MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.

Development of an Automatic Pipeline for Participation in the CELPP Challenge.

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining-whenever possible-empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein-ligand complexes, which will be addressed in future versions of the pipeline.

Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB).

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein-protein, protein-DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids.

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrödinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net/

Dynamics of hERG closure allow novel insights into hERG blocking by small molecules.

Today, drug discovery routinely uses experimental assays to determine very early if a lead compound can yield certain types of off-target activity. Among such off targets is hERG. The ion channel plays a primordial role in membrane repolarization and altering its activity can cause severe heart arrhythmia and sudden death. Despite routine tests for hERG activity, rather little information is available for helping medicinal chemists and molecular modelers to rationally circumvent hERG activity. In this article novel insights into the dynamics of hERG channel closure are described. Notably, helical pairwise closure movements have been observed. Implications and relations to hERG inactivation are presented. Based on these dynamics novel insights on hERG blocker placement are presented, compared to literature, and discussed. Last, new evidence for horizontal ligand positioning is shown in light of former studies on hERG blockers.

MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories.

MOTIVATION: A variety of pocket detection algorithms are now freely or commercially available to the scientific community for the analysis of static protein structures. However, since proteins are dynamic entities, enhancing the capabilities of these programs for the straightforward detection and characterization of cavities taking into account protein conformational ensembles should be valuable for capturing the plasticity of pockets, and therefore allow gaining insight into structure-function relationships. RESULTS: This article describes a new method, called MDpocket, providing a fast, free and open-source tool for tracking small molecule binding sites and gas migration pathways on molecular dynamics (MDs) trajectories or other conformational ensembles. MDpocket is based on the fpocket cavity detection algorithm and a valuable contribution to existing analysis tools. The capabilities of MDpocket are illustrated for three relevant cases: (i) the detection of transient subpockets using an ensemble of crystal structures of HSP90; (ii) the detection of known xenon binding sites and migration pathways in myoglobin; and (iii) the identification of suitable pockets for molecular docking in P38 Map kinase. AVAILABILITY: MDpocket is free and open-source software and can be downloaded at http://fpocket.sourceforge.net. CONTACT: pschmidtke@ub.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

fpocket: online tools for protein ensemble pocket detection and tracking.

Computational small-molecule binding site detection has several important applications in the biomedical field. Notable interests are the identification of cavities for structure-based drug discovery or functional annotation of structures. fpocket is a small-molecule pocket detection program, relying on the geometric alpha-sphere theory. The fpocket web server allows: (i) candidate pocket detection--fpocket; (ii) pocket tracking during molecular dynamics, in order to provide insights into pocket dynamics--mdpocket; and (iii) a transposition of mdpocket to the combined analysis of homologous structures--hpocket. These complementary online tools allow to tackle various questions related to the identification and annotation of functional and allosteric sites, transient pockets and pocket preservation within evolution of structural families. The server and documentation are freely available at http://bioserv.rpbs.univ-paris-diderot.fr/fpocket.

Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix).

BACKGROUND: Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.g. a hydroxyl map for the ethanol) are largely influenced by the rest of the solvent molecule and do not reflect the intrinsic affinity of the moiety in question. As such, they are hardly transferable to different molecules. METHOD: To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions. RESULT: We demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential. CONCLUSION: Atomic decomposition of binding free energies derived from MDmix simulations provides transferable and quantitative binding free energy maps.

Shielded hydrogen bonds as structural determinants of binding kinetics: application in drug design.

Time scale control of molecular interactions is an essential part of biochemical systems, but very little is known about the structural factors governing the kinetics of molecular recognition. In drug design, the lifetime of drug-target complexes is a major determinant of pharmacological effects but the absence of structure-kinetic relationships precludes rational optimization of this property. Here we show that almost buried polar atoms--a common feature on protein binding sites--tend to form hydrogen bonds that are shielded from water. Formation and rupture of this type of hydrogen bonds involves an energetically penalized transition state because it occurs asynchronously with dehydration/rehydration. In consequence, water-shielded hydrogen bonds are exchanged at slower rates. Occurrence of this phenomenon can be anticipated from simple structural analysis, affording a novel tool to interpret and predict structure-kinetics relationships. The validity of this principle has been investigated on two pairs of Hsp90 inhibitors for which detailed thermodynamic and kinetic data has been experimentally determined. The agreement between macroscopic observables and molecular simulations confirms the role of water-shielded hydrogen bonds as kinetic traps and illustrates how our finding could be used as an aid in structure-based drug discovery.

Large-scale comparison of four binding site detection algorithms.

