The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

Central European Institute of Technology Brno University of Technology Brno Czech Republic

Department of Chemistry and Biochemistry Mendel University in Brno Brno Czech Republic

Zobrazit více v PubMed

Wolynes PG (2015) Evolution, energy landscapes and the paradoxes of protein folding. Biochimie 119: 218–230. 10.1016/j.biochi.2014.12.007 PubMed DOI PMC

Liu H, Chen Q (2016) Computational protein design for given backbone: recent progresses in general method-related aspects. Curr Opin Struc Biol 39: 89–95. PubMed

Hatfield MPD, Lovas S (2014) Conformational Sampling Techniques. Curr Pharm Des 20: 3303–3313. 10.2174/13816128113199990603 PubMed DOI

Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A (2016) Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol. 12: 1–70. PubMed PMC

Muhammed MT, Aki‐Yalcin E (2019) Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem Biol Drug Des 93: 12–20. 10.1111/cbdd.13388 PubMed DOI

Cavasotto CN, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14: 676–683. 10.1016/j.drudis.2009.04.006 PubMed DOI

Krieger E, Nabuurs SB, Vriend G (2003) Homology modeling. Methods of Biochem Anal 44: 509–524. PubMed

Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89: 10915–10919. 10.1073/pnas.89.22.10915 PubMed DOI PMC

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402. 10.1093/nar/25.17.3389 PubMed DOI PMC

Krogh A, Brown M, Mian IS, Sjölander K, Haussler D (1994) Hidden Markov models in computational biology: Applications to protein modeling. J Mol Biol 235: 1501–1531. 10.1006/jmbi.1994.1104 PubMed DOI

Daga PR, Patel RY, Doerksen RJ (2010) Template-based protein modeling: recent methodological advances. Curr Top Med Chem 10: 84–94. 10.2174/156802610790232314 PubMed DOI PMC

Totrov M (2011) Loop simulations. Homology Modeling: Springer. pp. 207–229.

Wilson C, Gregoret LM, Agard DA (1993) Modeling side-chain conformation for homologous proteins using an energy-based rotamer search. J Mol Biol 229: 996–1006. 10.1006/jmbi.1993.1100 PubMed DOI

Dunbrack RL Jr, Karplus M (1993) Backbone-dependent rotamer library for proteins application to side-chain prediction. J Mol Biol 230: 543–574. 10.1006/jmbi.1993.1170 PubMed DOI

Liang S, Grishin NV (2002) Side‐chain modeling with an optimized scoring function. Protein Sci 11: 322–331. 10.1110/ps.24902 PubMed DOI PMC

Wang Q, Canutescu AA, Dunbrack RL Jr (2008) SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc 3: 1832–1847. 10.1038/nprot.2008.184 PubMed DOI PMC

Madhusudhan MS, Marti-Renom MA, Eswar N, John B, Pieper U, et al. (2005) Comparative protein structure modeling. The Proteomics Protocols Handbook: Springer. 831–860.

Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2018) Critical assessment of methods of protein structure prediction (CASP)—Round XII. Proteins 86: 7–15. 10.1002/prot.25415 PubMed DOI PMC

Guzenko D, Lafita A, Monastyrskyy B, Kryshtafovych A, Duarte JM (2019) Assessment of protein assembly prediction in CASP13. Proteins 1–10. PubMed PMC

Kryshtafovych A, Monastyrskyy B, Fidelis K, Moult J, Schwede T, et al. (2018) Evaluation of the template‐based modeling in CASP12. Proteins 86: 321–334. 10.1002/prot.25425 PubMed DOI PMC

Zhang C, Mortuza SM, He B, Wang Y, Zhang Y (2018) Template‐based and free modeling of I‐TASSER and QUARK pipelines using predicted contact maps in CASP12. Proteins 86: 136–151. 10.1002/prot.25414 PubMed DOI PMC

Croll TI, Sammito MD, Kryshtafovych A, Read RJ (2019) Evaluation of template‐based modeling in CASP13. Proteins 1–15. PubMed PMC

