The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Treatment pathway analysis of newly diagnosed dementia patients in four electronic health record databases in Europe.

PURPOSE: Real-world studies to describe the use of first, second and third line therapies for the management and symptomatic treatment of dementia are lacking. This retrospective cohort study describes the first-, second- and third-line therapies used for the management and symptomatic treatment of dementia, and in particular Alzheimer's Disease. METHODS: Medical records of patients with newly diagnosed dementia between 1997 and 2017 were collected using four databases from the UK, Denmark, Italy and the Netherlands. RESULTS: We identified 191,933 newly diagnosed dementia patients in the four databases between 1997 and 2017 with 39,836 (IPCI (NL): 3281, HSD (IT): 1601, AUH (DK): 4474, THIN (UK): 30,480) fulfilling the inclusion criteria, and of these, 21,131 had received a specific diagnosis of Alzheimer's disease. The most common first line therapy initiated within a year (± 365 days) of diagnosis were Acetylcholinesterase inhibitors, namely rivastigmine in IPCI, donepezil in HSD and the THIN and the N-methyl-D-aspartate blocker memantine in AUH. CONCLUSION: We provide a real-world insight into the heterogeneous management and treatment pathways of newly diagnosed dementia patients and a subset of Alzheimer's Disease patients from across Europe.

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

UNLABELLED: PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities. AVAILABILITY AND IMPLEMENTATION: The PysGeNET platform is freely available at http://www.psygenet.org/. The PsyGeNET database is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). CONTACT: lfurlong@imim.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular and clinical diseasome of comorbidities in exacerbated COPD patients.

The frequent occurrence of comorbidities in patients with chronic obstructive pulmonary disease (COPD) suggests that they may share pathobiological processes and/or risk factors.To explore these possibilities we compared the clinical diseasome and the molecular diseasome of 5447 COPD patients hospitalised because of an exacerbation of the disease. The clinical diseasome is a network representation of the relationships between diseases, in which diseases are connected if they co-occur more than expected at random; in the molecular diseasome, diseases are linked if they share associated genes or interaction between proteins.The results showed that about half of the disease pairs identified in the clinical diseasome had a biological counterpart in the molecular diseasome, particularly those related to inflammation and vascular tone regulation. Interestingly, the clinical diseasome of these patients appears independent of age, cumulative smoking exposure or severity of airflow limitation.These results support the existence of shared molecular mechanisms among comorbidities in COPD.

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Network medicine analysis of COPD multimorbidities.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest. METHODS: We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts. RESULTS: Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities. CONCLUSIONS: The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.

Expanding the frontiers of protein-protein modeling: from docking and scoring to binding affinity predictions and other challenges.

In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics.

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.

Optimization of pyDock for the new CAPRI challenges: Docking of homology-based models, domain-domain assembly and protein-RNA binding.

We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology-based modeling of the interacting subunits, domain-domain assembling, and protein-RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound-bound target T29, the second best model among scorers for the protein-RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking.

Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis: a multi-national multi-database case-control study.

BACKGROUND: Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk. METHODS: A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) who were identified from electronic health records in the UK, Spain, Denmark and the Netherlands. Conditional logistic regression models were fitted to estimate the risk of dementia. RESULTS: Prior methotrexate use was associated with a lower risk of dementia (OR 0.71, 95% CI 0.52-0.98). Furthermore, methotrexate use with therapy longer than 4 years had the lowest risk of dementia (odds ratio 0.37, 95% CI 0.17-0.79). Sulfasalazine use was not associated with dementia (odds ratio 0.88, 95% CI 0.57-1.37). CONCLUSIONS: Further studies are still required to clarify the relationship between prior methotrexate use and duration as well as biological treatments with dementia risk.

Identification of hot-spot residues in protein-protein interactions by computational docking.

BACKGROUND: The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex. RESULTS: We have applied here normalized interface propensity (NIP) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex. CONCLUSION: The NIP values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.

Prediction and scoring of docking poses with pyDock.

The two previous CAPRI experiments showed the success of our rigid-body and refinement approach. For this third edition of CAPRI, we have used a new faster protocol called pyDock, which uses electrostatics and desolvation energy to score docking poses generated with FFT-based algorithms. In target T24 (unbound/model), our best prediction had the highest value of fraction of native contacts (40%) among all participants, although it was not considered as acceptable by the CAPRI criteria. In target T25 (unbound/bound), we submitted a model with medium quality. In target T26 (unbound/unbound), we did not submit any acceptable model (but we would have submitted acceptable predictions if we had included available mutational information about the binding site). For targets T27 (unbound/unbound) and T28 (homo-dimer using model), nobody (including us) submitted any acceptable model. Intriguingly, the crystal structure of target T27 shows an alternative interface that correlates with available biological data (we would have submitted acceptable predictions if we had included this). We also participated in all targets of the SCORERS experiment, with at least acceptable accuracy in all valid cases. We submitted two medium and four acceptable scoring models of T25. Using additional distance restraints (from mutational data), we had two medium and two acceptable scoring models of T26. For target T27, we submitted two acceptable scoring models of the alternative interface in the crystal structure. In summary, CAPRI showed the excellent capabilities of pyDock in identifying near-native docking poses.

