Given by χ torsional angles, rotamers describe the side-chain conformations of amino acid residues in a protein based on the rotational isomers (hence the word rotamer). Constructed rotamer libraries, based on either protein crystal structures or dynamics studies, are the tools for classifying rotamers (torsional angles) in a way that reflect their frequency in nature. Rotamer libraries are routinely used in structure modeling and evaluation. In this perspective article, we would like to encourage researchers to apply rotamer analyses beyond their traditional use. Molecular dynamics (MD) of proteins highlight the in silico behavior of molecules in solution and thus can identify favorable side-chain conformations. In this article, we used simple computational tools to study rotamer dynamics (RD) in MD simulations. First, we isolated each frame in the MD trajectories in separate Protein Data Bank files via the cpptraj module in AMBER. Then, we extracted torsional angles via the Bio3D module in R language. The classification of torsional angles was also done in R according to the penultimate rotamer library. RD analysis is useful for various applications such as protein folding, study of rotamer-rotamer relationship in protein-protein interaction, real-time correlation between secondary structures and rotamers, study of flexibility of side chains in binding site for molecular docking preparations, use of RD as guide in functional analysis and study of structural changes caused by mutations, providing parameters for improving coarse-grained MD accuracy and speed, and many others. Major challenges facing RD to emerge as a new scientific field involve the validation of results via easy, inexpensive wet-lab methods. This realm is yet to be explored.

Department of Chemistry and Biochemistry Mendel University in Brno Brno Czech Republic; Central European Institute of Technology Brno University of Technology Brno Czech Republic

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Lovell S.C., Word J.M., Richardson D.C. The penultimate rotamer library. Proteins. 2000;40:389–408. PubMed

Lovell, S. C., J. M. Word, …, D. C. Richardson. 2000. The penultimate rotamer library. Proteins. 40:389-408. PubMed

Dunbrack R.L., Jr. Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 2002;12:431–440. PubMed

Dunbrack, R. L., Jr. 2002. Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 12:431-440. PubMed

Dunbrack R.L., Jr., Karplus M. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 1993;230:543–574. PubMed

Dunbrack, R. L., Jr., and M. Karplus. 1993. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230:543-574. PubMed

Bates P.A., Sternberg M.J. Model building by comparison at CASP3: using expert knowledge and computer automation. Proteins. 1999;37(Suppl 3):47–54. PubMed

Bates, P. A., and M. J. Sternberg. 1999. Model building by comparison at CASP3: using expert knowledge and computer automation. Proteins. 37(Suppl 3):47-54. PubMed

Scouras A.D., Daggett V. The Dynameomics rotamer library: amino acid side chain conformations and dynamics from comprehensive molecular dynamics simulations in water. Protein Sci. 2011;20:341–352. PubMed PMC

Scouras, A. D., and V. Daggett. 2011. The Dynameomics rotamer library: amino acid side chain conformations and dynamics from comprehensive molecular dynamics simulations in water. Protein Sci. 20:341-352. PubMed PMC

Carbonell P., del Sol A. Methyl side-chain dynamics prediction based on protein structure. Bioinformatics. 2009;25:2552–2558. PubMed

Carbonell, P., and A. del Sol. 2009. Methyl side-chain dynamics prediction based on protein structure. Bioinformatics. 25:2552-2558. PubMed

Engh R.A., Chen L.X.-Q., Fleming G.R. Conformational dynamics of tryptophan: a proposal for the origin of the non-exponential fluorescence decay. Chem. Phys. Lett. 1986;126:365–372.

Engh, R. A., L. X.-Q. Chen, and G. R. Fleming. 1986. Conformational dynamics of tryptophan: a proposal for the origin of the non-exponential fluorescence decay. Chem. Phys. Lett. 126:365-372.

Das S., Das S., Maiti N.C. Orientation of tyrosine side chain in neurotoxic Aβ differs in two different secondary structures of the peptide. R. Soc. Open Sci. 2016;3:160112. PubMed PMC

Das, S., S. Das, …, N. C. Maiti. 2016. Orientation of tyrosine side chain in neurotoxic Aβ differs in two different secondary structures of the peptide. R. Soc. Open Sci. 3:160112. PubMed PMC

Altis A., Nguyen P.H., Stock G. Dihedral angle principal component analysis of molecular dynamics simulations. J. Chem. Phys. 2007;126:244111. PubMed

Altis, A., P. H. Nguyen, …, G. Stock. 2007. Dihedral angle principal component analysis of molecular dynamics simulations. J. Chem. Phys. 126:244111. PubMed

Watanabe H., Elstner M., Steinbrecher T. Rotamer decomposition and protein dynamics: efficiently analyzing dihedral populations from molecular dynamics. J. Comput. Chem. 2013;34:198–205. PubMed

Watanabe, H., M. Elstner, and T. Steinbrecher. 2013. Rotamer decomposition and protein dynamics: efficiently analyzing dihedral populations from molecular dynamics. J. Comput. Chem. 34:198-205. PubMed

Schleif R. 2013. A Concise Guide to Charmm and the Analysis of Protein Structure and Function.http://pages.jh.edu/∼rschlei1/Random_stuff/publications/charmmbook.pdf

Schleif, R. 2013. A Concise Guide to Charmm and the Analysis of Protein Structure and Function. http://pages.jh.edu/∼rschlei1/Random_stuff/publications/charmmbook.pdf.

