No abstract

Drug resistance is a major obstacle faced by therapist in treating HIV infected patients. The reason behind these phenomena is either protein mutation or the changes in gene expression level that induces resistance to drug treatments. These mutations affect the drug binding activity, hence resulting in failure of treatment. Therefore, it is necessary to conduct resistance testing in order to carry out HIV effective therapy. This study combines both sequence and structural features for predicting HIV resistance by applying SVM and Random Forests classifiers. The model was tested on the mutants of HIV-1 protease and reverse transcriptase. Taken together the features we have used in our method, total contact energies among multiple mutations have a strong impact in predicting resistance as they are crucial in understanding the interactions of HIV mutants. The combination of sequence-structure features offers high accuracy with support vector machines as compared to Random Forests classifier. Both single and acquisition of multiple mutations are important in predicting HIV resistance to certain drug treatments. We have discovered the practicality of these features; hence, these can be used in the future to predict resistance for other complex diseases.

COVID-19 an outbreak of a novel corona virus originating from Wuhan, China in December 2019 has now spread across the entire world and has been declared a pandemic by WHO. Angiotensin converting enzyme 2 (ACE2) is a receptor protein that interacts with the spike glycoprotein of the host to facilitate the entry of coronavirus (SARS-CoV-2) hence causing the disease (COVID-19). Our experimental design is based on bioinformatics approach that combines sequence, structure and consensus based tools to label a protein coding single nucleotide polymorphism (SNP) as damaging/deleterious or neutral. The interaction of wildtype ACE2-spike glycoprotein and their variants were analyzed using docking studies. The mutations W461R, G405E and F588S in ACE2 receptor protein and population specific mutations P391S, C12S and G1223A in the spike glycoprotein were predicted as highly destabilizing to the structure of the bound complex. So far, no extensive in silico study has been reported that identifies the effect of SNPs on Spike glycoprotein-ACE2 interaction exploring both sequence and structural features. To this end, this study conducted an in-depth analysis that facilitates in identifying the mutations that blocks the interaction of two proteins that can result in stopping the virus from entering the host cell.

Extracting useful knowledge from an unstructured textual data is a challenging task for biologists, since biomedical literature is growing exponentially on a daily basis. Building an automated method for such tasks is gaining much attention of researchers. ZK DrugResist is an online tool that automatically extracts mutations and expression changes associated with drug resistance from PubMed. In this study we have extended our tool to include semantic relations extracted from biomedical text covering drug resistance and established a server including both of these features. Our system was tested for three relations, Resistance (R), Intermediate (I) and Susceptible (S) by applying hybrid feature set. From the last few decades the focus has changed to hybrid approaches as it provides better results. In our case this approach combines rule-based methods with machine learning techniques. The results showed 97.67% accuracy with 96% precision, recall and F-measure. The results have outperformed the previously existing relation extraction systems thus can facilitate computational analysis of drug resistance against complex diseases and further can be implemented on other areas of biomedicine.

The CRISPR-Cas9 system has recently evolved as a powerful mutagenic tool for targeted genome editing. The impeccable functioning of the system depends on the optimal design of single guide RNAs (sgRNAs) that mainly involves sgRNA specificity and on-target cleavage efficacy. Several research groups have designed algorithms and models, trained on mammalian genomes, for predicting sgRNAs cleavage efficacy. These models are also implemented in most plant sgRNA design tools due to the lack of on-target cleavage efficacy studies in plants. However, one of the major drawbacks is that almost all of these models are biased for considering only coding regions of the DNA while excluding ineffective regions, which are of immense importance in functional genomics studies especially for plants, thus making prediction less reliable. In the present study, we evaluate the on-target cleavage efficacy of experimentally validated sgRNAs designed against diverse ineffective regions of Arabidopsis thaliana genome using various statistical tests. We show that nucleotide preference in protospacer adjacent motif (PAM) proximal region, GC content in the PAM proximal seed region, intact RAR and 3rd stem loop structures, and free accessibility of nucleotides in seed and tracrRNA regions of sgRNAs are important determinants associated with their high on-target cleavage efficacy. Thus, our study describes the features important for plant sgRNAs high on-target cleavage efficacy against ineffective genomic regions previously shown to give rise to ineffective sgRNAs. Moreover, it suggests the need of developing an elaborative plant-specific sgRNA design model considering the entire genomic landscape including ineffective regions for enabling highly efficient genome editing without wasting time and experimental resources.