Guex N, Peitsch MC (1997) SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. Electrophoresis 18: 2714–2723. 10.1002/elps.1150181505 PubMed DOI

Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32: 526–531. PubMed PMC

Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10: 845–858. 10.1038/nprot.2015.053 PubMed DOI PMC

Källberg M, Wang H, Wang S, Peng J, Wang Z, et al. (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7: 1511–1522. 10.1038/nprot.2012.085 PubMed DOI PMC

McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16: 404–405. 10.1093/bioinformatics/16.4.404 PubMed DOI

Webb B, Sali A (2014) Protein structure modeling with MODELLER. Protein Structure Prediction: Springer. pp. 1–15. PubMed

Nayeem A, Sitkoff D, Krystek S Jr (2006) A comparative study of available software for high‐accuracy homology modeling: From sequence alignments to structural models. Protein Sci 15: 808–824. 10.1110/ps.051892906 PubMed DOI PMC

Adams PD, Afonine PV, Baskaran K, Berman HM, Berrisford J, et al. (2019) Announcing mandatory submission of PDBx/mmCIF format files for crystallographic depositions to the Protein Data Bank (PDB). Acta Cryst D Biol Crystallogr 75: 451–454. PubMed PMC

Green RK (2019) Beginner’s Guide to PDB Structures and the PDBx/mmCIF Format. PDB-101. Available from: http://pdb101.rcsb.org/learn/guide-to-understanding-pdb-data/beginner%E2%80%99s-guide-to-pdb-structures-and-the-pdbx-mmcif-format. [2019 Feb 10].

Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, et al. (2018) RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res 47: 464–474. PubMed PMC

Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285: 1735–1747. 10.1006/jmbi.1998.2401 PubMed DOI

Li Y, Roy A, Zhang Y (2009) HAAD: a quick algorithm for accurate prediction of hydrogen atoms in protein structures. PLoS ONE. 4:1–9. PubMed PMC

Dayhoff MO M. SR (1987) A model of evolutionary change in proteins. Atlas of Protein Sequence and Structure 5: 345–352.

Venclovas C (2011) Methods for sequence–structure alignment. Homology Modeling: Springer. pp. 55–82. PubMed

Haddad Y, Heger Z, Adam V (2016) Guidelines for homology modeling of dopamine, norepinephrine, and serotonin transporters. ACS Chem Neurosci 7: 1607–1613. 10.1021/acschemneuro.6b00242 PubMed DOI

Pascarella S, Argos P (1992) Analysis of insertions/deletions in protein structures. J Mol Biol 224: 461–471. 10.1016/0022-2836(92)91008-d PubMed DOI

Vyas VK, Ukawala RD, Ghate M, Chintha C (2012) Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 74: 1–17. 10.4103/0250-474X.102537 PubMed DOI PMC

Soto CS, Fasnacht M, Zhu J, Forrest L, Honig B (2008) Loop modeling: Sampling, filtering, and scoring. Proteins 70: 834–843. 10.1002/prot.21612 PubMed DOI PMC

Fiser A, Sali A (2003) ModLoop: automated modeling of loops in protein structures. Bioinformatics 19: 2500–2501. 10.1093/bioinformatics/btg362 PubMed DOI

Leman JK, Weitzner BD, Lewis SM, Bonneau R, Consortium R (2019) Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks.

Studer G, Tauriello G, Bienert S, Waterhouse AM, Bertoni M, et al. (2019) Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information. Computational Methods in Protein Evolution: Springer. pp. 301–316. PubMed

Choi Y, Deane CM (2010) FREAD revisited: accurate loop structure prediction using a database search algorithm. Proteins 78: 1431–1440. 10.1002/prot.22658 PubMed DOI

Karami Y, Guyon F, De Vries S, Tufféry P (2018) DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins. Sci Rep 8: 1–12. 10.1038/s41598-017-17765-5 PubMed DOI PMC

Liang S, Zhang C, Sarmiento J, Standley DM (2012) Protein loop modeling with optimized backbone potential functions. J Chem Theory Comput 8: 1820–1827. 10.1021/ct300131p PubMed DOI

Ramachandran KI, Deepa G, Namboori K (2008) Computational chemistry and molecular modeling: principles and applications: Springer Science & Business Media.

