Application of the Monte Carlo Method for the Prediction of Behavior of Peptides

Author(s): Alla P. Toropova*, Andrey A. Toropov

Journal Name: Current Protein & Peptide Science

Volume 20 , Issue 12 , 2019

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Prediction of physicochemical and biochemical behavior of peptides is an important and attractive task of the modern natural sciences, since these substances have a key role in life processes. The Monte Carlo technique is a possible way to solve the above task. The Monte Carlo method is a tool with different applications relative to the study of peptides: (i) analysis of the 3D configurations (conformers); (ii) establishment of quantitative structure – property / activity relationships (QSPRs/QSARs); and (iii) development of databases on the biopolymers. Current ideas related to application of the Monte Carlo technique for studying peptides and biopolymers have been discussed in this review.

Keywords: Monte Carlo method, conformer, QSPR, QSAR, database, peptides, biopolymers.

Carrer, D.C. Membrane proteins, a biophysical perspective. Curr. Protein Pept. Sci., 2011, 12(8), 684.
Ruprecht, V.; Axmann, M.; Wieser, S.; Schütz, G.J. What can we learn from single molecule trajectories? Curr. Protein Pept. Sci., 2011, 12(8), 714-724.
Hughes, S.R.; López-Núñez, J.C.; Jones, M.A.; Moser, B.R.; Cox, E.J.; Lindquist, M.; Galindo-Leva, L.Á.; Riaño-Herrera, N.M.; Rodriguez-Valencia, N.; Gast, F.; Cedeño, D.L.; Tasaki, K.; Brown, R.C.; Darzins, A.; Brunner, L. Sustainable conversion of coffee and other crop wastes to biofuels and bioproducts using coupled biochemical and thermochemical processes in a multi-stage biorefinery concept. Appl. Microbiol. Biotechnol., 2014, 98(20), 8413-8431.
Agoram, B.; Woltosz, W.S.; Bolger, M.B. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Deliv. Rev., 2001, 50(Suppl. 1), S41-S67.
Kaneko, M.; Narukawa, M. Assessment of cardiovascular risk with glucagon-like peptide 1 receptor agonists in patients with type 2 diabetes using an alternative measure to the hazard ratio. Ann. Pharmacother., 2018, 52(7), 632-638.
Hirota, M.; Ashikaga, T.; Kouzuki, H. Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter. J. Appl. Toxicol., 2018, 38(4), 514-526.
Vázquez-Prieto, S.; Paniagua, E.; Ubeira, F.M.; González-Díaz, H. QSPR-perturbation models for the prediction of b-epitopes from immune epitope database: A potentially valuable route for predicting “in silico” new optimal peptide sequences and/or boundary conditions for vaccine development. Int. J. Pept. Res. Ther., 2016, 22(4), 445-450.
Golubović, M.; Lazarević, M.; Zlatanović, D.; Krtinić, D.; Stoičkov, V.; Mladenović, B.; Milić, D.J.; Sokolović, D.; Veselinović, A.M. The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study. Comput. Biol. Chem., 2018, 75, 32-38.
Halder, A.K.; Moura, A.S.; Cordeiro, M.N.D.S. QSAR modelling: A therapeutic patent review 2010-present. Expert Opin. Ther. Pat., 2018, 28(6), 467-476.
Adhikari, N.; Amin, S.K.A.; Saha, A.; Jha, T. Structural exploration for the refinement of anticancer matrix metalloproteinase-2 inhibitor designing approaches through robust validated multi-QSARs. J. Mol. Struct., 2018, 1156, 501-515.
Amin, S.A.; Adhikari, N.; Bhargava, S.; Gayen, S.; Jha, T. An integrated QSAR modeling approach to explore the structure–property and selectivity relationships of N-benzoyl-l-biphenylalanines as integrin antagonists. Mol. Divers., 2018, 22(1), 129-158.
Amin, S.A.; Adhikari, N.; Baidya, S.K.; Gayen, S.; Jha, T. Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies. J. Biomol. Struct. Dyn., 2019, 37(1), 75-94.
Bhargava, S.; Adhikari, N.; Amin, S.A.; Das, K.; Gayen, S.; Jha, T. Hydroxyethylamine derivatives as HIV-1 protease inhibitors: A predictive QSAR modelling study based on Monte Carlo optimization. SAR QSAR Environ. Res., 2017, 28(12), 973-990.
