A pairwise residue contact area-based mean force potential for discrimination of native protein structure

Arab, Seyed Shahriar; Sadeghi, Mehdi; Eslahchi, Changiz; Pezeshk, Hamid; Sheari, Armita

doi:10.1186/1471-2105-11-16

Cited by 18 publications

(15 citation statements)

References 46 publications

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“…Two data sets, 12 figures, 14 tables, and references (44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54) are available at http://www.biophysj.org/biophysj/supplemental/S0006-3495(12)00503-6.…”

Section: Supporting Materialsmentioning

confidence: 99%

Self-Complementarity within Proteins: Bridging the Gap between Binding and Folding

Basu

Bhattacharyya

Banerjee

2012

Biophysical Journal

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Complementarity, in terms of both shape and electrostatic potential, has been quantitatively estimated at proteinprotein interfaces and used extensively to predict the specific geometry of association between interacting proteins. In this work, we attempted to place both binding and folding on a common conceptual platform based on complementarity. To that end, we estimated (for the first time to our knowledge) electrostatic complementarity (E m) for residues buried within proteins. E m measures the correlation of surface electrostatic potential at protein interiors. The results show fairly uniform and significant values for all amino acids. Interestingly, hydrophobic side chains also attain appreciable complementarity primarily due to the trajectory of the main chain. Previous work from our laboratory characterized the surface (or shape) complementarity (S m) of interior residues, and both of these measures have now been combined to derive two scoring functions to identify the native fold amid a set of decoys. These scoring functions are somewhat similar to functions that discriminate among multiple solutions in a protein-protein docking exercise. The performances of both of these functions on state-of-the-art databases were comparable if not better than most currently available scoring functions. Thus, analogously to interfacial residues of protein chains associated (docked) with specific geometry, amino acids found in the native interior have to satisfy fairly stringent constraints in terms of both S m and E m. The functions were also found to be useful for correctly identifying the same fold for two sequences with low sequence identity. Finally, inspired by the Ramachandran plot, we developed a plot of S m versus E m (referred to as the complementarity plot) that identifies residues with suboptimal packing and electrostatics which appear to be correlated to coordinate errors.

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“…Two data sets, 12 figures, 14 tables, and references (44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54) are available at http://www.biophysj.org/biophysj/supplemental/S0006-3495(12)00503-6.…”

Section: Supporting Materialsmentioning

confidence: 99%

Self-Complementarity within Proteins: Bridging the Gap between Binding and Folding

Basu

Bhattacharyya

Banerjee

2012

Biophysical Journal

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“…Based on the interaction type, statistical potentials can be further classified into several categories, including distancedependent potentials (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), contact-based potentials (21)(22)(23)(24), and numerous other interaction-type based potentials. One of the first reported statistical potentials was a residue-level contact-based potential that incorporated contact frequencies over short-range, medium-range, and long-range (21).…”

Section: Introductionmentioning

confidence: 99%

Using the Unfolded State as the Reference State Improves the Performance of Statistical Potentials

Liu

Gong

2012

Biophysical Journal

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Distance-dependent statistical potentials are an important class of energy functions extensively used in modeling protein structures and energetics. These potentials are obtained by statistically analyzing the proximity of atoms in all combinatorial amino-acid pairs in proteins with known structures. In model evaluation, the statistical potential is usually subtracted by the value of a reference state for better selectivity. An ideal reference state should include the general chemical properties of polypeptide chains so that only the unique factors stabilizing the native structures are retained after calibrating on reference state. However, reference states available as of this writing rarely model specific chemical constraints of peptide bonds and therefore poorly reflect the behavior of polypeptide chains. In this work, we proposed a statistical potential based on unfolded state ensemble (SPOUSE), where the reference state is summarized from the unfolded state ensembles of proteins produced according to the statistical coil model. Due to its better representation of the features of polypeptides, SPOUSE outperforms three of the most widely used distance-dependent potentials not only in native conformation identification, but also in the selection of close-to-native models and correlation coefficients between energy and model error. Furthermore, SPOUSE shows promising possibility of further improvement by integration with the orientation-dependent side-chain potentials.

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“…Knowledge-based or empirical mean force potential is and will still be the dominant energy function in prediction and simulation of protein structures in the near future. This is because the SEEF was extracted from the experiment-resolved protein structures and, since the structure of folded proteins reflects the free energy of the interaction of all their components, including all enthalpic and entropic contributions, as well as solvent effects, such potentials will surely provide an excellent shortcut toward a powerful prediction function (Arab et al 2010). The new potential energy established by Dehouck-Gilis-Rooman has provided the best performance in protein mutation analysis and structure prediction (Dehouck et al 2009), and most importantly, their potential energy can be decomposed into the sum of different coupling terms consisting of different combinations of sequence and structure descriptors.…”

Section: Discussionmentioning

confidence: 99%

Statistical energy potential: reduced representation of Dehouck–Gilis–Rooman function by selecting against decoy datasets

Huang

Wei

et al. 2011

Amino Acids

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Statistical effective energy function (SEEF) is derived from the statistical analysis of the database of known protein structures. Dehouck-Gilis-Rooman (DGR) group has recently created a new generation of SEEF in which the additivity of the energy terms was manifested by decomposing the total folding free energy into a sum of lower order terms. We have tried to optimize the potential function based on their work. By using decoy datasets as screening filter, and through modification of algorithms in calculation of accessible surface area and residue-residue interaction cutoff, four new combinations of the energy terms were found to be comparable to DGR potential in performance test. Most importantly, the term number was reduced from the original 30 terms to only 5 in our results, thereby substantially decreasing the computation time while the performance was not sacrificed. Our results further proved the additivity and manipulability of the DGR original energy function, and our new combination of the energy could be used in prediction of protein structures.

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A pairwise residue contact area-based mean force potential for discrimination of native protein structure

Cited by 18 publications

References 46 publications

Self-Complementarity within Proteins: Bridging the Gap between Binding and Folding

Self-Complementarity within Proteins: Bridging the Gap between Binding and Folding

Using the Unfolded State as the Reference State Improves the Performance of Statistical Potentials

Statistical energy potential: reduced representation of Dehouck–Gilis–Rooman function by selecting against decoy datasets

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