Recombinant HIV type 1 (MN) Glycoprotein 120 (a.a. 30-513) [His]

Acquired immune deficiency syndrome (AIDS) is one of the most lethal infectious diseases influencing human community. While fusion of HIV-1 and host cell membranes, viral envelope glycoprotein gp120 is dissociated and a cascade of refolding events is initiated in the viral fusion protein gp41. To promote formation of the co-receptor binding site on the gp120 and initial attachment, HIV-1 employs CD4 as its primary receptor. Ibalizumab, a humanized, anti-CD4 monoclonal antibody for HIV-1 infection, was investigated in silico to design a potential improved antibody. Computer-aided antibody engineering has been successful in the design of new biologics for disease diagnosis and therapeutic interventions. Here, crystal structure of CD4 along with monoclonal antibody Ibalizumab was explored. Thr30, Ser31, Asn52, Tyr53, Asn98 and Tyr99 in heavy chain of Ibalizumab were mutated with 19 standard amino acid residues using computational methods. A set of 720 mutant macromolecules were designed, and binding affinity of these macromolecules to CD4 was evaluated through Ag-Ab docking, binding free-energy calculations, and hydrogen binding estimation. In comparison to Ibalizumab, seven designed theoretical antibody demonstrated better result in all assessments. Therefore, these newly designed macromolecules were proposed as potential antibodies to serve as therapeutic options for HIV infection.

Despite the significant progress in managing patients infected with HIV through the development of Highly Active Anti-Retroviral Therapy (HAART), major challenges and opportunities remain to be explored. Of particular interest, is the binding of glycopmtein 120 (gp120) to the primary cellular receptor Cluster of Differentiation 4 (CD4). In this work we describe our two phased computational process to identify useful compounds capable of binding to the gp120 protein for therapeutic purposes. We identified 187 compounds from the literature that conform to active binding sites on these proteins and use these as training/test sets. The data in the form of quantitative structure-activity relationships (QSAR) is downloaded from the ZINC database and transformed using principal components analysis. In the first phase we developed a Radial Basis Function neural network model that identifies potential inhibitors from a virtual screen of a subset of the ZINC database. In the second phase we modelled the top performing compounds using the Discovery Studio docking and screening software. By employing this approach, we identified that those compounds with a LogP value of appmx 2-4 performed well in the binding simulations while the lower scoring compounds do not bind well.