We are investigating protein-protein interactions for more than 2000 human proteins whose structures are known, with particular focus on those proteins that play a role in neuromuscular diseases. The database of information that will be produced will help researchers design molecules to inhibit or enhance binding of particular macromolecules, hopefully leading to better treatments for muscular dystrophy and other neuromuscular diseases.

Phase 1 of Help Cure Muscular Dystrophy has ended in June 2007 and Phase 2 has been launched in may 2009 and ended in the fall 2013.

The first and second phase of the project is supported by World Community Grid and by Decrypthon (a partnership between AFM (French Muscular Dystrophy Association), CNRS (French National Center for Scientific Research) and IBM).

We are now continuing our investigations on protein-protein intercations within the framework of the MAPPING project (Investissement d'Avenir en Bioinformatique, funded by the French Ministry of Research).

October 28, 2014

Dear All,

Here is some information on how our study of Protein-Protein Interactions (PPI) is evolving within the framework of the MAPPING project (Investissement d'Avenir en Bioinformatique, funded by the French Ministry of Research). We are moving in several directions, all aimed at refining information on protein partnership within the cell that has previously been obtained [Lopes et al 2013; Sacquin-Mora et al. 2008]. The different directions taken by the project are explained below:

DOCKING COMBINED TO EXPERIMENTAL INTERFACES
We have previously shown that molecular docking simulations ("docking"), combined with the knowledge of experimental interfaces, improve the discrimination of protein partners in the cell from those that are not interacting [Lopes et al 2013, Sacquin-Mora et al. 2008]. The computational cost of docking simulations is important, even with a coarse-grain molecular representation, which we used in our WCG computations. We tested replacing these simulations with a very effective approach to rigid docking, which uses a scoring function based only on geometric complementarity of protein surfaces [Ritchie 2002]. Our results for a set of 186 protein complexes from different functional classes [Mintseris classes et al. 2005] indicate that this function fails and its score does not accurately reflect binding affinities. The conformational space that is generated and selected by the scoring function is actually very poor in conformations that are near the native complexes. However, the evaluation of a large number of docking conformations combined with the knowledge of experimental interfaces still allows us to identify protein partners with a precision that is almost equivalent to that obtained with coarse-grain simulations [Laine & Carbone 2013].

Fig. Prediction of partners among 46 proteins, by combining rigid docking based on geometrical complementarity and knowledge of experimental interfaces. 500 (pink, black), 1000 (blue) and 2000 (cyan) docking conformations have been analyzed. The docking has been realized starting from original PDB positions (S) or from random positions (M).

WHEN DOCKING IS CROSSED WITH PREDICTIONS
As argued in [Sacquin-Mora et al. 2008, Lopes et al 2013], docking can be successfully crossed with the prediction of interaction surfaces. We recall that the HCMD2 calculations were heavily based on this result. We have used JET predictions [Engelen et al. 2009] to reduce the computational space for the 2200 proteins launched on WCG. We have pointed out that accurate predictions will improve partner identification scores and for this we are working on improving JET. The account of our new results is described below.

MORE ACCURATE PREDICTIONS OF INTERACTION SITES
We previously developed a tool, Joint Evolutionary Tree (JET) for the prediction of protein-protein interfaces [Engelen et al. 2009]. JET assumes that interaction sites are formed by a highly conserved core, surrounded by multiple concentric layers of less conserved residues, but with specific physico-chemical properties. Although this assumption is verified in the majority of interfaces, there are some cases where the interaction site only extends in a preferred direction from the retained core. In order to refine the definition of interaction sites predicted by JET, we have introduced a geometric criterion to describe the protein surface and have coupled this with conservation and physico-chemical properties. The new version of JET encourages extensions of interaction sites to protruding areas in the protein that appear to be highly exposed to the solvent. Depending on the system studied, JET is now able to automatically determine the combination of the most relevant criteria. These changes have significantly improved the performance of JET on different types of protein [Laine et al complexes. 2013- post Berlin]. We are currently setting up a database for all PDB structures, including those structures studied on the WCG grid in the HCMD2 project.

