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Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. [ABSTRACT FROM AUTHOR]

Abstract: Background: The details of the functional interaction between G proteins and the G protein coupled receptors (GPCRs) have long been subjected to extensive investigations with structural and functional assays and a large number of computational studies. Scope of review: The nature and sites of interaction in the G-protein/GPCR complexes, and the specificities of these interactions selecting coupling partners among the large number of families of GPCRs and G protein forms, are still poorly defined. Major conclusions: Many of the contact sites between the two proteins in specific complexes have been identified, but the three dimensional molecular architecture of a receptor-Gα interface is only known for one pair. Consequently, many fundamental questions regarding this macromolecular assembly and its mechanism remain unanswered. General significance: In the context of current structural data we review the structural details of the interfaces and recognition sites in complexes of sub-family A GPCRs with cognate G-proteins, with special emphasis on the consequences of activation on GPCR structure, the prevalence of preassembled GPCR/G-protein complexes, the key structural determinants for selective coupling and the possible involvement of GPCR oligomerization in this process. [Copyright &y& Elsevier]

Protein P53 is involved in more than 50% of the human cancers and the P53–MDM2 complex is a target for anticancer drug design. It is possible to engineer small P53 mimics that would be expected to disrupt the P53–MDM2 complex, and release P53 to initiate cell-cycle arrest or apoptosis. These small peptides should bind to the functional epitopes of the protein–protein interface, and prevent the interaction between P53 and MDM2. Here, we apply an improved computational alanine scanning mutagenesis method, which allows the determination of the hot spots present in both monomers, P53 and MDM2, of three protein complexes (the P53-binding domain of human MDM2, its analogue from Xenopus laev is, and the structure of human MDM2 in complex with an optimized P53 peptide). The importance of the hydrogen bonds formed by the protein backbone has been neglected due to the difficulty of measuring experimentally their contribution to the binding free energy. In this study we present a computational approach that allows the estimation of the contribution to the binding free energy of the C=O and N–H groups in the backbone of the P53 and MDM2 proteins. We have noticed that the hydrogen bond between the HE1 atom of the hot spot Trp23 and the O atom of the residue Leu54, as well as the NH-pi hydrogen bond between the Ile57 and Met58 were observed in the Molecular dynamics simulation, and their contribution to the binding free energy measured. This study not only shows the reliability of the computational mutagenesis method to detect hot spots but also demonstrates an excellent correlation between the quantitative calculated binding free energy contribution of the C=O and N–H backbone groups of the interfacial residues and the qualitative values expected for this kind of interaction. The study also increases our understanding of the P53–MDM2 interaction. [ABSTRACT FROM AUTHOR]

Influenza (flu) is a contagious viral disease, which targets the human respiratory tract and spreads throughout the world each year. Every year, influenza infects around 10% of the world population and between 290,000 and 650,000 people die from it according to the World Health Organization (WHO). Influenza viruses belong to the Orthomyxoviridae family and have a negative sense eight-segment single-stranded RNA genome that encodes 11 different proteins. The only control over influenza seasonal epidemic outbreaks around the world are vaccines, annually updated according to viral strains in circulation, but, because of high rates of mutation and recurrent genetic assortment, new viral strains of influenza are constantly emerging, increasing the likelihood of pandemics. Vaccination effectiveness is limited, calling for new preventive and therapeutic approaches and a better understanding of the virus–host interactions. In particular, grasping the role of influenza non-structural protein 1 (NS1) and related known interactions in the host cell is pivotal to better understand the mechanisms of virus infection and replication, and thus propose more effective antiviral approaches. In this review, we assess the structure of NS1, its dynamics, and multiple functions and interactions, to highlight the central role of this protein in viral biology and its potential use as an effective therapeutic target to tackle seasonal and pandemic influenza. [ABSTRACT FROM AUTHOR]