Structural Bioinformatics

UCSD Pharm201 Structural Bioinformatics Futures

Phil — Sat, 03 Dec 2011 07:10:00 -0800

This is a podcast and slides of a lecture given on Dec 2, 2011 in the course Phar201 (http://www.sdsc.edu/pb/edu/pharm201/) at the University of California San Diego. The lecture considers the future of structural bioinformatics and biological data more generally.

Structural Bioinformatics

UCSD Phar201 Computational Systems Medicine

Phil — Wed, 23 Nov 2011 21:39:51 -0800

This is a podcast and slides of a lecture given on Nov 23, 2011 in the course Phar201 (http://www.sdsc.edu/pb/edu/pharm201/) at the University of California San Diego by graduate student Roger Chang. It covers the interface between structural bioinformatics and systems biology form the viewpoint of off-target drug effects and large scale metabolic modeling.

Structural Bioinformatics

UCSD Phar201 Structural Domains in Proteins

Phil — Thu, 17 Nov 2011 10:04:41 -0800

This is a podcast and slides of a lecture given on Nov 16, 2011 in the course Phar201 (http://www.sdsc.edu/pb/edu/pharm201/) at the University of California San Diego by Prof. Phil Bourne. It covers determining domains from 3D protein structure.

Structural Bioinformatics

UCSD Phar201 Chemical Data and Computer-Aided Drug Discovery

Phil — Thu, 17 Nov 2011 08:55:07 -0800

This is a podcast and slides of a lecture given on Nov 9, 2011 in the course Phar201 (http://www.sdsc.edu/pb/edu/pharm201/) at the University of California San Diego by Prof. Michael Gilson. It covers structure and ligand based structure methods used in drug discovery.

Structural Bioinformatics

UCSD Phar201 Structural Aspects of Protein Protein Interactions

Phil — Thu, 17 Nov 2011 08:06:16 -0800

This is a podcast and slides of a lecture given on Nov 4, 2011 in the course Phar201 (http://www.sdsc.edu/pb/edu/pharm201/) at the University of California San Diego by Phil Bourne. It covers one methodology for determining the site of protein-protein interaction when one only has a single structural component of the complex. It is based on the paper found here http://www.sdsc.edu/pb/papers/chung05.pdf

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 10 2010

Phil — Sun, 14 Nov 2010 08:39:28 -0800

This is a Podcast from the University of California San Diego Graduate Student Bioinformatics I class Lecture 10 for 2010. Using Protein Structure to Study Evolution. See http://www.sdsc.edu/pb/edu/pharm201/ for details.

Traditionally protein structure has been studied through looking at evolution. Most recently evolution has been studied through looking at protein structure. Goal: Provide an appreciation of what protein structure brings to the study of evolution. Reading: Chapter 23 of Structural Bioinformatics 2nd Edition Ed Gu & Bourne

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 18

Phil — Mon, 07 Dec 2009 12:33:13 -0800

This is lecture 18 (the final lecture) from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers the a brief discussion of some outstanding problems.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 17

Phil — Sun, 06 Dec 2009 10:03:46 -0800

This is lecture 17 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers the features of protein-protein interactions and one method to predict such interactions.

Urban Design Master Planning 2009

Structural Bioinformatics Pharm 201 UCSD Lecture 16

Phil — Sat, 28 Nov 2009 11:45:43 -0800

This is lecture 16 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers molecular graphics and following a brief introduction is a class discussion of the good and bad features of various molecular graphics programs based on an assignment the class had undertaken. Namely:

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 15

Phil — Sat, 28 Nov 2009 08:54:26 -0800

This is lecture 15 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers a method for studying protein-ligand interactions. Specifically finding off-target binding sites that has implications for studying the side effects and repositioning existing drugs.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 14

Phil — Thu, 19 Nov 2009 06:48:16 -0800

This is lecture 14 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers the principles of assigning protein secondary structure using teh 3D structure as a starting point with a focus on the Kabsch-Sander algorithm as used in DSSP.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 11

Phil — Sun, 08 Nov 2009 19:58:57 -0800

This is lecture 11 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers the field of structural immunology and is presented by Dr. Julia Ponomarenko.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 10

