ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment

Abstract

Background The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials. Results We developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, “effective atomic interaction” is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination. Conclusions Distance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/ . Electronic supplementary material The online version of this article (10.1186/s12859-019-2898-y) contains supplementary material, which is available to authorized users.

Identification of native protein structures captured by principal interactions

“…A non-redundant structural dataset of 6944 protein chains was culled by PISCES [40] from Protein Data Bank with pairwise sequence identity < 50%, resolution < 1.6 Å, R-factor < 0.25, protein length > 40 and < 1000 residues. We also excluded the proteins with more than 50% sequence identity with targets in test sets (proteins in the 3DRobot [41] and CASP10–13 [19]). The final list contains 6384 protein chains.…”

“…The principal interaction model was assessed in discrimination of native structure, using the decoy sets 3DRobot [41] and CASP10–13 [19]. The 3DRobot set includes decoy structures for 200 non-homologous proteins randomly selected from the PDB library.…”

“…Each protein contains 300 structural decoys with RMSD ranging from 0 to 12 Å. The CASP10–13 decoy set includes a total of 13,474 structures for 175 proteins, which were collected and trimmed from CASP10-CASP13 experiments by Yu et.al [19]. In this data set, all prediction sets contain experimental structures that are sequentially consecutive and all non-first prediction models (the second to fifth models of predictors) have been removed.…”

“…RMSD of the minimum energy: The root mean square deviation of the Cα-Cα pairs between the native structure and the structure with the minimum energy.PC: The Pearson correlation coefficient between Cα RMSD from the native structure and the total energies.Z-score b : The energy gap between the best decoy structure (the model with lowest RMSD from the native structure) and the remaining structures is calculated as follows: where score best decoy scorenative is the score (energy) calculated for the best decoy structure, 〈 score decoys 〉 and σ decoys are the average and standard deviation of score distributions of decoys proteins (the native and the best decoy are excluded), respectively. F.E (20% Fraction Enrichment): which measures the fraction of the most accurate 20% decoys (the top 20% lowest RMSD structures) among the 20% best scoring decoys (by excluding the native structure) compared to that for the entire decoy set [19]. …”

An information gain-based approach for evaluating protein structure models

“…Despite the pertinence of this probabilistic framework, the misleading justification based on physics is still recurrent in the literature (e.g. [30] , [31] , [32] , [33] , [34] ), presumably because it does not interfere with the practical success of statistical potentials.…”