Becker OM, MacKerell AD Jr, Roux B, Watanabe M (2001) Computational Biochemistry and Biophysics: CRC Press; 10.1023/a:1011642500501 DOI

Hinchliffe A (2005) Molecular modelling for beginners: John Wiley & Sons.

Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, et al. (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25: 1605–1612. 10.1002/jcc.20084 PubMed DOI

DeLano WL (2002) Pymol: An open-source molecular graphics tool. CCP4 Newsletter on protein crystallography 40: 82–92.

Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14: 33–38. 10.1016/0263-7855(96)00018-5 PubMed DOI

Chen VB, Arendall WB, Headd JJ, Keedy DA, Immormino RM, et al. (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Cryst D Biol Crystallogr 66: 12–21. PubMed PMC

Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8: 52–56. 10.1016/0263-7855(90)80070-v PubMed DOI

Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26: 283–291.

Benkert P, Tosatto SCE, Schomburg D (2008) QMEAN: A comprehensive scoring function for model quality assessment. Proteins 71: 261–277. 10.1002/prot.21715 PubMed DOI

Shen M, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15: 2507–2524. 10.1110/ps.062416606 PubMed DOI PMC

Sippl MJ (1993) Recognition of errors in three‐dimensional structures of proteins. Proteins 17: 355–362. 10.1002/prot.340170404 PubMed DOI

Pawlowski M, Bogdanowicz A, Bujnicki JM (2013) QA-RecombineIt: a server for quality assessment and recombination of protein models. Nucleic Acids Res 41: 389–397. PubMed PMC

Pawlowski M, Gajda MJ, Matlak R, Bujnicki JM (2008) MetaMQAP: a meta-server for the quality assessment of protein models. BMC Bioinformatics 9: 1–20. 10.1186/1471-2105-9-1 PubMed DOI PMC

Eramian D, Shen M, Devos D, Melo F, Sali A, et al. (2006) A composite score for predicting errors in protein structure models. Protein Sci 15: 1653–1666. 10.1110/ps.062095806 PubMed DOI PMC

Eswar N, John B, Mirkovic N, Fiser A, Ilyin VA, et al. (2003) Tools for comparative protein structure modeling and analysis. Nucleic Acids Res 31: 3375–3380. 10.1093/nar/gkg543 PubMed DOI PMC

Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen‐bonded and geometrical features. Biopolymers: Original Research on Biomolecules 22: 2577–2637. PubMed

Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. Journal of molecular biology 292: 195–202. 10.1006/jmbi.1999.3091 PubMed DOI

Zemla A (2003) LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 31: 3370–3374. 10.1093/nar/gkg571 PubMed DOI PMC

Antczak PLM, Ratajczak T, Lukasiak P, Blazewicz J. SphereGrinder-reference structure-based tool for quality assessment of protein structural models; 2015. IEEE. pp. 665–668.

Song Y, DiMaio F, Wang RY-R, Kim D, Miles C, et al. (2013) High-resolution comparative modeling with RosettaCM. Structure 21: 1735–1742. 10.1016/j.str.2013.08.005 PubMed DOI PMC

Xu D, Zhang J, Roy A, Zhang Y (2011) Automated protein structure modeling in CASP9 by I‐TASSER pipeline combined with QUARK‐based ab initio folding and FG‐MD‐based structure refinement. Proteins 79: 147–160. 10.1002/prot.23111 PubMed DOI PMC

Adiyaman R, McGuffin LJ (2019) Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 20: 1–20. PubMed PMC

Heo L, Feig M (2017) PREFMD: a web server for protein structure refinement via molecular dynamics simulations. Bioinformatics 34: 1063–1065. PubMed PMC

