An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45.

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2-p53 complex.

Structural Basis for Mutations of Human Aquaporins Associated to Genetic Diseases.

Aquaporins (AQPs) are among the best structural-characterized membrane proteins, fulfilling the role of allowing water flux across cellular membranes. Thus far, 34 single amino acid polymorphisms have been reported in HUMSAVAR for human aquaporins as disease-related. They affect AQP2, AQP5 and AQP8, where they are associated with nephrogenic diabetes insipidus, keratoderma and colorectal cancer, respectively. For half of these mutations, although they are mostly experimentally characterized in their dysfunctional phenotypes, a structural characterization at a molecular level is still missing. In this work, we focus on such mutations and discuss what the structural defects are that they appear to cause. To achieve this aim, we built a 3D molecular model for each mutant and explored the effect of the mutation on all of their structural features. Based on these analyses, we could collect the structural defects of all the pathogenic mutations (here or previously analysed) under few main categories, that we found to nicely correlate with the experimental phenotypes reported for several of the analysed mutants. Some of the structural analyses we present here provide a rationale for previously experimentally observed phenotypes. Furthermore, our comprehensive overview can be used as a reference frame for the interpretation, on a structural basis, of defective phenotypes of other aquaporin pathogenic mutants.

Prevention of Vγ9Vδ2 T Cell Activation by a Vγ9Vδ2 TCR Nanobody.

Vγ9Vδ2 T cell activation plays an important role in antitumor and antimicrobial immune responses. However, there are conditions in which Vγ9Vδ2 T cell activation can be considered inappropriate for the host. Patients treated with aminobisphosphonates for hypercalcemia or metastatic bone disease often present with a debilitating acute phase response as a result of Vγ9Vδ2 T cell activation. To date, no agents are available that can clinically inhibit Vγ9Vδ2 T cell activation. In this study, we describe the identification of a single domain Ab fragment directed to the TCR of Vγ9Vδ2 T cells with neutralizing properties. This variable domain of an H chain-only Ab (VHH or nanobody) significantly inhibited both phosphoantigen-dependent and -independent activation of Vγ9Vδ2 T cells and, importantly, strongly reduced the production of inflammatory cytokines upon stimulation with aminobisphosphonate-treated cells. Additionally, in silico modeling suggests that the neutralizing VHH binds the same residues on the Vγ9Vδ2 TCR as the Vγ9Vδ2 T cell Ag-presenting transmembrane protein butyrophilin 3A1, providing information on critical residues involved in this interaction. The neutralizing Vγ9Vδ2 TCR VHH identified in this study might provide a novel approach to inhibit the unintentional Vγ9Vδ2 T cell activation as a consequence of aminobisphosphonate administration.