Prediction of Protein Solubility using Primary Structure Compositional Features: A Machine Learning Perspective

Rasool, Nouman; Hussain, Waqar; Mahmood, Sajid

doi:10.4172/jpb.1000458

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Bioinformatics that is an interdisciplinary field of mathematics, science, and computer science provides very meaningful results regarding the analysis of exome sequencing [10]. Computational tools make sure to assess users from data storage to data retrieval, data analysis, its annotation, and ultimately provide visualization of results for the understanding of biological system thoroughly [11][12][13][14]. In silico methods use computational approaches that are cost-effective and are predictive methods for chemical compounds before carrying out a scientific laboratory experiment [15].…”

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confidence: 99%

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

2020

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At the end of December 2019, a novel strain of coronavirus, given the name of 2019-nCoV, emerged for exhibiting symptoms of severe acute respiratory syndrome. The virus is spreading rapidly in China and around the globe, affecting thousands of people leading to a pandemic. To control the mortality rate associated with the 2019-nCoV, prompt steps are needed. Until now there is no effective treatment or drug present to control its life-threatening effects in the humans. The scientist is struggling to find new inhibitors of this deadly virus. In this study, to identify the effective inhibitor candidates against the main protease (Mpro) of 2019-nCoV, computational approaches were adopted. Phytochemicals having immense medicinal properties as ligands were docked against the Mpro of 2019-nCoV to study their binding properties. ADMET and DFT analyses were also further carried out to analyze the potential of these phytochemicals as an effective inhibitor against Mpro of 2019-nCoV.

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confidence: 99%

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

2020

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“…For pattern recognition, statistical moments are used to centralize some key arrangements in the collected data. 48 – 56 To describe different properties of data, various statistical moments are utilized such as polynomial and distribution functions. In this study, raw, Hahn, and central moments are employed, as reported in various previous studies such as.…”

Section: Methodsmentioning

confidence: 99%

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features

et al. 2023

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Objective The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information. Methods Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/ . Results XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately. Conclusions The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.

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“…Computer-aided protocols of drug discovery [57][58][59][60] have proven to be very effi cient and effective, as reported in various previous studies [61][62][63][64][65]. Opting these protocols, presently, the fi ve phytochemicals which were screened out for further in vitro and in vivo analysis have been reported in many studies for their pharmaceutical properties [66][67][68].…”

Section: Analysis Of Reactivity Using Dftmentioning

confidence: 99%

Computer-aided Analysis of Selective Phytochemicals as Potent Inhibitors of Parkin: Major Biological Target of Parkinson’s disease

Arif¹,

Subhani²,

Hussain³

et al. 2020

Glob J Biotechnol Biomater Sci

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Parkinson's disease, caused by mutations in the Parkin that leads to loss of neuron is the second most widespread neurodegenerative disorder in the world. Phytochemicals are being considered due to their medicinal properties to cure many human diseases. The present study targets the inhibition of Parkin, a major biological target for Parkinson using 3150 phytochemicals from various medicinal plants. These plants are naturally growing in a local climate of Pakistan, India and China and being used for a long time for the medicinal purpose. A total of 3150 phytochemicals from various medicinal plants were collected for this in silico study. The pharmacological assessments prediction, molecular docking and density functional theory (DFT) based studies were done to fi nd out the latent inhibitory properties of these phytochemicals against Parkin. Out of 3150 phytochemicals, 175 showed human-suitable pharmacological properties and among those 175 compounds, 5 phytochemicals, i.e. Liquirtin, Shinfl avanone, Glabrone, GlycyrdioneB and IsoangustoneA to have potent inhibitory properties against Parkin and can be deliberated for additional in vitro and in vivo studies to evaluate their inhibitory eff ects against Parkin. They revealed binding affi nity greater than various previously reported inhibitors against Parkin. Additionally, DFT based analysis exhibited high reactivity for these fi ve phytochemicals in the binding pocket of Parkin, based on E LUMO , E HOMO and band energy gap. A total of 5 out of 175 phytochemicals are reported as highly potent inhibitors against Parkin which are liquirtin, Shinfl avanone, Glabrone, Glycyrdione B and IsoangustoneA from the same medicinal plant Glycyrrhiza glabra. However, these 5 phytochemicals can be considered for further in vivo and in vitro analysis for the clinical development of a drug against the world's second most common brain disorder, the Parkinson's disease.

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Prediction of Protein Solubility using Primary Structure Compositional Features: A Machine Learning Perspective

Cited by 11 publications

References 26 publications

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features

Computer-aided Analysis of Selective Phytochemicals as Potent Inhibitors of Parkin: Major Biological Target of Parkinson’s disease

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