Fig. Prediction of interaction sites for a set of proteins of Huang database. From Left to right, results from the old JET version, the new version, and the experimental interface with the trace (associated to conservation of amino-acids in sequences and to conservation of physico-chemical properties expected at the interface). Proteins in the blue square correspond to those where a small molecule appears.

COEVOLUTION ANALYSIS AND PROTEIN-PROTEIN INTERACTIONS
We planned to obtain extra evidence of protein-protein interactions, and a better understanding of their nature, by exploiting information coming from co-evolution of residues at the interface of the two proteins. Novel kinds of interaction between potential partners, identified on interaction surfaces by coevolution analysis, are currently under investigation. We also developed a very fast version of the algorithm for the analysis of co-evolution "Blocks In Sequences" (BIS) [Dib & Carbone 2012]. Today, this implementation allows us to analyse long genomic sequences, such as complete viral genomes and to study the interaction of sets of proteins. This was previously impossible and it allows us to identify contacts among multiple proteins for which no interaction information was available. Using this approach, we considered the entire coding portion of the genome of hepatitis C virus (HCV) and identified, with BIS, all potential points of contact between the 10 proteins of this virus. In collaboration with F. Penin we are currently analyzing the interaction data.

Fig. HCV protein-protein interaction network [Champeimont et al. unpublished results]. The blue network reports the result of coevolution analysis performed on the full HCV polyprotein (for three genotypes). The width of the lines is proportional to the number of predicted direct and indirect interactions (at the domain level), while the circles representing the proteins have an area proportional to the protein length. The red network reports all experimentally known HCV protein-protein interactions. Coevolution analysis is realized with a new version of the BIS tool [Dib&Carbone, 2012].

PROTEIN-PROTEIN INTERACTION AND DOCKING
Our collaborators in Lyon (R.Lavery team) have made progress in simulation as well as in the prediction of protein interactions. Protein-protein recognition has been studied by advanced all-atom molecular dynamics techniques. The separation of complexes by these methods provides access to energy profile and the role of different factors such as conformational flexibility and the impact of water and ions surrounding the protein partners. The separation of the complex constituted by ubiquitin (an enzyme responsible for marking proteins for degradation) and its recognition domain has helped shed light on the mechanism for the recognition of this particular system [Bouvier 2014].

In parallel, R.Lavery's team has developed a new coarse-grain docking approach, using a simplified representation of protein structures, based on the PaLaCe model [Pasi et al. 2013]. This model provides very encouraging results for the prediction of binding affinity constants for the formation of binary protein complexes [N Ceres, unpublished results]. The development of more refined energy terms representing both electrostatics and interactions with the solvent [Ceres et al. 2012] are underway and should lead to further improved affinity predictions. In terms of docking, the team has developed a multiple minimization approach that can deal with the flexibility of the interacting proteins (either limiting movements to side chains, to protein loops at the interface, or treating the complete protein as flexible). The use of internal coordinates (notably torsion angles) makes minimization much more efficient than it would be using Cartesian coordinates.

Fig. Comparison between experimentally measured binding affinities of different types of protein-protein complexes (x-axis, data from the "Affinity Benchmark" [Kastritis et al. Protein Science 2013]) and values predicted by PaLaCe (y-axis). The global correlation coefficient is 0.8.

Fig. Docking study between the Alpha Chymotrypsin (left) and the Eglin C (right) proteins, minimized (on 50000 randomly chosen starting points) with the PaLaCe potential. The interaction surface is colored depending on the probability that a residue will participate to the protein-protein interface (red: strong, white: medium, blue: weak). The favored surfaces are in excellent correlations with the experimental structure (visualized on the bottom).

