Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

By Mikhail Karasikov, Guillaume Pagès, Sergei Grudinin. 2018

52 citations recorded by the source corpus.

Motivation Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models. Results We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue-residue orientations, contacts between backbone atoms, hydrogen bonding, and solvent-solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12, and MOULDER). Availability The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS, and Windows. Supplementary information Supplementary data are available at Bioinformatics online.

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

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Citations in the corpus

Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “…representations include residue contacts (Olechnovi ˇ c & Venclovas, 2017), orientations or positions collectively projected to local coordinates (Karasikov et al., 2019), physics-inspired energy terms (O’Connell et al., 2018; Uziela et al., 2017), or context-free grammars of protein topology…”
Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13 (2020, methodology): “We collected initial models from CASP13 stage-2 server submissions and ranked them using the SBROD orientation-dependent backbone-only scoring function.(39) We picked the top five models and refined them iteratively using a gradient-based optimization.”
Assessment of chemical-crosslink-assisted protein structure modeling in CASP13 (2019, methodology): “39 We picked the top five models and refined them iteratively using a gradient-based optimization.”