A Smoothed Backbone-dependent Rotamer Library for Proteins Derived from Adaptive Kernel Density Estimates and Regressions

Overview

Journal Structure

Publisher Cell Press

Specialties Biochemistry
Biotechnology
Cell Biology
Molecular Biology

Date 2011 Jun 8

PMID 21645855

Citations 417

Authors

Maxim V Shapovalov

Roland L Dunbrack Jr

Affiliations

Soon will be listed here.

Abstract

Rotamer libraries are used in protein structure determination, prediction, and design. The backbone-dependent rotamer library consists of rotamer frequencies, mean dihedral angles, and variances as a function of the backbone dihedral angles. Structure prediction and design methods that employ backbone flexibility would strongly benefit from smoothly varying probabilities and angles. A new version of the backbone-dependent rotamer library has been developed using adaptive kernel density estimates for the rotamer frequencies and adaptive kernel regression for the mean dihedral angles and variances. This formulation allows for evaluation of the rotamer probabilities, mean angles, and variances as a smooth and continuous function of phi and psi. Continuous probability density estimates for the nonrotameric degrees of freedom of amides, carboxylates, and aromatic side chains have been modeled as a function of the backbone dihedrals and rotamers of the remaining degrees of freedom. New backbone-dependent rotamer libraries at varying levels of smoothing are available from http://dunbrack.fccc.edu.

Citing Articles

A Comparative Analysis of Cockroach and Mosquito, Octopamine Receptor Homologues Produced Using Chimera, Swiss-Model, and AlphaFold Molecular Modeling Tools.

Kamguia S, Njabon E, Patouossa I, Emadak A, Forlemu N ACS Omega. 2025; 10(8):7907-7919.

PMID: 40060804 PMC: 11886639. DOI: 10.1021/acsomega.4c08755.

Any1 is a phospholipid scramblase involved in endosome biogenesis.

Gao J, Franzkoch R, Rocha-Roa C, Psathaki O, Hensel M, Vanni S J Cell Biol. 2025; 224(4).

PMID: 40047640 PMC: 11893163. DOI: 10.1083/jcb.202410013.

Phosphorylation-State Modulated Binding of HSP70: Structural Insights and Compensatory Protein Engineering.

Stewart M, Paththamperuma C, McCann C, Cottingim K, Zhang H, DelVecchio R bioRxiv. 2025; .

PMID: 40027613 PMC: 11870554. DOI: 10.1101/2025.02.17.637997.

(Fr) Keissl Crude Extract Inhibits HIV Subtypes and Integrase Drug-Resistant Strains at Different Stages of HIV Replication.

Naidu D, Oduro-Kwateng E, Soliman M, Ndlovu S, Mkhwanazi N Pharmaceuticals (Basel). 2025; 18(2).

PMID: 40006004 PMC: 11859181. DOI: 10.3390/ph18020189.

Using AlphaFold2 to Predict the Conformations of Side Chains in Folded Proteins.

Maisuradze G, Thakur A, Khatri K, Haldane A, Levy R bioRxiv. 2025; .

PMID: 39990457 PMC: 11844428. DOI: 10.1101/2025.02.10.637534.

References

Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J . Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins. 2009; 77 Suppl 9:114-22. PMC: 2922016. DOI: 10.1002/prot.22570. View

Smith C, Kortemme T . Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. J Mol Biol. 2008; 380(4):742-56. PMC: 2603262. DOI: 10.1016/j.jmb.2008.05.023. View

Zhang C, Liu S, Zhou Y . Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential. Protein Sci. 2004; 13(2):391-9. PMC: 2286705. DOI: 10.1110/ps.03411904. View

Amir E, Kalisman N, Keasar C . Differentiable, multi-dimensional, knowledge-based energy terms for torsion angle probabilities and propensities. Proteins. 2008; 72(1):62-73. DOI: 10.1002/prot.21896. View

Canutescu A, Shelenkov A, Dunbrack Jr R . A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 2003; 12(9):2001-14. PMC: 2323997. DOI: 10.1110/ps.03154503. View

Friedland G, Linares A, Smith C, Kortemme T . A simple model of backbone flexibility improves modeling of side-chain conformational variability. J Mol Biol. 2008; 380(4):757-74. PMC: 3574579. DOI: 10.1016/j.jmb.2008.05.006. View

Bower M, Cohen F, Dunbrack Jr R . Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. J Mol Biol. 1997; 267(5):1268-82. DOI: 10.1006/jmbi.1997.0926. View

Bradley P, Misura K, Baker D . Toward high-resolution de novo structure prediction for small proteins. Science. 2005; 309(5742):1868-71. DOI: 10.1126/science.1113801. View

Mendes J, Nagarajaram H, Soares C, Blundell T, Carrondo M . Incorporating knowledge-based biases into an energy-based side-chain modeling method: application to comparative modeling of protein structure. Biopolymers. 2001; 59(2):72-86. DOI: 10.1002/1097-0282(200108)59:2<72::AID-BIP1007>3.0.CO;2-S. View

10.

Lovell S, Word J, Richardson J, Richardson D . Asparagine and glutamine rotamers: B-factor cutoff and correction of amide flips yield distinct clustering. Proc Natl Acad Sci U S A. 1999; 96(2):400-5. PMC: 15148. DOI: 10.1073/pnas.96.2.400. View

11.

Dahiyat B, Mayo S . De novo protein design: fully automated sequence selection. Science. 1997; 278(5335):82-7. DOI: 10.1126/science.278.5335.82. View

12.

Dunbrack Jr R, Karplus M . Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J Mol Biol. 1993; 230(2):543-74. DOI: 10.1006/jmbi.1993.1170. View

13.

Kuhlman B, Baker D . Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci U S A. 2000; 97(19):10383-8. PMC: 27033. DOI: 10.1073/pnas.97.19.10383. View

14.

Andrusier N, Nussinov R, Wolfson H . FireDock: fast interaction refinement in molecular docking. Proteins. 2007; 69(1):139-59. DOI: 10.1002/prot.21495. View

15.

Word J, Lovell S, Richardson J, Richardson D . Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol. 1999; 285(4):1735-47. DOI: 10.1006/jmbi.1998.2401. View

16.

Saraf M, Moore G, Goodey N, Cao V, Benkovic S, Maranas C . IPRO: an iterative computational protein library redesign and optimization procedure. Biophys J. 2006; 90(11):4167-80. PMC: 1459523. DOI: 10.1529/biophysj.105.079277. View

17.

Calhoun J, Kono H, Lahr S, Wang W, DeGrado W, Saven J . Computational design and characterization of a monomeric helical dinuclear metalloprotein. J Mol Biol. 2003; 334(5):1101-15. DOI: 10.1016/j.jmb.2003.10.004. View

18.

Harder T, Boomsma W, Paluszewski M, Frellsen J, Johansson K, Hamelryck T . Beyond rotamers: a generative, probabilistic model of side chains in proteins. BMC Bioinformatics. 2010; 11:306. PMC: 2902450. DOI: 10.1186/1471-2105-11-306. View

19.

Smith R, Lovell S, Burke D, Montalvao R, Blundell T . Andante: reducing side-chain rotamer search space during comparative modeling using environment-specific substitution probabilities. Bioinformatics. 2007; 23(9):1099-105. DOI: 10.1093/bioinformatics/btm073. View

20.

Davis I, Murray L, Richardson J, Richardson D . MOLPROBITY: structure validation and all-atom contact analysis for nucleic acids and their complexes. Nucleic Acids Res. 2004; 32(Web Server issue):W615-9. PMC: 441536. DOI: 10.1093/nar/gkh398. View