Abraham M.J., Spoel D. v. d., Hess B., The GROMACS Development Team . 2018. GROMACS User Manual version 2018.www.gromacs.org

Abraham, M. J., D. v. d. Spoel, …, B. Hess; The GROMACS Development Team. 2018. GROMACS User Manual version 2018. www.gromacs.org.

Sandia National Laboratories . 2018. LAMMPS User’s Manual: compute Dihedral/Local Command.https://lammps.sandia.gov/doc/compute_dihedral_local.html

Sandia National Laboratories. 2018. LAMMPS User’s Manual: compute Dihedral/Local Command. https://lammps.sandia.gov/doc/compute_dihedral_local.html.

Gordon J.C., Myers J.B., Onufriev A. H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res. 2005;33:W368–W371. PubMed PMC

Gordon, J. C., J. B. Myers, …, A. Onufriev. 2005. H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res. 33:W368-W371. PubMed PMC

Nguyen H., Roe D.R., Simmerling C. Improved generalized born solvent model parameters for protein simulations. J. Chem. Theory Comput. 2013;9:2020–2034. PubMed PMC

Nguyen, H., D. R. Roe, and C. Simmerling. 2013. Improved generalized born solvent model parameters for protein simulations. J. Chem. Theory Comput. 9:2020-2034. PubMed PMC

Travaglia A., Pietropaolo A., Rizzarelli E. A small linear peptide encompassing the NGF N-terminus partly mimics the biological activities of the entire neurotrophin in PC12 cells. ACS Chem. Neurosci. 2015;6:1379–1392. PubMed

Travaglia, A., A. Pietropaolo, …, E. Rizzarelli. 2015. A small linear peptide encompassing the NGF N-terminus partly mimics the biological activities of the entire neurotrophin in PC12 cells. ACS Chem. Neurosci. 6:1379-1392. PubMed

Haddad Y., Adam V., Heger Z. Trk receptors and neurotrophin cross-interactions: new perspectives toward manipulating therapeutic side-effects. Front. Mol. Neurosci. 2017;10:130. PubMed PMC

Haddad, Y., V. Adam, and Z. Heger. 2017. Trk receptors and neurotrophin cross-interactions: new perspectives toward manipulating therapeutic side-effects. Front. Mol. Neurosci. 10:130. PubMed PMC

Chavent M., Kuentz-Simonet V., Saracco J. Multivariate analysis of mixed data: the PCAmixdata R package. arXiv. 2014 http://arxiv.org/abs/1411.4911 arXiv:1411.4911.

Chavent, M., V. Kuentz-Simonet, …, J. Saracco. 2014. Multivariate analysis of mixed data: the PCAmixdata R package. arXiv, arXiv:1411.4911, http://arxiv.org/abs/1411.4911.

Ferreira L.G., Dos Santos R.N., Andricopulo A.D. Molecular docking and structure-based drug design strategies. Molecules. 2015;20:13384–13421. PubMed PMC

Ferreira, L. G., R. N. Dos Santos, …, A. D. Andricopulo. 2015. Molecular docking and structure-based drug design strategies. Molecules. 20:13384-13421. PubMed PMC

Hurley J.H. The Role of Interior Side-Chain Packing in Protein Folding and Stability. The Protein Folding Problem and Tertiary Structure Prediction. In: Merz K., LeGrand S., editors. Birkhauser; 1994. pp. 549–578.

Hurley, J. H. 1994. The Role of Interior Side-Chain Packing in Protein Folding and Stability. The Protein Folding Problem and Tertiary Structure Prediction, K. Merz and S. LeGrand, eds. (Birkhauser), pp. 549-578.