RECENT PUBLICATIONS

A.Lopes, S.Sacquin-Mora, V.Dimitrova, E.Laine, Y.Ponty, A.Carbone, Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information, PLoS Computational Biology, 2013.

E. Laine, A. Carbone, Identification of Protein Interaction Partners from Shape Complementarity Molecular Cross-Docking. In A. Petrosino, L. Maddalena, P. Pala (Eds.), IEEE International Conference on Image Analysis and Processing (ICIAP) 2013 Workshops, LNCS 8158, pp. 318–325. Springer, Heidelberg, 2013.

A.Carbone, Extracting co-evolving characters from a tree of species. In Discrete and Topological Models in Molecular Biology, N.Jonoska, M.Saito, G.Rozenberg (eds.), Springer, 2013.

Pasi, M., Lavery, R., & Ceres, N. PaLaCe: A Coarse-Grain  Protein Model for Studying  Mechanical Properties. Journal of Chemical Theory and Computation, 9, 785–793, 2013.

N. Ceres, M. Pasi, R. Lavery. A Protein Solvation Model Based on Residue Burial. Journal of Chemical Theory and Computation 2012 8:2141-2144.

WEBLECTURE

A webLecture was organized by World Community Grid in February 2014. The video in on YouTube.

November 13, 2012

Dear All,

We are at the end of HCMD2 and I would like to thank you for the patience and persistence in running our docking program in your machines. The huge amount of cross-docking data that we collected, thanks to you (!), has been for the first time realized. It is a mine of information for our research in protein-protein interactions and it will constitute a precious amount of information also for our colleagues in the world interested in molecular docking.

We finished to analyze the data on the 168 protein complexes run on HCMD1 and we now know what has to be done next. We shall integrate novel and quantitative, experimental data on protein binding to predict not only the conformation of interacting proteins, but also which proteins will interact and how strongly. This involves four specific challenges:

1) Obtain quantitative experimental data on protein interactions with a wide range of binding affinities. We will use surface plasmon resonance
(SPR), followed by isothermal titration calorimetry (ITC) to fully characterize the thermodynamics of protein interactions over a wide range of affinities and physical conditions (concentration, pH, temperature, …). These methods constitute ideal tools for our purpose. They will be used to quantify interactions between a set of commercially available proteins, including known interacting partners. However, we will also characterize nominally non-functional "cross-interactions" within this set to test, for the first time, the common assumption that choosing single proteins from known binary complexes, or choosing proteins from different cellular compartments, implies the absence of interaction.

2) Use evolutionary sequence data to detect protein residues involved in interaction interfaces and pairs of interacting proteins. We will identify key residues within interaction sites and co-evolution signals between pairs of interaction sites in order to predict interacting partners and integrate this information into a refined molecular docking approach, with the aim of identifying binary interactions within a large set of proteins. This goal will include constructing an automated pipeline for co-evolution analysis of single proteins and protein pairs.

3) Formulate new protein-protein interaction potentials using experimental data, molecular simulations and existing structural data. Molecular simulations coupled with free energy calculations will be used to obtain an atomic-scale view of the dissociation of a limited number of the weak and strong protein interactions studied by microcalorimetry. We will determine the extent to which complexes have well-defined conformations and fully desolvated interfaces. This data will be used to formulate and iteratively refine new interaction potentials within a coarse-grain model, which will be sensitive to binding affinity.

4) Carry out a refined analysis of the large database of protein interactions that you generated (!) to characterize interaction networks and binding promiscuity. During stage two of the Help Cure Muscular Dystrophy project (HCMD2), the resources of the World Community Grid (WCG) were used to dock all possible protein pairs within a set of 2200 proteins, potentially important for understanding and treating
neurodegenerative diseases. This data will be analyzed to characterize key “hub” proteins and network structures, first, with the existing energetic and residue conservation data and then with the new methods resulting from 1-3.

The methods and interaction data derived from our studies will be freely available to the scientific community by the implementation of web servers and web databases.

