Computational Molecular Biology 2016, Vol.6, No.3, 1-6
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In this regard it is important to study biochemical characteristics of proteins. Xu et al. studied the geometry of the
hydrogen bonds across protein interfaces. The software HBPLUS is designed to analyze hydrogen bonds
(McDonald et al., 1993; McDonald and Thornton, 1994). The program determines the positions of missing
hydrogen’s in the PDB and checks each donor–acceptor pair to ascertain its fitness to the geometric criteria.
As researches goes on for antiretroviral therapy for HIV-AID, the database of HIV is created which consist of
various mutant and strains of HIV. The biologist Julio-Septiembre (2000) studied HIV-2 strains from Los Alamos
database; the largest and oldest database of HIV; and its immunogenicity is compared with HIV-1. The
antigenicity profile obtained with the Surface Plot program for epitope II shows that this region has a group of
exposed amino acids in this middle part with a high degree of immunogenic potential in both HIV-1 and HIV-2
viruses. Andersson (2001) studied comparative immune response of HIV1 and HIV2 especially during the
asymptomatic phase of HIV-2 infection by taking population groups because HIV-2 is less immunogenic.
As protein sequence database is developed, Wu (2002) studied functionally annotated protein sequences. The
annotation problems are addressed by a classification-driven and rule-based method with evidence attribution,
coupled with an integrated knowledge base system. This approach allows sensitive identification, consistent and
rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified
computationally predicted features.
Medical practiseners identify HIV infection by viral- load, CD4 count in blood samples of infected persons. P24 is
a surface antigen of HIV its enhancement would be the indication of HIV infection. Schüpbach (2003) studied
HIV progression by p24 antigen test and viral load test for antiretroviral treatment, which identify specific
expression pattern shared by a group of well-characterized genes classified based on relevant biological functions,
treatment for HIV infection is not possible till date. Researchers are trying to find the potential drug target sites for
drug designing of HIV infection. Membrane proteins are the ones which comes first in cell-cell contact hence
these are also the better sites for drug targets. To study membrane proteins Mitaku et al. (2004) gives SOSUI
prediction software for transmembrane helices. It also gives the type of transmembrane protein with 80 %
accuracy by using hidden markov models. As the statistical methods developed Toshiyoki 2004 studied membrane
proteins and soluble proteins on the basis of principle component analysis. The verification was done by Jacknife
test. Yang (2006) studied membrane protein types on the basis of amino acid and peptide composition. Dubey et al.
(2009; 2010) have done the similar work by machine learning techniques.
Major research in HIV enzymes have been done to look for perfect drug design. The possibility is mostly shown
in HIV-1 protease. It is a retroviral aspartyl protease (retropepsin) that is essential for the life-cycle of HIV. HIV
protease cleaves newly synthesized polyproteins at the appropriate places to create the mature protein components
of an infectious HIV virion. Thus, mutation of HIV protease's active site or inhibition of its activity disrupts HIV’s
ability to replicate and infect additional cells is studied by Seelmeier et al. 1988), and making HIV protease
inhibition the subject of considerable pharmaceutical research is shown by McPhee (1996). Lumini and Nanni
(2009) jointly proposed hierarchical classifiers architecture which is a successful attempt to obtain a drastically
error reduction with respect to the performance of linear classifiers. This hierarchy is useful for HIV-1 protease
cleavage site prediction with greater accuracy.
This cleavage site prediction is important because proteinases play critical roles in both intra and extracellular
processes by binding and cleaving their protein substrates. This would help to find protease cleavage sites for
identifying potential drug target sites in HIV-1 protease. Various bioinformatics techniques like molecular docking
along with machine learning techniques are used for classification. The team of Glick (2010) presented an
investigation of the application of machine learning to improve the results of high throughput docking against the
HIV-1 protease by Naive Bayes classifier also shows good results.
MicroRNAs are very small pieces of RNA, which have a strong position in the cell. They can bind to RNA those
codes for a protein, to repress this protein. MicroRNAs are involved in a variety of disease processes, playing a
