PocketMatch (version 2.0): A parallel algorithm for the detection of structural similarities between protein ligand binding-sites

Knowledge of protein-ligand interactions is essential to understand several biological processes and important for applications ranging from understanding protein function to drug discovery and protein engineering. Here, we describe an algorithm for the comparison of three-dimensional ligand-binding sites in protein structures. A previously described algorithm, PocketMatch (version 1.0) is optimised, expanded, and MPI-enabled for parallel execution. PocketMatch (version 2.0) rapidly quantifies binding-site similarity based on structural descriptors such as residue nature and interatomic distances. Atomic-scale alignments may also be obtained from amino acid residue pairings generated. It allows an end-user to compute database-wide, all-to-all comparisons in a matter of hours. The use of our algorithm on a sample dataset, performance-analysis, and annotated source code is also included.

Analysis of Sequence Periodicity in E. coli Proteins: Empirical Investigation of the ?Duplication and Divergence? Theory of Protein Evolution

Gatherer, D., and McEwan, N.R. (2003). Analysis of sequence periodicity in E. coli proteins: empirical investigation of the 'duplication and divergence' theory of protein evolution. Journal of Molecular Evolution 57, 149-158. RAE2008

Individuals with mutations in XPNPEP3, which encodes a mitochondrial protein, develop a nephronophthisis-like nephropathy.

The autosomal recessive kidney disease nephronophthisis (NPHP) constitutes the most frequent genetic cause of terminal renal failure in the first 3 decades of life. Ten causative genes (NPHP1-NPHP9 and NPHP11), whose products localize to the primary cilia-centrosome complex, support the unifying concept that cystic kidney diseases are "ciliopathies". Using genome-wide homozygosity mapping, we report here what we believe to be a new locus (NPHP-like 1 [NPHPL1]) for an NPHP-like nephropathy. In 2 families with an NPHP-like phenotype, we detected homozygous frameshift and splice-site mutations, respectively, in the X-prolyl aminopeptidase 3 (XPNPEP3) gene. In contrast to all known NPHP proteins, XPNPEP3 localizes to mitochondria of renal cells. However, in vivo analyses also revealed a likely cilia-related function; suppression of zebrafish xpnpep3 phenocopied the developmental phenotypes of ciliopathy morphants, and this effect was rescued by human XPNPEP3 that was devoid of a mitochondrial localization signal. Consistent with a role for XPNPEP3 in ciliary function, several ciliary cystogenic proteins were found to be XPNPEP3 substrates, for which resistance to N-terminal proline cleavage resulted in attenuated protein function in vivo in zebrafish. Our data highlight an emerging link between mitochondria and ciliary dysfunction, and suggest that further understanding the enzymatic activity and substrates of XPNPEP3 will illuminate novel cystogenic pathways.

Novel Computational Protein Design Algorithms with Applications to Cystic Fibrosis and HIV

Proteins are essential components of cells and are crucial for catalyzing reactions, signaling, recognition, motility, recycling, and structural stability. This diversity of function suggests that nature is only scratching the surface of protein functional space. Protein function is determined by structure, which in turn is determined predominantly by amino acid sequence. Protein design aims to explore protein sequence and conformational space to design novel proteins with new or improved function. The vast number of possible protein sequences makes exploring the space a challenging problem.

Computational structure-based protein design (CSPD) allows for the rational design of proteins. Because of the large search space, CSPD methods must balance search accuracy and modeling simplifications. We have developed algorithms that allow for the accurate and efficient search of protein conformational space. Specifically, we focus on algorithms that maintain provability, account for protein flexibility, and use ensemble-based rankings. We present several novel algorithms for incorporating improved flexibility into CSPD with continuous rotamers. We applied these algorithms to two biomedically important design problems. We designed peptide inhibitors of the cystic fibrosis agonist CAL that were able to restore function of the vital cystic fibrosis protein CFTR. We also designed improved HIV antibodies and nanobodies to combat HIV infections.

On the mechanism of the inhibition of transducin function by farnesylcysteine analogs

The gamma subunits of heterotrimeric G proteins are isoprenylated/methylated on their carboxy termini. The photoreceptor G protein, transducin, is farnesylated/methylated at this position. Since the isoprenyl group is required for G protein function, it is of great interest to determine the mechanism by which the farnesyl group of T gamma interacts with the other transducin subunits and/or the activated photoreceptor, rhodopsin. Farnesylcysteine derivatives (N-acetyl-S-farnesyl-L-cysteine and farnesylated peptides) have been previously shown to have effects on transducin activity at high concentrations. Here, an extensive survey is done of farnesylcysteine analogs and other lipid molecules, which are tested for their ability to inhibit GTP/GDP exchange in transducin catalyzed by photolyzed rhodopsin. These studies are carried out to determine the nature of the inhibition process. While it does not appear that these molecules exhibit the specificity which would characterize a ligand-receptor type mechanism, the results suggest that these compounds are not acting in a nonspecific detergent-like manner either. The most likely mode of action of farnesylcysteine analogs is that they interfere with the lipid-lipid based association of T alpha and T beta gamma through the lipid modifications present on each subunit.

