanford.edu
Our group's primary research objective is to understand the flow of genetic information from the genome to the phenotype of an organism using bioinformatics. This includes understanding the sequence-structure relationships and the structure-function dependencies of macromolecules. Specifically, we develop computer representations that can discover structural and functional properties of DNA, RNA and proteins from sequences and from first principles. We use statistical methods and machine learning to discover first principles of molecular and structural biology from known examples. We are also interested in predicting the interactions between ligands and proteins and between two interacting macromolecules. We are actively studying the mechanisms of ligand-protein and protein-protein docking.
The problems described above are fundamental to understanding molecular biology and medicine. Functional genomics leads to novel diagnostic methods for both inherited and infectious disease. Analyses of protein structure and interactions results in discovery of new drug targets and rational drug design
We attack these critical problems using a variety of different representations of sequences and structures. Multiple representations of sequences include simple motif consensus sequence patterns, parametric representations, probabilistic techniques, graph theoretic approaches as well as computer simulations. Much of our work consists of developing a new representation of a structure or a function of a macromolecule, applying the methods of machine learning to this representation, and then evaluating the accuracy of the method. We have developed novel representations of sequence correlations that have predicted amino acid side chain interactions that stabilize protein strands and helices. We have developed novel algorithms for aligning sequences that give insight into the secondary structure of proteins. We have developed novel methods for discovering both sequence and structural motifs in proteins that help establish semantics of protein structure and function.
Brutlag, D. L., Dautricourt, J. P., Diaz, R., Fier, J., Moxon, B. and Stamm, R. (1993). BLAZE: An implementation of the Smith-Waterman Comparison Algorithm on a Massively Parallel Computer. Computers and Chemistry 17, 203-207.
Brutlag, D. L. (1994). Understanding the Human Genome. In Leder, P., Clayton, D. A. and Rubenstein, E. (Ed.), Scientific American: Introduction to Molecular Medicine (pp. 153-168). New York NY: Scientific American Inc.
Galper, A. R. and Brutlag, D. L. (1994). Computational Simulations of Biological Systems. In Smith, D. W. (Ed.), Biocomputing: Informatics and Genome Projects (pp. 269-306.). New York NY: Academic Press.
Klingler, T. M. and Brutlag, D. L. (1994). Discovering structural correlations in alpha-helices. Protein Sci 3 (10), 1847-57.
Naor, D. and Brutlag, D. L. (1994). On Near-Optimal Alignments of Biological Sequences. J. Computational Biology 1 (4), 349-366.
Wu, T. D. and Brutlag, D. L. (1995). Identification of protein motifs using conserved amino acid properties and partitioning techniques. ISMB-95 2, 402-10.
Brutlag, D. L. and Sternberg, M. (1996). Sequences and Topology: Challenges for Algorithms and Experts. Current Opinions in Structural Biology 6 (3), 343-345.
Wu, T. D. and Brutlag, D. L. (1996). Discovering Empirically Conserved Amino Acid Substitution Groups in Databases of Protein Families. Intelligent Systems for Molecular Biology-96 3, 230-240.
