| 1. |
- Isarankura-Na-Ayudhya, Chartchalerm, et al.
(författare)
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Computational Insights on Sulfonamide Imprinted Polymers
- 2008
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Ingår i: Molecules. - : MDPI AG. - 1420-3049. ; 13:12, s. 3077-3091
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Tidskriftsartikel (refereegranskat)abstract
- Molecular imprinting is one of the most efficient methods for preparing synthetic receptors that possess user defined recognition properties. Despite general success of non-covalent imprinting for a large variety of templates, some groups of compounds remain difficult to tackle due to their structural complexity. In this study we investigate preparation of molecularly imprinted polymers that can bind sulfonamide compounds, which represent important drug candidates. Compared to the biological system that utilizes metal coordinated interaction, the imprinted polymer provided pronounced selectivity when hydrogen bond interaction was employed in an organic solvent. Computer simulation of the interaction between the sulfonamide template and functional monomers pointed out that although methacrylic acid had strong interaction energy with the template, it also possessed high non-specific interaction with the solvent molecules of tetrahydrofuran as well as being prone to self-complexation. On the other hand, 1-vinylimidazole was suitable for imprinting sulfonamides as it did not cross-react with the solvent molecules or engage in self-complexation structures.
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| 2. |
- Lapins, Maris, et al.
(författare)
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A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:6, s. e66566-
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Tidskriftsartikel (refereegranskat)abstract
- A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.
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| 3. |
- Li, Hao, et al.
(författare)
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Computational study on the origin of the cancer immunotherapeutic potential of B and T cell epitope peptides.
- 2017
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Ingår i: Molecular Biosystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 13:11, s. 2310-2322
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Tidskriftsartikel (refereegranskat)abstract
- Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities. This study utilizes a machine learning method together with interpretable descriptors in an attempt to identify parameters determining the immunotherapeutic activity of cancer epitope peptides.
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| 4. |
- Li, Hao, et al.
(författare)
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Roles of d-Amino Acids on the Bioactivity of Host Defense Peptides
- 2016
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 17:7
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Forskningsöversikt (refereegranskat)abstract
- Host defense peptides (HDPs) are positively-charged and amphipathic components of the innate immune system that have demonstrated great potential to become the next generation of broad spectrum therapeutic agents effective against a vast array of pathogens and tumor. As such, many approaches have been taken to improve the therapeutic efficacy of HDPs. Amongst these methods, the incorporation of d-amino acids (d-AA) is an approach that has demonstrated consistent success in improving HDPs. Although, virtually all HDP review articles briefly mentioned about the role of d-AA, however it is rather surprising that no systematic review specifically dedicated to this topic exists. Given the impact that d-AA incorporation has on HDPs, this review aims to fill that void with a systematic discussion of the impact of d-AA on HDPs.
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| 5. |
- Mandi, Prasit, et al.
(författare)
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Exploring the origins of structure-oxygen affinity relationship of human haemoglobin allosteric effector
- 2015
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Ingår i: Molecular Simulation. - : Informa UK Limited. - 0892-7022 .- 1029-0435. ; 41:15, s. 1283-1291
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Tidskriftsartikel (refereegranskat)abstract
- A data set comprising 27 myo-inositol derivatives based on tetrakisphosphates and bispyrophosphates were used in the development of quantitative structure-activity relationship model for investigating its allosteric effector property against human haemoglobin (Hb). Three-dimensional structures of the investigated compounds were subjected to geometry optimisations at the density functional theory level. Physicochemical features of low-energy conformers were represented by quantum chemical and molecular descriptors. Feature selection by means of unsupervised forward selection and stepwise linear regression resulted in a set of four important descriptors. Multivariate analysis was performed using multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). Robustness of the predictive performance of all methods was deduced from internal and external validation, which afforded Q(CV)(2) values of 0.6306, 0.7484 and 0.8722 using MLR, ANN and SVM, respectively, for the former and Q(Ext)(2) values of 0.8332, 0.8847 and 0.9694, respectively, for the latter. The predictive model is anticipated to be useful for further guiding the rational design of robust allosteric effectors of human Hb.
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| 6. |
- Nabu, Sunanta, et al.
(författare)
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Proteochemometric model for predicting the inhibition of penicillin-binding proteins
- 2015
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Ingår i: Journal of Computer-Aided Molecular Design. - : Springer Science and Business Media LLC. - 0920-654X .- 1573-4951. ; 29:2, s. 127-141
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Tidskriftsartikel (refereegranskat)abstract
- Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing beta-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic beta-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability (R (2) = 0.91, Q (2) = 0.77, Q (Ext) (2) = 0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.
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| 7. |
- Nantasenamat, Chanin, et al.