We will do all this with a 4 years funding from the French ministry of research that was awarded to our group this year. We shall devote this grant to the development of the new tools (in biophysics and bioinformatics) mentioned above, as well as on the analysis of the HCMD2 dataset to arrive to the best prediction possible on the human protein-protein interaction network that you generated in these two last years.

To keep you informed on the development of the project, I shall provide news on the advancements in my webpage. Pointers to the publications will be given there. If by any chance I do not post news from more than 6 months, send me a reminder!

THANK YOU again to all of you from all the scientists of the HCMD1 and HCMD2 projects.

Best regards to all,

Alessandra

December 21, 2011

Hi to all, with Sophie and Richard we have written up an account of the docking analysis of the 168 protein complexes of the Mintseris dataset tested in phase 1. The paper is under review right now and will give you the link as soon as it is published.

The analysis of the dataset of 168 protein complexes is not finished yet! In fact, we try to improve the signals for the detection of partnership. There are two main points that one needs to keep in mind. In phase 2 we do not know the real partners and we had to use predictions of interaction sites to run MaXDO. This was because the search space on a protein surface could not be exhaustively explored, even with the help of WCG. it would be far too big! This means that we need to understant on a pool of proteins that we know (that is the 168 protein complexes) how the predictions of protein interaction sites will impact partners predictions. This is what we carefully investigate right now. It takes time! There are a number of intermediate results that you might like to know about:

1. the analysis realized in [Sacquin-Mora et al. 2008] on 12 complexes, has been scaled to 168 complexes, and it highlighted a predictive protein-protein interaction power of AUC=0.84 (see Figure A below) when using knowledge on real interaction surfaces and when exploring the whole protein surface. It is important to stress that this successful scaling of the analysis in [Sacquin-Mora et al. 2008] to 168 proteins was not an obvious guess! Why successful? The AUC is a probability measure used to evaluate the accuracy of the test. Values vary from 0 to 1, where 1 represents a perfect test and 0.5 represents a worthless test. Roughly speaking, one can think of the following ranking:
.90-1 = excellent, .80-.90 = good, .70-.80 = fair, .60-.70 = poor, .50-.60 = fail.

2. We also observed that amongst the 168 protein complexes several had the tendency to bind to nearly all other proteins and others showed very few strong interactions. Both these families of proteins negatively contribute to partnership prediction, and, when eliminated, enable the predictive power to be increased to an AUC=0.98 (see Figure B).

3. When experimental information on interaction surfaces is replaced with data from JET [Engelen 2009] (the tool for conservation analysis developed within our consortium) the predictive power only decreases slightly, with an AUC=0.82. This suggests that coupling protein interface predictions with docking is a very promising approach.

4. Nevertheless improvements are still required, since when JET predictions are used to delimit the docking area, as well as to compute the numerical index that discriminates partners, the predictive power falls at an AUC=0.59. This implies that better interaction patch detection has to be developed. However, we note that a subgroup of 20 complexes was identified where JET predictions already yielded very good predictions (AUC=0.97; Figure C below), suggesting that generating subgroups by categorizing protein interaction proclivities could improve performance.

5. Lastly, we systematically analyzed complexes in terms of the functional classes of the interacting proteins. The complexes could be grouped into: Enzyme-Inhibitors (46 proteins), Antigen-Antibody (20), Antigen-Antibody Bound (24), Others (78), and also as, Rigid Body (126), Medium (26) and Difficult (16). Interactions within certain classes, such as Enzyme-Inhibitors, were clearly easier to predict suggesting that such classifications should be considered in partnership prediction.

Figure. Matrices of pairwise interaction indexes for different subsets of proteins. High interaction scores (between 0.7 and 1, blue and black in the color scale) indicate a high probability of interaction. Proteins are ordered in the matrix such that true interacting partners lie on the diagonal. A: full dataset of 168 protein complexes. Interaction scores were computed using knowledge of the experimental interfaces (AUC=0.84). B: subset of 44 protein complexes leading to an AUC=0.98. Interaction scores were computed using knowledge on experimental interfaces. C: subset of 20 proteine complexes leading to an AUC=0.97. Interaction scores were computed using interfaces predicted by JET.