Phil — Wed, 04 Nov 2009 19:19:41 -0800

This is lecture 10 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covers the issues associated with protein structure comparison and delves into the CE method in particular.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 9

Phil — Thu, 29 Oct 2009 07:27:52 -0700

This is lecture 9 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covered the interrelationship between protein sequence-structure and function. This is a precursor to a study of data reduction and establishing a non-redundant set of information.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 8

Phil — Tue, 27 Oct 2009 09:53:15 -0700

This is lecture 8 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture covered two items. Prof Rik Belew described work he is doing on an HIVortal - a systems biology drill down of HIV. Prof Phil Bourne described the basics of the Gene Ontology.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 7

Phil — Thu, 22 Oct 2009 10:00:54 -0700

This is lecture 7 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The lecture illustrates to students how biocurators accept and process data both within the Protein Data Bank (PDB) and UniProt with the goal of helping students understand the strengths and weaknesses of the data they analyze.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 6

Phil — Mon, 19 Oct 2009 17:45:33 -0700

This is lecture 6 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ Teh title is "the multipolar parametrization: an alternative representation of protein shape/structure" presented by Dr. Apostol Gramada

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 5

Phil — Mon, 19 Oct 2009 17:35:28 -0700

This is lecture 5 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The topic is "Data Representation and the Protein Data Bank" presented by Drs. Peter Rose and Andreas Prlic

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 4

Phil — Mon, 19 Oct 2009 14:58:20 -0700

This is lecture 4 from a graduate student class in structural bioinformatics offered at UCSD see http://www.sdsc.edu/pb/edu/pharm201/ The topic is "Know How Best to represent Your Data." Historically the PDB format expresses the Lingua Franca of structural bioinformatics, but it has serious flaws. These will be explored and understood in the context of one replacement - mmCIF. Goal: To understand why good data representation is important.

Structural Bioinformatics

Structural Bioinformatics Pharm 201 UCSD Lecture 3

Phil — Wed, 07 Oct 2009 20:18:11 -0700

This is lecture 3 from a graduate student class in structural bioinformatics offered at UCSD

Structural Bioinformatics

Bookcast - Structural Bioinformatics 2nd Edition Gu & Bourne (Eds.)

Phil — Tue, 16 Jun 2009 17:11:36 -0700

Further details of the book can be found at http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470181052.html and on Google Books http://books.google.com/books?id=4H_ai7ivRIcC&pg=PP1&dq=structural+bioin....

SciVee Conferences Demo

Song Yang: My Curriculum Vitae

Song — Fri, 21 Nov 2008 14:07:04 -0800

This video gives a current brief overview of my Curriculum Vitae for prospective employers to review. I am looking for research and development positions in Bioinformatics, Genome analysis and computational evolution areas. I welcome the opportunity to visit with prospective employees about opportunities that would suit my experience and interest as described in this video, slides and linked CV. I may be reached via the SciVee messaging system. Thanks again for your consideration and for taking the time to review my video CV on SciVee!

Sincerely,
Song Yang

Structural Bioinformatics

Rethinking proteasome evolution: two novel bacterial proteasomes.

drnknmstrr — Mon, 11 Aug 2008 16:41:39 -0700

The proteasome is a multisubunit structure that degrades proteins. Protein degradation is an essential component of regulation because proteins can become misfolded, damaged, or unnecessary. Proteasomes and their homologues vary greatly in complexity: from HslV (heat shock locus v), which is encoded by 1 gene in bacteria, to the eukaryotic 20S proteasome, which is encoded by more than 14 genes. Despite this variation in complexity, all the proteasomes are composed of homologous subunits.

