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AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

Computers, Materials & Continua 2021年第10期69卷 1039-1055页

作者： Fahad Alotaibi Muhammad Attique yaser daanial khan Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Management and TechnologyLahore54000Pakistan Department of Information Technology University of GujratGujrat50700Pakistan

Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial ***,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain ***,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development *** anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and *** exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep *** proposed model performs best in conjunction with deep *** testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set *** proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and ***,the proposed model was also extensively probed in comparison with other existing *** performance of the proposed model also out-performs other existing *** outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based ***,it has the potential to assiduously support medical and bioinformatics research.

关键词： Prediction feature extraction machine learning bootstrap aggregation deep learning bioinformatics computational intelligence antiinflammatory peptides

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iPhosD-PseAAC:Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC

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BIOCELL 2021年第5期45卷 1287-1298页

作者： ALAA OMRAN ALMAGRABI yaser daanial khan SHER AFZAL khan Faculty of Computing and Information Technology Department of Information SystemsKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54770Pakistan Department of Computer Sciences Abdul Wali Khan University MardanMardan23200Pakistan

Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the DNA alongside transcription features and initiates the transcription of correct response ***,the prediction of phosphoaspartate sites is critical,and its experimental identification can be expensive,time-consuming,and *** this purpose,we propose iPhosD-PseAAC,a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues:(1)Benchmark dataset.(2)The feature extraction techniques such as pseudo amino acid composition(PseAAC),statistical moments,and position relative features.(3)For the classification,artificial neural network AAN will be used.(4)In this step,10-fold cross-validation and self-consistency testing will be used for *** self-consistency testing,100%Acc is achieved,whereas,for 10-fold crossvalidation 95.14%Acc,95.58%Sn,94.70%Sp and 0.95 MCC are observed.(5).The final step is the development of a user-friendly web server for the ease of ***,the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.

关键词： Phosphorylation Phosphoaspartate Prediction 5-step rule Statistical moments PseAAC

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ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

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Computers, Materials & Continua 2022年第4期71卷 215-230页

作者： Talha Imtiaz Baig yaser daanial khan Talha Mahboob Alam Bharat Biswal Hanan Aljuaid Durdana Qaiser Gillani School of Science and Technology University of Management and TechnologyLahorePakistan Department of Computer Science and Information Technology Virtual University of PakistanLahorePakistan Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdul Rahman University(PNU)RiyadhSaudi Arabia Department of Economics University of LahoreLahorePakistan

Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and *** is connected with many biological processes and closely linked with many metabolic *** develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this ***,most of the techniques and different traditional experimental models have a very high *** are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation *** study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset *** the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of ***,the predictor model is a very useful and helpful tool for lipoylation sites *** of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature *** wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.

关键词： Lipoylation lysine feature vector post translational modification amino acid Mathew’s correlation coefficient neural network

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Identication of Antimicrobial Peptides Using Chou’s 5 Step Rul

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Computers, Materials & Continua 2021年第6期 2863-2881页

作者： Sharaf J.Malebary yaser daanial khan Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahorePakistan

With the advancement in cellular biology,the use of antimicrobial peptides(AMPs)against many drug-resistant pathogens has *** have a broad range of activity and can work as antibacterial,antifungal,antiviral,and sometimes even as anticancer *** traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab *** experiments are both time-consuming and *** the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such *** study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and *** proposed methodology uses machine learning algorithms to predict such sequences.A dataset was formulated based on 1902 samples of AMPs and 3997 samples of *** learning algorithms are trained on a xed number of succinct coefcients retaining sequence and composition information of primary *** features are extracted using position relative incidence and statistical *** performance is validated via various validation tests including a 10-fold cross-validation *** overall accuracy of 95.43%was achieved.A comparison of results with existing methodologies shows that the proposed methodology outperformed existing methodologies in terms of prediction accuracy.

关键词： Antimicrobial peptides multidrug-resistant antiviral antibacterial cytokine classication

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