Computational coarse protein modeling of HIV-1 sequences using evolutionary search algorithm

There are extensive research works on HIV-1 genome and its encoded proteins. The genome comprises of nine genes that code for at least 15 proteins. Although these sequences are available to the public, the structure information is incomplete. The structure information is vital to understand the pathogenesis as well as for preventive measures. There are experimental efforts such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy to solve the structures of HIV-1 proteins. However, there are some limitations with these methods, for instance, membrane associated proteins are difficult to crystalize and NMR has size limitation. Moreover, these methods are very expensive and time consuming. Hence, computational methods can be of use. This chapter deals with a computational protein structure prediction (PSP) based on the primary structure. One of the popular approaches for modeling coarse protein structure is Dill’s HP-model. This work presents a revised HP model for HIV-1 proteins. These proteins were modeled over 2D square lattice with optimal conformation using evolutionary programming. The modeled conformations were also evaluated against the experimental structure.