Castellano, G.; Redondo, L.; Torrens, F. QSAR of natural sesquiterpene lactones as inhibitors of Myb-dependent gene expression. Curr. Top. Med. Chem., 2017, 17(30), 3256-3268.
Aranda, J.F.; Bacelo, D.E.; Leguizamón Aparicio, M.S.; Ocsachoque, M.A.; Castro, E.A.; Duchowicz, P.R. Predicting the bioconcentration factor through a conformation-independent QSPR study. SAR QSAR Environ. Res., 2017, 28(9), 749-763.
Rescifina, A.; Floresta, G.; Marrazzo, A.; Parenti, C.; Prezzavento, O.; Nastasi, G.; Dichiara, M.; Amata, E. Development of a sigma-2 receptor affinity filter through a Monte Carlo based QSAR analysis. Eur. J. Pharm. Sci., 2017, 106, 94-101.
Rescifina, A.; Floresta, G.; Marrazzo, A.; Parenti, C.; Prezzavento, O.; Nastasi, G.; Dichiara, M.; Amata, E. Sigma-2 receptor ligands QSAR model dataset. Data Brief, 2017, 13, 514-535.
Scotti, L.; Scotti, M.T. In silico studies applied to natural products with potential activity against Alzheimer’s disease. Neuromethods, 2018, 132, 513-531.
Scotti, M.T.; Scotti, L.; Ishiki, H.M.; Peron, L.M.; de Rezende, L.; do Amaral, A.T. Variable-selection approaches to generate QSAR models for a set of antichagasic semicarbazones and analogues. Chemom. Intell. Lab. Syst., 2016, 154, 137-149.
Speck-Planche, A.; Kleandrova, V.V.; Scotti, M.T.; Cordeiro, M.N.D.S. 3D-QSAR methodologies and molecular modeling in bioinformatics for the search of novel anti-HIV therapies: Rational design of entry inhibitors. Curr. Bioinform., 2013, 8(4), 452-464.
Toropov, A.A.; Toropova, A.P.; Leszczynska, D.; Leszczynski, J. “Ideal correlations” for biological activity of peptides. Biosystems, 2019, 181, 51-57.
Golmohammadi, H.; Dashtbozorgi, Z.; Vander Heyden, Y. Support vector regression based qspr for the prediction of retention time of peptides in reversed-phase liquid chromatography. Chromatographia, 2015, 78(1-2), 7-19.
Silla, J.M.; Nunes, C.A.; Cormanich, R.A.; Guerreiro, M.C.; Ramalho, T.C.; Freitas, M.P. MIA-QSPR and effect of variable selection on the modeling of kinetic parameters related to activities of modified peptides against dengue type 2. Chemom. Intell. Lab. Syst., 2011, 108(2), 146-149.
Liu, K.P.; Xia, B.B.; Zhang, X.Y. Review of QSPR modeling of mobilities of peptides in capillary zone electrophoresis. J. Liq. Chromatogr. Relat. Technol., 2008, 31(11-12), 1808-1822.
Zhou, P.; Zeng, H.; Tian, F.F.; Li, B.; Li, Z.A. Applying novel molecular electronegativity-interaction vector (MEIV) to QSPR study on collision cross section of singly protonated peptides. QSAR Comb. Sci., 2007, 26(1), 117-121.
Zaliani, A.; Gancia, E. MS-WHIM scores for amino acids: A new 3D-description for peptide QSAR and QSPR studies. J. Chem. Inf. Comput. Sci., 1999, 39(3), 525-533.
Andrade-Ochoa, S.; García-Machorro, J.; Bello, M.; Rodríguez-Valdez, L.M.; Flores-Sandoval, C.A.; Correa-Basurto, J. QSAR, DFT and molecular modeling studies of peptides from HIV-1 to describe their recognition properties by MHC-I. J. Biomol. Struct. Dyn., 2018, 36(9), 2312-2330.
Toropova, A.P.; Toropov, A.A.; Beeg, M.; Gobbi, M.; Salmona, M. Utilization of the Monte Carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides. Curr. Drug Discov. Technol., 2017, 14(4), 229-243.
Tong, J.; Li, L.; Li, K.; Bai, M. Peptide drugs QSAR study based on topomer CoMFA. Lett. Drug Des. Discov., 2017, 14(10), 1114-1121.