A large-scale evaluation and comparison of four cavity detection algorithms was carried out. The algorithms SiteFinder, fpocket, PocketFinder, and SiteMap were evaluated on a protein test set containing 5416 protein-ligand complexes and 9900 apo forms, corresponding to a subset of the set used earlier for benchmarking the PocketFinder algorithm. For the holo structures, all four algorithms correctly identified a similar amount of pockets (around 95%). SiteFinder, using optimized parameters, SiteMap, and fpocket showed similar pocket ranking performance, which was defined by ranking the correct binding site on rank 1 of the predictions or within the first 5 ranks of the predictions. On the apo structures, PocketFinder especially and also SiteFinder (optimized parameters) performed best, identifying 96% and 84% of all binding sites, respectively. The fpocket program predicts binding sites most accurately among the algorithms evaluated here. SiteFinder needed an average calculation time of 1.6 s compared with 2 min for SiteMap and around 2 s for fpocket.

Fpocket: an open source platform for ligand pocket detection.

BACKGROUND: Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces. RESULTS: Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster. CONCLUSION: Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at http://fpocket.sourceforge.net.

Halogens in Protein-Ligand Binding Mechanism: A Structural Perspective.

Halogen atoms have been at the center of many rational medicinal chemistry applications in drug design. While fluorine and chlorine atoms are often added to enhance physicochemical properties, bromine and iodine elements are generally inserted to improve selectivity. Favorable halogen interactions such as halogen bond have been thoroughly studied through quantum mechanics and statistical analyses. Although most of the studies focus on halogen interaction through its σ-hole, hydrogen bonding also has a significant impact. Here, we present an analysis describing the interacting environment of halogen atoms in protein-ligand context. With consideration of structural redundancy in the PDB, tendencies toward specific molecular interactions consideration have been refined and implications for rational drug design with halogens further discussed. Finally, we highlight the moderate occurrence of halogen bonding and present the other roles of halogen in protein-ligand complexes, completing the medicinal chemistry guide to rational halogen interactions.

Cordycepin is an immunoregulatory active ingredient of Cordyceps sinensis.

We have reported that cordycepin, an adenosine derivative from the fungus Cordyceps, increased interleukin (IL)-10 expression, decreased IL-2 expression and suppressed T lymphocyte activity. In the present study, we further characterized the regulatory effects of cordycepin on human immune cells. Moreover, a traditional Chinese drug, Cordyceps sinensis (CS) that contains cordycepin, was also investigated. Cytometric Bead Array (CBA) was used to determine the concentrations of IL-1beta, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, TNF-alpha and IFN-gamma in culture of peripheral blood mononuclear cells (PBMCs). The results showed that both cordycepin and CS up-regulated IL-10, IL-1beta, IL-6, IL-8 and TNF-alpha; at the same time, they suppressed phytohemagglutinin (PHA)-induced production of IL-2, IL-4, IL-5, IFN-gamma and IL-12. As compared to cordycepin, CS displayed its regulatory effects on IL-2 and IL-10 in a similar dose-dependent manner even with higher efficiency. The binding activity of transcription factors in a human monocytic cell line THP-1 was tested by the trans-AM method, and a higher binding activity of SP1 and SP3 was observed in cordycepin or CS treated cells compared to the control. These results led to the opinion that cordycepin and CS pleiotropically affected the actions of immune cells and cytokine network in a similar fashion. Cordycepin could be an important immunoregulatory active ingredient in Cordyceps sinensis. In addition, CS may contain substances which possess synergism with cordycepin, as CS showed a higher efficiency in the production of IL-10 and IL-2 than cordycepin. However, merits of these effects in pharmacology and clinical medicine have yet to be proven and the precise mechanism of these immune regulatory actions should be researched.

Premature senescence is a primary fail-safe mechanism of ERBB2-driven tumorigenesis in breast carcinoma cells.

The receptor tyrosine kinase ERBB2 plays a central role in the development of breast cancer and other epithelial malignancies. Elevated ERBB2 activity is believed to transform cells by transmitting mitogenic and antiapoptotic signals. Here we show that tightly regulated overexpression of oncogenic ERBB2 in human breast carcinoma cells does not stimulate proliferation but provokes premature senescence, accompanied by up-regulation of the cyclin-dependent kinase inhibitor P21(WAF1/CIP1). A similar effect was caused by retrovirus-mediated overexpression of oncogenic ERBB2 in low-passage murine embryonic fibroblasts. In contrast to previous observations based on constitutively overexpressing cell lines, P21 induced by tetracycline-regulated ERBB2 localizes to the nucleus in arrested cells. P21 up-regulation seems to be independent of the P53 tumor suppressor protein, and senescence-associated phenotypic alterations are reversed by specific inhibition of P38 mitogen-activated protein kinases. Functional inactivation of P21 by antisense oligonucleotides is sufficient to prevent cell cycle arrest as well as the senescent phenotype, thereby identifying the P21 protein as the key mediator of hypermitogenic cell cycle arrest and premature senescence in breast carcinoma cells. Our results may thus indicate that premature senescence represents an inherent anticarcinogenic program during ERBB2-driven mammary tumorigenesis. We propose a multistep model for the process of malignant transformation by ERBB2 wherein secondary lesions either target P21 or downstream effectors of senescence to bypass this primary fail-safe mechanism.