Liu J., Wang R. Classification of current scoring functions. J. Chem. Inf. Model. 2015;55:475–482. PubMed

Liu, J., and R. Wang. 2015. Classification of current scoring functions. J. Chem. Inf. Model. 55:475-482. PubMed

Cheng T., Li Q., Bryant S.H. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 2012;14:133–141. PubMed PMC

Cheng, T., Q. Li, …, S. H. Bryant. 2012. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 14:133-141. PubMed PMC

De Vivo M., Masetti M., Cavalli A. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem. 2016;59:4035–4061. PubMed

De Vivo, M., M. Masetti, …, A. Cavalli. 2016. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem. 59:4035-4061. PubMed

Bonvin A.M. Flexible protein-protein docking. Curr. Opin. Struct. Biol. 2006;16:194–200. PubMed

Bonvin, A. M. 2006. Flexible protein-protein docking. Curr. Opin. Struct. Biol. 16:194-200. PubMed

Dagliyan O., Proctor E.A., Dokholyan N.V. Structural and dynamic determinants of protein-peptide recognition. Structure. 2011;19:1837–1845. PubMed PMC

Dagliyan, O., E. A. Proctor, …, N. V. Dokholyan. 2011. Structural and dynamic determinants of protein-peptide recognition. Structure. 19:1837-1845. PubMed PMC

Allam A., Maigre L., Artaud I. New peptides with metal binding abilities and their use as drug carriers. Bioconjug. Chem. 2014;25:1811–1819. PubMed

Allam, A., L. Maigre, …, I. Artaud. 2014. New peptides with metal binding abilities and their use as drug carriers. Bioconjug. Chem. 25:1811-1819. PubMed

Buß O., Rudat J., Ochsenreither K. FoldX as protein engineering tool: better than random based approaches? Comput. Struct. Biotechnol. J. 2018;16:25–33. PubMed PMC

Buß, O., J. Rudat, and K. Ochsenreither. 2018. FoldX as protein engineering tool: better than random based approaches? Comput. Struct. Biotechnol. J. 16:25-33. PubMed PMC

Jumper J.M., Freed K.F., Sosnick T.R. Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics. arXiv. 2016 http://arxiv.org/abs/1610.07277?context=physics arXiv:1610.07277. PubMed PMC

Jumper, J. M., K. F. Freed, and T. R. Sosnick. 2016. Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics. arXiv, arXiv:1610.07277, http://arxiv.org/abs/1610.07277?context=physics. PubMed PMC

Greenfield N.J. Using circular dichroism spectra to estimate protein secondary structure. Nat. Protoc. 2006;1:2876–2890. PubMed PMC

Greenfield, N. J. 2006. Using circular dichroism spectra to estimate protein secondary structure. Nat. Protoc. 1:2876-2890. PubMed PMC

Yang H., Yang S., Yu S. Obtaining information about protein secondary structures in aqueous solution using Fourier transform IR spectroscopy. Nat. Protoc. 2015;10:382–396. PubMed

Yang, H., S. Yang, …, S. Yu. 2015. Obtaining information about protein secondary structures in aqueous solution using Fourier transform IR spectroscopy. Nat. Protoc. 10:382-396. PubMed

Rygula A., Majzner K., Baranska M. Raman spectroscopy of proteins: a review. J. Raman Spectrosc. 2013;44:1061–1076.

Rygula, A., K. Majzner, …, M. Baranska. 2013. Raman spectroscopy of proteins: a review. J. Raman Spectrosc. 44:1061-1076.

Barth A. Infrared spectroscopy of proteins. Biochim. Biophys. Acta. 2007;1767:1073–1101. PubMed

Barth, A. 2007. Infrared spectroscopy of proteins. Biochim. Biophys. Acta. 1767:1073-1101. PubMed

Clayton A.H., Sawyer W.H. Tryptophan rotamer distributions in amphipathic peptides at a lipid surface. Biophys. J. 1999;76:3235–3242. PubMed PMC

Clayton, A. H., and W. H. Sawyer. 1999. Tryptophan rotamer distributions in amphipathic peptides at a lipid surface. Biophys. J. 76:3235-3242. PubMed PMC

Saraiva M.A., Jorge C.D., Maçanita A.L. Earliest events in α-synuclein fibrillation probed with the fluorescence of intrinsic tyrosines. J. Photochem. Photobiol. B. 2016;154:16–23. PubMed

Saraiva, M. A., C. D. Jorge, …, A. L. Maçanita. 2016. Earliest events in α-synuclein fibrillation probed with the fluorescence of intrinsic tyrosines. J. Photochem. Photobiol. B. 154:16-23. PubMed

Kecel S., Ozel A.E., Celik S. Conformational analysis and vibrational spectroscopic investigation of L-alanyl-L-glutamine dipeptide. J. Spectrosc. 2010;24:219–232.

Kecel, S., A. E. Ozel, …, S. Celik. 2010. Conformational analysis and vibrational spectroscopic investigation of L-alanyl-L-glutamine dipeptide. J. Spectrosc. 24:219-232.

Celik S., Ozel A.E., Agaeva G. Conformational preferences, experimental and theoretical vibrational spectra of cyclo (Gly–Val) dipeptide. J. Mol. Struct. 2011;993:341–348.

Celik, S., A. E. Ozel, …, G. Agaeva. 2011. Conformational preferences, experimental and theoretical vibrational spectra of cyclo (Gly-Val) dipeptide. J. Mol. Struct. 993:341-348.