Collective variable driven molecular dynamics to improve protein-protein docking scoring.

In biophysics, the structural prediction of protein-protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study, we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein-protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein-protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations.

Protein-protein docking and hot-spot prediction for drug discovery.

Most processes in living organisms occur through an intricate network of protein-protein interactions, in which any malfunctioning can lead to pathological situations. Therefore, current research in biomedicine is starting to focus on protein interaction networks. A detailed structural knowledge of these interactions at molecular level will be necessary for drug discovery targeting protein-protein interactions. The challenge from a structural biology point of view is determining the structure of the specific complex formed upon interaction of two or several proteins, and/or locating the surface residues involved in the interaction and identify which of them are the most important ones for binding (hot-spots). In this line, an increasing number of computer tools are available to complement experimental efforts. Docking algorithms can achieve successful predictive rates in many complexes, as shown in the community assessment experiment CAPRI, and have already been applied to a variety of cases of biomedical interest. On the other side, many methods for interface and hotspot prediction have been reported, based on a variety of evolutionary, geometrical and physico-chemical parameters. Computer predictions are reaching a significant level of maturity, and can be very useful to guide experiments and suggest mutations, or to provide a mechanistic framework to the experimental results on a given interaction. We will review here existing computer approaches for proteinprotein docking, interface prediction and hot-spot identification, with focus to drug discovery targeting protein-protein interactions.

Allosteric conversation in the androgen receptor ligand-binding domain surfaces.

Androgen receptor (AR) is a major therapeutic target that plays pivotal roles in prostate cancer (PCa) and androgen insensitivity syndromes. We previously proposed that compounds recruited to ligand-binding domain (LBD) surfaces could regulate AR activity in hormone-refractory PCa and discovered several surface modulators of AR function. Surprisingly, the most effective compounds bound preferentially to a surface of unknown function [binding function 3 (BF-3)] instead of the coactivator-binding site [activation function 2 (AF-2)]. Different BF-3 mutations have been identified in PCa or androgen insensitivity syndrome patients, and they can strongly affect AR activity. Further, comparison of AR x-ray structures with and without bound ligands at BF-3 and AF-2 showed structural coupling between both pockets. Here, we combine experimental evidence and molecular dynamic simulations to investigate whether BF-3 mutations affect AR LBD function and dynamics possibly via allosteric conversation between surface sites. Our data indicate that AF-2 conformation is indeed closely coupled to BF-3 and provide mechanistic proof of their structural interconnection. BF-3 mutations may function as allosteric elicitors, probably shifting the AR LBD conformational ensemble toward conformations that alter AF-2 propensity to reorganize into subpockets that accommodate N-terminal domain and coactivator peptides. The induced conformation may result in either increased or decreased AR activity. Activating BF-3 mutations also favor the formation of another pocket (BF-4) in the vicinity of AF-2 and BF-3, which we also previously identified as a hot spot for a small compound. We discuss the possibility that BF-3 may be a protein-docking site that binds to the N-terminal domain and corepressors. AR surface sites are attractive pharmacological targets to develop allosteric modulators that might be alternative lead compounds for drug design.

Docking and scoring: applications to drug discovery in the interactomics era.

BACKGROUND: Computational approaches such as docking and scoring are becoming routine in drug discovery as a complement to other more traditional techniques. However, so far, computer drug design methods have been applied to inhibit the function of individual proteins, and there is little available data on the use of these computational techniques to target protein-protein interactions. OBJECTIVE: To establish a strategy for the use of current computational tools in drug discovery targeting protein-protein interactions. METHOD: Individual techniques applied to specific cases could be studied to derive a general strategy for targeting protein-protein interactions. CONCLUSION: Protein docking, interface prediction and hot-spot identification can contribute to the discovery of small molecule inhibitors targeting protein interactions of therapeutic interest, especially when little structural information is available.

Computer applications for prediction of protein-protein interactions and rational drug design.

In recent years, protein-protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein-protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein-protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein-protein interactions of therapeutic significance. While rational design of protein-protein interaction inhibitors is at its very early stage, the first results are promising.