Computational prediction of anti HIV-1 peptides andin vitroevaluation of anti HIV-1 activity of HIV-1 P24-derived peptides

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Computational prediction of antifungal peptides via Chou’s PseAAC and SVM

With the increase in immunocompromised patients in the recent years, fungal infections have emerged as new and serious threat in hospitals. This, and the insufficiency of current antifungal therapies alongside their toxic effects on patients, has led to the increased interest in seeking new antifungal peptides. In the present study, we have developed a prediction method for screening of antifungal peptides. For this, we have chosen Chou’s pseudo amino acid composition (PseAAC) to translate peptide sequences into numeric values. Thus, the SVM classifier was performed for binomial classification of antifungal peptides. The performance of the classifier was evaluated via ten-fold cross-validation and an independent dataset. For further validation of the model developed, 22 P24-derived peptides were predicted using the classifier and in vitro assays were performed on the three peptides with the highest prediction score. The results showed that the PseAAC [Formula: see text] SVM method is able to predict AFPs with ACC of 94.76%. In vitro results also validate the SEN and SPC of the classifier. The results suggest that the computational approach used in this study is highly efficient for prediction of antifungal peptides, which can save time and money in AFP screening and synthesis of novel peptides.

Sequence-based analysis and prediction of lantibiotics: A machine learning approach

Lantibiotics, an important group of ribosomally synthesized peptides, represent an important arsenal of novel promising antimicrobials showing high potency in fighting against the prevalence of antibiotic resistance among microbial pathogens. However, due to the lack of high throughput strategies for the isolation and identification of these compounds, our information regarding their structure and especially sequence-based properties is far from complete. Therefore, in the present study, a comprehensive sequence-based analysis of these peptides was performed with the help of machine learning approach together with a feature selection technique. Meanwhile, an attempt to develop an accurate computational model for prediction of lantibiotics was made via constructing two datasets of 280 and 190 lantibiotic and non-lantibiotic antimicrobial peptide sequences, respectively. Based on the conducted approach and as a result of our search for a subset of relevant features of lantibiotics, particular types of sequenced-based features were observed to be preferred in lantibiotics, the knowledge-based implementation of which can be used as strategies for lantibiotic bioengineering purposes. Moreover, a SMO-based classifier was developed for the prediction of lantibiotics with the accuracy and specificity values of 88.5% and 94%, respectively which shows the great potential of the developed algorithm for the prediction of lantibiotcs. Conclusively, the accurate predictor algorithm as well as the identified sequence-based distinctiveness properties of lantibiotics can give valuable information in both the fields of lantibiotic discovery and bioengineering.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.