Celik S., Ozel A.E., Akyuz S. Comparative study of antitumor active cyclo (Gly-Leu) dipeptide: a computational and molecular modeling study. Vib. Spectrosc. 2016;83:57–69.

Celik, S., A. E. Ozel, and S. Akyuz. 2016. Comparative study of antitumor active cyclo (Gly-Leu) dipeptide: a computational and molecular modeling study. Vib. Spectrosc. 83:57-69.

Çelik S., Akyuz S., Ozel A.E. Vibrational spectroscopic and structural investigations of bioactive molecule Glycyl-Tyrosine (Gly-Tyr) Vib. Spectrosc. 2017;92:287–297.

Celik, S., S. Akyuz, and A. E. Ozel. 2017. Vibrational spectroscopic and structural investigations of bioactive molecule Glycyl-Tyrosine (Gly-Tyr). Vib. Spectrosc. 92:287-297.

Kecel-Gunduz S., Bicak B., Ozel A.E. Structural and spectroscopic investigation on antioxidant dipeptide, L-Methionyl-L-Serine: a combined experimental and DFT study. J. Mol. Struct. 2017;1137:756–770.

Kecel-Gunduz, S., B. Bicak, …, A. E. Ozel. 2017. Structural and spectroscopic investigation on antioxidant dipeptide, L-Methionyl-L-Serine: a combined experimental and DFT study. J. Mol. Struct. 1137:756-770.

Celik S., Ozel A.E., Akyuz S. Structural and IR and Raman spectral analysis of cyclo (His-Phe) dipeptide. Vib. Spectrosc. 2012;61:54–65.

Celik, S., A. E. Ozel, …, S. Akyuz. 2012. Structural and IR and Raman spectral analysis of cyclo (His-Phe) dipeptide. Vib. Spectrosc. 61:54-65.

Maglia G., Jonckheer A., Engelborghs Y. An unusual red-edge excitation and time-dependent Stokes shift in the single tryptophan mutant protein DD-carboxypeptidase from Streptomyces: the role of dynamics and tryptophan rotamers. Protein Sci. 2008;17:352–361. PubMed PMC

Maglia, G., A. Jonckheer, …, Y. Engelborghs. 2008. An unusual red-edge excitation and time-dependent Stokes shift in the single tryptophan mutant protein DD-carboxypeptidase from Streptomyces: the role of dynamics and tryptophan rotamers. Protein Sci. 17:352-361. PubMed PMC

Shao Q., Zhu W. Assessing AMBER force fields for protein folding in an implicit solvent. Phys. Chem. Chem. Phys. 2018;20:7206–7216. PubMed

Shao, Q., and W. Zhu. 2018. Assessing AMBER force fields for protein folding in an implicit solvent. Phys. Chem. Chem. Phys. 20:7206-7216. PubMed

Mittal J., Best R.B. Tackling force-field bias in protein folding simulations: folding of Villin HP35 and Pin WW domains in explicit water. Biophys. J. 2010;99:L26–L28. PubMed PMC

Mittal, J., and R. B. Best. 2010. Tackling force-field bias in protein folding simulations: folding of Villin HP35 and Pin WW domains in explicit water. Biophys. J. 99:L26-L28. PubMed PMC

Freddolino P.L., Park S., Schulten K. Force field bias in protein folding simulations. Biophys. J. 2009;96:3772–3780. PubMed PMC

Freddolino, P. L., S. Park, …, K. Schulten. 2009. Force field bias in protein folding simulations. Biophys. J. 96:3772-3780. PubMed PMC

Best R.B., Zhu X., Mackerell A.D., Jr. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J. Chem. Theory Comput. 2012;8:3257–3273. PubMed PMC

Best, R. B., X. Zhu, …, A. D. Mackerell, Jr. 2012. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J. Chem. Theory Comput. 8:3257-3273. PubMed PMC

Krieger E., Nabuurs S.B., Vriend G. Homology modeling. Methods Biochem. Anal. 2003;44:509–523. PubMed

Krieger, E., S. B. Nabuurs, and G. Vriend. 2003. Homology modeling. Methods Biochem. Anal. 44:509-523. PubMed

Malyshev D.A., Dhami K., Romesberg F.E. A semi-synthetic organism with an expanded genetic alphabet. Nature. 2014;509:385–388. PubMed PMC

Malyshev, D. A., K. Dhami, …, F. E. Romesberg. 2014. A semi-synthetic organism with an expanded genetic alphabet. Nature. 509:385-388. PubMed PMC

Service R.F. Synthetic biology. Designer microbes expand life’s genetic alphabet. Science. 2014;344:571. PubMed

Service, R. F. 2014. Synthetic biology. Designer microbes expand life’s genetic alphabet. Science. 344:571. PubMed

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