Improved prediction of protein side-chain conformations with SCWRL4

By Georgii G. Krivov, Maxim V. Shapovalov, Roland L. Dunbrack. 2009

384 citations recorded by the source corpus.

Determination of side‐chain conformations is an important step in protein structure prediction and protein design. Many such methods have been presented, although only a small number are in widespread use. SCWRL is one such method, and the SCWRL3 program (2003) has remained popular because of its speed, accuracy, and ease‐of‐use for the purpose of homology modeling. However, higher accuracy at comparable speed is desirable. This has been achieved in a new program SCWRL4 through: (1) a new backbone‐dependent rotamer library based on kernel density estimates; (2) averaging over samples of conformations about the positions in the rotamer library; (3) a fast anisotropic hydrogen bonding function; (4) a short‐range, soft van der Waals atom–atom interaction potential; (5) fast collision detection using k‐discrete oriented polytopes; (6) a tree decomposition algorithm to solve the combinatorial problem; and (7) optimization of all parameters by determining the interaction graph within the crystal environment using symmetry operators of the crystallographic space group. Accuracies as a function of electron density of the side chains demonstrate that side chains with higher electron density are easier to predict than those with low‐electron density and presumed conformational disorder. For a testing set of 379 proteins, 86% of χ1 angles and 75% of χ1+2 angles are predicted correctly within 40° of the X‐ray positions. Among side chains with higher electron density (25–100th percentile), these numbers rise to 89 and 80%. The new program maintains its simple command‐line interface, designed for homology modeling, and is now available as a dynamic‐linked library for incorporation into other software programs. Proteins 2009. © 2009 Wiley‐Liss, Inc.

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

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Citations in the corpus

SWISS-MODEL: homology modelling of protein structures and complexes (2018, positive): “The optimal configuration of rotamers is estimated us-ing the graph based TreePack algorithm (53) by minimizing the SCWRL4 energy function (54).”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, background): “43 Finally, the over-all stereochemical properties were refined with locPREFMD 27 before the submission.”
Application of docking methodologies to modeled proteins (2020, methodology): “Finally, full-atom models with side chains were constructed by program SCWRL, 42 which uses a backbone-dependent library of side chain rotamers with associated probabilities derived from frequency of observation in the PDB. SCWRL attempts to fit the highest probability rotamers for each amino acid w”
Structure guided forcefield optimization (2011, neutral): “24 Gaussian kernel smoothing was applied to both the backbone dependency and the terminal v angles for the sidechains listed above; for the remaining fully rotameric sidechains, no smoothing was applied.”
2018). CCBuilder 2.0: Powerful and accessible coiled-coil modeling (2017, methodology): “bone structure using an interface that ISAMBARD provides to SCWRL4.(62) There are a range of analysis”
An end-to-end deep learning method for rotamer-free protein side-chain packing (2022, methodology): “Currently, the fastest methods (OSCAR-star[25], FASPR[15], SCWRL4[9]) do not employ deep learning (DL), and are rotamer library-based.”
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2 (2022, methodology): “The hZIP4 in silico double mutants were produced through the SCWRL4 algorithm based on graph theory, which employs an energy function to determine the best conformations for the side-chains (Krivov et al., 2009).”
Improved antibody structure prediction by deep learning of side chain conformations (2021, methodology): “For instance, SCWRL4 uses backbone-dependent libraries to calculate rotamer frequencies based on kernel density estimates and kernel regressions [17].”
A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions (2011, positive): “SCWRL4 was run on a set of 379 high-resolution proteins used previously (Krivov et al., 2009).”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “To compensate for possible biases introduced by SCWRL4, the same relaxation procedure was applied to the same residues of the wild-type structure.”
Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server (2014, methodology): “The average accuracy of side chains modeled using SCWRL 4.0 is 0.82 ± 1.05 Å, with the predicted structures of exposed side chains being less reliable than those of buried ones; for a more detailed analysis, the user may refer to the SCWRL 4.0 original paper 74 .”
OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors (2021, methodology): “Many traditional protein side-chain modeling programs [1,2,5,6] are composed of three key components: a rotamer library, an energy function and a search method.”
Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets (2017, background): “While rotamer libraries have alleviated the sampling challenges for surface side chains (6), backbone flexibility remains the principal challenge in protein-protein docking.”
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021, background): “This is almost sufﬁcient for protein folding, as there are several off-the-shelf tools [14, 20, 40] for determining side-chain conformations from backbone or C α -trace structures.”
Smooth orientation-dependent scoring function for coarse-grained protein quality assessment (2018, methodology): “These can be modeled with the widely-used SCWRL4 tool (15), as in (3), or any other method (16).”