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Author: Watshara Shoombuatong

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Now showing 1 - 10 of 23
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    Meta-iavp: A sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation
    (2019-11-02) Nalini Schaduangrat; Chanin Nantasenamat; Virapong Prachayasittikul; Watshara Shoombuatong; Mahidol University
    (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs... as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide
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    IQSP: A sequence-based tool for the prediction and analysis of quorum sensing peptides via chou’s 5-steps rule and informative physicochemical properties
    (2020-01-01) Phasit Charoenkwan; Nalini Schaduangrat; Chanin Nantasenamat; Theeraphon Piacham; Watshara Shoombuatong; Mahidol University; Chiang Mai University
    . With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although..., few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules
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    iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides
    (2020-01-01) Phasit Charoenkwan; Janchai Yana; Chanin Nantasenamat; Md Mehedi Hasan; Watshara Shoombuatong; Chiang Mai Rajabhat University; Kyushu Institute of Technology; Mahidol University; Chiang Mai University
    developed yet. In this study, we have proposed the first sequence-based predictor named iUmami-SCM using primary sequence information for the identification and characterization of umami peptides. iUmami-SCM utilized a newly developed scoring card method... and the food industry. Experimental approaches for predicting umami peptides are labor intensive, time consuming, and expensive. To date, computational models for the prediction and analysis of umami peptides as a function of sequence information have not been
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    A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
    (2021-12-01) Phasit Charoenkwan; Warot Chotpatiwetchkul; Vannajan Sanghiran Lee; Chanin Nantasenamat; Watshara Shoombuatong; Universiti Malaya; King Mongkut's Institute of Technology Ladkrabang; Mahidol University; Chiang Mai University
    , their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853... of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins
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    Evolution of sequence-based bioinformatics tools for protein-protein interaction prediction
    (2020-01-01) Mst Shamima Khatun; Watshara Shoombuatong; Md Mehedi Hasan; Hiroyuki Kurata; Kyushu Institute of Technology; Japan Society for the Promotion of Science; Mahidol University
    . In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches
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    Sequence based human leukocyte antigen gene prediction using informative physicochemical properties
    (2015-01-01) Watshara Shoombuatong; Panuwat Mekha; Jeerayut Chaijaruwanich; Mahidol University; Maejo University; Chiang Mai University
    to develop an efficient and easily interpretable method for predicting HLA gene class compared to existing methods. We investigated the HLA gene prediction problem as follows: (a) establishing a dataset (HLA262) such that the sequence identity of the complete
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    Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
    (2020-01-01) Phasit Charoenkwan; Chanin Nantasenamat; Md Mehedi Hasan; Watshara Shoombuatong; Kyushu Institute of Technology; Mahidol University; Chiang Mai University
    improving our understanding of the biological function and mechanisms of PVPs. Therefore, it is desirable to develop a computational method that is capable of fast and accurate identification of PVPs. To address this, we propose a novel sequence-based meta... learning (ML) algorithms making use of seven feature encodings. To the best of our knowledge, the Meta-iPVP is the first meta-based approach that has been developed for PVP prediction. Independent test results indicated that the Meta-iPVP could discern
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    Ibitter‐fuse: A novel sequence‐based bitter peptide predictor by fusing multi‐view features
    (2021-08-02) Phasit Charoenkwan; Chanin Nantasenamat; Md Mehedi Hasan; Mohammad Ali Moni; Pietro Lio; Watshara Shoombuatong; Department of Computer Science and Technology; The University of Queensland; Tulane University School of Medicine; Mahidol University; Chiang Mai University
    Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning‐based methods have be-come effective approaches for providing a good avenue... for identifying potential bitter peptides from large‐scale protein datasets. Although few machine learning‐based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed
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    Erratum: Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties. Int. J. Mol. Sci. 2020, 21, 75 (International journal of molecular sciences (2019) 21 1 PII: E2629)
    (2020-04-10) Phasit Charoenkwan; Nalini Schaduangrat; Chanin Nantasenamat; Theeraphon Piacham; Watshara Shoombuatong; Mahidol University; Chiang Mai University
    The authors wish to make the following corrections to this paper: [...].
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    HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage
    (2019-06-01) Sayamon Hongjaisee; Chanin Nantasenamat; Tanawan Samleerat Carraway; Watshara Shoombuatong; Mahidol University; Chiang Mai University
    © 2019 Elsevier Ltd Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.