Abstract
The relation between distribution of hydrophobic amino acids along with protein chains and their structure is far from being completely understood. No reliable method allows ab initio prediction of the folded structure from this distribution of physicochemical properties, even when they are highly degenerated by considering only two classes: hydrophobic and polar. Establishment of long-range hydrophobic three dimension (3D) contacts is essential for the formation of the nucleus, a key process in the early steps of protein folding. Thus, a large number of 3D simulation studies were developed to challenge this issue. They are nowadays evaluated in a specific chapter of the molecular modeling competition, Critical Assessment of Protein Structure Prediction. We present here a simulation of the early steps of the folding process for 850 proteins, performed in a discrete 3D space, which results in peaks in the predicted distribution of intra-chain noncovalent contacts. The residues located at these peak positions tend to be buried in the core of the protein and are expected to correspond to critical positions in the sequence, important both for folding and structural (or similarly, energetic in the thermodynamic hypothesis) stability. The degree of stabilization or destabilization due to a point mutation at the critical positions involved in numerous contacts is estimated from the calculated folding free energy difference between mutated and native structures. The results show that these critical positions are not tolerant towards mutation. This simulation of the noncovalent contacts only needs a sequence as input, and this paper proposes a validation of the method by comparison with the prediction of stability by well-established programs.
Acknowledgments
Many thanks are due to the RPBS platform.PMR has benefited of an Erasmus grant for this study.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
[1] Del Giudice R, Arciello A, Itri F, Merlino A, Monti M, Buonanno M, et al. Protein conformational perturbations in hereditary amyloidosis: differential impact of single point mutations in ApoAI amyloidogenic variants. Biochim Biophys Acta BBA – Gen Subj 2016;1860:434–44.10.1016/j.bbagen.2015.10.019Search in Google Scholar
[2] Street AG, Mayo SL. Computational protein design. Structure 1999;7:R105–9.10.1016/S0969-2126(99)80062-8Search in Google Scholar PubMed
[3] Coluzza I. Computational protein design: a review. J Phys Condens Matter Inst Phys J 2017;29:143001.10.1088/1361-648X/aa5c76Search in Google Scholar PubMed
[4] Smaoui MR, Waldispühl J. Complete characterization of the mutation landscape reveals the effect on amylin stability and amyloidogenicity. Proteins Struct Funct Bioinforma 2015;83:1014–26.10.1002/prot.24795Search in Google Scholar PubMed
[5] Lee C, Levitt M. Packing as a structural basis of protein stability: understanding mutant properties from wildtype structure. Pac Symp Biocomput Pac Symp Biocomput 1997;245–55.Search in Google Scholar
[6] Kellogg EH, Leaver-Fay A, Baker D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins Struct Funct Bioinforma 2011;79:830–8.10.1002/prot.22921Search in Google Scholar PubMed PubMed Central
[7] Abkevich V, Gutin A, Shakhnovich E. Specific nucleus as the transition state for protein folding: evidence from the lattice model. Biochemistry (Mosc) 1994;33:10026–36.10.1021/bi00199a029Search in Google Scholar PubMed
[8] Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction: progress and new directions in round XI. Proteins 2016;84:4–14.10.1002/prot.25064Search in Google Scholar PubMed PubMed Central
[9] Travasso R, telo da Goma M, Faisca P. Pathways to folding, nucleation events, and native geometry. J Chem Phys 2007;127:145106.10.1063/1.2777150Search in Google Scholar PubMed
[10] Noivirt-Brik O, Unger R, Horovitz A. Analysing the origin of long-range interactions in proteins using lattice models. BMC Struct Biol 2009;9:4.10.1186/1472-6807-9-4Search in Google Scholar PubMed PubMed Central