At the moment we work on coevolution between protein interfaces and on improving JET interaction predictions. With both advancements we count improving identification of new partners, and increase the AUC above. We have done a lot of work already on this. A new approach to coevolution analysis, treating especially conserved sites like protein interfaces, has been recently developed at the lab. More on this soon.

Merry Christmas and a Happy New Year to all!

Alessandra

July 31, 2010

Hi to all! Thought to try to explain what we are doing right now before you take some vacation, like the scientists here. Hope it will help to feel that things are improving and that the project is very active from this side!! Actually, someone new will join the group on september, Anne Lopes. Anne is assistant professor in structural bioinformatics and has a background in physical-chemistry. She is very interested in working on the protein partnership problem with the numerical approach we developed and on the data analysis of the huge amount of information you are producing!

The state of the art here is the following.

In the paper [S. Sacquin-Mora, A. Carbone and R. Lavery (2008), Identification of protein interaction partners and protein-protein interaction sites, J. Mol. Biol. 382, p1276-1289] we developed a numerical method to detect protein partners. The method was presented and tested on a small quantity of known protein complexes. As you can imagine, as soon as the data from HCMD Phase 1 arrived (THANKS TO YOUR CONTRIBUTION!!) we retested the approach to verify whether we could confirm the results on a larger dataset. This is indeed the case, the method works, and we can distinguish protein partners within the about 150 proteins tested. We observed that the signal is much less sharp when we work with 150 proteins than with 12 proteins (like in the paper) though and that some extra work should be done to improve the numerical method. Remember that for HCMD Phase 2 we shall search for partners among about 2200 proteins.

At the moment, we have improved the formula introduced in the paper and we are developing an "intelligent" approach to arrive fast and surely to identify a small number of potential partners for any protein.

Let me give you an insight on the complexity underlying the problem. It has something to do with the understanding of protein population. This is an important point to assimilate, if you like to understand a bit more of our analysis. When we consider a protein, we do not just study one protein (that is, its geometry and its physico-chemical properties: this is already taken into account in the docking algorithm running on your computers and into JET, the program that allowed us to predict protein binding sites) but we rather study its behaviour with the population of proteins that are around it (in the cell; for the HCMD phase 2, population means the 2200 proteins analyzed in your computers). In other words, when we look at a protein we hope to get a signal on its partnership by looking at her way to interact with all other proteins in the population. This means that we hope to learn from bad interactions as well as from good interactions. The information that YOU are giving us provides to us some insight on what is bad and what is good! but this is not enough and we shall use also some extra observation on the interaction of the protein within a population.

Some proteins are slippery, meaning that they do not seem to glue to any partner. Some others are gluing, meaning that they do glue to essentially everybody. Then there are many other proteins (about a half) that seem to stick on the right place with some specificity. They are the easiest to study. When we use, in our calculations, contributions coming from the entire population, one should think that these contributions come, in principle, from slippery proteins, gluing proteins and many other proteins whose behaviour is less sharply characterizable. "Noise" might enter into the calculation and we wish to reduce it. Learning from the whole set of interactions of a protein, means to learn to which group the protein belongs to. Once this is determined, the numerical criteria that we developed could be adjusted to accurately predict a partner or a small set of potential partners, whenever possible. The understanding of the whole set of behaviours that we need to take into account to know how to correctly evaluate the data coming from WCG is our goal today.

There are a few other concerns that are present in our analysis, and they have something to do with :

1. the algorithmic aspects concerning the handling of large amount of information to be combined for the "learning" approach I mentioned above.

