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A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
(2021-12-01)
Charoenkwan, Phasit
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Chotpatiwetchkul, Warot
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Lee, Vannajan Sanghiran
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Nantasenamat, Chanin
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Shoombuatong, Watshara
Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development 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, 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 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906–0.910) and 2–17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.
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StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors
(2021-10-01)
Malik, Aijaz Ahmad
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Chotpatiwetchkul, Warot
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Phanus-umporn, Chuleeporn
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Nantasenamat, Chanin
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Charoenkwan, Phasit
Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV. It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.
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Towards reproducible computational drug discovery
(2020-01-28)
Schaduangrat, Nalini
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Lampa, Samuel
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Simeon, Saw
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Gleeson, Matthew Paul
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Spjuth, Ola
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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A novel potent autophagy inhibitor ECDD-S27 targets vacuolar ATPase and inhibits cancer cell survival
(2019-12-01)
Paha, Jiraporn
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Kanjanasirirat, Phongthon
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Munyoo, Bamroong
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Tuchinda, Patoomratana
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Suvannang, Naravut
Autophagy is a conserved lysosomal-dependent cellular degradation process and its dysregulation has been linked to numerous diseases including neurodegeneration, infectious diseases, and cancer. Modulation of autophagy is therefore considered as an attractive target for disease intervention. We carried out a high-content image analysis screen of natural product-derived compounds to discover novel autophagy modulating molecules. Our screen identified ECDD-S27 as the most effective compound for increasing the number of autophagic vacuoles inside cells. The structure of ECDD-S27 revealed that it is a derivative of cleistanthin A, a natural arylnaphthalene lignan glycoside found in plants. ECDD-S27 increases the number of autophagic vacuoles by inhibiting the autophagic flux and is able to restrict the survival of different cancer cells at low nanomolar concentrations. Molecular docking and SERS analysis showed that ECDD-S27 may potentially target the V-ATPase. Upon treatment of various cancer cells with ECDD-S27, the V-ATPase activity is potently inhibited thereby resulting in the loss of lysosomal acidification. Taken together, these data indicated that ECDD-S27 retards the autophagy pathway by targeting the V-ATPase and inhibits cancer cell survival. The observed antitumor activity without cytotoxicity to normal cells suggests the therapeutic potential warranting further studies on lead optimization of the compound for cancer treatment.
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Synthesis, plasmodium falciparum inhibitory activity, cytotoxicity and solubility of n2,n4-disubstituted quinazoline-2,4-diamines
(2019-01-01)
Pobsuk, Nattakarn
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Suphakun, Praphasri
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Hannongbua, Supa
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Nantasenamat, Chanin
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Choowongkomon, Kiattawee
Background: Despite the development of extensive control strategies and treatment options, approximately 200 million malaria cases, leading to approximately 450,000 deaths, were reported in 2015. Due to issue of disease resistance, additional drug development efforts are needed to produce new, more effective treatments. Quinazoline-2,4-diamines were identified as anti-parasitic compounds over three decades ago and have remained of interest to date in industry and academia. Objective: An anti-malarial SAR evaluation of previously unreported N2,N4-disubstituted quina-zoline-2,4-diamines have been undertaken in this study. We have synthesized and evaluated new derivatives against P. falciparum in our attempt to better characterize their biological activity and overall physical properties. Method: The synthesis of N2,N4-disubstituted quinazoline-2,4-diamines inhibitors is reported along with activities in a radioactive labeled hypoxanthine incorporation assay against the f Plasmodium falciparum (Pf.) K1 strain. In addition, cytotoxicity was determined in the A549 and Vero cell lines using an MTT based. The aqueous solubility of key compounds was assessed at pH 7.4 using a shake flask-based approach. Results: We identified compounds 1 and 6p as sub µM inhibitors of P. falciparum, having equivalent anti-malarial activity to Chloroquine. Compounds 1 and 6m are low µM inhibitors of P. falciparum with improved cytotoxicity profiles. Compound 6m displayed the best balance between P. falciparum Inhibitory activity (2 µM) and cytotoxicity, displaying >49 fold selectivity over A549 and Vero cell lines. Conclusion: Twenty one N2,N4-Disubstituted Quinazoline-2,4-diamines have been prepared in our group and characterized in terms of their antimalarial activity, cytotoxicity and physical properties. Compounds with good activity and reasonable selectivity over mammalian cell lines have been identified. SAR analyses suggest further exploration is are necessary to improve the balance of P. falciparum Inhibitory activity, cytotoxicity and solubility.
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PepBio: Predicting the bioactivity of host defense peptides
(2017-01-01)
Simeon, Saw
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Li, Hao
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Win, Thet Su
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Malik, Aijaz Ahmad
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Kandhro, Abdul Hafeez
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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