The Anti-Apoptotic Bcl-xL Protein, a New Piece in the Puzzle of Cytochrome C Interactome

A structural model of the adduct between human cytochrome c and the human anti-apoptotic protein Bcl-xL, which defines the protein-protein interaction surface, was obtained from solution NMR chemical shift perturbation data. The atomic level information reveals key intermolecular contacts identifying new potentially druggable areas on cytochrome c and Bcl-xL. Involvement of residues on cytochrome c other than those in its complexes with electron transfer partners is apparent. Key differences in the contact area also exist between the Bcl-xL adduct with the Bak peptide and that with cytochrome c. The present model provides insights to the mechanism by which cytochrome c translocated to cytosol can be intercepted, so that the apoptosome is not assembled

False positive reduction in protein-protein interaction predictions using gene ontology annotations

<p>Abstract</p> <p>Background</p> <p>Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.</p> <p>Results</p> <p>Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The '<it>strength</it>', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the <it>strength </it>varies between two and ten-fold of randomly removing protein pairs from the datasets.</p> <p>Conclusion</p> <p>Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.</p

Assessment of protein-protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein–protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein–protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM

NMDA receptors are selectively partitioned into complexes and supercomplexes during synapse maturation

How neuronal proteomes self-organize is poorly understood because of their inherent molecular and cellular complexity. Here, focusing on mammalian synapses we use blue-native PAGE and ‘gene-tagging’ of GluN1 to report the first biochemical purification of endogenous NMDA receptors (NMDARs) directly from adult mouse brain. We show that NMDARs partition between two discrete populations of receptor complexes and B1.5MDa supercomplexes. We tested the assembly mechanism with six mouse mutants, which indicates a tripartite requirement of GluN2B, PSD93 and PSD95 gate the incorporation of receptors into B1.5MDa supercomplexes, independent of either canonical PDZ-ligands or GluN2A. Supporting the essential role of GluN2B, quantitative gene-tagging revealed a fourfold molar excess of GluN2B over GluN2A in adult forebrain. NMDAR supercomplexes are assembled late in postnatal development and triggered by synapse maturation involving epigenetic and activity-dependent mechanisms. Finally, screening the quaternary organization of 60 native proteins identified numerous discrete supercomplexes that populate the mammalian synapse

The Dynamics of the Human Leukocyte Antigen Head Domain Modulates Its Recognition by the T-Cell Receptor

Generating the immune response requires the discrimination of peptides presented by the human leukocyte antigen complex (HLA) through the T-cell receptor (TCR). However, how a single amino acid substitution in the antigen bonded to HLA affects the response of T cells remains uncertain. Hence, we used molecular dynamics computations to analyze the molecular interactions between peptides, HLA and TCR. We compared immunologically reactive complexes with non-reactive and weakly reactive complexes. MD trajectories were produced to simulate the behavior of isolated components of the various p-HLA-TCR complexes. Analysis of the fluctuations showed that p-HLA binding barely restrains TCR motions, and mainly affects the CDR3 loops. Conversely, inactive p-HLA complexes displayed significant drop in their dynamics when compared with its free versus ternary forms (p-HLA-TCR). In agreement, the free non-reactive p-HLA complexes showed a lower amount of salt bridges than the responsive ones. This resulted in differences between the electrostatic potentials of reactive and inactive p-HLA species and larger vibrational entropies in non-elicitor complexes. Analysis of the ternary p-HLA-TCR complexes also revealed a larger number of salt bridges in the responsive complexes. To summarize, our computations indicate that the affinity of each p-HLA complex towards TCR is intimately linked to both, the dynamics of its free species and its ability to form specific intermolecular salt-bridges in the ternary complexes. Of outstanding interest is the emerging concept of antigen reactivity involving its interplay with the HLA head sidechain dynamics by rearranging its salt-bridgesFinancial support was provided by the Spanish Ministry of Economy and Competiveness (Grant No. BFU2012-31670/BMC); The Andalusian Government (Grant PAI, BIO198); The Spanish Fund for Health Research (FIS; code PI11/02366, FI12/00189) and the Ramón Areces Foundation.Peer reviewe