Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage

<div><p>The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived ‘bulk’ sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under <a href="http://structure.bioinf.mpi-inf.mpg.de/" target="_blank">http://structure.bioinf.mpi-inf.mpg.de/</a>.</p> </div

Important V3 positions and amino acid indices on the V3 structure.

<p>(A) 2B4C V3 structure used in this study. Cα atoms are marked with small black spheres along the loop backbone. Representative atoms are represented by gray spheres with the size proportional to the number of features of the clonal model mapped on the respective V3 position. Positions assigned to core sites are numbered. (B) V3 structure in a bound conformation (2QAD, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi.1002977-Huang3" target="_blank">[19]</a>) with the same sphere representation as in panel (A). Positions informative of tropism are located close to each other in this conformation and form interactions between two sides of the loop stem. (C, D) Structure representation of V3 as in panel (A) with positions of the loop colored according to the ratio of selected features related to “Positive charge” (C) and “Hydrophobicity factor” (D) to the overall number of the selected features present on the respective V3 position with red indicating high ratio and gray low. Structures were visualized using Pymol <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi.1002977-Schrodinger1" target="_blank">[48]</a>.</p

Distribution of scores of features selected using SVM and Lasso.

<p>(A) Distribution of scores of the SVM method. The vertical line indicates the cutoff for the selection of features for the clonal model. The scores of the top-scoring features are listed. (B) Distribution of scores of the Lasso method. Top-scoring features in the distribution are indicated. On both panels, positions of the features mapped on the V3 loop structure are indicated in brackets, labels are colored according to the clusters shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi-1002977-g004" target="_blank">Figure 4</a>.</p

Performance of models based on features selected using RF, SVM and Lasso.

<p>ROCR of different models are plotted. The legend lists the number of features, AUC and sensitivity at the specificity of the 11/25 rule (6.28%. indicated by the vertical dashed line) in brackets. The clonal model is represented by a black solid line. Vertical segments show the standard deviations of the 10×10-fold cross validation curves of the clonal and g2p models. Comparison of clonal and g2p models via precision-recall curves is shown in the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi.1002977.s014" target="_blank">Figure S14</a>.</p

Performance of the clonal and clinical models on different datasets.^*

*<p>The performance of the clonal model on the Sander and Dybowski datasets is compared to the performance of the original methods <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi.1002977-Dybowski1" target="_blank">[20]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi.1002977-Sander1" target="_blank">[21]</a> developed on these datasets. The clinical model constructed on the HOMER dataset is compared to the performance of g2p method in a 10×10-fold cross validation. Additionally the clinical model trained on the HOMER dataset coupled with clinical correlates (VL and CD4⁺ T cell counts) (HOMER-clinical) is compared to the g2p model also coupled with clinical correlates.</p

Schematic illustration of the sphere-shaped proximities of the structural descriptor.

<p>Atoms of the 2B4C V3 loop structure are represented by dots, representative atoms by larger dots. The black line connects representative atoms of the adjacent loop residues. Atoms of each residue are colored according to the “Hydrophobicity factor” amino acid index. Spheres are centered at the representative atoms of the loop residues. The spheres for residues 299, 317 and 323 are shown. The physicochemical features of residues within each sphere were averaged and used as a part of the structural descriptor.</p

Hierarchical clustering of the amino acid indices.

<p>The vertical line indicates the separation of the tree into four clusters analyzed in this study. Labels of the tree are colored according to the clusters.</p

V3 positions and amino acid indices among the features of the clonal model.

<p>(A) Structure locations of features of the clonal model were mapped on the positions on the reference sequence. Numbers of selected features mapped to adjacent sequence positions were summed and averaged over a sequence window of size three. The resulting distribution of all features is represented by the black line, distributions of features of the four clusters are represented by lines in the relevant colors as defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002977#pcbi-1002977-g004" target="_blank">Figure 4</a>. (B) Amino acid indices of the clonal model features. Bars are colored according to the clusters of amino acid indices. Significantly overrepresented indices are marked with an asterisk.</p

Summary statistic of the used datasets.

*<p>For the MVC dataset in the columns “X4 sequences” and “R5 sequences” the numbers of therapy failures and successes are shown respectively.</p

Profiling of Parkin-Binding Partners Using Tandem Affinity Purification

<div><p>Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting approximately 1–2% of the general population over age 60. It is characterized by a rather selective loss of dopaminergic neurons in the substantia nigra and the presence of α-synuclein-enriched Lewy body inclusions. Mutations in the <i>Parkin</i> gene (<i>PARK2</i>) are the major cause of autosomal recessive early-onset parkinsonism. The Parkin protein is an E3 ubiquitin ligase with various cellular functions, including the induction of mitophagy upon mitochondrial depolarizaton, but the full repertoire of Parkin-binding proteins remains poorly defined. Here we employed tandem affinity purification interaction screens with subsequent mass spectrometry to profile binding partners of Parkin. Using this approach for two different cell types (HEK293T and SH-SY5Y neuronal cells), we identified a total of 203 candidate Parkin-binding proteins. For the candidate proteins and the proteins known to cause heritable forms of parkinsonism, protein-protein interaction data were derived from public databases, and the associated biological processes and pathways were analyzed and compared. Functional similarity between the candidates and the proteins involved in monogenic parkinsonism was investigated, and additional confirmatory evidence was obtained using published genetic interaction data from <i>Drosophila melanogaster</i>. Based on the results of the different analyses, a prioritization score was assigned to each candidate Parkin-binding protein. Two of the top ranking candidates were tested by co-immunoprecipitation, and interaction to Parkin was confirmed for one of them. New candidates for involvement in cell death processes, protein folding, the fission/fusion machinery, and the mitophagy pathway were identified, which provide a resource for further elucidating Parkin function.</p></div