| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- Pratiwi, Reny, et al.
(författare)
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CryoProtect : A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins
- 2017
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Ingår i: Journal of Chemistry. - : Hindawi Publishing Corporation. - 2090-9063 .- 2090-9071.
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Tidskriftsartikel (refereegranskat)abstract
- Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.
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| 5. |
- Shoombuatong, Watshara, et al.
(författare)
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Extending proteochemometric modeling for unraveling the sorption behavior of compound-soil interaction
- 2016
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 151, s. 219-227
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Tidskriftsartikel (refereegranskat)abstract
- Contamination of ground water by industrial chemicals presents a major environmental and health problem. Soil sorption plays an important role in the transport and movement of such pollutant chemicals. In this study, proteochemometric (PCM) modeling was used to unravel the origins of interactions of 17 phthalic acid esters (PAEs) against 3 soil types by predicting the organic carbon content normalized sorption coefficient (logK(oc)) values as a function of fingerprint descriptors of 17 PAEs and physical and textural properties of 3 soils. The results showed that PCM models provided excellent predictivity (R-2 = 0.94, Q(2) = 0.89,Q(Ext)(2) = 0.85). In further validation of the model, our proposed PCM model was assessed by leave-one-compound-out (Q(LOCO)(2) = 0.86) and leave-one-soil-out (Q(LOCO)(2) = 0.86) cross-validations. The transparency of the PCM model allowed interpretation of the underlying importance of descriptors, which potentially contributes to a better understanding on the outcome of PAEs in the environment. A thorough analysis of descriptor importance revealed the contribution of secondary carbon atoms on the hydrophobicity and flexibility of PAEs as significant properties in influencing the soil sorption capacity.
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| 6. |
- Shoombuatong, Watshara, et al.
(författare)
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Towards Predicting the Cytochrome P450 Modulation : From QSAR to proteochemometric modeling.
- 2017
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Ingår i: Current drug metabolism. - : Bentham Science Publishers Ltd.. - 1389-2002 .- 1875-5453. ; 18:6, s. 540-555
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Tidskriftsartikel (refereegranskat)abstract
- Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically convert xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus is given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is provided.
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| 7. |
- Simeon, Saw, et al.
(författare)
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osFP : a web server for predicting the oligomeric states of fluorescent proteins
- 2016
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Background: Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence. Results: After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment. Conclusion: osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.
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| 8. |
- Simeon, Saw, et al.
(författare)
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PepBio : predicting the bioactivity of host defense peptides
- 2017
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Ingår i: RSC Advances. - : Royal Society of Chemistry (RSC). - 2046-2069. ; 7:56, s. 35119-35134
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Tidskriftsartikel (refereegranskat)abstract
- Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at https://github.com/chaninn/pepbio/.
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| 9. |
- Simeon, Saw, et al.
(författare)
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Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking
- 2016
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Ingår i: PeerJ. - : PeerJ. - 2167-8359. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential Ifor cognition arid memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained frorn ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive rnodels were constructed from 100 different data splits using random forest. Generated models afforded R-2, Q(cv)(2) and Q(Ext)(2) values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and lexternal set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R-2, Q(CV)(2) and Q(Ext)(2) values of 0.92 +/- 0.01, 0.78 +/- 0.06 and 018 +/- 0.05, respectively. Furthermore, IT-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover Kennard Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of -12.2, -12.0 and -12.0 kcal/mol, respectively, against human AChE, which is modulated by, hydrogen bonding, pi-pi stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.
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| 10. |
- Win, Thet Su, et al.
(författare)
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HemoPred : a web server for predicting the hemolytic activity of peptides
- 2017
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Ingår i: Future Medicinal Chemistry. - : FUTURE SCI LTD. - 1756-8919 .- 1756-8927. ; 9:3, s. 275-291
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Tidskriftsartikel (refereegranskat)abstract
- Aim: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates. Materials & methods: This study describes a sequence-based predictor based on a random forest classifier using amino acid composition, dipeptide composition and physicochemical descriptors (named HemoPred). Results: This approach could outperform previously reported method and typical classification methods (e.g., support vector machine and decision tree) verified by fivefold cross-validation and external validation with accuracy and Matthews correlation coefficient in excess of 95% and 0.91, respectively. Results revealed the importance of hydrophobic and Cys residues on alpha-helix and beta-sheet, respectively, on the hemolytic activity. Conclusion: A sequence-based predictor which is publicly available as the web service of HemoPred, is proposed to predict and analyze the hemolytic activity of peptides.
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