Kenneth J. Addess

Computational Biology Branch,
National Center for Biotechnology Information
National Library of Medicine, National Institutes of Health,
Building 38A, 8600 Rockville Pike, Bethesda, MD 20894.

Biography:

Dr. Ken Addess is a senior scientist in NCBI’s Computational Biology Branch. Dr. Addess’s work focuses on developing and maintaining the software of the NCBI Entrez Structure database and visualization services. Dr. Addess has held several positions in the public and private sectors including a previous tenure at NCBI, Xencor, and the RCSB Protein Data Bank. Dr. Addess received his Ph.D from UCLA Department of Chemistry under Juli Feigon and then was a postdoctoral fellow with Arthur Pardi at the University of Colorado, Boulder, focusing on the determination of the 3D structure of nucleic acids by NMR spectroscopy.

Talk Title:3D/2D/1D similarity searching in NCBI's protein and chemical structure databases

Abstract: Similarity in protein sequences, protein structures and small molecules is a powerful method for understanding the relationship between structure and function. It can also be used to understand the molecular basis of disease and provide information to help identify important leads for drug discovery and other treatments for disease. Entrez, NCBI’s biomedical search engine, provides the molecular database resources and tools to help users search and identify these relationships. Similarity relationships among structures in any of the databases are pre-computed, so that a user may instantly link from a given chemical structure record, for example, to its neighbors, structurally similar chemicals. Search services allow a user to compare a novel structure, for example a newly determined protein 3D structure, against all previously deposited structures The methods used to calculate similarity scores vary with molecular structure representation, sequences (1D), chemical connectivity (2D) or spatial structure (3D). In the talk I will giving an overview of the similarities and differences among these methods with examples illustrating how structural similarities are a practical and powerful tool in biological structure-function analysis.