Locating Large, Flexible Ligands on Proteins.

Many biologically important ligands of proteins are large, flexible, and in many cases charged molecules that bind to extended regions on the protein surface. It is infeasible or expensive to locate such ligands on proteins with standard methods such as docking or molecular dynamics (MD) simulation. The alternative approach proposed here is scanning of a spatial and angular grid around the protein with smaller fragments of the large ligand. Energy values for complete grids can be computed efficiently with a well-known fast Fourier transform-accelerated algorithm and a physically meaningful interaction model. We show that the approach can readily incorporate flexibility of the protein and ligand. The energy grids (EGs) resulting from the ligand fragment scans can be transformed into probability distributions and then directly compared to probability distributions estimated from MD simulations and experimental structural data. We test the approach on a diverse set of complexes between proteins and large, flexible ligands, including a complex of sonic hedgehog protein and heparin, three heparin sulfate substrates or nonsubstrates of an epimerase, a multibranched supramolecular ligand that stabilizes a protein-peptide complex, a flexible zwitterionic ligand that binds to a surface basin of a Kringle domain, and binding of ATP to a flexible site of an ion channel. In all cases, the EG approach gives results that are in good agreement with experimental data or MD simulations.

A simple structure-based model for the prediction of HIV-1 co-receptor tropism.

BACKGROUND: Human Immunodeficiency Virus 1 enters host cells through interaction of its V3 loop (which is part of the gp120 protein) with the host cell receptor CD4 and one of two co-receptors, namely CCR5 or CXCR4. Entry inhibitors binding the CCR5 co-receptor can prevent viral entry. As these drugs are only available for CCR5-using viruses, accurate prediction of this so-called co-receptor tropism is important in order to ensure an effective personalized therapy. With the development of next-generation sequencing technologies, it is now possible to sequence representative subpopulations of the viral quasispecies. RESULTS: Here we present T-CUP 2.0, a model for predicting co-receptor tropism. Based on our recently published T-CUP model, we developed a more accurate and even faster solution. Similarly to its predecessor, T-CUP 2.0 models co-receptor tropism using information of the electrostatic potential and hydrophobicity of V3-loops. However, extracting this information from a simplified structural vacuum-model leads to more accurate and faster predictions. The area-under-the-ROC-curve (AUC) achieved with T-CUP 2.0 on the training set is 0.968±0.005 in a leave-one-patient-out cross-validation. When applied to an independent dataset, T-CUP 2.0 has an improved prediction accuracy of around 3% when compared to the original T-CUP. CONCLUSIONS: We found that it is possible to model co-receptor tropism in HIV-1 based on a simplified structure-based model of the V3 loop. In this way, genotypic prediction of co-receptor tropism is very accurate, fast and can be applied to large datasets derived from next-generation sequencing technologies. The reduced complexity of the electrostatic modeling makes T-CUP 2.0 independent from third-party software, making it easy to install and use.