Fluid Shear Induces Conformation Change in Human Blood Protein von Willebrand Factor in Solution

Many of the physiological functions of von Willebrand Factor (VWF), including its binding interaction with blood platelets, are regulated by the magnitude of applied fluid/hydrodynamic stress. We applied two complementary strategies to study the effect of fluid forces on the solution structure of VWF. First, small-angle neutron scattering was used to measure protein conformation changes in response to laminar shear rates (G) up to 3000/s. Here, purified VWF was sheared in a quartz Couette cell and protein conformation was measured in real time over length scales from 2-140 nm. Second, changes in VWF structure up to 9600/s were quantified by measuring the binding of a fluorescent probe 1,1'-bis(anilino)-4-,4'-bis(naphtalene)-8,8'-disulfonate (bis-ANS) to hydrophobic pockets exposed in the sheared protein. Small angle neutron scattering studies, coupled with quantitative modeling, showed that VWF undergoes structural changes at G < 3000/s. These changes were most prominent at length scales <10 nm (scattering vector (q) range >0.6/nm). A mathematical model attributes these changes to the rearrangement of domain level features within the globular section of the protein. Studies with bis-ANS demonstrated marked increase in bis-ANS binding at G > 2300/s. Together, the data suggest that local rearrangements at the domain level may precede changes at larger-length scales that accompany exposure of protein hydrophobic pockets. Changes in VWF conformation reported here likely regulate protein function in response to fluid shear.

New insights into small molecules inhibitors and protein-protein interactions of VirB8 : a critical conserved component of the type IV secretion system.

Les systèmes bactériens de sécrétion de type IV (T4SS) sont constitués d’un ensemble de 8 à 12 protéines conservées. Ces dernières sont utilisées lors de la translocation de protéines, la translocation de complexes ADN-protéines mais aussi pour le transport de ces derniers au travers de la membrane cellulaire. Les T4SS, en tant que facteurs de virulence pour beaucoup de pathogènes comme Brucella suis, sont donc d’excellents modèles cibles pour le développement de médicaments d’antivirulence. Ces médicaments, en privant le pathogène de son facteur essentiel de virulence : le T4SS, constituent une alternative ou encore une amélioration des traitements antibiotiques utilisés actuellement. VirB8, un facteur d’assemblage conservé dans le T4SS, forme des dimères qui sont importants pour la fonction des T4SS dans ces pathogènes. De par ses interactions multiples, VirB8 est un excellent modèle pour l’analyse des facteurs d’assemblage mais aussi en tant que cible de médicaments qui empêcheraient son interaction avec d’autres protéines et qui, in fine, désarmeraient les bactéries en les privant de leur fonctions essentielles de virulence. À ce jour, nous savons qu’il existe un équilibre monomère-dimère et un processus d’homodimerization de VirB8 dont l’importance est vitale pour la fonctionnement biologique des T4SSs. En se basant sur des essais quantitatifs d’interaction, nous avons identifié (i) des sites potentiels d’interaction avec d’autres protéines VirB du T4SS mais aussi (ii) isolé des petites molécules inhibitrices afin de tester la fonction protéique de VirB8. Afin de déterminer les acides aminés importants pour l’hétérodimérization de VirB8 avec VirB10, nous avons effectué des expériences de mutagenèse aléatoire, de phage display et d’arrimage moléculaire in silico. Ces expériences ont démontré l’importance de trois acides aminés localisés sur le feuillet β : R160, S162, T164 et I165. Ces derniers seraient importants pour l’association de VirB8 avec VirB10 étant donné que leur mutagenèse entraine une diminution de la formation du complexe VirB8-VirB10. L’objectif actuel de notre projet de recherche est de pouvoir mieux comprendre mais aussi d’évaluer le rôle de VirB8 dans l’assemblage du T4SS. Grace à un méthode de criblage adaptée à partir de la structure de VirB8, nous avons pu identifié une petite molécule inhibitrice BAR-068, qui aurait un rôle prometteur dans l’inhibition du T4SS. Nous avons utilisé la spectroscopie par fluorescence, l’essai à deux hybrides, le cross-linking et la cristallographie afin de déterminer le mécanisme d'interaction existant entre VirB8 et BAR-068. Ces travaux pourraient permettre de nombreuses avancées, notamment en termes de compréhension des mécanismes d’inhibition du T4SS. Notre objectif ultime est de pouvoir caractériser la séquence d’évènements essentiels à l’assemblage et au fonctionnement du T4SS. De manière globale, notre projet de recherche permettrait de révéler les grands principes d’assemblage des protéines membranaires, les processus de sécrétion de protéines chez les bactéries mais aussi de proposer une nouvelle stratégie lors du développement de drogues antimicrobiennes.

The FunFOLD2 server for the prediction of protein-ligand interactions

The FunFOLD2 server is a new independent server that integrates our novel protein–ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein–ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html.

