NIH VideoCast - Network Models for Protein Dynamics and Allostery: Bridging Between Physics-Based and Information Theoretic Approaches

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Network Models for Protein Dynamics and Allostery: Bridging Between Physics-Based and Information Theoretic Approaches

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Air date:	Tuesday, April 24, 2007, 1:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 110 * This only includes stats from October 2011 and forward.
Category:	Special
Runtime:	01:03:02
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	In recent years, there has been a surge in the number of publications using network models for understanding biomolecular systems dynamics. Essentially, two different groups of studies have been performed, driven by two different communities. The first is based on molecular biophysics and statistical mechanical concepts (see for example normal mode analyses using elastic network models). Many studies in this group have shown that the most cooperative modes of motions accessible near physiological conditions are those relevant to their functional mechanisms. The second, on the other hand, is based on information theory and spectral graph methods. While our group has essentially specialized in the former (Bahar & Rader (2005) Curr. Opi. Struct. Bio. 15, 586-92), we recently observed that the latter approach can signficiantly help in addressing the challenging problem of allosteric communication in biomolecular systems as recently published [Chennubhotla & Bahar (2006) Molecular Systems Biology, 2:36]. We will present both approaches with application to GroEL-GroES machinery and allosteric communication. We will show how signal transduction events derived from spectral-graph theoretic methods directly depend on inter-residue fluctuation dynamics, thus establishing the bridge between the (newly proposed) information-theoretic and the (well-established) physically-inspired approaches. The results provide us with important insights on protein design and mechanisms of allostery. http://www.nih.gov/sigs/sysbio

NLM Title:	Network models for protein dynamics and allostery : bridging between physics-based and information theoretic approaches / Ivet Bahar and Chakra Chennubhotla ; Systems Biology Special Interest Group.
Author:	Bahar, Ivet. National Institutes of Health (U.S.). Systems Biology Special Interest Group.
Publisher:
Abstract:	(CIT): In recent years, there has been a surge in the number of publications using network models for understanding biomolecular systems dynamics. Essentially, two different groups of studies have been performed, driven by two different communities. The first is based on molecular biophysics and statistical mechanical concepts (see for example normal mode analyses using elastic network models). Many studies in this group have shown that the most cooperative modes of motions accessible near physiological conditions are those relevant to their functional mechanisms. The second, on the other hand, is based on information theory and spectral graph methods. While our group has essentially specialized in the former (Bahar & Rader (2005) Curr. Opi. Struct. Bio. 15, 586-92), we recently observed that the latter approach can signficiantly help in addressing the challenging problem of allosteric communication in biomolecular systems as recently published [Chennubhotla & Bahar (2006) Molecular Systems Biology, 2:36]. We will present both approaches with application to GroEL-GroES machinery and allosteric communication. We will show how signal transduction events derived from spectral-graph theoretic methods directly depend on inter-residue fluctuation dynamics, thus establishing the bridge between the (newly proposed) information-theoretic and the (well-established) physically-inspired approaches. The results provide us with important insights on protein design and mechanisms of allostery. http://www.nih.gov/sigs/sysbio.
Subjects:	Allosteric Regulation--physiology Models, Molecular Protein Conformation
Publication Types:	Lecture Webcast
NLM Classification:	QU 55.9
NLM ID:	101306084
CIT Live ID:	5877
Permanent link:	https://videocast.nih.gov/watch=5877

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