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Riyaphan J, Pham DC, Leong MK, Weng CF. In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes. Biomolecules 2021; 11:1877. [PMID: 34944521 PMCID: PMC8699780 DOI: 10.3390/biom11121877] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023] Open
Abstract
Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.
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Affiliation(s)
| | - Dinh-Chuong Pham
- Biomaterials and Nanotechnology Research Group, Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam;
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Hualien 97401, Taiwan
| | - Ching-Feng Weng
- Functional Physiology Section, Department of Basic Medical Science, Xiamen Medical College, Xiamen 361023, China
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Vázquez NAR. Adsorption of terbium ion on Fc/dymethylacrylamide: application of Monte Carlo simulation. POLIMEROS 2020. [DOI: 10.1590/0104-1428.08419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Andersson K, Karlsson R, Löfås S, Franklin G, Hämäläinen MD. Label-free kinetic binding data as a decisive element in drug discovery. Expert Opin Drug Discov 2013; 1:439-46. [PMID: 23495944 DOI: 10.1517/17460441.1.5.439] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The emerging possibilities to obtain label-free, kinetic-based binding data for drug-target and drug absorption, distribution, metabolism and excretion (ADME)-marker interactions have proven useful in many drug discovery related issues. Multiple reports have demonstrated that the common use of affinity as an early measure of drug potency may be directly misleading. This review summarises findings in the literature related to compound selection in the drug discovery process. It is important to understand the different properties of association and dissociation rates, the former being related to both structure and dosage and the latter depending solely on molecular structure. By performing parallel optimisations of association and dissociation rates, compounds with desirable kinetic profiles for target binding may be generated. In addition, compound selection may also consider the kinetic properties of the drug-ADME-marker binding profiles, further refining the quality of compounds early in the drug discovery process. The promising results found in the literature indicate that kinetic data on drug-protein interactions may soon become a crucial decisive element in modern drug discovery.
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Tang SN, Sun JM, Xiong WW, Cong PS, Li TH. Identification of the subcellular localization of mycobacterial proteins using localization motifs. Biochimie 2012; 94:847-53. [DOI: 10.1016/j.biochi.2011.12.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2011] [Accepted: 12/02/2011] [Indexed: 01/28/2023]
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Dell'Orco D. Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology. MOLECULAR BIOSYSTEMS 2009; 5:323-34. [PMID: 19396368 DOI: 10.1039/b821580d] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The increasing call for an overall picture of the interactions between the components of a biological system that give rise to the observed function is often summarized by the expression systems biology. Both the interpretative and predictive capabilities of holistic models of biochemical systems, however, depend to a large extent on the level of physico-chemical knowledge of the individual molecular interactions making up the network. This review is focused on the structure-based quantitative characterization of protein-protein interactions, ubiquitous in any biochemical pathway. Recently developed, fast and effective computational methods are reviewed, which allow the assessment of kinetic and thermodynamic features of the association-dissociation processes of protein complexes, both in water soluble and membrane environments. The performance and the accuracy of fast and semi-empirical structure-based methods have reached comparable levels with respect to the classical and more elegant molecular simulations. Nevertheless, the broad accessibility and lower computational cost provide the former methods with the advantageous possibility to perform systems-level analyses including extensive in silico mutagenesis screenings and large-scale structural predictions of multiprotein complexes.
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Affiliation(s)
- Daniele Dell'Orco
- Department of Chemistry, University of Modena and Reggio Emilia, Via Campi 183, 41100, Modena, Italy.
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Gaussian process: an alternative approach for QSAM modeling of peptides. Amino Acids 2009; 38:199-212. [DOI: 10.1007/s00726-008-0228-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2008] [Accepted: 12/18/2008] [Indexed: 10/21/2022]
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Dell’Orco D, De Benedetti PG. Quantitative structure–activity relationship analysis of canonical inhibitors of serine proteases. J Comput Aided Mol Des 2008; 22:469-78. [DOI: 10.1007/s10822-008-9175-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2007] [Accepted: 01/09/2008] [Indexed: 10/22/2022]
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Mandrika I, Prusis P, Yahorava S, Tars K, Wikberg JES. QSAR of multiple mutated antibodies. J Mol Recognit 2007; 20:97-102. [PMID: 17421049 DOI: 10.1002/jmr.817] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The aim of this study was to develop predictive quantitative structure-activity relationship (QSAR) modeling for antibody-peptide interactions. A small single chain antibody library was designed and manufactured around the murine anti-p24 (HIV-1) monoclonal antibody CB4-1 by use of statistical molecular design (SMD) principles and site directed mutagenesis, and its affinity for a p24 derived antigen was determined by fluorescence polarization. A satisfactory QSAR model (Q(2) = 0.74, R(2) = 0.88) was derived by correlating the affinity data to physicochemical property scales of the amino acids varied in the library. The model explains most of the antibody-antigen interactions of the studied set, and provides insights into the molecular mechanism involved in antigen binding.