Structural Bioinformatics

The Past, Present and Future of the Protein Data Bank

WomenInBioinformatics — Wed, 07 May 2008 14:24:02 -0700

Structural Bioinformatics

Predicting structural disorder and induced folding: From theoretical principles to practical applications

WomenInBioinformatics — Mon, 21 Apr 2008 09:55:35 -0700

Structural Bioinformatics

Challenges in Automated Assignment of Function for Proteins From the Genome Projects

WomenInBioinformatics — Mon, 21 Apr 2008 06:11:51 -0700

Structural Bioinformatics

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

apryl — Tue, 26 Feb 2008 13:10:50 -0800

Background

tRNA on Ribosome

Multipolar representation of protein structure

jmath — Thu, 20 Sep 2007 18:52:42 -0700

Background

That the structure determines the function of proteins is a central paradigm in biology. However, protein functions are more directly related to cooperative effects at the residue and multi-residue scales. As such, current representations based on atomic coordinates can be considered inadequate.

Structural Bioinformatics

Modern proteomes contain putative imprints of ancient shifts in trace metal geochemistry

cdupont — Thu, 20 Sep 2007 18:30:41 -0700

Because of the rise in atmospheric oxygen 2.3 billion years ago (Gya) and the subsequent changes in oceanic redox state over the last 2.3–1 Gya, trace metal bioavailability in marine environments has changed dramatically. Although theorized to have influenced the biological usage of metals leaving discernable genomic signals, a thorough and quantitative test of this hypothesis has been lacking. Using structural bioinformatics and whole-genome sequences, the Fe-, Zn-, Mn-, and Co-binding metallomes of 23 Archaea, 233 Bacteria, and 57 Eukarya were constructed.

Structural Bioinformatics

The Association of Tetrameric Acetylcholinesterase with ColQ Tail: A Block Normal Mode Analysis

dzhang — Mon, 17 Sep 2007 15:12:38 -0700

Acetylcholinesterase (AChE) rapidly hydrolyzes acetylcholine in the neuromuscular junctions and other cholinergic synapses to terminate the neuronal signal. In physiological conditions, AChE exists as tetramers associated with the proline-rich attachment domain (PRAD) of either collagen-like Q subunit (ColQ) or proline-rich membrane-anchoring protein. Crystallographic studies have revealed that different tetramer forms may be present, and it is not clear whether one or both are relevant under physiological conditions.

Structural Bioinformatics

Color Deficiencies Caused by Rhodopsin

jlfink — Wed, 05 Sep 2007 15:00:03 -0700

A video accompanying a poster presentation by Erika Gracia from Chula Vista High School. From the RCSB Summer Course in Introductory Bioinformatics as part of the Howard Hughes Scholars Program, UCSD.

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Structural Comparison of Mammalian Insulin and their Immunological Effects of Humans

jlfink — Tue, 04 Sep 2007 20:42:14 -0700

A video accompanying a poster presentation by Julian Sesma and Jesus Sesma from Castle Park High School. From the RCSB Summer Course in Introductory Bioinformatics as part of the Howard Hughes Scholars Program, UCSD.

SciVee Conferences Demo

Wiggle—Predicting Functionally Flexible Regions from Primary Sequence

jgu — Thu, 19 Jul 2007 16:42:49 -0700

The Wiggle series are support vector machine–based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility.

tRNA on Ribosome

Phylogeny determined by protein domain content

Song — Thu, 19 Jul 2007 11:01:11 -0700

A simple classification scheme that uses only the presence or absence of a protein domain architecture has been used to determine the phylogeny of 174 complete genomes. The method correctly divides the 174 taxa into Archaea, Bacteria, and Eukarya and satisfactorily sorts most of the major groups within these superkingdoms. The most challenging problem involved 119 Bacteria, many of which have reduced genomes. When a weighting factor was used that takes account of difference in genome size (number of considered folds), small-genome taxa were mostly grouped with their full-sized counterparts.

Structural Bioinformatics

Structural Evolution of the Protein Kinase–Like Superfamily

escheeff — Thu, 19 Jul 2007 10:11:23 -0700

The protein kinase family is large and important, but it is only one family in a larger superfamily of homologous kinases that phosphorylate a variety of substrates and play important roles in all three superkingdoms of life. We used a carefully constructed structural alignment of selected kinases as the basis for a study of the structural evolution of the protein kinase–like superfamily. The comparison of structures revealed a “universal core” domain consisting only of regions required for ATP binding and the phosphotransfer reaction.