(författare)
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AutoWeka : toward an automated data mining software for QSAR and QSPR studies.
- 2015
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Ingår i: Methods in Molecular Biology. - New York, NY : Springer New York. - 1064-3745 .- 1940-6029. ; 1260, s. 119-47
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Tidskriftsartikel (refereegranskat)abstract
- UNLABELLED: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models.AVAILABILITY: The software is freely available at http://www.mt.mahidol.ac.th/autoweka.
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| 8. |
- Nantasenamat, Chanin, et al.
(författare)
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Illuminating the Origins of Spectral Properties of Green Fluorescent Proteins via Proteochemometric and Molecular Modeling
- 2014
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Ingår i: Journal of Computational Chemistry. - : Wiley. - 0192-8651 .- 1096-987X. ; 35:27, s. 1951-1966
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Tidskriftsartikel (refereegranskat)abstract
- Green fluorescent protein (GFP) has immense utility in biomedical imaging owing to its autofluorescent nature. In efforts to broaden the spectral diversity of GFP, there have been several reports of engineered mutants via rational design and random mutagenesis. Understanding the origins of spectral properties of GFP could be achieved by means of investigating its structure-activity relationship. The first quantitative structure-property relationship study for modeling the spectral properties, particularly the excitation and emission maximas, of GFP was previously proposed by us some years ago in which quantum chemical descriptors were used for model development. However, such simplified model does not consider possible effects that neighboring amino acids have on the conjugated pi-system of GFP chromophore. This study describes the development of a unified proteochemometric model in which the GFP chromophore and amino acids in its vicinity are both considered in the same model. The predictive performance of the model was verified by internal and external validation as well as gamma-scrambling. Our strategy provides a general solution for elucidating the contribution that specific ligand and protein descriptors have on the investigated spectral property, which may be useful in engineering novel GFP variants with desired characteristics.
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| 9. |
- Nantasenamat, Chanin, et al.
(författare)
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Molecular Modeling of the Human Hemoglobin-Haptoglobin Complex Sheds Light on the Protective Mechanisms of Haptoglobin
- 2013
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:4
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Tidskriftsartikel (refereegranskat)abstract
- Hemoglobin (Hb) plays a critical role in human physiological function by transporting O-2. Hb is safe and inert within the confinement of the red blood cell but becomes reactive and toxic upon hemolysis. Haptoglobin (Hp) is an acute-phase serum protein that scavenges Hb and the resulting Hb-Hp complex is subjected to CD163-mediated endocytosis by macrophages. The interaction between Hb and Hp is extraordinarily strong and largely irreversible. As the structural details of the human Hb-Hp complex are not yet available, this study reports for the first time on insights of the binding modalities and molecular details of the human Hb-Hp interaction by means of protein-protein docking. Furthermore, residues that are pertinent for complex formation were identified by computational alanine scanning mutagenesis. Results revealed that the surface of the binding interface of Hb-Hp is not flat and protrudes into each binding partner. It was also observed that the secondary structures at the Hb-Hp interface are oriented as coils and alpha-helices. When dissecting the interface in more detail, it is obvious that several tyrosine residues of Hb, particularly beta 145Tyr, alpha 42Tyr and alpha 140Tyr, are buried in the complex and protected from further oxidative reactions. Such finding opens up new avenues for the design of Hp mimics which may be used as alternative clinical Hb scavengers.
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| 10. |
- Prachayasittikul, Veda, et al.
(författare)
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Computer-Aided Drug Design of Bioactive Natural Products.
- 2015
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Ingår i: Current Topics in Medicinal Chemistry. - : Bentham Science Publishers Ltd.. - 1568-0266 .- 1873-4294. ; 15:18, s. 1780-800
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Tidskriftsartikel (refereegranskat)abstract
- Natural products have been an integral part of sustaining civilizations because of their medicinal properties. Past discoveries of bioactive natural products have relied on serendipity, and these compounds serve as inspiration for the generation of analogs with desired physicochemical properties. Bioactive natural products with therapeutic potential are abundantly available in nature and some of them are beyond exploration by conventional methods. The effectiveness of computational approaches as versatile tools for facilitating drug discovery and development has been recognized for decades, without exception, in the case of natural products. In the post-genomic era, scientists are bombarded with data produced by advanced technologies. Thus, rendering these data into knowledge that is interpretable and meaningful becomes an essential issue. In this regard, computational approaches utilize the existing data to generate knowledge that provides valuable understanding for addressing current problems and guiding the further research and development of new natural-derived drugs. Furthermore, several medicinal plants have been continuously used in many traditional medicine systems since antiquity throughout the world, and their mechanisms have not yet been elucidated. Therefore, the utilization of computational approaches and advanced synthetic techniques would yield great benefit to improving the world's health population and well-being.
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