Scarpino A, Ferenczy GG, Keserű GM (2018) Comparative Evaluation of Covalent Docking Tools. J Chem Inf Model 58: 1441–1458. 10.1021/acs.jcim.8b00228 PubMed DOI

Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, et al. (2005) H++: a server for estimating p K as and adding missing hydrogens to macromolecules. Nucleic Acids Res 33: 368–371. PubMed PMC

Senn HM, Thiel W (2007) QM/MM studies of enzymes. Curr Opin Chem Biol 11: 182–187. 10.1016/j.cbpa.2007.01.684 PubMed DOI

Farah K, Müller‐Plathe F, Böhm MC (2012) Classical reactive molecular dynamics implementations: State of the art. ChemPhysChem 13: 1127–1151. 10.1002/cphc.201100681 PubMed DOI

Warshel A, Weiss RM (1980) An empirical valence bond approach for comparing reactions in solutions and in enzymes. J Am Chem Soc 102: 6218–6226.

Ponder JW, Case DA (2003) Force fields for protein simulations. Advances in protein chemistry: Elsevier. pp. 27–85. PubMed

Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25: 1157–1174. 10.1002/jcc.20035 PubMed DOI

Mobley D, Bannan CC, Rizzi A, Bayly CI, Chodera JD, et al. (2018) Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0. 1. BioRxiv: 286542.

Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, et al. (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65: 712–725. 10.1002/prot.21123 PubMed DOI PMC

Best RB, Zhu X, Shim J, Lopes PEM, Mittal J, et al. (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 8: 3257–3273. 10.1021/ct300400x PubMed DOI PMC

Schmid N, Eichenberger AP, Choutko A, Riniker S, Winger M, et al. (2011) Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur Biophys J 40: 843–856. 10.1007/s00249-011-0700-9 PubMed DOI

Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118: 11225–11236.

Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105: 9954–9960.

Nagata K, Randall A, Baldi P (2014) Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures. Bioinformatics 30: 1681–1689. 10.1093/bioinformatics/btu106 PubMed DOI PMC

Warnecke A, Sandalova T, Achour A, Harris RA (2014) PyTMs: a useful PyMOL plugin for modeling common post-translational modifications. BMC Bioinformatics 15: 1–12. 10.1186/1471-2105-15-1 PubMed DOI PMC

Wang J, Wang W, Kollman PA, Case DA (2001) Antechamber: an accessory software package for molecular mechanical calculations. J Am Chem Soc 222: 403–403.

Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, et al. (2010) CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields. J Comput Chem 31: 671–690. 10.1002/jcc.21367 PubMed DOI PMC

Malde AK, Zuo L, Breeze M, Stroet M, Poger D, et al. (2011) An Automated Force Field Topology Builder (ATB) and Repository: Version 1.0. J Chem Theory Comput 7: 4026–4037. 10.1021/ct200196m PubMed DOI

Schüttelkopf AW, Van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Cryst D Biol Crystallogr 60: 1355–1363. PubMed

Cramer CJ (2013) Essentials of computational chemistry: theories and models: John Wiley & Sons.

Carvalho ATP, Barrozo A, Doron D, Kilshtain AV, Major DT, et al. (2014) Challenges in computational studies of enzyme structure, function and dynamics. J Mol Graph Model 54: 62–79. 10.1016/j.jmgm.2014.09.003 PubMed DOI

Haddad Y, Heger Z, Adam V (2017) Targeting neuroblastoma cell surface proteins: recommendations for homology modeling of hNET, ALK, and TrkB. Front Mol Neurosci 10: 1–7. 10.3389/fnmol.2017.00001 PubMed DOI PMC

Aricescu AR, Siebold C, Choudhuri K, Chang VT, Lu W, et al. (2007) Structure of a tyrosine phosphatase adhesive interaction reveals a spacer-clamp mechanism. Science 317: 1217–1220. 10.1126/science.1144646 PubMed DOI

Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs

Toward structure-based drug design against the epidermal growth factor receptor (EGFR)

Homology modeling in the time of collective and artificial intelligence