Genetic, structural, and molecular insights into the function of ras of complex proteins domains

Ras of complex proteins (ROC) domains were identified in 2003 as GTP binding modules in large multidomain proteins from Dictyostelium discoideum. Research into the function of these domains exploded with their identification in a number of proteins linked to human disease, including leucine-rich repeat kinase 2 (LRRK2) and death-associated protein kinase 1 (DAPK1) in Parkinson’s disease and cancer, respectively. This surge in research has resulted in a growing body of data revealing the role that ROC domains play in regulating protein function and signaling pathways. In this review, recent advances in the structural informa- tion available for proteins containing ROC domains, along with insights into enzymatic function and the integration of ROC domains as molecular switches in a cellular and organismal context, are explored.

Regulation of gene and protein expression in cardiac myocyte hypertrophy and apoptosis

Considerable efforts have been expended in elucidating the inter-cellular and intra-cellular signaling pathways which elicit cardiac myocyte hypertrophy or apoptosis, and in identifying the changes which are associated with the end-stage of the response. The challenge now is to link the two. Although some of the signaling effects will be the acute modulation of existing protein function, long-term effects which bring about and maintain the hypertrophic state or which culminate in cell death are mediated at the level of gene and protein expression. With the advances in micro-array technology and genome sequencing, it is now possible to obtain a picture of the global gene expression profile in myocytes or in whole heart which dictates the proteins which could be made. This is not the final picture since additional regulation at the level of translation modulates the relative proportions of each protein that can be made from the transcriptome. Even here, further regulation of protein stability and turnover means that ultimately it is still necessary to examine the proteome to determine what may cause the functional changes in a cell. Thus, in order to gain a full picture of events which regulate the response and gain some insight into possible points of intervention for therapy, it is necessary to examine gene expression, mRNA translation and protein expression in concert.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Conformational changes in redox pairs of protein structures

Disulfides are conventionally viewed as structurally stabilizing elements in proteins but emerging evidence suggests two disulfide subproteomes exist. One group mediates the well known role of structural stabilization. A second redox-active group are best known for their catalytic functions but are increasingly being recognized for their roles in regulation of protein function. Redox-active disulfides are, by their very nature, more susceptible to reduction than structural disulfides; and conversely, the Cys pairs that form them are more susceptible to oxidation. In this study, we searched for potentially redox-active Cys Pairs by scanning the Protein Data Bank for structures of proteins in alternate redox states. The PDB contains over 1134 unique redox pairs of proteins, many of which exhibit conformational differences between alternate redox states. Several classes of structural changes were observed, proteins that exhibit: disulfide oxidation following expulsion of metals such as zinc; major reorganisation of the polypeptide backbone in association with disulfide redox-activity; order/disorder transitions; and changes in quaternary structure. Based on evidence gathered supporting disulfide redox activity, we propose disulfides present in alternate redox states are likely to have physiologically relevant redox activity.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.

Iteratively predict protein functions from protein-protein interactions

Current similarity-based approaches of predicting protein functions from protein-protein interaction (PPI) data usually make use of available information in the PPI network to predict functions of un-annotated proteins, and the prediction is a one-off procedure. However the interactions between proteins are more likely to be mutual rather than static and mono-directed. In other words, the un-annotated proteins, once their functions are predicted, will in turn affect the similarities between proteins. In this paper, we propose an innovative iteration algorithm that incorporates this dynamic feature of protein interaction into the protein function prediction, aiming to achieve higher prediction accuracies and get more reasonable results. With our algorithm, instead of one-off function predictions, functions are assigned to an unannotated protein iteratively until the functional similarities between proteins achieve a stable state. The experimental results show that our iterative method can provide better prediction results than one-off prediction methods with higher prediction accuracies, and is stable for large protein datasets.

Dynamically predicting protein functions from semantic associations of proteins

Predicting functions of un-annotated proteins is a significant challenge in the post-genomics era. Among existing computational approaches, exploiting interactions between proteins to predict functions of un-annotated proteins is widely used. However, it remains difficult to extract semantic associations between proteins (i.e. protein associations in terms of protein functionality) from protein interactions and incorporate extracted semantic associations to more effectively predict protein functions. Furthermore, existing approaches and algorithms regard the function prediction as a one-off procedure, ignoring dynamic and mutual associations between proteins. Therefore, deriving and exploiting semantic associations between proteins to dynamically predict functions are a promising and challenging approach for achieving better prediction results. In this paper, we propose an innovative algorithm to incorporate semantic associations between proteins into a dynamic procedure of protein function prediction. The semantic association between two proteins is measured by the semantic similarity of two proteins which is defined by the similarities of functions two proteins possess. To achieve better prediction results, function similarities are also incorporated into the prediction procedure. The algorithm dynamically predicts functions by iteratively selecting functions for the un-annotated protein and updating the similarities between the un-annotated protein and its neighbour annotated proteins until such suitable functions are selected that the similarities no longer change. The experimental results on real protein interaction datasets demonstrated that our method outperformed the similar and non-dynamic function prediction methods. Incorporating semantic associations between proteins into a dynamic procedure of function prediction reflects intrinsic relationships among proteins as well as dynamic features of protein interactions, and therefore, can significantly improve prediction results.