HotSpot Wizard is a web server used for the automated identification of hotspots in semi-rational protein design to give improved protein stability, catalytic activity, substrate specificity and enantioselectivity. Since there are three orders of magnitude fewer protein structures than sequences in bioinformatic databases, the major limitation to the usability of previous versions was the requirement for the protein structure to be a compulsory input for the calculation. HotSpot Wizard 3.0 now accepts the protein sequence as input data. The protein structure for the query sequence is obtained either from eight repositories of homology models or is modeled using Modeller and I-Tasser. The quality of the models is then evaluated using three quality assessment tools-WHAT_CHECK, PROCHECK and MolProbity. During follow-up analyses, the system automatically warns the users whenever they attempt to redesign poorly predicted parts of their homology models. The second main limitation of HotSpot Wizard's predictions is that it identifies suitable positions for mutagenesis, but does not provide any reliable advice on particular substitutions. A new module for the estimation of thermodynamic stabilities using the Rosetta and FoldX suites has been introduced which prevents destabilizing mutations among pre-selected variants entering experimental testing. HotSpot Wizard is freely available at http://loschmidt.chemi.muni.cz/hotspotwizard.

International Centre for Clinical Research St Anne's University Hospital Brno 65691 Brno Czech Republic

IT4Innovations Centre of Excellence Faculty of Information Technology Brno University of Technology Bozetechova 2 61266 Brno Czech Republic

Loschmidt Laboratories Department of Experimental Biology Masaryk University 62500 Brno Czech Republic

Zobrazit více v PubMed

Hawkins M.J., Soon-Shiong P., Desai N.. Protein nanoparticles as drug carriers in clinical medicine. Adv. Drug Deliv. Rev. 2008; 60:876–885. PubMed

Godfrey T., Reichelt J.. Industrial applications. Industrial Enzymology: The Application of Enzymes in Industry. 1982; London: Macmillan, The Nature Press; 582.

Bromley E.H., Channon K., Moutevelis E., Woolfson D.N.. Peptide and protein building blocks for synthetic biology: from programming biomolecules to self-organized biomolecular systems. ACS Chem. Biol. 2008; 3:38–50. PubMed

De La Rica R., Matsui H.. Applications of peptide and protein-based materials in bionanotechnology. Chem. Soc. Rev. 2010; 39:3499–3509. PubMed PMC

Cheng F., Zhu L., Schwaneberg U.. Directed evolution 2.0: improving and deciphering enzyme properties. Chem. Commun. 2015; 51:9760–9772. PubMed

Romero P.A., Arnold F.H.. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 2009; 10:866–876. PubMed PMC

Lutz S. Beyond directed evolution—semi-rational protein engineering and design. Curr. Opin. Biotechnol. 2010; 21:734–743. PubMed PMC

Cheng Z., Peplowski L., Cui W., Xia Y., Liu Z., Zhang J., Kobayashi M., Zhou Z.. Identification of key residues modulating the stereoselectivity of nitrile hydratase towards rac‐Mandelonitrile by Semi‐rational engineering. Biotechnol. Bioeng. 2017; 115:1–12. PubMed

Bendl J., Stourac J., Sebestova E., Vavra O., Musil M., Brezovsky J., Damborsky J.. HotSpot Wizard 2.0: automated design of site-specific mutations and smart libraries in protein engineering. Nucleic Acids Res. 2016; 44:W479–W487. PubMed PMC

Talukdar P., Talapatra S.N.. Oxy-haemoglobin protein engineering: an automated design for hotspots stability, site-specific mutations and smart libraries by using HotSpot Wizard 2.0 software. Int. J. Adv. Res. Comput. Sci. 2017; 8:220–228.

Wang X., Ma R., Xie X., Liu W., Tu T., Zheng F., You S., Ge J., Xie H., Yao B. et al. . Thermostability improvement of a Talaromyces leycettanus xylanase by rational protein engineering. Sci. Rep. 2017; 7:15287. PubMed PMC

Vatansever R., Uras M.E., Sen U., Ozyigit I.I., Filiz E.. Isolation of a transcription factor DREB1A gene from Phaseolus vulgaris and computational insights into its characterization: protein modeling, docking and mutagenesis. J. Biomol. Struct. Dyn. 2016; 35:1–12. PubMed

Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E.. The Protein Data Bank. Nucleic Acids Res. 2000; 28:235–242. PubMed PMC

UniProt Consortium UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017; 45:D158–D169. PubMed PMC

Baker D., Sali A.. Protein structure prediction and structural genomics. Science. 2001; 294:93–96. PubMed

Cavasotto C.N., Phatak S.S.. Homology modeling in drug discovery: current trends and applications. Drug Discov. Today. 2009; 14:676–683. PubMed

Schwede T. Protein modeling: what happened to the ‘protein structure gap’. Structure. 2013; 21:1531–1540. PubMed PMC

Haas J., Roth S., Arnold K., Kiefer F., Schmidt T., Bordoli L., Schwede T.. The Protein Model Portal—a comprehensive resource for protein structure and model information. Database. 2013; 2013:bat031. PubMed PMC

Csmp.ucsf.edu CSMP | Home. 2017; (20 December 2017, date last accessed)http://csmp.ucsf.edu/index.htm.

Jcsg.org The Joint Center for Structural Genomics (JCSG) Homepage. 2017; (20 December 2017, date last accessed)http://www.jcsg.org/.

Mcsg.anl.gov 2017; (20 December 2017, date last accessed)http://www.mcsg.anl.gov/.