Urbisch, D.; Honarvar, N.; Kolle, S.N.; Mehling, A.; Ramirez, T.; Teubner, W.; Landsiedel, R. Peptide reactivity associated with skin sensitization: The QSAR Toolbox and TIMES compared to the DPRA. Toxicol. In Vitro, 2016, 34, 194-203.
Tong, J.; Li, L.; Liu, S.; Chang, J. Peptide drugs QSAR modeling based on a new descriptor of amino acids-SVGT. Lett. Drug Des. Discov., 2016, 13(3), 262-267.
Nongonierma, A.B.; Fitzgerald, R.J. Learnings from quantitative structure-activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: A review. RSC Advances, 2016, 6(79), 75400-75413.
Tong, J.; Chang, J.; Li, L.; Bai, M. QSAR study of peptide drugs by 3D-HoVAIF. J. Struct. Chem., 2015, 56(7), 1268-1274.
Toropova, M.A.; Veselinović, A.M.; Veselinović, J.B.; Stojanović, D.B.; Toropov, A.A. QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids. Comput. Biol. Chem., 2015, 59, 126-130.
Tong, J.B.; Chang, J.; Liu, S.L.; Bai, M. A quantitative structure-activity relationship (QSAR) study of peptide drugs based on a new of amino acids. J. Serb. Chem. Soc., 2015, 80(3), 343-353.
Jahangiri, R.; Soltani, S.; Barzegar, A. A review of QSAR studies to predict activity of ACE peptide inhibitors. Pharm. Sci., 2014, 20(3), 122-129.
Toropova, A.P.; Toropov, A.A.; Rasulev, B.F.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES. Struct. Chem., 2012, 23(6), 1873-1878.
O’Toole, E.M.; Panagiotopoulos, A.Z. Monte Carlo simulation of folding transitions of simple model proteins using a chain growth algorithm. J. Chem. Phys., 1992, 97, 8644-8651.
Noguti, T.; Go, N. Efficient Monte Carlo method for simulation of fluctuating conformations of native proteins. Biopolymers, 1985, 24, 527-546.
Abagyan, R.; Totrov, M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J. Mol. Biol., 1994, 235, 983-1002.
Tong, L.; Pan, C.; Wang, H.; Bertolini, M.; Lew, E.; Meneghini, L.F. Impact of delaying treatment intensification with a glucagon-like peptide-1 receptor agonist in patients with type 2 diabetes uncontrolled on basal insulin: A longitudinal study of a US administrative claims database. Diabetes Obes. Metab., 2018, 20(4), 831-839.
Boye, K.S.; Botros, F.T.; Haupt, A.; Woodward, B.; Lage, M.J. Glucagon-like peptide-1 receptor agonist use and renal impairment: A retrospective analysis of an electronic health records database in the U.S. population. Diabetes Ther., 2018, 9(2), 637-650.
Dorl, S.; Winkler, S.; Mechtler, K.; Dorfer, V. PhoStar: Identifying tandem mass spectra of phosphorylated peptides before database search. J. Proteome Res., 2018, 17(1), 290-295.
Usmani, S.S.; Kumar, R.; Bhalla, S.; Kumar, V.; Raghava, G.P.S. In silico tools and databases for designing peptide-based vaccine and drugs. Adv. Protein Chem. Struct. Biol., 2018, 112, 221-263.
Elguoshy, A.; Hirao, Y.; Xu, B.; Saito, S.; Quadery, A.F.; Yamamoto, K.; Mitsui, T.; Yamamoto, T. Identification and validation of human missing proteins and peptides in public proteome databases: data mining strategy. J. Proteome Res., 2017, 16(12), 4403-4414.
Nielsen, S.D.; Beverly, R.L.; Qu, Y.; Dallas, D.C. Milk bioactive peptide database: A comprehensive database of milk protein-derived bioactive peptides and novel visualization. Food Chem., 2017, 232, 673-682.
Loukil, H.; Tmar, M.; Louati, M.; Masmoudi, A.; Gargouri, F. Impact of a priori MS/MS intensity distributions on database search for peptide identification. Digit. Signal Process., 2017, 67, 52-60.
Tran, T.T.; Bollineni, R.C.; Strozynski, M.; Koehler, C.J.; Thiede, B. Identification of alternative splice variants using unique tryptic peptide sequences for database searches. J. Proteome Res., 2017, 16(7), 2571-2578.
