Search

Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein-protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein-protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein-protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.

Nonhomologous end joining (NHEJ) is a DNA repair mechanism that religates double-strand DNA breaks to maintain genomic integrity during the entire cell cycle. The Ku70/80 complex recognizes DNA breaks and serves as an essential hub for recruitment of NHEJ components. Here, we describe intramolecular interactions of the Ku70 C-terminal domain, known as the SAP domain. Using single-particle cryo-electron microscopy, mass spectrometric analysis of intermolecular cross-linking and molecular modelling simulations, we captured variable positions of the SAP domain depending on DNA binding. The first position was localized at the DNA aperture in the Ku70/80 apo form but was not observed in the DNA-bound state. The second position, which was observed in both apo and DNA-bound states, was found below the DNA aperture, close to the helical arm of Ku70. The localization of the SAP domain in the DNA aperture suggests a function as a flexible entry gate for broken DNA. DATABASES: EM maps have been deposited in EMDB (EMD-11933). Coordinates have been deposited in Protein Data Bank (PDB 7AXZ). Other data are available from corresponding authors upon a request.

Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein-carbohydrate interfaces. In order to better understand protein-carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D X-ray crystal structures of protein-carbohydrate complexes. In ProCarbDB, the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein-carbohydrate interfaces. The ProCarbDB web server is freely available at http://www.procarbdb.science/procarb.

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

For over four decades structural biology has been used to understand the mechanisms of disease, and structure-guided approaches have demonstrated clearly that they can contribute to many aspects of early drug discovery, both computationally and experimentally. Structure can also inform our understanding of impacts of mutations in human genetic diseases and drug resistance in cancers and infectious diseases. We discuss the ways that structural insights might be useful in both repurposing off-licence drugs and guide the design of new molecules that might be less susceptible to drug resistance in the future.

Despite interest in associating polymorphisms with clinical or experimental phenotypes, functional interpretation of mutation data has lagged behind generation of data from modern high-throughput techniques and the accurate prediction of the molecular impact of a mutation remains a non-trivial task. We present here an integrated knowledge-driven computational workflow designed to evaluate the effects of experimental and disease missense mutations on protein structure and interactions. We exemplify its application with analyses of saturation mutagenesis of DBR1 and Gal4 and show that the experimental phenotypes for over 80% of the mutations correlate well with predicted effects of mutations on protein stability and RNA binding affinity. We also show that analysis of mutations in VHL using our workflow provides valuable insights into the effects of mutations, and their links to the risk of developing renal carcinoma. Taken together the analyses of the three examples demonstrate that structural bioinformatics tools, when applied in a systematic, integrated way, can rapidly analyse a given system to provide a powerful approach for predicting structural and functional effects of thousands of mutations in order to reveal molecular mechanisms leading to a phenotype. Missense or non-synonymous mutations are nucleotide substitutions that alter the amino acid sequence of a protein. Their effects can range from modifying transcription, translation, processing and splicing, localization, changing stability of the protein, altering its dynamics or interactions with other proteins, nucleic acids and ligands, including small molecules and metal ions. The advent of high-throughput techniques including sequencing and saturation mutagenesis has provided large amounts of phenotypic data linked to mutations. However, one of the hurdles has been understanding and quantifying the effects of a particular mutation, and how they translate into a given phenotype. One approach to overcome thi

How is information communicated both within and between cells of living systems with high signal to noise? We discuss transmembrane signaling models involving two receptor tyrosine kinases: the fibroblast growth factor receptor (FGFR) and the MET receptor. We suggest that simple dimerization models might occur opportunistically giving rise to noise but cooperative clustering of the receptor tyrosine kinases observed in these systems is likely to be important for signal transduction. We propose that this may be a more general prerequisite for high signal to noise in transmembrane receptor signaling.

UNLABELLED: Familial renal cell carcinoma (RCC) is genetically heterogeneous and may be caused by mutations in multiple genes, including VHL, MET, SDHB, FH, FLCN, PTEN, and BAP1. However, most individuals with inherited RCC do not have a detectable germline mutation. To identify novel inherited RCC genes, we undertook exome resequencing studies in a familial RCC kindred and identified a CDKN2B nonsense mutation that segregated with familial RCC status. Targeted resequencing of CDKN2B in individuals (n = 82) with features of inherited RCC then revealed three candidate CDKN2B missense mutations (p.Pro40Thr, p.Ala23Glu, and p.Asp86Asn). In silico analysis of the three-dimensional structures indicated that each missense substitution was likely pathogenic through reduced stability of the mutant or reduced affinity for cyclin-dependent kinases 4 and 6, and in vitro studies demonstrated that each of the mutations impaired CDKN2B-induced suppression of proliferation in an RCC cell line. These findings identify germline CDKN2B mutations as a novel cause of familial RCC. SIGNIFICANCE: Germline loss-of-function CDKN2B mutations were identified in a subset of patients with features of inherited RCC. Detection of germline CDKN2B mutations will have an impact on familial cancer screening and might prove to influence the management of disseminated disease.