2. the fact that on HCMD phase 2 data analysis, we use JET predictions of protein interaction in our numerical criteria instead of actual real interfaces as done in the paper cited above. This implies a loss of precision that we should consider in our numerical evaluations of the interactions.

These informations should give you some insight on the complexity of the question we face today. Hope that everyone will be feeling that we are advancing, together, for a project that runs alive and hopefully will reserve exciting surprises to all. We expect it.

Have a good summer! Alessandra

Computational grids are emerging as a new paradigm for sharing and aggregation of geographically distributed resources with the aim of solving large-scale computational and data intensive problems in science. This project proposes to apply this powerful computational schema to the detection of protein-protein interactions. Identifying pairs or larger complexes of functionally interacting proteins, or determining the binding of a protein to a DNA sequence or to a ligand are fundamental problems in biology with immediate consequences in drug design. This multidisciplinary project directly addresses this question by setting the goal of screening a database containing thousands of proteins, predict functional sites involved in binding to other proteins or ligand targets, and determine whether two proteins are potential interacting partners in the cell. The project will determine information on the structure of macromolecular complexes which is important not only for identifying functionally important partners, but also for determining how such interactions will be perturbed by natural or engineered site mutations in either of the interacting partners, or as the result of exogenous molecules, and, notably, pharmacophores. A database of such information would be of significant medical interest since, while it now becomes feasible to design a small molecule to inhibit or enhance the binding of a given macromolecule to a given partner, it is much more difficult to know how the same small molecule could directly or indirectly influence other existing interactions.

Given n protein structures, they are docked one against the other, that is cross-docked.
Notice that nxn interactions are tested.

Molecular modeling refers to theoretical methods and computational techniques to model or mimic the behavior of molecules. These methods and techniques are used to investigate the structure of biological systems such as protein folding or molecular recognition of protein-ligand binding, ranging from small chemical systems to large biological molecules and assemblies of material (protein complexes). Protein-ligand docking is a molecular modeling technique to predict the position and orientation (the 3D-structure) of a protein in relation to a ligand (another protein, DNA, drug, etc.). Docking methods are based on purely physical principles; even proteins of unknown function (or which have been studied relatively little) may be docked. The only prerequisite is that their 3D-structure has been either determined experimentally, or can be estimated by some theoretical technique. The docking approach generally starts with a database of known molecules and attempts to find pairs of molecules which have an affinity to bind to one another. The affinity is estimated using a so-called scoring function. In the end, a list of the best-binding molecules for a targeted protein is returned. The quality of fit has a geometric and a chemical component. The geometric component measures how well the surface shapes (the 3D-structures) complement each other like a hand in glove. The chemical component measures the quality of the atomic interactions between the partner molecules (i.e. are the interactions strong or weak?). For complex structures like proteins (the smallest are composed of hundreds of atoms), it takes considerably computer time to determine the fit of correct protein-protein interactions. Without World Community Grid, the computations required to conduct the docking was prohibitively time consuming. For the first 168 selected proteins, the estimated CPU time on a 2 GHz PC should have been about 8,000 years.

A solution to this computational barrier is to use evolutionary information to predict potential binding sites and realize localized docking only on surfaces which are most likely to interact. By using the prediction of protein binding sites, based on protein evolution, highly reduces computational time by a factor of 100 and therefore allow us to extend the analysis at large scale with the crucial help of World Community Grid. Without World Community Grid, the computations required to conduct the (localized) docking at large scale would be also prohibitively time consuming.

Volunteers donating their computer time to World Community Grid searched (in phase I) and will search (in phase II) for the best protein-protein partners.

The main pool of calculations runs on World Community Grid:
Phase I, with cross-docking of 168 proteins, has been completed.
Phase II, with targeted cross-docking of about 2200 human proteins whose three dimensional structures are known (and stored in the Protein Data Bank www.rcsb.org/pdb) will be launched at the end of march-beginning april 2009. The set of proteins include those found to be mutated in neuromuscular disord

Preliminary calculations and a posteriori analysis run on Decrypthon Grid and on Grid'5000:
Preliminary calculations for Phase I and Phase II and analysis of data for phase I have been done on French University grids.

