Scoring predictive models using a reduced representation of proteins: model and energy definition

Fogolari, Federico; Pieri, L.; Dovier, Agostino; Bortolussi, Luca; Giugliarelli, G.; Corazza, Alessandra; Esposito, Gennaro; Viglino, Paolo

doi:10.1186/1472-6807-7-15

Cited by 23 publications

(34 citation statements)

References 68 publications

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“…Here we develop an empirical scoring function for predicting enzyme-inhibitor binding affinity, one that relies on an all-atom, four-body statistical potential derived by implementing the computational geometry technique of Delaunay tessellation. Our atomic potential compares well with other atomic energy functions [11,12] in identifying the native structure as a global minimum, extensive work to be reported elsewhere.…”

Section: Introductionmentioning

confidence: 88%

A multibody atomic statistical potential for the prediction of enzyme-inhibitor binding energy

Masso¹

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Accurate prediction of enzyme-inhibitor binding energy has the capacity to speed drug design and chemical genomics efforts by helping to narrow the focus of experiments. Here a non-redundant set of three hundred high-resolution crystallographic enzyme-inhibitor structures was compiled for analysis, complexes with known binding energies (ΔG) based on the availability of experimentally determined inhibition constants (ki). Additionally, a separate set of over 1400 diverse high-resolution macromolecular crystal structures was collected for the purpose of creating an all-atom knowledge-based statistical potential, via application of the Delaunay tessellation computational geometry technique. Next, two hundred of the enzyme-inhibitor complexes were randomly selected to develop a model for predicting binding energy, first by tessellating structures of the complexes as well as the enzymes without their bound inhibitors, then by using the statistical potential to calculate a topological score for each structure tessellation. We derived as a predictor of binding energy an empirical linear function of the difference between topological scores for a complex and its isolated enzyme. A correlation coefficient (r) of 0.79 was obtained for the experimental and calculated ΔG values, with a standard error of 2.34 kcal/mol. Lastly, the model was evaluated with the held-out set of one hundred complexes, for which structure tessellations were performed in order to calculate topological score differences, and binding energy predictions were generated from the derived linear function. Calculated binding energies for the test data also compared well with their experimental counterparts, displaying a correlation coefficient of r= 0.77 with a standard error of 2.50 kcal/mol.

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Section: Introductionmentioning

confidence: 88%

A multibody atomic statistical potential for the prediction of enzyme-inhibitor binding energy

Masso¹

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…The most common way of discriminating among predicted structures is by employing either knowledge‐based or physics‐based energy (scoring) functions . Knowledge based potentials that can be applied to reduced representations of proteins with either one center, two centers or more centers (heavy atoms) of interaction per AA are widely used for identifying and ranking near‐native models from a pool of generated decoys. The main difficulty in using any of these energy functions is to recognize both secondary and tertiary structure features that resemble to the native structure .…”

Section: Introductionmentioning

confidence: 99%

Selective refinement and selection of near-native models in protein structure prediction

et al. 2015

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In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions.

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“…To balance the accuracy and computational time, the intermediate models between the atom-level and residue-level representations have also been developed [15,21,22,24]. In these models, the side chains are considered, which are usually simplified as C β atoms or the side chain center of mass.…”

Section: Introductionmentioning

confidence: 99%

NCACO-score: An effective main-chain dependent scoring function for structure modeling

Tian

Cao

et al. 2011

BMC Bioinformatics

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BackgroundDevelopment of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.ResultsBased on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.ConclusionsAlthough NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.

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Scoring predictive models using a reduced representation of proteins: model and energy definition

Cited by 23 publications

References 68 publications

A multibody atomic statistical potential for the prediction of enzyme-inhibitor binding energy

A multibody atomic statistical potential for the prediction of enzyme-inhibitor binding energy

Selective refinement and selection of near-native models in protein structure prediction

NCACO-score: An effective main-chain dependent scoring function for structure modeling

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