New statistical potential for quality assessment of protein models and a survey of energy functions

By Dmitry Rykunov, András Fiser. 2010

81 citations recorded by the source corpus.

BackgroundScoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment.ResultsThe performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation. Partially based on the observations made, we present a novel residue based statistical potential, which employs a shuffled reference state definition and takes into account the mutual orientation of residue side chains. Atom- and residue-level statistical potentials and Linux executables to calculate the energy of a given protein proposed in this work can be downloaded from http://www.fiserlab.org/potentials.ConclusionsAmong the most influential terms we observed a critical role of a proper reference state definition and the benefits of including information about the microenvironment of interaction centers. Molecular mechanical potentials were also tested and found to be over-sensitive to small local imperfections in a structure, requiring unfeasible long energy relaxation before energy scores started to correlate with model quality.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Toward the estimation of the absolute quality of individual protein structure models (2010, background): “The performance of QMEAN with respect to other stateof-the-art methods such as ProSA (Sippl, 1993) and DFIRE (Zhou and Zhou, 2002) has also been recently assessed in an independent study (Rykunov and Fiser, 2010).”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, methodology): “…on several decoy sets including the Rosetta set (Qian et al., 2007), the CASP9 models, the I-TASSER data set (Zhang and Zhang, 2010), the CASP5-8 data set (Rykunov and Fiser, 2010), and the Decoy 'R' Us (Samudrala and Levitt, 2000) set, as well as an in-house large set of template-based models.”
Assessment of chemical-crosslink-assisted protein structure modeling in CASP13 (2019, background): “05.(25,26) This comparison could not be performed between pairs of groups that shared less than 2 common targets (Figure 5).”
Computational protein structure refinement: Almost there, yet still so far to go (2017, background): “35,170,173,174 As a consequence, it is often very dif ﬁ cult to identify the most re ﬁ ned models, or even distinguish re ﬁ ned models from models that have become worse in snapshots from MD simulations.”
De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, background): “Another kind of energy function is knowledge-based, which is constructed by using the known structural information, such as the statistical potential based on the atom pair distance distribution [12], [13] and generic contact restraint of side-chain center [14].”
Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles (2015, background): “Likewise, other features like shape complementarity and the statistical potentials DCOMPLEX and RF_CB that normally perform well also perform poorly in this category.”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “…discrete optimized protein energy (DOPE) (27), distance-scaled, ﬁnite ideal-gas reference (DFIRE) (10), dipolar DFIRE (dDFIRE) (75,76), random walk (RW) and RWplus (29), generalized orientation-dependent all-atom potential (GOAP) (77), OPUS-PSP (78), RF-HA-SRS (28), and RF-CB-SRS-OD (79).”
Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13 (2020, background): “05.(25,26) This comparison could not be performed between pairs of groups that shared less than 2 common targets (Figure 5).”
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized (2010, background): “Clearly, the assumption that the joint probability can be written as a product of pairwise probabilities is not justified [12,13,43], but in practice this assumption often provides useful results [22].”