Computational biology: Alessandra Carbone (PI) team, Analytical Genomics, FRE 3214 CNRS-UPMC, Université Pierre et Marie Curie, Paris.

Grid computing: Jean-Marie Chesneaux team, Laboratory of Computer Science (LIP6), UMR 7606 CNRS-UPMC, Université Pierre et Marie Curie, Paris.

Genetic analysis of myopathies: Pascale Guicheney team, INSERM Laboratory U582, Myology Institute, "Pitié-Salpetrière" Hospital, Paris.

Molecular modelling: Richard Lavery team, Intitut de Biologie et Chimie des Protéines, UMR 5086 CNRS-Université de Lyon, Lyon.

Thanks to the funding of AFM and CNRS, three postdocs actively partecipated from 2005 to 2008 to the advancement of the project :

Stefan Engelen - IR Genoscope, Evry
Postdoc AFM in 2006-2008
Prediction of protein binding sites and development of JET

Yann Ponty - Postdoc at Laboratoire d'Informatique de Paris 6, CNRS-UPMC
Postdoc AFM/CNRS in 2008
Analysis of data from phase 1, interface between JET and MAXDo and criteria for protein partnership prediction

Sophie Sacquin-Mora - CR CNRS - Laboratoire de Biochimie Théorique, UMR 9080 CNRS, Institut de Biologie Physico-Chimique, Paris
Postdoc AFM in 2005-2006 AFM
Development of MAXDO, criteria for protein partnership prediction

Sophie Sacquin-Mora and Yann Ponty are working actively on the project even if associated to other institutions now.

Decrypthon Program provided the infrastructure necessary to assure the portability of the software to the Decrypthon Grid and to WCG. The Laboratoire d'Informatique du Parallelisme (ENS, Lyon), partner of AFM/CNRS, gave us the possibility to run tests and a posteriori analysis on the French University Grid5000. For this, we acknowledge the work of Raphael Bolze (LIP, ENS Lyon). Nicolas Bard (LIP, ENS Lyon) and Michael Heymann (LIP, ENS Lyon) will soon replace Raphael on the same tasks.

The project run on WCG. Thousands of internautes offered their computer time to the project. To them we are most grateful.

Information obtained on the structure of macromolecular complexes is important for identifying functionally important partners, but also for determining how such interactions will be perturbed by natural or engineered site mutations. Hence, to fully understand or to control biological processes we need to predict in the most accurate manner protein interfaces for a protein structure, possibly without knowing its partners. Joint Evolutionary Trees (JET) is a method designed to detect very different types of interactions of a protein with another protein, ligands, DNA and RNA. JET uses evolutionary information, namely how certain positions within the primary sequence of a protein are conserved in a given protein family, and also physico-chemical properties of residues to finally predict the location of protein-protein interface sites on the protein surface. It uses a carefully designed sampling method making sequence analysis more sensitive to the functional and structural importance of individual residues, and a clustering method parameterized on the target structure for the detection of patches on protein surfaces and their extension into predicted interaction sites. JET is a large scale method, highly accurate and applicable to search protein partners.

JET has been conceived and developed in different stages along 4 years of work which now finalized in a publically available software package and a publication [Engelen et al 2008]. The information obtained with JET is extremely interesting for the HCMD project because we can save a large amount of computational time by restricting the search of the conformational space between the two proteins to the area surrounding the predicted interface. The data resulting from phase 1 of our project were used to adjust various parameters of the JET program and see how far we could go in filtering the starting positions in the docking calculations without loosing relevant information on protein interactions.