[11] Dotu I, Cebrian M, Hentenryck PV, Clote P. On lattice protein structure prediction Revisited. IEEE/ACM Trans Comput Biol Bioinform 2011;8:1620–32.10.1109/TCBB.2011.41Search in Google Scholar PubMed
[12] Will S. Constraint-based hydrophobic core construction for protein structure prediction in the face-centered-cubic lattice. Pac Symp Biocomput 2002;661–72.10.1142/9789812799623_0061Search in Google Scholar PubMed
[13] Mann M, Saunders R, Smith C, Backofen R, Deane CM. Producing high-accuracy lattice models from protein atomic coordinates including side chains. Adv Bioinforma 2012;2012:148045.10.1155/2012/148045Search in Google Scholar
[14] Callebaut I, Labesse G, Durand P, Poupon A, Canard L, Chomilier J, et al. Deciphering protein sequence information through Hydrophobic Cluster Analysis (HCA) : current status and perspectives. Cell Mol Life Sci 1997;53:621–45.10.1007/s000180050082Search in Google Scholar PubMed
[15] Chomilier J, Lamarine M, Mornon J-P, Torres JH, Eliopoulos E, Papandreou N. Analaysis of fragments induced by simulated lattice protein folding. Comptes Rendus Acad Sci 2004;327:431–43.Search in Google Scholar
[16] Tsong TY, Baldwin RL, McPhie P, Elson EL. A sequential model of nucleation-dependent protein folding: kinetic studies of ribonuclease A. J Mol Biol 1972 14;63:453–69.10.1016/0022-2836(72)90440-8Search in Google Scholar
[17] Wetlaufer D. Nucleation, rapid folding, and globular intrachain regions in proteins. PNAS 1973;70:697–701.10.1073/pnas.70.3.697Search in Google Scholar PubMed PubMed Central
[18] Papandreou N, Eliopoulos E, Berezovsky I, Lopes A, Chomilier J. Universal positions in globular proteins: observation to simulation. Eur J Biochem 2004;271:4762–8.10.1111/j.1432-1033.2004.04440.xSearch in Google Scholar PubMed
[19] Faisca P. The nucleation mechanism of protein folding: a survey of computer simulaiton studies. J Phys Condens Matter 2009;21:373102.10.1088/0953-8984/21/37/373102Search in Google Scholar PubMed
[20] Guo Y, Tao F, Wu Z, Wang Y. Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations. BMC Syst Biol 2017;11:93.10.1186/s12918-017-0459-4Search in Google Scholar PubMed PubMed Central
[21] Levitt M, Gerstein M, Huang E, Subbiah S, Tsai J. Protein folding: the endgame. Annu Rev Biochem 1997;66:549–79.10.1146/annurev.biochem.66.1.549Search in Google Scholar PubMed
[22] Prudhomme N, Chomilier J. Prediction of the protein folding core: application to the immunoglobulin fold. Biochimie 2009;91:1465–74.10.1016/j.biochi.2009.07.016Search in Google Scholar PubMed
[23] Banach M, Prudhomme N, Carpentier M, Duprat E, Papandreou N, Kalinowska B, et al. Contribution to the prediction of the Fold Code: application to immunoglobulin and flavodoxin cases. PLoS One 2015;10:e0125098.10.1371/journal.pone.0125098Search in Google Scholar PubMed PubMed Central
[24] Lonquety M, Chomilier J, Papandreou N, Lacroix Z. SPROUTS: a database for evaluation of the protein stability upon point mutation. Nucleic Acids Res 2008;37:D374–9.10.1093/nar/gkn704Search in Google Scholar PubMed PubMed Central
[25] Acuna R, Lacroix Z, Papandreou N, Chomilier J. Protein intrachain contact prediction with Most Interacting Residues (MIR). BAMS 2014;10:227–42.10.1515/bams-2014-0015Search in Google Scholar
[26] Kwasigroch J-M, Chomilier J, Mornon J-P. A global taxonomy of loops in globular proteins. J Mol Biol 1996;259:855–72.10.1006/jmbi.1996.0363Search in Google Scholar PubMed
[27] Acuña R, Lacroix Z, Chomilier J, Papandreou N. SMIR: a web server to predict residues involved in the protein folding core. In: Emerging trends in computational biology, bioinformatics and sytems biology. Quoc Nam Tran, Hamid Arabnia; 437–54.10.1016/B978-0-12-802508-6.00024-7Search in Google Scholar
[28] Bordner AJ, Abagyan RA. Large-scale prediction of protein geometry and stability changes for arbitrary single point mutations. Proteins 2004;57:400–13.10.1002/prot.20185Search in Google Scholar PubMed