Zia, Q.; Azhar, A.; Ahmad, S.; Afsar, M.; Hasan, Z.; Owais, M.; Alam, M.; Akbar, S.; Ganash, M.; Ashraf, G.M.; Zubair, S.; Aliev, G. PeMtb: A database of MHC antigenic peptide of Mycobacterium tuberculosis. Curr. Pharm. Biotechnol., 2017, 18(8), 648-652.
Dilger, J.M.; Glover, M.S.; Clemmer, D.E. A database of transition-metal-coordinated peptide cross-sections: Selective interaction with specific amino acid residues. J. Am. Soc. Mass Spectrom., 2017, 28(7), 1293-1303.
Ravichandran, G.; Kumaresan, V.; Bhatt, P.; Arasu, M.V.; Al-Dhabi, N.A.; Arockiaraj, J. A cumulative strategy to predict and characterize Antimicrobial Peptides (AMPs) from protein database. Int. J. Pept. Res. Ther., 2017, 23(2), 281-290.
Kosinsky, Y.A.; Dubovskii, P.V.; Nolde, D.E.; Arseniev, A.S.; Efremov, R.G. Fusion peptide interaction with lipid bilayer: Modeling with Monte Carlo simulation and continuum electrostatics calculation. Mol. Simul., 2000, 24(4-6), 341-349.
Keseru, G.M.; Menyhárd, D.K. Role of proximal His93 in nitric oxide binding to metmyoglobin. Application of continuum solvation in Monte Carlo protein simulations. Biochemistry, 1999, 38(20), 6614-6622.
Ferreira, N.S.; Neto, A.M.J.C.; Mota, G.V.S. Infrared theoretical spectra of triolein obtained by density functional theory from a conformational search for low-energy conformers by the Monte Carlo method. J. Comput. Theor. Nanosci., 2014, 11(11), 2313-2317.
Villa, F.; Panel, N.; Chen, X.; Simonson, T. Adaptive landscape flattening in amino acid sequence space for the computational design of protein: peptide binding. J. Chem. Phys., 2018, 149(7)art. no.072302
Kang, W.B.; He, C.; Liu, Z.X.; Wang, J.; Wang, W. Composition-related structural transition of random peptides: insight into the boundary between intrinsically disordered proteins and folded proteins. J. Biomol. Struct. Dyn., 2019, 37(8), 1956-1967.
Archirel, P.; Bergès, J.; Houée-Lévin, C. Radical cations of the monomer and van der waals dimer of a methionine residue as prototypes of (2 center-3 electron) SN and SS bonds. molecular simulations of their absorption spectra in water. J. Phys. Chem. B, 2016, 120(37), 9875-9886.
Cardone, A.; Bornstein, A.; Pant, H.C.; Brady, M.; Sriram, R.; Hassan, S.A. Detection and characterization of nonspecific, sparsely populated binding modes in the early stages of complexation. J. Comput. Chem., 2015, 36(13), 983-995.
Gao, S.; Zeng, J.; Elsheikh, A.M.; Naji, G.; Alhajj, R.; Rokne, J.; Demetrick, D. A closer look at “social” boundary genes reveals knowledge to gene expression profiles. Curr. Protein Pept. Sci., 2011, 12(7), 602-613.
Keedy, D.A.; Fraser, J.S.; van den Bedem, H. Exposing hidden alternative backbone conformations in X-ray crystallography using qFit. PLOS Comput. Biol., 2015, 11(10)e1004507
Jardon, E.V.; Bond, P.J.; Ulmschneider, M.B. Ab Initio Folding of Glycophorin A and Acetylcholine M2 Transmembrane Segments Via Simplified Environment Molecular Simulations. In: Olivares- Quiroz, L.; Guzmán-López, O.; Jardón-Valadez, H. (eds) Physical Biology of Proteins and Peptides. ; Springer, Cham ,. , 2015; pp. 115-139.
Tong, J.; Chang, J.; Xu, X.; Liu, S.; Bai, M. A new descriptor for amino acids and its applications in peptide QSAR. Revista Chimie, 2014, 65(5), 550-555.
Srivastava, A.K.; Shukla, N.; Pathak, V.K. Quantitative structure-activity relationship (QSAR) studies on a series of carbamate-appended N-alkylsulphonamides as inhibitors of peptide amyloid-β (Aβ). Oxid. Commun., 2013, 36(4), 1090-1101.
