Characteritzation of protein-protein interfaces and identification of transient cavities for its modulation

Protein-protein interactions (PPIs) play an essential role in many biological processes, including disease conditions. Strategies to modulate PPIs with small molecules have therefore attracted increasing interest over the last few years, where successful PPI inhibitors have been reported into transient cavities from previously flat PPIfs. Recent studies emphasize on hot-spots (those residues contribute for most of the energy of binding) as promising targets for the modulation of PPI. PyDock is the only computational method that uses docking to predict PPIfs and hot-spots (HS) residues. Using Normalized Interface Propensity (NIP) values derived from rigid-body protein docking simulation, we are able to predict the PPIfs and HS residues without any prior structural knowledge of the complex. We benchmarked the protocol in a small set of protein-protein complexes for which both structural data and PPI inhibitors are known. We present an approach aimed at identifying HS and transient pockets from predicted PPIfs in order to find potential small molecules capable of modulating PPIs. The method uses pyDock to identify PPIfs and HS and molecular dynamics (MD) techniques to describe the possible fluctuations of the interacting proteins in order to suggest transient pockets. Afterwards, we evaluated the validity of predicted HS and pockets for in silico drug design by using ligand docking. We present a strategy based on MD and NIP which allows to identify cavities as potentially good targets to bind inhibitors when there is no information at all about the protein-protein complex structure

Prediction of protein-binding areas by small-world residue networks and application to docking

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.</p> <p>Results</p> <p>Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved.</p> <p>Conclusions</p> <p>The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.</p

Structural assembly of two-domain proteins by rigid-body docking.

BACKGROUND: Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requires ab initio methods. Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking. RESULTS: Here we focus on interacting domain pairs that are part of the same peptide chain and thus have an inter-domain peptide region (so called linker). We have developed a method called pyDockTET (tethered-docking), which uses rigid-body docking to generate domain-domain poses that are further scored by binding energy and a pseudo-energy term based on restraints derived from linker end-to-end distances. The method has been benchmarked on a set of 77 non-redundant pairs of domains with available X-ray structure. We have evaluated the docking method ZDOCK, which is able to generate acceptable domain-domain orientations in 51 out of the 77 cases. Among them, our method pyDockTET finds the correct assembly within the top 10 solutions in over 60% of the cases. As a further test, on a subset of 20 pairs where domains were built by homology modelling, ZDOCK generates acceptable orientations in 13 out of the 20 cases, among which the correct assembly is ranked lower than 10 in around 70% of the cases by our pyDockTET method. CONCLUSION: Our results show that rigid-body docking approach plus energy scoring and linker-based restraints are useful for modelling domain-domain interactions. These positive results will encourage development of new methods for structural prediction of macromolecules with multiple (more than two) domains.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

pyDock performance in 5th CAPRI edition: from docking and scoring to binding affinity predictions and other challenges

Proteins form the executive machinery underlying all the biological processes that occur within and between cells, from DNA replication to protein degradation. Although genome-scale technologies enable to clarify their large, intricate and highly dynamics networks, they fail to elucidate the detailed molecular mechanism that underlies the protein association process. Therefore, one of the most challenging objectives in biological research is to functionally characterize protein interactions by solving 3D complex structures. This is, however, not a trivial task as confirmed by the large gap that exist between the number of complexes identified by large-scale proteomics efforts and those for which high-resolution 3D experimental structures are available. For these reasons, computational docking methods, aimed to predict the binding mode of two proteins starting from the coordinates of the individual subunits, are bound to become a complementary approach to solve the structural interactome. Given its importance, the field of protein docking has experienced an explosion in recent years partially propelled by CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical Assessment of PRedicted Interaction) is a community-wide blind experiment aimed at objectively assessing the performance of computational methods for modeling protein interactions by inviting developers to test their algorithms on the same target system and quantitatively evaluating the results. In order to test pyDock,1 a docking scoring algorithm developed in our group, the PID (Protein Interaction and Docking) group of the BSC Life Science Department, we have participated in all the 15 targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our automated protocol confirmed to be highly successful to provide correct models in easy-to-medium difficulty protein-protein docking cases placing among the Top5 ranked groups out of more than 60 participants. Key words: Complex structure, CAPRI, protein-protein docking, pyDock, protein interactions

Exploring the relationship between gene expression and topological properties of Arabidopsis thaliana interactome network.