[29] Religa T, Markson J, Mayor U, Freund S, Fersht A. Solution structure of a protein denatured state and folding intermediate. Nature 2005;437:1053–6.10.1038/nature04054Search in Google Scholar PubMed
[30] Rose PW, Beran B, Bi C, Bluhm WF, Dimitropoulos D, Goodsell DS, et al. The RCSB Protein Data Bank: redesigned web site and web services. Nucleic Acids Res 2011;39:D392–401.10.1093/nar/gkq1021Search in Google Scholar PubMed PubMed Central
[31] Pappu RV, Marshall GR, Ponder JW. A potential smoothing algorithm accurately predicts transmembrane helix packing. Nat Struct Biol 1999;6:50–5.10.1038/4922Search in Google Scholar PubMed
[32] Shahmoradi A, Wilke CO. Dissecting the roles of local packing density and longer-range effects in protein sequence evolution. Proteins Struct Funct Bioinforma 2016;84:841–54.10.1002/prot.25034Search in Google Scholar PubMed PubMed Central
[33] Cheng J, Randall A, Baldi P. Prediction of protein stability changes for single site mutations using support vector machines. Proteins 2006;62:1125–32.10.1002/prot.20810Search in Google Scholar PubMed
[34] Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005;33:W306–10.10.1093/nar/gki375Search in Google Scholar PubMed
[35] Zhou H, Zhou Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci Publ Protein Soc 2002;11:2714–26.10.1110/ps.0217002Search in Google Scholar
[36] Kwasigroch JM, Gillis D, Dehouck Y, Rooman M. PoPMuSiC, rationally designing point mutations in protein structures. Bioinformatics 2002;18:1701–2.10.1093/bioinformatics/18.12.1701Search in Google Scholar PubMed
[37] Guerois R, Nielsen J, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. JMB 2002;320:369–87.10.1016/S0022-2836(02)00442-4Search in Google Scholar
[38] Quan L, Lv Q, Zhang Y. STRUM: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics 2016;32:2936–46.10.1093/bioinformatics/btw361Search in Google Scholar PubMed PubMed Central
[39] Christensen NJ, Kepp KP. Accurate stabilities of Laccase mutants predicted with a modified FoldX protocol. J Chem Inf Model 2012;52:3028–42.10.1021/ci300398zSearch in Google Scholar PubMed
[40] Isayev O, Gorb L, Leszczynski J. Theoretical calculations: can Gibbs free energy for intermolecular complexes be predicted efficiently and accurately? J Comput Chem 2007;28:1598–609.10.1002/jcc.20696Search in Google Scholar PubMed
[41] Siegert TR, Bird M, Kritzer JA. Identifying loop-mediated protein-protein interactions using loopFinder. Methods Mol Biol Clifton NJ 2017;1561:255–77.10.1007/978-1-4939-6798-8_15Search in Google Scholar PubMed
[42] Tokuriki N, Stricher F, Schymkowitz J, Serrano L, Tawfik DS. The stability effects of protein mutations appear to be universally distributed. J Mol Biol 2007;369:1318–32.10.1016/j.jmb.2007.03.069Search in Google Scholar PubMed
[43] Lonquety M, Lacroix Z, Chomilier J. Benchmarking stability tools: comparison of softwares devoted to protein stability changes induced by point mutations prediction. In: Comput Sys Bioinf Conference CSB2007 San Diego, USA, 2007.Search in Google Scholar
[44] R Core Team. R: a language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; Available from: https://www.R-project.org.Search in Google Scholar
[45] Acuña R, Chomilier J, Lacroix Z. Managing and documenting legacy scientific workflows. J Integr Bioinforma 2015;12:277.10.1515/jib-2015-277Search in Google Scholar
[46] Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012;28:3150–2.10.1093/bioinformatics/bts565Search in Google Scholar PubMed
[47] Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol 2009;9:51.10.1186/1472-6807-9-51Search in Google Scholar PubMed
[48] Siderius M, Jagodzinski F. Mutation sensitivity maps: identifying residue substitutions that impact protein structure via a rigidity analysis in silico mutation approach. J Comput Biol J Comput Mol Cell Biol 2018;25:89–102.10.1089/cmb.2017.0165Search in Google Scholar