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

The growth/motility factor hepatocyte growth factor/scatter factor (HGF/SF) and its receptor, the tyrosine kinase MET, constitute a signalling system essential for embryogenesis and for tissue/organ regeneration in post-natal life. HGF/SF-MET signalling, however, also plays a key role in the onset of metastasis of a large number of human tumours. Both HGF/SF and MET are high molecular weight proteins that bury an extensive interface upon complex formation and thus constitute a challenging target for the development of low molecular weight inhibitors. Here we have used surface plasmon resonance (SPR), nuclear magnetic resonance (NMR) and X-ray crystallography to screen a diverse fragment library of 1338 members as well as a range of piperazine-like compounds. Several small molecules were found to bind in the lysine-binding pocket of the kringle 1 domain of HGF/SF and its truncated splice variant NK1. We have defined the binding mode of these compounds, explored their biological activity and we show that selected fragments inhibit MET downstream signalling. Thus we demonstrate that targeting the lysine-binding pocket of NK1 is an effective strategy to generate MET receptor antagonists and we offer proof of concept that the HGF/SF-MET interface may be successfully targeted with small molecules. These studies have broad implications for the development of HGF/SF-MET therapeutics and cancer treatment.

Drug resistance is a major challenge for the treatment of many diseases and a significant concern throughout the drug development process. The ability to understand and predict the effects of mutations on protein-ligand affinities and their roles in the emergence of resistance would significantly aid treatment and drug design strategies. In order to study and understand the impacts of missense mutations on the interaction of ligands with the proteome, we have developed Platinum (http://structure.bioc.cam.ac.uk/platinum). This manually curated, literature-derived database, comprising over 1000 mutations, associates for the first time experimental information on changes in affinity with three-dimensional structures of protein-ligand complexes. To minimize differences arising from experimental techniques and to directly compare binding affinities, Platinum considers only changes measured by the same group and with the same amino-acid sequence used for structure determination, providing a direct link between protein structure, how a ligand binds and how mutations alter the affinity of the ligand of the protein. We believe Platinum will be an invaluable resource for understanding the effects of mutations that give rise to drug resistance, a major problem emerging in pandemics including those caused by the influenza virus, in infectious diseases such as tuberculosis, in cancer and in many other life-threatening illnesses.

The folliculin/Fnip complex has been demonstrated to play a crucial role in the mechanisms underlying Birt-Hogg-Dubé (BHD) syndrome, a rare inherited cancer syndrome. Lst4 has been previously proposed to be the Fnip1/2 orthologue in yeast and therefore a member of the DENN family. In order to confirm this, we solved the crystal structure of the N-terminal region of Lst4 from Kluyveromyces lactis and show it contains a longin domain, the first domain of the full DENN module. Furthermore, we demonstrate that Lst4 through its DENN domain interacts with Lst7, the yeast folliculin orthologue. Like its human counterpart, the Lst7/Lst4 complex relocates to the vacuolar membrane in response to nutrient starvation, most notably in carbon starvation. Finally, we express and purify the recombinant Lst7/Lst4 complex and show that it exists as a 1 : 1 heterodimer in solution. This work confirms the membership of Lst4 and the Fnip proteins in the DENN family, and provides a basis for using the Lst7/Lst4 complex to understand the molecular function of folliculin and its role in the pathogenesis of BHD syndrome.

The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein-protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein-protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of "orthosteric", competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein-protein interaction interfaces for which successful chemical modulators are being developed.

Mutations in the von Hippel-Lindau (VHL) gene are pathogenic in VHL disease, congenital polycythaemia and clear cell renal carcinoma (ccRCC). pVHL forms a ternary complex with elongin C and elongin B, critical for pVHL stability and function, which interacts with Cullin-2 and RING-box protein 1 to target hypoxia-inducible factor for polyubiquitination and proteasomal degradation. We describe a comprehensive database of missense VHL mutations linked to experimental and clinical data. We use predictions from in silico tools to link the functional effects of missense VHL mutations to phenotype. The risk of ccRCC in VHL disease is linked to the degree of destabilization resulting from missense mutations. An optimized binary classification system (symphony), which integrates predictions from five in silico methods, can predict the risk of ccRCC associated with VHL missense mutations with high sensitivity and specificity. We use symphony to generate predictions for risk of ccRCC for all possible VHL missense mutations and present these predictions, in association with clinical and experimental data, in a publically available, searchable web server.

MOTIVATION: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein-protein and protein-nucleic acid interactions. RESULTS: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario. AVAILABILITY AND IMPLEMENTATION: A web server is available at http://structure.bioc.cam.ac.uk/mcsm.

Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.