GUILDify v2.0:A Tool to Identify Molecular Networks Underlying Human Diseases, Their Comorbidities and Their Druggable Targets

The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease-gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein-protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2).The authors received support from: ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026); IMI-JU under grants agreements no. 116030 (TransQST) and no. 777365 (eTRANSAFE), resources of which are composed of financial contribution from the EU-FP7 (FP7/2007- 2013) and EFPIA companies in kind contribution; the EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate); the Spanish Ministry of Economy (MINECO) [BIO2017-85329-R] [RYC-2015-17519]; "Unidad de Excelencia María de Maeztu", funded by the Spanish Ministry of Economy [ref: MDM-2014-0370]. The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I+D+i 2013-2016, funded by ISCIII and FEDER

On the mechanisms of protein interactions : predicting their affinity from unbound tertiary structures

Motivation: The characterization of the protein–protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein–protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures. Results: We present a new approach that relies on the unbound protein structures and protein docking to predict protein–protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol. Availability and implementation: The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock

An ensemble learning approach for modeling the systems biology of drug-induced injury

Background: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. Results: We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. Conclusions: When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.The authors received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements TransQST and eTRANSAFE (refs: 116030, 777365). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA companies in kind contribution. The authors also received support from Spanish Ministry of Economy (MINECO, refs: BIO2017–85329-R (FEDER, EU), RYC-2015-17519) as well as EU H2020 Programme 2014–2020 under grant agreement No. 676559 (Elixir-Excelerate) and from Agència de Gestió D’ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR, ref.: 2017SGR01020). L.I.F. received support from ISCIII-FEDER (ref: CPII16/00026). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013–2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). J.A.P. received support from the CAMDA Travel Fellowship

In silico tools to study diseases and polypharmacology through the lens of network medicine

El funcionament intern de les cèl·lules pot entendre’s com un conjunt d’interaccions entre biomolècules, formant una xarxa que coneixem amb el nom d’interactoma. Els fàrmacs i malalties poden considerar-se pertorbacions d’aquesta xarxa, modulant directament molècules específiques, però indirectament comunitats de molècules les interaccions de les quals es veuen afectades per la pertorbació. La medicina de xarxes busca representar i analitzar amb precisió les xarxes biològiques, per tal d’entendre millor les malalties i aconseguir tractaments més segurs i eficaços. En aquesta tesi, presento diverses eines in silico basades en la medicina de xarxes, pensades per estudiar els mecanismes moleculars de malalties i fàrmacs. Aquestes eines s’utilitzen en un ampli ventall d’aplicacions de la medicina de xarxes, com per exemple l’estudi de comorbiditats, endofenotips, efectes secundaris, reutilització i combinació de fàrmacs

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods

Network, transcriptomic and genomic features differentiate genes relevant for drug response

Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.We received support from ISCIII-FEDER (PI13/00082, CP10/00524, and CPII16/00026), IMI-JU under grant agreements no. 116030 (TransQST) and no. 777365 (eTRANSAFE) resources of which are composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU H2020 Program 2014–2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and was supported by grant PT13/0001/0023, of the PE I+D+i 2013-2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). AG-P was supported by a Ramón y Cajal contract (RYC-2013-14554). BO and JA-P were supported by MINECO grant BIO2014-57518-R

Delta9-tetrahydrocannabinol modulates the proteasome system in the brain

Cannabis is the most consumed illicit drug worldwide. Its principal psychoactive component, Δ9-tetrahydrocannabinol (THC), affects multiple brain functions, including cognitive performance, by modulating cannabinoid type-1 (CB1) receptors. These receptors are strongly enriched in presynaptic terminals, where they modulate neurotransmitter release. We analyzed, through a proteomic screening of hippocampal synaptosomal fractions, those proteins and pathways modulated 3 h after a single administration of an amnesic dose of THC (10 mg/kg, i.p.). Using an isobaric labeling approach, we identified 2040 proteins, 1911 of them previously reported in synaptic proteomes, confirming the synaptic content enrichment of the samples. Initial analysis revealed a significant alteration of 122 proteins, where 42 increased and 80 decreased their expression. Gene set enrichment analysis indicated an over-representation of mitochondrial associated functions and cellular metabolic processes. A second analysis focusing on extreme changes revealed 28 proteins with altered expression after THC treatment, 15 of them up-regulated and 13 down-regulated. Using a network topology-based scoring algorithm we identified those proteins in the mouse proteome with the greatest association to the 28 modulated proteins. This analysis pinpointed a significant alteration of the proteasome function, since top scoring proteins were related to the proteasome system (PS), a protein complex involved in ATP-dependent protein degradation. In this regard, we observed that THC decreases 20S proteasome chymotrypsin-like protease activity in the hippocampus. Our data describe for the first time the modulation of the PS in the hippocampus following THC administration under amnesic conditions that may contribute to an aberrant plasticity at synapses.This study was supported by Grants from the Ministerio de Economía, Innovación y Competitividad (MINECO) (#BFU2015-68568-P (MINECO/FEDER, UE) to A.O., #BFU2015-69717-P (MINECO/FEDER, UE) to RRV and AB, #RYC-2011-08391 to AB, #SAF2017-90664-REDT to RRV and AB, #SAF2014-59648-P and #SAF2017-84060-R to R.M.; a grant from the Instituto de Salud Carlos III (#RD16/0017/0020) to R.M.; Generalitat de Catalunya (2017SGR-669 to R.M. and 2017SGR1776 to RRV and AB) and ICREA (Institució Catalana de Recerca i Estudis Avançats) Academia to A.O. and R.M. Mass spectrometric analyses were performed at the CRG/UPF Proteomics Unit which is part of the of Proteored, PRB3 and is supported by grant PT17/0019, of the PE I + D + i 2013-2016, funded by ISCIII and ERDF. Grant “Unidad de Excelencia María de Maeztu”, funded by the MINECO (#MDM-2014-0370); PLAN E (Plan Español para el Estímulo de la Economía y el Empleo); FEDER funding is also acknowledge

SPServer: split-statistical potentials for the analysis of protein structures and protein–protein interactions

Background: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. Results: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. Conclusions: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. Server address: https://sbi.upf.edu/spserver/ .Medicine, Faculty ofOther UBCNon UBCMedical Genetics, Department ofReviewedFacult