Tan, J.; Tian, F.; Lv, Y.; Liu, W.; Zhong, L.; Liu, Y.; Yang, L. Integration of QSAR modelling and QM/MM analysis to investigate functional food peptides with antihypertensive activity. Mol. Simul., 2013, 39(12), 1000-1006.
Tundidor-Camba, A.; Caballero, J.; Coll, D. 3D-QSAR modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor. Med. Chem., 2013, 9(4), 560-570.
Wang, Z.M.; Han, N.; Yuan, Z.M.; Wu, Z.H. Feature selection for high-dimensional data based on ridge regression and SVM and its application in peptide QSAR modeling. Wuli Huaxue Xuebao. Wuli Huaxue Xuebao, Acta Physico - Chimica Sinica, . 2013, 29(3), 498-507.
Gao, J.; Cheng, Y.; Cui, W.; Chen, Q.; Zhang, F.; Du, Y.; Ji, M. 3D-QSAR and molecular docking studies of hydroxamic acids as peptide deformylase inhibitors. Med. Chem. Res., 2012, 21(8), 1597-1610.
Hemmateenejad, B.; Miri, R.; Elyasi, M. A segmented principal component analysis-regression approach to QSAR study of peptides. J. Theor. Biol., 2012, 305, 37-44.
He, R.; Ma, H.; Zhao, W.; Qu, W.; Zhao, J.; Luo, L.; Zhu, W. Modeling the QSAR of ACE-inhibitory peptides with ANN and its applied illustration. Int. J. Pept., 2012, 2012620609
Wang, Y.; Cheng, X.; Lin, Y.; Wen, H.; Wang, L.; Xia, Q.; Lin, Z. TAP-binding peptides prediction by QSAR modeling based on amino acid structural information. Curr. Comput.-. Aid. Drug Des., 2012, 8(1), 50-54.
Toropov, A.A.; Toropova, A.P. Quasi-SMILES and nano-QFAR: United model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere, 2015, 139, 18-22.
Toropov, A.A.; Rallo, R.; Toropova, A.P. Use of Quasi-SMILES and monte carlo optimization to develop quantitative feature property/activity relationships (QFPR/QFAR) for nanomaterials. Curr. Top. Med. Chem., 2015, 15(18), 1837-1844.
Toropova, A.P.; Toropov, A.A.; Manganelli, S.; Leone, C.; Baderna, D.; Benfenati, E.; Fanelli, R. Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials. NanoImpact, 2016, 1, 60-64.
Toropov, A.A.; Toropova, A.P.; Begum, S.; Achary, P.G.R. Towards predicting the solubility of CO2 and N2 in different polymers using a quasi-SMILES based QSPR approach. SAR QSAR Environ. Res., 2016, 27(4), 293-301.
Achary, P.G.R.; Begum, S.; Toropova, A.P.; Toropov, A.A. A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization. Materials Discov., 2016, 5, 22-28.
Toropov, A.A.; Achary, P.G.R.; Toropova, A.P. Quasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles. Chem. Phys. Lett., 2016, 660, 107-110.
Toropova, A.P.; Achary, P.G.R.; Toropov, A.A. Quasi-SMILES for Nano-QSAR prediction of toxic effect of Al2O3 nanoparticles. Mater.Sci. Eng.,, 2017, 3-3, 1624-1635.
Toropova, A.P.; Toropov, A.A.; Veselinović, A.M.; Veselinović, J.B.; Leszczynska, D.; Leszczynski, J. Quasi-SMILES as a novel tool for prediction of nanomaterials’ endpoints. Multi-Scale Approach. Drug Discov., 2017, 8, 191-221.
Trinh, T.X.; Choi, J.S.; Jeon, H.; Byun, H.G.; Yoon, T.H.; Kim, J. Quasi-SMILES-based nano-quantitative structure-activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem. Res. Toxicol., 2018, 31(3), 183-190.
Toropova, A.P.; Toropov, A.A.; Benfenati, E.; Leszczynska, D.; Leszczynski, J. Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES. Biosystems, 2018, 169-170, 5-12.
