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

Become EABM
Become Reviewer
Call for Editor


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.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Published on: 16 December, 2019
Page: [1151 - 1157]
Pages: 7
DOI: 10.2174/1389203720666190123163907
Price: $65

Article Metrics

PDF: 26