Mikhail Karasikov1,2,3, Guillaume Pagès1, Sergei Grudinin1. 1. Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France. 2. Center for Energy Systems, Skolkovo Institute of Science and Technology, Moscow, Russia. 3. Moscow Institute of Physics and Technology, Moscow, Russia.
Abstract
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 AND IMPLEMENTATION: 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.
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 AND IMPLEMENTATION: 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.
Authors: J Eduardo Fajardo; Rojan Shrestha; Nelson Gil; Adam Belsom; Silvia N Crivelli; Cezary Czaplewski; Krzysztof Fidelis; Sergei Grudinin; Mikhail Karasikov; Agnieszka S Karczyńska; Andriy Kryshtafovych; Alexander Leitner; Adam Liwo; Emilia A Lubecka; Bohdan Monastyrskyy; Guillaume Pagès; Juri Rappsilber; Adam K Sieradzan; Celina Sikorska; Esben Trabjerg; Andras Fiser Journal: Proteins Date: 2019-10-07
Authors: Jianlin Cheng; Myong-Ho Choe; Arne Elofsson; Kun-Sop Han; Jie Hou; Ali H A Maghrabi; Liam J McGuffin; David Menéndez-Hurtado; Kliment Olechnovič; Torsten Schwede; Gabriel Studer; Karolis Uziela; Česlovas Venclovas; Björn Wallner Journal: Proteins Date: 2019-07-16