In order to evaluate the quality of the protein interactions modelled with the MAXDo program (which ran on WCG during phase 1 of the HCMD project) we developed an Interaction Index (II), based on the interaction energies between proteins and the residues found at the protein interface. The II value ranges from O (no complex formation) and 1 (excellent interaction between the two proteins). If we compare the distribution of the II value for all protein pairs (over 28,000) and only pairs of experimental partners (168) we see that "real partners" usually form complexes with a significantly larger II than randomly chosen partners: The average II value is only 0.18 for the whole database, while the "real" couples yield an average II of 0.32. There are also some very important variations depending on the type of complex considered. For example, in a reduced dataset excluding enzyme-inhibitor complexes, the experimental partners had a remarkable II average value of 0.91, which make then very easily distinguishable from the random partners (see Fig.1).

Fig. 1: Normalized Interaction Index matrix for a reduced dataset of ten proteins. The rows and columns are ordered so that experimentally observed complexes lie on the trailing diagonal of the matrix. These complexes clearly distinguish themselves, with a higher II, from the incorrect off-diagonal complexes.

We also worked on a faster, more efficient version of MAXDo. This new version takes into account the information produced by the JET program concerning the location of protein interfaces, so that we now need to test only around 15% of the starting positions for docking that we previously studied with MAXDo (see Fig. 3). Combined with other improvements made to MAXDo, the docking of a protein pair should take only 3% of the computation time that was necessary during Phase1.

Fig. 3: Cartoon representation of the RacGTApase/P67Phox complex (pdb code 1E96). The systematically generated starting positions for the receptor and ligand proteins are plotted as blue and red points. After filtration using JET information, docking calculations are only performed for those starting points that are located in the vicinity of JET predicted interface residues (plotted as green spheres on the proteins), thus considerably reducing the computation time.

The list of proteins considered for phase 2 was initially selected by experts on three major classes: Neuromuscular diseases, experimentally-determined (Guicheney and Carbone labs) and predicted structures (Laboratoire de Bioinformatique et Genomique Integratives, IGBMC); others (like heart and brain-related) experimental structures (Carbone lab). This initial list was later semi-automatically filtered to weed out very similar proteins. To that purpose, we used the ASTRAL structurally non-redundant database, and added a final filter based on sequence similarity (PDBSelect95 database) in order to cover as much of the structural and functional diversity as possible. Overall, 2263 single-chain proteins of lengths ranging from 25 to 800 residues will be run on the grid (See Fig. 4). Since our ability to run MAXDo on such a large dataset relies on an initial restriction of the conformation spaces, we have installed JET on the Decrypthon grid in Lyon. At the moment, JET is being run simultaneously on the machines of the grid and has already yielded results for more than half of the proteins. Full results should be available during the upcoming weeks. These results will be carefully post-processed, using conclusions from our analysis of the first phase, before we feed the JET-annotated PDB files to MAXDo.

Fig. 4: Distribution of the number of residues (x-axis) for the proteins (y-axis) in the list that will be investigated during phase 2 of the HCMD project.

S. Sacquin-Mora, A. Carbone and R. Lavery (2008), Identification of protein interaction partners and protein-protein interaction sites, J. Mol. Biol. 382, p1276-1289.

S. Engelen, L.A. Trojan, S. Sacquin-Mora, R. Lavery and A. Carbone (2009), Joint Evolutionary Trees: a large scale method to predict protein interfaces based on sequence sampling, PLoS Comp. Biol. 5(1): e1000267. doi:10.1371/journal.pcbi.1000267.

HCMD2 studies cross docking of 2246 human protein structures. In total we shall dock 2 466 753 pairs of proteins among the 2246^2 possible ones. This means that 913 627 781 945 docking initial positions should be computed by a full cross-docking. By using JET, we can reduce the docking space of 85%, that is we shall have only 137 652 178 995 conformations to be analyzed.

Phase 1 of the project studied 28 224 pairs (168 proteins) and explored a total of 10 391 124 240 conformations, that is 13,25 times less than what we shall have to compute in Phase II (in terms of docking initial positions).