Protein Structure Analysis in Bioinformatics, UCSC extension

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I will teach this course next from August 14 through October 9, 2000 (no meeting September 4) on Mondays 6-9 pm at the UCSC extension Cupertino facility. When enrollment opens up, you can go to UCSC extension web site .

Week-by-week schedule added at the bottom of this page: 8/3.

Course Description

Proteins manifest themselves in a diverse set of structures. Along with sequence analysis, structure analysis is often key to gaining insights into the functions of a protein. Sequence analysis alone is often not sufficient, because sequence-dissimilar proteins can share structural similarities, which give rise to similar functions. Structure analysis also has numerous applications, for instance to guiding the design of protein based drugs.

Largely due to structural diversity, the analysis of protein structures poses unique challenges in bioinformatics. A variety of methods and tools have therefore been developed. This course covers the main methods, tools, and databases that are used on protein structure analysis. Covered methods include neural networks for protein secondary structure prediction, threading methods for aligning a sequence to a structure, and methods for aligning a structure to a structure (VAST, DALI). The Molecular Docking Problem and methods for it are also covered. Covered tools include VAST, DALI, and neural network servers on the web, and also several viewers that permit visualization of the structures. Covered databases include PDB (Protein Data Bank), MMDB (Molecular Modeling Database) and a whole array of specialized databases (as an example, FSSP, a database that stores structure-structure alignments of all pairs of structures from PDB).

Guest lecturers will provide additional background on some of the non-computational aspects of the course. These include protein structure (alpha helices, beta sheets, secondary structure, tertiary structure) and further detail on experimental structure determination methods (NMR, X-ray crystallography).

This course is aimed towards professionals in the biotech industry and those in the computer industry interested in applications of computational methods in biotechnology.

Prerequisites

Bioinformatics Tools, Databases, and Methods course. Experimental Methods in Molecular Biology course. Programming experience is not required. Students must have web-browsing experience and access to the internet.

Week-by-week schedule

Week 1: Protein Chemistry and Protein Structure Overview: Amino acid residues, Various types of bonds, Primary structure, Secondary structure, Alpha helices, Beta sheets, Tertiary structures, structure motifs, domains, Ligand binding and protein function.

Week 2: Experimental Methods for Protein Structure Determination: NMR spectroscopy, X-ray crystallography, others.

Week 3: The Main Structure Databases: Protein Data Bank (PDB), File format, Benefits and limitations, Molecular Modeling Database (MMDB), File format, advantages over PDB, limitations, Structure viewers for PDB and MMDB. Structure alignment and search tools for PDB and MMDB.

Week 4: Part I: Other Structure Databases; Part II: Structure Profiles. Databases of precomputed structure-to-structure alignments. Protein classification databases. Structure motif databases. Structure Profiles (microenvironments around amino acids).

Week 5: Structure-to-Structure Alignment Methods and Tools: Practical utility of this approach. VAST, DALI, CE, others. Web servers for these tools.

Week 6: Sequence-to-Structure Alignment Methods and Tools: The Threading Problem. Practical utility of this approach. Scoring functions. Algorithmic methods. Threading software on the web.

Week 7: Structure-from-Sequence Prediction: Practical utility of this approach. Predicting secondary structure from sequence. Predicting secondary structure from sequence + evolutionary information. Various prediction methods: neural networks, others. Predicting tertiary structure from sequence. Web servers for these tools.

Week 8: The Molecular Docking Problem: Practical utility of this problem. Ligands and receptors. Computer vision algorithms applied to molecular docking. Other computational issues in molecular docking.