Information and discrimination in pairwise contact potentials

Solis, Armando D.; Rackovsky, S.

doi:10.1002/prot.21733

Cited by 17 publications

(53 citation statements)

References 35 publications

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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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Section: Discussionmentioning

confidence: 99%

Application of information theory to feature selection in protein docking

et al. 2011

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“…The conversion of the probability into an energy function is normally done employing Boltzmann's law 3 . Theory of conditional probabilities 5,6 , linear and quadratic programming 7 and information theory 8 have been invoked to justify the approach. The simplest observable 1,2 that one can use to characterize a structure is the presence of a contact between two specific residues.…”

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confidence: 99%

A simple and efficient statistical potential for scoring ensembles of protein structures

Cossio

Granata

Laio

et al. 2012

Sci Rep

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In protein structure prediction it is essential to score quickly and reliably large sets of models by selecting the ones that are closest to the native state. We here present a novel statistical potential constructed by Bayesian analysis measuring a few structural observables on a set of 500 experimental protein structures. Even though employing much less parameters than current state-of-the-art methods, our potential is capable of discriminating with an unprecedented reliability the native state in large sets of misfolded models of the same protein. We also introduce the new idea that thermal fluctuations cannot be neglected for scoring models that are very similar to each other. In these cases, the best structure can be recognized only by comparing the probability distributions of our potential over short finite temperature molecular dynamics simulations starting from the competing models. 1-4 are energy functions derived from databases of known protein conformations that empirically aim to capture the most relevant aspects of the physical chemistry of protein structure and function. They are derived by measuring the probability of an observable in an ensemble of experimental structures relative to a reference state 2,6,[16][17][18] . The conversion of the probability into an energy function is normally done employing Boltzmann's law 3 . Theory of conditional probabilities 5,6 , linear and quadratic programming 7 and information theory 8 have been invoked to justify the approach. The simplest observable 1,2 that one can use to characterize a structure is the presence of a contact between two specific residues. This procedure has been generalized to include more and more complex observables 3,6,7,[9][10][11][12][13][14][15] , making the KBPs more and more accurate. , a scoring function derived using an elegant Bayesian analysis, the composite scoring function QMEAN6 35,36 and the potential RF_CB_SRS_OD introduced by Rykunov and Fiser 18 are particularly successfull, even when tested on CASP targets 18,28 . However, even the best performing KBP is not capable of distinguishing the folded state in all the decoy sets, indicating that improvements are still possible. Moreover, the state-of-the-art KBPs exploit many complex observables, such as the distance between pair of residues or atoms, which significantly boosts the number of parameters. The weights of the various terms (which may include correlated observables, such as value of the torsions and the presence of secondary structure elements) are optimized on the decoy sets in order to obtain the best performance 10,35 . This may affect the robustness of the potentials and the possibility to use them for different purposes other than fold recognition.We here introduce a statistical potential that we call BACH (Bayesian Analysis Conformation Hunt). Its definition relies on a few binary structural observables, such as the presence of short range contacts between pair of residues, in the spirit of the original works 1,2 on KBPs. As a consequence, the energy functio...

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“…In this work, we revisit the contact reference state problem using information-theoretic tools we have developed in previous work. We have found that a key determinant of the correct discrimination of native folds amidst an ensemble of incorrect or “decoy” conformations is the total divergence, an information-theoretic entity that quantifies the distance between the score of the correct structure and the mean score of the decoys 8. Here, we demonstrate how the definition of “expected” probability affects the total divergence of contact potentials, and evaluate the impact of the definitions on their effectiveness in actual threading.…”

Section: Introductionmentioning

confidence: 86%

Information‐theoretic analysis of the reference state in contact potentials used for protein structure prediction

Solis¹,

Rackovsky²

2009

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Using information-theoretic concepts, we examine the role of the reference state, a crucial component of empirical potential functions, in protein fold recognition. We derive an information-based connection between the probability distribution functions of the reference state and those that characterize the decoy set used in threading. In examining commonly used contact reference states, we find that the quasi-chemical (QC) approximation is informatically superior to other variant models designed to include characteristics of real protein chains, such as finite length and variable amino acid composition from protein to protein. We observe that in these variant models, the total divergence, the operative function that quantifies discrimination, descreases along with threading performance. We find that any amount of nativeness encoded in the reference state model does not significantly improve threading performance. A promising avenue for the development of better potentials is suggested by our information-theoretic analysis of the action of contact potentials on individual protein sequences. Our results show that contact potentials perform better when the compositional properties of the data set used to derive the score function probabilities are similar to the properties of the sequence of interest. Results also suggest to use only sequences of similar composition in deriving contact potentials, to tailor the contact potential specifically for a test sequence.

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Information and discrimination in pairwise contact potentials

Cited by 17 publications

References 35 publications

Application of information theory to feature selection in protein docking

Application of information theory to feature selection in protein docking

A simple and efficient statistical potential for scoring ensembles of protein structures

Information‐theoretic analysis of the reference state in contact potentials used for protein structure prediction

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