The aim of this study is to integrate and link up transcriptomic data with biological networks approaches. The main objective was to determinate the correlation of transcriptomic profiles with PPI topology, seeking to demonstrate relational or structural patterns within the network internal organization

pyDock performance in 5th CAPRI edition: from docking and scoring to binding affinity predictions and other challenges

Proteins form the executive machinery underlying all the biological processes that occur within and between cells, from DNA replication to protein degradation. Although genome-scale technologies enable to clarify their large, intricate and highly dynamics networks, they fail to elucidate the detailed molecular mechanism that underlies the protein association process. Therefore, one of the most challenging objectives in biological research is to functionally characterize protein interactions by solving 3D complex structures. This is, however, not a trivial task as confirmed by the large gap that exist between the number of complexes identified by large-scale proteomics efforts and those for which high-resolution 3D experimental structures are available. For these reasons, computational docking methods, aimed to predict the binding mode of two proteins starting from the coordinates of the individual subunits, are bound to become a complementary approach to solve the structural interactome. Given its importance, the field of protein docking has experienced an explosion in recent years partially propelled by CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical Assessment of PRedicted Interaction) is a community-wide blind experiment aimed at objectively assessing the performance of computational methods for modeling protein interactions by inviting developers to test their algorithms on the same target system and quantitatively evaluating the results. In order to test pyDock,1 a docking scoring algorithm developed in our group, the PID (Protein Interaction and Docking) group of the BSC Life Science Department, we have participated in all the 15 targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our automated protocol confirmed to be highly successful to provide correct models in easy-to-medium difficulty protein-protein docking cases placing among the Top5 ranked groups out of more than 60 participants. Key words: Complex structure, CAPRI, protein-protein docking, pyDock, protein interactions

In silico docking of urokinase plasminogen activator and integrins

Background: Urokinase, its receptor and the integrins are functionally associated and involved in regulation of cell signaling, migration, adhesion and proliferation. No structural information is available on this potential multimolecular complex. However, the tri-dimensional structure of urokinase, urokinase receptor and integrins is known. Results: We have modeled the interaction of urokinase on two integrins, alpha IIb beta 3 in the open configuration and alpha v beta 3 in the closed configuration. We have found that multiple lowest energy solutions point to an interaction of the kringle domain of uPA at the boundary between alpha and beta chains on the surface of the integrins. This region is not far away from peptides that have been previously shown to have a biological role in urokinase receptor/integrins dependent signaling. Conclusions: We demonstrated that in silico docking experiments can be successfully carried out to identify the binding mode of the kringle domain of urokinase on the scaffold of integrins in the open and closed conformation. Importantly we found that the binding mode was the same on different integrins and in both configurations. To get a molecular view of the system is a prerequisite to unravel the complex protein-protein interactions underlying urokinase/urokinase receptor/integrin mediated cell motility, adhesion and proliferation and to design rational in vitro experiments

Protein docking by Rotation-Based Uniform Sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test <url>http://www.ebi.ac.uk/msd-srv/capri</url>, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.</p> <p>Results</p> <p>In these work we present new computational tools for protein-protein docking. We describe here the RotBUS (Rotation-Based Uniform Sampling) method to generate uniformly distributed sets of rigid-body docking poses, with a new fast calculation of the optimal contacting distance between molecules. We have tested the method on a standard benchmark of unbound structures and we can find near-native solutions in 100% of the cases. After applying a new fast filtering scheme based on residue-based desolvation, in combination with FTDock plus pyDock scoring, near-native solutions are found with rank ≤ 50 in 39% of the cases. Knowledge-based experimental restraints can be easily included to reduce computational times during sampling and improve success rates, and the method can be extended in the future to include flexibility of the side-chains.</p> <p>Conclusions</p> <p>This new sampling algorithm has the advantage of its high speed achieved by fast computing of the intermolecular distance based on a coarse representation of the interacting surfaces. In addition, a fast desolvation scoring permits the screening of millions of conformations at low computational cost, without compromising accuracy. The protocol presented here can be used as a framework to include restraints, flexibility and ensemble docking approaches.</p

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection

Motivation: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results: Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability and Implementation: IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/ approximately SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online