The mitotic regulator Pin1 plays an important role in protein quality control and age-related medical conditions such as Alzheimer disease and Parkinson disease. Although its cellular role has been thoroughly investigated during the past decade, the molecular mechanisms underlying its function remain elusive. We provide evidence for interactions between the two domains of Pin1. Several residues displayed unequivocal peak splits in nuclear magnetic resonance spectra, indicative of two different conformational states in equilibrium. Pareto analysis of paramagnetic relaxation enhancement data demonstrates that the two domains approach each other upon addition of a nonpeptidic ligand. Titration experiments with phosphorylated peptides monitored by fluorescence anisotropy and chemical shift perturbation indicate that domain interactions increase Pin1's affinity toward peptide ligands. We propose this interplay of the domains and ligands to be a general mechanism for a large class of two-domain proteins.

BackgroundHuman 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.ResultsHere 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.ConclusionsWe 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.

Sonic Hedgehog (Shh) is a representative of the evolutionary closely related class of Hedgehog proteins that have essential signaling functions in animal development. The N-terminal domain (ShhN) is also assigned to the group of LAS proteins (LAS = Lysostaphin type enzymes, D-Ala-D-Ala metalloproteases, Sonic Hedgehog), of which all members harbor a structurally well-defined center; however, it is remarkable that ShhN so far is the only LAS member without proven peptidase activity. Another unique feature of ShhN in the LAS group is a double- center close to the zinc. We have studied the effect of these calcium ions on ShhN structure, dynamics, and interactions. We find that the presence of calcium has a marked impact on ShhN properties, with the two calcium ions having different effects. The more strongly bound calcium ion significantly stabilizes the overall structure. Surprisingly, the binding of the second calcium ion switches the putative catalytic center from a state similar to LAS enzymes to a state that probably is catalytically inactive. We describe in detail the mechanics of the switch, including the effect on substrate co-ordinating residues and on the putative catalytic water molecule. The properties of the putative substrate binding site suggest that ShhN could degrade other ShhN molecules, e.g. by cleavage at highly conserved glycines in ShhN. To test experimentally the stability of ShhN against autodegradation, we compare two ShhN mutants in vitro: (1) a ShhN mutant unable to bind calcium but with putative catalytic center intact, and thus, according to our hypothesis, a constitutively active peptidase, and (2) a mutant carrying additionally mutation E177A, i.e., with the putative catalytically active residue knocked out. The in vitro results are consistent with ShhN being a cannibalistic zinc-peptidase. These experiments also reveal that the peptidase activity depends on .

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.

The epidemiology of HIV-1 in China has unique features that may have led to unique viral strains. We therefore tested the hypothesis that it is possible to find distinctive patterns in HIV-1 genomes sampled in China. Using a rule inference algorithm we could indeed extract from sequences of the third variable loop (V3) of HIV-1 gp120 a set of 14 signature patterns that with 89% accuracy distinguished Chinese from non-Chinese sequences. These patterns were found to be specific to HIV-1 subtype, i.e. sequences complying with pattern 1 were of subtype B, pattern 2 almost exclusively covered sequences of subtype 01_AE, etc. We then analyzed the first of these signature patterns in depth, namely that L and W at two V3 positions are specifically occurring in Chinese sequences of subtype B/B' (3% false positives). This pattern was found to be in agreement with the phylogeny of HIV-1 of subtype B inside and outside of China. We could neither reject nor convincingly confirm that the pattern is stabilized by immune escape. For further interpretation of the signature pattern we used the recently developed measure of Direct Information, and in this way discovered evidence for physical interactions between V2 and V3. We conclude by a discussion of limitations of signature patterns, and the applicability of the approach to other genomic regions and other countries.