Carles Pons

A global genetic interaction network maps a wiring diagram of cellular function

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships. A global network of genetic interaction profile similarities. (Left) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. (Right) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms. We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

Towards the prediction of protein interaction partners using physical docking

Deciphering the whole network of protein interactions for a given proteome (‘interactome’) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well‐established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non‐redundant potential interactors. We additionally show that true interactions can be distinguished from non‐likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel‐energy model’; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non‐binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks.

pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring

UNLABELLED pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. AVAILABILITY AND IMPLEMENTATION The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Pushing Structural Information into the Yeast Interactome by High-Throughput Protein Docking Experiments

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.

Present and future challenges and limitations in protein-protein docking.

The study of protein–protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein–protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid‐body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid‐body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain–domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy‐based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run. Proteins 2010.

Structural Characterization of Protein-Protein Complexes by Integrating Computational Docking with Small-angle Scattering Data

X-ray crystallography and NMR can provide detailed structural information of protein-protein complexes, but technical problems make their application challenging in the high-throughput regime. Other methods such as small-angle X-ray scattering (SAXS) are more promising for large-scale application, but at the cost of lower resolution, which is a problem that can be solved by complementing SAXS data with theoretical simulations. Here, we propose a novel strategy that combines SAXS data and accurate protein-protein docking simulations. The approach has been benchmarked on a large pool of known structures with synthetic SAXS data, and on three experimental examples. The combined approach (pyDockSAXS) provided a significantly better success rate (43% for the top 10 predictions) than either of the two methods alone. Further analysis of the influence of different docking parameters made it possible to increase the success rates for specific cases, and to define guidelines for improving the data-driven protein-protein docking protocols.

Scoring by Intermolecular Pairwise Propensities of Exposed Residues (SIPPER): A New Efficient Potential for Protein―Protein Docking

A detailed and complete structural knowledge of the interactome is one of the grand challenges in Biology, and a variety of computational docking approaches have been developed to complement experimental efforts and help in the characterization of protein-protein interactions. Among the different docking scoring methods, those based on physicochemical considerations can give the maximum accuracy at the atomic level, but they are usually computationally demanding and necessarily noisy when implemented in rigid-body approaches. Coarser-grained knowledge-based potentials are less sensitive to details of atomic arrangements, thus providing an efficient alternative for scoring of rigid-body docking poses. In this study, we have extracted new statistical potentials from intermolecular pairs of exposed residues in known complex structures, which were then used to score protein-protein docking poses. The new method, called SIPPER (scoring by intermolecular pairwise propensities of exposed residues), combines the value of residue desolvation based on solvent-exposed area with the propensity-based contribution of intermolecular residue pairs. This new scoring function found a near-native orientation within the top 10 predictions in nearly one-third of the cases of a standard docking benchmark and proved to be also useful as a filtering step, drastically reducing the number of docking candidates needed by energy-based methods like pyDock.

Exploring genetic suppression interactions on a global scale

A global genetic suppression network The genetic background of an organism can influence the overall effects of new genetic variants. Some mutations can amplify a deleterious phenotype, whereas others can suppress it. Starting with a literature survey and expanding into a genomewide assay, van Leeuwen et al. generated a large-scale suppression network in yeast. The data set reveals a set of general properties that can be used to predict suppression interactions. Furthermore, the study provides a template for extending suppression studies to other genes or to more complex organisms. Science, this issue p. 599 A large-scale study in yeast reveals how defects associated with a mutation in one gene can be compensated for by a second mutation in a suppressor gene. INTRODUCTION Genetic suppression occurs when the phenotypic defects caused by a mutated gene are rescued by a mutation in another gene. These genetic interactions can connect genes that work within the same pathway or biological process, providing new mechanistic insights into cellular function, or they can correct defects in gene expression or protein production. More generally, suppression interactions may play an important role in the genetics underlying human diseases, such as the diverse penetrance of Mendelian disease variants. Our ability to interpret personal genome sequences remains limited, in part, because we lack an understanding of how sequence variants interact in nonadditive ways to generate profound phenotypes, including genetic suppression. RATIONALE Genetic interactions, in which mutations in two different genes combine to generate an unexpected phenotype, may underlie a significant component of trait heritability. Although genetic interactions that compromise fitness, such as synthetic lethality, have been mapped extensively, suppression interactions have not been explored systematically. To understand the general principles of genetic suppression and to examine the extent to which these interactions reflect cellular function, we harnessed the powerful genetics of the budding yeast Saccharomyces cerevisiae to assemble aglobal network of genetic suppression interactions. RESULTS By analyzing hundreds of published papers, we assembled a network of genetic suppression interactions involving ~1300 different yeast genes and ~1800 unique interactions. Through automated genetic mapping and whole-genome sequencing, we also isolated an unbiased, experimental set of ~200 spontaneous suppressor mutations that correct the fitness defects of deletion or hypomorphic mutant alleles. Integrating these results yielded a global suppression network. The majority of suppression interactions identified novel gene-gene connections, thus providing new information about the functional wiring diagram of a cell. Most suppression pairs connected functionally related genes, including genes encoding members of the same pathway or complex. The functional enrichments observed for suppression gene pairs were several times as high as those found for other types of genetic interactions; this highlighted their discovery potential for assigning gene function. Our systematic suppression analysis also identified a prevalent allele-specific mechanism of suppression, whereby growth defects of hypomorphic alleles can be overcome by mutations that compromise either protein or mRNA degradation machineries. From whole-genome sequencing of suppressor strains, we also identified additional secondary mutations, the vast majority of which appeared to be random passenger mutations. However, a small subset of genes was enriched for secondary mutations, several of which did not affect growth rate but rather appeared to delay the onset of the stationary phase. This delay suggests that they are selected for under laboratory growth conditions because they increase cell abundance within a propagating population. CONCLUSION A global network of genetic suppression interactions highlights the major potential for systematic studies of suppression to map cellular function. Our findings allowed us to formulate and quantify the general mechanisms of genetic suppression, which has the potential to guide the identification of modifier genes affecting the penetrance of genetic traits, including human disease. Global analysis of genetic suppression. Genetic suppression interactions occur when the detrimental effects of a primary mutation can be overcome by a secondary mutation. Both literature-curated and experimentally derived suppression interactions were collected and yielded a genetic suppression network. This global network was enriched for functional relationships and defined distinct mechanistic classes of genetic suppression. Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell. In addition to suppressor mutations, we identified frequent secondary mutations,in a subset of genes, that likely cause a delay in the onset of stationary phase, which appears to promote their enrichment within a propagating population. These findings allow us to formulate and quantify general mechanisms of genetic suppression.

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

BackgroundProtein-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.ResultsHere 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.ConclusionsThe 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.