Nesg.org NESG - NorthEast Structural Genomics consortium. 2017; (20 December 2017, date last accessed)http://www.nesg.org/.

Venkatagiriyappa V. NYSGRC. 2017; (20 December 2017, date last accessed)http://www.nysgxrc.org/psi3-cgi/index.cgi.

Jcmm.burnham.org Joint Center for Molecular Modeling (JCMM). 2017; (20 December 2017, date last accessed)http://jcmm.burnham.org/.

Pieper U., Webb B.M., Dong G.Q., Schneidman-Duhovny D., Fan H., Kim S.J., Tainer J.A.. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 2013; 42:D336–D346. PubMed PMC

Kiefer F., Arnold K., Künzli M., Bordoli L., Schwede T.. The SWISS-MODEL repository and associated resources. Nucleic Acids Res. 2008; 37:D387–D392. PubMed PMC

Biasini M., Bienert S., Waterhouse A., Arnold K., Studer G., Schmidt T., Schwede T.. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014; 42:W252–W258. PubMed PMC

Song Y., DiMaio F., Wang R.Y.R., Kim D., Miles C., Brunette T.J., Baker D.. High-resolution comparative modeling with RosettaCM. Structure. 2013; 21:1735–1742. PubMed PMC

Kim D.E., Chivian D., Baker D.. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 2004; 32:W526–W531. PubMed PMC

Kelley L.A., Mezulis S., Yates C.M., Wass M.N., Sternberg M.J.. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 2015; 10:845–858. PubMed PMC

Larsson P., Skwark M.J., Wallner B., Elofsson A.. Improved predictions by Pcons. net using multiple templates. Bioinformatics. 2010; 27:426–427. PubMed PMC

Webb B., Sali A.. Protein structure modeling with MODELLER. Methods Mol. Biol. 2014; 1137:151–115. PubMed

Yang J., Yan R., Roy A., Xu D., Poisson J., Zhang Y.. The I-TASSER Suite: protein structure and function prediction. Nat. Methods. 2015; 12:7–8. PubMed PMC

McGuffin L.J., Atkins J.D., Salehe B.R., Shuid A.N., Roche D.B.. IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences. Nucleic Acids Res. 2015; 43:W169–W173. PubMed PMC

Russel D., Lasker K., Webb B., Velázquez-Muriel J., Tjioe E., Schneidman-Duhovny D., Sali A.. Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies. PLoS Biol. 2012; 10:e1001244. PubMed PMC

Hildebrand A., Remmert M., Biegert A., Söding J.. Fast and accurate automatic structure prediction with HHpred. Proteins. 2009; 77:128–132. PubMed

Källberg M., Wang H., Wang S., Peng J., Wang Z., Lu H., Xu J.. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 2012; 7:1511–1522. PubMed PMC

Yang Y., Faraggi E., Zhao H., Zhou Y.. Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics. 2011; 27:2076–2082. PubMed PMC

Kryshtafovych A., Fidelis K., Moult J.. CASP10 results compared to those of previous CASP experiments. Proteins. 2014; 82:164–174. PubMed PMC

Laskowski R.A., MacArthur M.W., Moss D.S., Thornton J.M.. PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 1993; 26:283–291.

Chen V.B., Arendall W.B., Headd J.J., Keedy D.A., Immormino R.M., Kapral G.J., Richardson D.C.. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 2010; 66:12–21. PubMed PMC

Hooft R W., Vriend G., Sander C., Abola E.E.. Errors in protein structures. Nature. 1996; 381:272–272. PubMed

Kellogg E.H., Leaver‐Fay A., Baker D.. Role of conformational sampling in computing mutation‐induced changes in protein structure and stability. Proteins. 2011; 79:830–838. PubMed PMC

Schymkowitz J., Borg J., Stricher F., Nys R., Rousseau F., Serrano L.. The FoldX web server: an online force field. Nucleic Acids Res. 2005; 33:W382–W388. PubMed PMC

Kellogg E.H., Leaver‐Fay A., Baker D.. Role of conformational sampling in computing mutation‐induced changes in protein structure and stability. Proteins. 2011; 79:830–838. PubMed PMC

Bednar D., Beerens K., Sebestova E., Bendl J., Khare S., Chaloupkova R., Prokop Z., Brezovsky J., Baker D., Damborsky J.. FireProt: energy-and evolution-based computational design of thermostable multiple-point mutants. PLoS Comput. Biol. 2015; 11:e1004556. PubMed PMC

Chaloupková R., Sykorova J., Prokop Z., Jesenska A., Monincova M., Pavlova M., Tsuda M., Nagata Y., Damborsky J.. Modification of activity and specificity of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 by engineering of its entrance tunnel. J. Biol. Chem. 2003; 278:52622–52628. PubMed

Nagata Y., Prokop Z., Marvanova S., Sykorova J., Monincova M., Tsuda M., Damborsky J.. Reconstruction of mycobacterial dehalogenase Rv2579 by cumulative mutagenesis of haloalkane dehalogenase LinB. Appl. Environ. Microbiol. 2003; 69:2349–2355. PubMed PMC

Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics

A computational workflow for analysis of missense mutations in precision oncology

Illuminating the mechanism and allosteric behavior of NanoLuc luciferase

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

Machine Learning-Guided Protein Engineering

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Multimeric structure of a subfamily III haloalkane dehalogenase-like enzyme solved by combination of cryo-EM and x-ray crystallography

SoluProtMutDB: A manually curated database of protein solubility changes upon mutations

Enzyme catalysis prior to aromatic residues: Reverse engineering of a dephospho-CoA kinase

FireProtDB: database of manually curated protein stability data