Go Back   SEQanswers > Literature Watch

Similar Threads
Thread Thread Starter Forum Replies Last Post
RNA-Seq: HITS-CLIP: panoramic views of protein-RNA regulation in living cells. Newsbot! Literature Watch 1 09-11-2015 12:48 AM
NEXTflex ChIP-Seq Kit for Improved Genome Wide Analysis of DNA-Protein Interactions Bioo Scientific Vendor Forum 0 11-07-2011 09:25 AM
ChIP-Seq: Systematic characterization of protein-DNA interactions. Newsbot! Literature Watch 0 01-06-2011 04:40 AM
ChIP-Seq: Q&A: ChIP-seq technologies and the study of gene regulation. Newsbot! Literature Watch 0 06-10-2010 03:00 AM
PubMed: Bind-n-Seq: high-throughput analysis of in vitro protein-DNA interactions usi Newsbot! Literature Watch 0 10-22-2009 03:00 AM

Thread Tools
Old 05-17-2011, 03:10 AM   #1
RSS Posting Maniac

Join Date: Feb 2008
Posts: 1,443
Default ChIP-Seq: Physics approaches to protein interactions and gene regulation.

Syndicated from PubMed RSS Feeds

Physics approaches to protein interactions and gene regulation.

Phys Biol. 2011 Jun;8(3):030301

Authors: Nussinov R, Panchenko AR, Przytycka T

Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue we have assembled a representative set of papers written by experts with diverse scientific backgrounds, each offering a unique viewpoint on using computational and physics methods to study biological systems at different levels of organization. We start with studies that aim to decipher the mechanisms of molecular recognition using biophysics methods and then expand our scale, concluding the issue with studies of interaction networks at cellular and population levels. Biomolecules interact with each other in a highly specific manner and selectively recognize their partners among hundreds of thousands of other molecules. As the paper by Zhang et al points out, this recognition process should be fast and guided by long-range electrostatic forces that select and bring the interacting partners together. The authors show that the increase of salt concentration leads to destabilization of protein complexes, suggesting an optimization of the charge-charge interactions across the protein binding interfaces. The following paper by Berezovsky further explores the balance of different interactions in protein complexes and uses physical concepts to explain the entire spectrum of protein structural classes, from intrinsically disordered to hyperthermostable proteins. The author describes highly unstructured viral proteins at one end of the spectrum and discusses the balance of stabilizing interactions in protein complexes from thermophilic organisms at the other. Recently accumulated evidence has indicated that native proteins do not necessarily require a unique structure to be biologically active, and in some cases structural disorder or intrinsic flexibility can be a prerequisite for their function. From the physical point of view, these disordered/flexible proteins exist in dynamic equilibrium between different conformational states, some of which could be selected upon binding to another partner. Such a property allows disordered proteins to achieve specific binding and at the same time reversibility and diversity in their interactions. Interestingly, as is shown in the paper by Mészáros et al, even though some disordered regions and proteins have a tendency to fold upon binding, the structures of their complexes still reveal their inherent flexibility. Indeed, disordered proteins and their complexes have certain properties which distinguish them from proteins with well-defined structures. This is evident from the papers by Lobanov and Galzitskaya, and Mészáros et al, which show that such characteristic features of disordered proteins allow their successful computational prediction from the sequence alone. Computational prediction of protein disorder has been used in another study by Takeda et al where the authors investigate the role of disorder in the function of a specific actin capping protein. The paper presents normal mode analysis with the elastic network model to examine the mechanisms of intrinsic flexibility and its biological role in actin function. Analysis of the underlying mechanisms and key factors in protein recognition might be essential for the prediction of protein-protein interactions. The papers by Tuncbag et al and Hashimoto et al demonstrate how incorporating the physico-chemical properties of binding interfaces and their atomic details obtained from protein crystal structures might be used to increase the accuracy of predicted protein-protein interactions and provide data on relative orientations of interacting proteins and on the locations of binding sites. Moreover, analysis of protein-protein interactions might require further fine-tuning for different types of assemblies, like that shown in the example of homooligomers by Hashimoto et al. Studies of protein-protein interactions at the molecular level have contributed considerably to understanding the principles of large-scale organization of the cellular interactome. Using graph theory as a unifying language, many characteristic properties of bimolecular networks have been identified, including scale free distribution of the vertex degree, network motifs, and modularity, to name a few. These studies of network organization require the network to be as complete as possible, which given the limitations of experimental techniques is not currently the case. Therefore, experimental procedures for detecting biomolecular interactions should be complemented by computational approaches. The paper by Lees et al provides a review of computational methods, integrating multiple independent sources of data to infer physical and functional protein-protein interaction networks. One of the important aspects of protein interactions that should be accounted for in the prediction of protein interaction networks is that many proteins are composed of distinct domains. Protein domains may mediate protein interactions while proteins and their interaction networks may gain complexity through gene duplication and expansion of existing domain architectures via domain rearrangements. The latter mechanisms have been explored in detail in the paper by Cohen-Gihon et al. Protein-protein interactions are not the only component of the cell's interactome. Regulation of cell activity can be achieved at the level of transcription and involve a transcription factor-DNA binding which typically requires recognition of a specific DNA sequence motif. Chip-Chip and the more recent Chip-Seq technologies allow in vivo identification of DNA binding sites and, together with novel in vitro approaches, provide data necessary for deciphering the corresponding binding motifs. Such information, complemented by structures of protein-DNA complexes and knowledge of the differences in binding sites among homologs, opens the door to constructing predictive binding models. The paper by Persikov and Singh provides an example of such a model in the Cys(2)His(2) zinc finger family. Recent studies have indicated that the presence of such binding motifs is, however, neither necessary nor sufficient for transcription factor activity. Transcription regulation is a complex and still not fully understood process involving, in addition to protein-DNA binding, other factors such as epigenetic modifications and three-dimensional DNA organization. In this issue, Levens and Benham discuss another important mechanism which is likely to contribute to overall gene regulation-changes of DNA secondary structure in response to supercoiling-induced stress. Pointing out that DNA is "more than a cipher", they argue that the DNA structural transitions driven by negative supercoiling may have profound consequences for the cell and have to be accounted for in detailed models. There is considerable progress in physical modeling of DNA dynamics in response to stress. Such efforts, supported by experimental data, will bring us closer to an understanding of the role of supercoiling in gene regulation. Large-scale biomolecular interaction networks not only provide a system-level view of cellular processes, but are also increasingly used to model communications between molecules. The lack of sufficient biochemical data and the gigantic scale of the network prevented detailed modeling of network dynamics and have stimulated the development of simplified models such as the information flow approach described by Kim et al in this issue. Importantly, despite their simplicity, such models proved to be extremely useful for identifying network modules, essential nodes, and molecular pathways which are dysregulated in complex diseases such as cancer. Finally, moving from studies of single cells towards populations, one has to recognize the heterogeneity present within a population of cells. In the context of protein abundance, such cell-to-cell variation within clonal populations of cells, referred to as expression noise, has recently become a focus of intense cross-disciplinary research. (ABSTRACT TRUNCATED)

PMID: 21572176 [PubMed - in process]

Newsbot! is offline   Reply With Quote

Thread Tools

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

All times are GMT -8. The time now is 07:03 PM.

Powered by vBulletin® Version 3.8.9
Copyright ©2000 - 2020, vBulletin Solutions, Inc.
Single Sign On provided by vBSSO