InteractoMIX:A suite of computational tools to exploit interactomes in biological and clinical research

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com)

New Developments in Protein Structure Modelling for Biological and Clinical Research New Developments in Protein Structure Modelling for Biological and Clinical Research InteractoMIX: a suite of computational tools to exploit interactomes in biological an

Abstract Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com)

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

Discovery of biological paths activating main regulators : Application on Salmonella infection and drug-drug interacions

Cellular behaviour is regulated by a very precise system in which the complex interplay between various types of biomolecules plays a crucial role for the proper functioning of the system itself. Proteins interacting with DNA are in charged of its replication, packaging repair and recombination, among them, transcription factors regulate gene expression, but in turn they are part of a larger network of proteins interactions. In this thesis I addressed these aspects, in relation to a certain phenotype, in first instance by identifying common regulators of genes with similar expression signatures derived from high-throughput experiments, and then by computationally modelling the signal transduction by means of a message-passing algorithm. This allowed the identification of main regulators in the specific systems describing the infection process of Salmonella spp. in two different hosts: Arabidopsis thaliana and Homo sapiens. The same approach showed encouraging results in the pharmaco-dynamic study of drug-drug interactions.El comportament cel•lular és regulat per un complex conjunt de relacions entre diferents tipus de biomol•lècules, que juga un paper fonamental per al correcte funcionament del propi sistema cel•lular. Diferents proteïnes s’encarreguen de la replicació, empaquetament, reparació i recombinació de l’ADN, i en regulen la seva expressió per mitjà de factors de transcripció. Aquests modulen la seva activitat mitjançant interaccions amb d’altres proteïnes, formant part d’una xarxa d’interaccions molt més extensa. En aquesta tesi m’interesso per aquests aspectes en relació a certs fenotips. Primer, identificant reguladors comuns de gens amb patrons d’expressió similars, obtinguts d’experiments d’alt rendiment; després, usant d’algorismes de transmissió del missatge per al modelat computacional de la transducció del senyal. Així he identificat reguladors principals en el procés d’infecció de Salmonella spp. en Arabidopsis thaliana i Homo sapiens. Una aproximació idèntica aporta resultats esperançadors en l’estudi de la farmacodinàmica de les interaccions entre drogues

Discovery of biological paths activating main regulators : Application on Salmonella infection and drug-drug interacions

Cellular behaviour is regulated by a very precise system in which the complex interplay between various types of biomolecules plays a crucial role for the proper functioning of the system itself. Proteins interacting with DNA are in charged of its replication, packaging repair and recombination, among them, transcription factors regulate gene expression, but in turn they are part of a larger network of proteins interactions. In this thesis I addressed these aspects, in relation to a certain phenotype, in first instance by identifying common regulators of genes with similar expression signatures derived from high-throughput experiments, and then by computationally modelling the signal transduction by means of a message-passing algorithm. This allowed the identification of main regulators in the specific systems describing the infection process of Salmonella spp. in two different hosts: Arabidopsis thaliana and Homo sapiens. The same approach showed encouraging results in the pharmaco-dynamic study of drug-drug interactions.El comportament cel•lular és regulat per un complex conjunt de relacions entre diferents tipus de biomol•lècules, que juga un paper fonamental per al correcte funcionament del propi sistema cel•lular. Diferents proteïnes s’encarreguen de la replicació, empaquetament, reparació i recombinació de l’ADN, i en regulen la seva expressió per mitjà de factors de transcripció. Aquests modulen la seva activitat mitjançant interaccions amb d’altres proteïnes, formant part d’una xarxa d’interaccions molt més extensa. En aquesta tesi m’interesso per aquests aspectes en relació a certs fenotips. Primer, identificant reguladors comuns de gens amb patrons d’expressió similars, obtinguts d’experiments d’alt rendiment; després, usant d’algorismes de transmissió del missatge per al modelat computacional de la transducció del senyal. Així he identificat reguladors principals en el procés d’infecció de Salmonella spp. en Arabidopsis thaliana i Homo sapiens. Una aproximació idèntica aporta resultats esperançadors en l’estudi de la farmacodinàmica de les interaccions entre drogues

Understanding protein recognition using structural features

Protein-Protein interactions (PPIs) play a crucial role in virtually all cell processes. Thus, understanding the molecular mechanism of protein recognition is a critical challenge in molecular biology. Previous works in this field show that not only the binding region but also the rest of the protein is involved in the interac- tion, suggesting a funnel-like recognition model as responsible of facilitating the interacting process. Fur- ther more, we have previously shown that three-dimensional local structural features (groups of protein loops) define characteristic patterns (interaction signatures) that can be used to predict whether two pro- teins will interact or not. A notable trait of this prediction system is that interaction signatures can be denoted as favouring or disfavouring depending on their role on the promotion of the molecular binding. Here, we use such features in order to determine differences between the binding interface and the rest of the protein surface in known PPIs. Particularly, we study computationally three different groups of protein-protein interfaces: i) native interfaces (the actual binding patches of the interacting pairs), ii) par- tial interfaces (the docking between a binding patch and a non-interacting patch), and iii) back-to-back interfaces (the docking between non-interacting patches for both of the interacting proteins). Our results show that the interaction signatures in partial interfaces are much less favoured than the ones observed in native and back-to-back interfaces. We hypothesise that this phenomenon is related to the dynamics of the molecular association process. Back-to-back interfaces preserve the exposure of the real interacting patches (thus, allowing the formation of a native interface), while in a partial interface one interacting patch is sequestered and becomes unavailable to form a native interaction

Inferring causal molecular networks: empirical assessment through a community-based effor

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.This work was supported in part by the US National Institutes of Health (National Cancer Institute (NCI) grants U54 CA 112970 (to J.W.G.) and 5R01CA180778 (to J.M.S.), NCI award U54CA143869 to M.F.C. and National Institute of General Medical Sciences award 1R01GM109031 to J.M.S.), the Susan G. Komen Foundation (SAC110012 to J.W.G.), the Prospect Creek Foundation (grant to J.W.G.), the EuroinvesXgacion program of MICINN (Spanish Ministry of Science and InnovaXon), partners of the ERASysBio+ iniXaXve supported under the EU ERA-NET Plus Scheme in FP7 (SHIPREC), MICINN (FEDER BIO2008-0205, FEDER BIO2011-22568 and EUI2009-04018 to B.O.), the Royal Society (Wolfson Research Merit Award to S.M.), the German Federal Ministry of Education and Research GANI_MED Consortium (grant 03IS2061A to T.K.), and the US National Library of Medicine (grants R00LM010822 (to X.J.) and R01LM011663 (to X.J. and R.E.N.)