[49] Gekko K, Obu N, Li J, Lee JC. A linear correlation between the energetics of allosteric communication and protein flexibility in the Escherichia coli cyclic AMP receptor protein revealed by mutation-induced changes in compressibility and amide hydrogen−deuterium exchange. Biochemistry (Mosc) 2004;43:3844–52.10.1021/bi036271eSearch in Google Scholar
[50] Rathi PC, Jaeger K-E, Gohlke H. Structural rigidity and protein thermostability in variants of lipase A from Bacillus subtilis. PLoS One 2015;10:e0130289.10.1371/journal.pone.0130289Search in Google Scholar PubMed
[51] Jagodzinski F, Hardy J, Streinu I. Using rigididy analysis to probe mutation induced structural changes in proteins. J Bioinform Comput Biol 2012;10:1242010.10.1142/S0219720012420103Search in Google Scholar PubMed
[52] Shakhnovich EI, Gutin AM. Influence of point mutations on protein structure: probability of a neutral mutation. J Theor Biol 1991;149:537–46.10.1016/S0022-5193(05)80097-9Search in Google Scholar PubMed
[53] Cortés J, Al-Bluwi I. A robotics approach to enhance conformational sampling of proteins. ASME 2012;1177–86.10.1115/DETC2012-70105Search in Google Scholar
[54] Fisher RA. Confidence limits for a cross-product ratio. Aust J Stat 1962;4:41.10.1111/j.1467-842X.1962.tb00285.xSearch in Google Scholar
[55] Sillitoe I, Dawson N, Thornton J, Orengo C. The history of the CATH structural classification of protein domains. Biochimie. 2015;119:209–17.10.1016/j.biochi.2015.08.004Search in Google Scholar PubMed
[56] Cordes MH, Walsh NP, McKnight CJ, Sauer RT. Evolution of a protein fold in vitro. Science 1999;284:325–8.10.1126/science.284.5412.325Search in Google Scholar PubMed
[57] Shanthirabalan S, Chomilier J, Carpentier M. Structural effects of point mutations in proteins. Proteins 2018;86:853–867.10.1002/prot.25499Search in Google Scholar PubMed
[58] Ye L, Wu Z, Eleftheriou M, Zhou R. Single-mutation-induced stability loss in protein lysozyme. Biochem Soc Trans 2007;35:1551–7.10.1042/BST0351551Search in Google Scholar PubMed
[59] Kumagai I, Kojima S, Tamaki E, Miura K. Conversion of Trp 62 of hen egg-white lysozyme to Tyr by site-directed mutagenesis. J Biochem (Tokyo) 1987;102:733–40.10.1093/oxfordjournals.jbchem.a122111Search in Google Scholar
[60] Bresler S, Talmud D. On the nature of globular proteins. I. Comptes Rendus Dokady Acdémie Sci URSS 1944;43:310–49.Search in Google Scholar
[61] Berezovsky IN, Grosberg AY, Trifonov EN. Closed loops of nearly standard size: common basic element of protein structure. FEBS Lett 2000;466:283–6.10.1016/S0014-5793(00)01091-7Search in Google Scholar PubMed
[62] Lamarine M, Mornon J-P, Berezovsky IN, Chomilier J. Distribution of tightened end fragments of globular proteins statistically match that of topohydrophobic positions: towards an efficient punctuation of protein folding? Cell Mol Life Sci 2001;58:492–8.10.1007/PL00000873Search in Google Scholar PubMed
[63] Angelov B, Sadoc J-F, Jullien R, Soyer A, Mornon J-P, Chomilier J. Voronoï tessellation of proteins: a novel concept for analysis of protein folding. Proteins 2002;49:446–52.10.1002/prot.10220Search in Google Scholar PubMed
[64] Chintapalli SV, Yew BK, Illingworth CJ, Upton GJ, Reeves PJ, Parkes KE, et al. Closed loop folding units from structural alignments: Experimental foldons revisited. J Comput Chem 2010;31:2689–701.10.1002/jcc.21562Search in Google Scholar PubMed
[65] Nepomnyachiy S, Ben-Tal N, Kolodny R. Complex evolutionary footprints revealed in an analysis of reused protein segments of diverse lengths. Proc Natl Acad Sci 2017;114:11703–8.10.1073/pnas.1707642114Search in Google Scholar PubMed PubMed Central
[66] Chintapalli SV, Illingworth CJR, Upton GJG, Sacquin-Mora S, Reeves PJ, Mohammedali HS, et al. Assessing the effect of dynamics on the closed-loop protein-folding hypothesis. J R Soc Interface 2014;11:20130935.10.1098/rsif.2013.0935Search in Google Scholar PubMed PubMed Central
Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/bams-2018-0026).
©2018 Walter de Gruyter GmbH, Berlin/Boston