Toropova, A.P.; Toropov, A.A.; Benfenati, E.; Castiglioni, S.; Bagnati, R.; Passoni, A.; Zuccato, E.; Fanelli, R. Quasi-SMILES as a tool to predict removal rates of pharmaceuticals and dyes in sewage. Process Saf. Environ., 2018, 118, 227-233.
Toropov, A.A.; Toropova, A.P.; Benfenati, E.; Diomede, L.; Salmona, M. Use of quasi-SMILES to model biological activity of “micelle–polymer” samples. Struct. Chem., 2018, 29(4), 1213-1223.
Weininger, D. SMILES, a chemical language and information system: 1: Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci., 1988, 28(1), 31-36.
Weininger, D.; Weininger, A.; Weininger, J.L. SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comput. Sci., 1989, 29(2), 97-101.
Weininger, D. Smiles. 3. depict. graphical depiction of chemical structures. J. Chem. Inf. Comput. Sci., 1990, 30(3), 237-243.
Weininger, D. SMILES - a language for molecules and reactions. Handbook Chemoinform., 2008, 1, 80-102.
Siani, M.A.; Weininger, D.; Blaney, J.M. CHUCKLES: A method for representing and searching peptide and peptoid sequences on both monomer and atomic levels. J. Chem. Inf. Comput. Sci., 1994, 34(3), 588-593.
Toropova, A.P.; Toropov, A.A. Mutagenicity: QSAR -quasi-QSAR -nano-QSAR. Mini Rev. Med. Chem., 2015, 15(8), 608-621.
Toropova, A.P.; Toropov, A.A. Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol. Lett., 2017, 275, 57-66.
Toropova, A.P.; Toropov, A.A.; Leszczynska, D.; Leszczynski, J. CORAL and Nano-QFAR: Quantitative feature – Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicol. Environ. Saf., 2017, 139, 404-407.
Holm, L.; Sander, C. Database algorithm for generating protein backbone and side-chain co-ordinates from a Cα trace. Application to model building and detection of co-ordinate errors. J. Mol. Biol., 1991, 218(1), 183-194.
Mathiowetz, A.M.; Goddard, W.A., III Building proteins from Cα coordinates using the dihedral probability grid Monte Carlo method. Protein Sci., 1995, 4(6), 1217-1232.
Evans, J.S.; Chan, S.I.; Mathiowetz, A.M.; Goddard, W.A. III. De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology. Protein Sci., 1995, 4(6), 1203-1216.
Diller, D.J.; Redinbo, M.R.; Pohl, E.; Hol, W.G.J. A database method for automated map interpretation in protein crystallography. Proteins, 1999, 36(4), 526-541.
Skolnick, J.; Kolinski, A.; Ortiz, A. Derivation of protein-specific pair potentials based on weak sequence fragment similarity. Proteins, 2000, 38(1), 3-16.
Podtelezhnikov, A.A.; Ghahramarai, Z.; Wild, D.L. Learning about protein hydrogen bonding by minimizing contrastive divergence. Proteins, 2007, 66(3), 588-599.
Feng, J.; Naiman, D.Q.; Cooper, B. Probability-based pattern recognition and statistical framework for randomization: Modeling tandem mass spectrum/peptide sequence false match frequencies. Bioinformatics, 2007, 23(17), 2210-2217.
Paraskevopoulou, M.D.; Vlachos, I.S.; Athanasiadis, E.; Spyrou, G. BiDaS: A web-based Monte Carlo BioData simulator based on sequence/ feature characteristics. Nucleic Acids Res.,, 2013, 41(Web Server issue), W582-W586.
Toropova, M.A. Drug metabolism as an object of computational analysis by the Monte Carlo method. Curr. Drug Metab., 2017, 18(12), 1123-1131.
Harada, K.; Sakaguchi, H.; Sada, S.; Ishida, R.; Hayasaka, Y.; Tsuboi, T. Bitter tastant quinine modulates glucagon-like peptide-1 exocytosis from clonal GLUTag enteroendocrine L cells via actin reorganization. Biochem. Biophys. Res. Commun., 2018, 500(3), 723-730.
Hallasch, S.; Frick, S.; Jung, M.; Hilger, I. How gastrin-releasing peptide receptor (GRPR) and αvβ3 integrin expression reflect reorganization features of tumors after hyperthermia treatments. Sci. Rep., 2017, 7(1), 6100.

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Year: 2019
Page: [1151 - 1157]
Pages: 7
DOI: 10.2174/1389203720666190123163907
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