A signal processing-based Bioinformatics approach to assessing drug resistance: Human Immunodeficiency Virus as a case study

Measuring drug resistance is one of the challenging and essential pharmaceutical activities. It is a laborious and costly laboratory-based experimentation. Various clinical and experimental analyses for measuring drug resistance have been carried out. Results have been obtained for different types of therapeutic agents as a consequence of changes in the amino acids compositions in the sequence (mutation) of the organisms involved. In the same manner, the positions of these amino acids alterations and the level of resistance (folds) have also been experimentally identified. For example, G36S and V38M mutation in the Human Immunodeficiency Virus (HIV) Transmembrane glycoprotein (gp41) has been found to cause 100-fold resistance. However, there does not seem to have bioinformatics method developed in which the amino acid information of the proteins involved in the studies were used to computationally assess the degree of drug resistance without involving laboratory-based experimental procedure. The post-genomic era has witnessed the relevance of Bioinformatics approaches in the analysis of huge biomedical data. One such approach is the analysis of protein residues using digital signal processing technique such as informational spectrum method (ISM). Therefore, we propose a new bioinformatics method that is capable of assessing drug resistance without the use of any laboratory-based experiments. This new method incorporates ISM, sequence information of the proteins and other relevant information. By using the ISM and EIIP amino acid scale, the technique was applied in three classes of anti-HIV/AIDS drugs as a case study. It is observed that the protein residues of the susceptible strains attained the maximal peak amplitude at the consensus frequency while the resistant strains maintained lower amplitudes. This result signifies lower contribution from the resistant strains due to the mutation. The findings are consistent with those of the experimental ones and therefore suggest that the approach taken can be used to help assess drug resistance without laboratory-based experimentation. It should also be noted that the method can be applied in other drug resistance studies where sequence information of proteins is available and help design a computer-aided drug resistance calculator.