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Affiliation(s)
- Ilona Mandrika
- Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24 Uppasala, Sweden
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Doytchinova IA, Flower DR. In silico identification of supertypes for class II MHCs. THE JOURNAL OF IMMUNOLOGY 2005; 174:7085-95. [PMID: 15905552 DOI: 10.4049/jimmunol.174.11.7085] [Citation(s) in RCA: 149] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The development of epitope-based vaccines, which have wide population coverage, is greatly complicated by MHC polymorphism. The grouping of alleles into supertypes, on the basis of common structural and functional features, addresses this problem directly. In the present study we applied a combined bioinformatics approach, based on analysis of both protein sequence and structure, to identify similarities in the peptide binding sites of 2225 human class II MHC molecules, and thus define supertypes and supertype fingerprints. Two chemometric techniques were used: hierarchical clustering using three-dimensional Comparative Similarity Indices Analysis fields and nonhierarchical k-means clustering using sequence-based z-descriptors. An average consensus of 84% was achieved, i.e., 1872 of 2225 class II molecules were classified in the same supertype by both techniques. Twelve class II supertypes were defined: five DRs, three DQs, and four DPs. The HLA class II supertypes and their fingerprints given in parenthesis are DR1 (Trp(9beta)), DR3 (Glu(9beta), Gln(70beta), and Gln/Arg(74beta)), DR4 (Glu(9beta), Gln/Arg(70beta), and Glu/Ala(74beta)), DR5 (Glu(9beta), Asp(70beta)), and DR9 (Lys/Gln(9beta)); DQ1 (Ala/Gly(86beta)), DQ2 (Glu(86beta), Lys(71beta)), and DQ3 (Glu(86beta), Thr/Asp(71beta)); DPw1 (Asp(84beta) and Lys(69beta)), DPw2 (Gly/Val(84beta) and Glu(69beta)), DPw4 (Gly/Val(84beta) and Lys(69beta)), and DPw6 (Asp(84beta) and Glu(69beta)). Apart from the good agreement between known binding motifs and our classification, several new supertypes, and corresponding thematic binding motifs, were also defined.
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Rong J, Xu X, Ewen C, Bleackley RC, Kane KP. Isolation and characterization of novel single-chain Fv specific for human granzyme B. ACTA ACUST UNITED AC 2005; 23:219-31. [PMID: 15319069 DOI: 10.1089/1536859041651349] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Granzyme B, a neutral serine protease, has been demonstrated to be a pivotal molecule for protective immunity against viral infection and cellular malignant transformation. To facilitate monitoring of granzyme B levels, we have recently applied phage display technology to produce single-chain Fv antibodies specific for granzyme B, as versatile alternatives and complementary reagents to currently available monoclonal antibodies. Through four rounds of panning on purified human granzyme B-coated on solid phase, three unique clones were isolated. Expressed soluble scFv antibodies demonstrated specific immunological applications including ELISA, Western blotting, immunoprecipitation and intracellular staining. Based on sequence analyses and structural modeling, one scFv, Fv17, may have overlapping antigen binding specificity with monoclonal antibodies 2C5/F5 and GB11. Owing to the availability of its DNA sequence and large scale production capability, Fv17 should be a superior reagent for monitoring granzyme B expression in natural killer cells and antigen specific CD8+ T cell immunity.
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Affiliation(s)
- Jianhui Rong
- Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, Canada
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Freyhult E, Prusis P, Lapinsh M, Wikberg JES, Moulton V, Gustafsson MG. Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling. BMC Bioinformatics 2005; 6:50. [PMID: 15760465 PMCID: PMC555743 DOI: 10.1186/1471-2105-6-50] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2004] [Accepted: 03/10/2005] [Indexed: 12/05/2022] Open
Abstract
Background Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis. Results A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small. Conclusion The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.
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MESH Headings
- Algorithms
- Animals
- Computational Biology/methods
- Computer Simulation
- Data Interpretation, Statistical
- Humans
- Ligands
- Models, Biological
- Models, Chemical
- Models, Molecular
- Models, Statistical
- Models, Theoretical
- Oligonucleotide Array Sequence Analysis/methods
- Predictive Value of Tests
- Programming Languages
- Protein Binding
- Protein Conformation
- Rats
- Receptors, Adrenergic, alpha-1/chemistry
- Receptors, G-Protein-Coupled/chemistry
- Regression Analysis
- Reproducibility of Results
- Selection, Genetic
- Software
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Affiliation(s)
- Eva Freyhult
- The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden
| | - Peteris Prusis
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, S-751 24 Uppsala, Sweden
| | - Maris Lapinsh
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, S-751 24 Uppsala, Sweden
| | - Jarl ES Wikberg
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, S-751 24 Uppsala, Sweden
| | - Vincent Moulton
- The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden
| | - Mats G Gustafsson
- Department of Engineering Sciences, Uppsala University, Box 528, S-751 20 Uppsala, Sweden
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