Information‐theoretic dissection of pairwise contact potentials

Cline, Melissa; Karplus, Kevin; Lathrop, Richard H.; Smith, Temple F.; Rogers, Robert; Haussler, David

doi:10.1002/prot.10198

Cited by 53 publications

(62 citation statements)

References 24 publications

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“…For example, in protein threading (Jones et al, 1992), a primary sequence is matched to a structural template using an amino acid contact potential and other similar potentials derived from sequence-structure correlations. Recently, the information contained in amino acid contacts was estimated to be about 0.04 bits per contact, or 0.06 bits per residue (Cline et al, 2002), which can be compared with our estimate of 0.16 bits per residue of primary with secondary structure mutual information. Therefore, local structure potentials may be of under-appreciated importance to threading and other similar statistical structure prediction methods.…”

Section: Discussionsupporting

confidence: 54%

Protein secondary structure: entropy, correlations and prediction

Crooks

Brenner

2004

Bioinformatics

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Motivation: Is protein secondary structure primarily determined by local interactions between residues closely spaced along the amino acid backbone or by non-local tertiary interactions? To answer this question, we measure the entropy densities of primary and secondary structure sequences, and the local inter-sequence mutual information density. Results: We find that the important inter-sequence interactions are short ranged, that correlations between neighboring amino acids are essentially uninformative and that only one-fourth of the total information needed to determine the secondary structure is available from local inter-sequence correlations. These observations support the view that the majority of most proteins fold via a cooperative process where secondary and tertiary structure form concurrently. Moreover, existing single-sequence secondary structure prediction algorithms are almost optimal, and we should not expect a dramatic improvement in prediction accuracy. Availability: Both the data sets and analysis code are freely available from our Web site at http:/

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Section: Discussionsupporting

confidence: 54%

Protein secondary structure: entropy, correlations and prediction

Crooks

Brenner

2004

Bioinformatics

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“…In addition, mutual information proved to be successful for the identification of class-specific molecular descriptors [37]. Information theory has been applied for fold recognition and for the identification of correct protein models [38][39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Application of information theory to feature selection in protein docking

et al. 2011

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In the era of structural genomics, the prediction of protein interactions using docking algorithms is an important goal. The success of this method critically relies on the identification of good docking solutions among a vast excess of false solutions. We have adapted the concept of mutual information (MI) from information theory to achieve a fast and quantitative screening of different structural features with respect to their ability to discriminate between physiological and nonphysiological protein interfaces. The strategy includes the discretization of each structural feature into distinct value ranges to optimize its mutual information. We have selected 11 structural features and two datasets to demonstrate that the MI is dimensionless and can be directly compared for diverse structural features and between datasets of different sizes. Conversion of the MI values into a simple scoring function revealed that those features with a higher MI are actually more powerful for the identification of good docking solutions. Thus, an MI-based approach allows the rapid screening of structural features with respect to their information content and should therefore be helpful for the design of improved scoring functions in future. In addition, the concept presented here may also be adapted to related areas that require feature selection for biomolecules or organic ligands.

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“…To ensure that MI is not overestimated, we considered only those alignment column pairs that resulted in statistically significant MI estimates corrected for insufficient sampling bias (26). Statistical significance was estimated by permuting the order in the alignment columns 1,000 times and recalculating MI.…”

Section: Datasetmentioning

confidence: 99%

Structure, function, and evolution of transient and obligate protein–protein interactions

Mintseris

Weng

2005

Proc. Natl. Acad. Sci. U.S.A.

326

329

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Recent analyses of high-throughput protein interaction data coupled with large-scale investigations of evolutionary properties of interaction networks have left some unanswered questions. To what extent do protein interactions act as constraints during evolution of the protein sequence? How does the type of interaction, specifically transient or obligate, play into these constraints? Are the mutations in the binding site of an interacting protein correlated with mutations in the binding site of its partner? We address these and other questions by relying on a carefully curated dataset of protein complex structures. Results point to the importance of distinguishing between transient and obligate interactions. We conclude that residues in the interfaces of obligate complexes tend to evolve at a relatively slower rate, allowing them to coevolve with their interacting partners. In contrast, the plasticity inherent in transient interactions leads to an increased rate of substitution for the interface residues and leaves little or no evidence of correlated mutations across the interface.interaction networks ͉ obligate interactions ͉ protein interactions ͉ protein recognition ͉ transient interactions T he recent debate on the degree of constraint that proteinprotein interactions confer on protein evolution (1-4) has highlighted the problems of reliability in high-throughput interaction data and the processing and interpretation of those data. With increasing amounts of data on protein-protein interactions for several species as well as the emphasis on representing and understanding basic biological processes in terms of networks of interactions, it is important to focus on the precise definition and classification of these underlying interactions. Some computational analyses tend to group together disparate datasets originating from different experimental methods to get more robust answers (5), which sometimes tends to blur the definitions of the nodes and edges of the merged networks. Although the simplest approach to networks as sets of binary interactions provides some rudimentary understanding of the data, the more realistic and nuanced view in terms of modular complexes and subcomplex structures is needed, and such characterizations have recently started appearing in the literature (6).An important distinction between transient and obligate protein-protein interactions, overlooked in many studies, has important implications for the construction of protein interaction networks. Constructing a network with each node representing a single protein sequence is hardly realistic from a biological perspective. It is well known that many proteins exist as parts of permanent obligate complexes such as multisubunit enzymes, which may often fold and bind simultaneously (7,8). Other interactions are fleeting encounters between single proteins or the aforementioned larger complexes (9). These often include complexes involved in enzyme-inhibitor, enzymesubstrate, hormone-receptor, and signaling-effector types of interactions. Th...

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Information‐theoretic dissection of pairwise contact potentials

Cited by 53 publications

References 24 publications

Protein secondary structure: entropy, correlations and prediction

Protein secondary structure: entropy, correlations and prediction

Application of information theory to feature selection in protein docking

Structure, function, and evolution of transient and obligate protein–protein interactions

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