Dynamic undocking and the quasi-bound state as tools for drug discovery.

There is a pressing need for new technologies that improve the efficacy and efficiency of drug discovery. Structure-based methods have contributed towards this goal but they focus on predicting the binding affinity of protein-ligand complexes, which is notoriously difficult. We adopt an alternative approach that evaluates structural, rather than thermodynamic, stability. As bioactive molecules present a static binding mode, we devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a quasi-bound state at which the ligand has just broken the most important native contact with the receptor. This non-equilibrium property is surprisingly effective in virtual screening because true ligands form more-resilient interactions than decoys. Notably, DUck is orthogonal to docking and other 'thermodynamic' methods. We demonstrate the potential of the docking-undocking combination in a fragment screening against the molecular chaperone and oncology target Hsp90, for which we obtain novel chemotypes and a hit rate that approaches 40%.

Boosting interleukin-10 production: therapeutic effects and mechanisms.

More than forty cytokines have been extensively researched on the molecular structure, cell signaling and transduction pathway. With respect to cytokine-regulating therapy in immunological imbalance however, the reported results are conflicting because of the pleiotropic functions and the intricate interactions of the cytokine network. In this review, we outline the observations on interleukin-10 (IL-10) upregulatory therapy. Despite varying opinions on its therapeutic effects for different disorders, IL-10 has been considered a potential anti-inflammatory cytokine. Numerous studies support the view that IL-10 shows a strong suppressive effect on Th1 lymphocytes, antigen presenting cells and the production of inflammatory mediators. It is also noticeable that recent research has revealed the relationship between IL-10 induced antigen specific regulatory CD4+ T cells and antigen specific immune tolerance. This specific regulation was mediated in part through IL-10 secretion, because anti-IL-10 treatment reverted the inhibitory effect of regulatory T cell clones. In different models, these cells were shown to inhibit both Th1 and Th2-type inflammatory responses through the secretion of IL-10. With the presence of IL-10, regulatory T cells may induce peripheral immune tolerance. Exogenous administration, transgenic expression and endogenous stimulative agents of IL-10 have been used for a variety of inflammatory diseases, autoimmune diseases and allograft rejection in patients and experimental models. A therapeutic intervention with drug inducing endogenous IL-10 may be more practical than an exogenous administration of IL-10 with transient effect. Although further investigation on gene regulation of IL-10 is necessary, increasing studies have been reported concerning the attempt to develop the agents, which could promote endogenous IL-10 production for the treatment of immunological disorders and inflammatory diseases. With some unclear mechanisms, these agents have strongly upregulated IL-10 production in vitro or in vivo. Reported IL-10 upregulatory agents have shown promising prospects for remission of autoimmune diseases and inflammatory diseases and have even induced antigen specific immune tolerance. It is interesting that the IL-10 upregulatory effect of several traditional immunosuppressive drugs has been detected, e.g. glucocorticoid, which is considered "not more as an immunosuppressive drug but an immune modulating agent". Approximately twenty IL-10 upregulatory agents as instances are described in the present review. In addition, their therapeutic effects in various diseases are discussed.

Effect of cordycepin on interleukin-10 production of human peripheral blood mononuclear cells.

Therapeutic options for controlling autoimmune diseases are still very limited. Interleukin-10 has been reported to be a promising approach to therapeutic intervention. In the search for a drug which results in the selective upregulation of interleukin-10, we investigated the immunoregulative effects of cordycepin. We have measured interleukin-10 and interleukin-2 secretion of human peripheral blood mononuclear cells that were incubated with cordycepin and assessed the influence of cordycepin on the expression of interleukin-10 mRNA, the proliferative response and the expression of surface markers on T lymphocytes. In addition, the subsets of interleukin-10-secreting cells, the influence of anti-interleukin-10 neutralizing antibody and cytotoxicity of cordycepin were evaluated. Our results suggest that cordycepin has a significantly upregulative effect on interleukin-10 production and interleukin-10 mRNA expression. Interleukin-10-producing cells included in CD4+, CD8+, CD19+, CD56+ and CD14+ cells. At the same time, cordycepin inhibited phytohaemagglutinin-induced interleukin-2 production and proliferation of peripheral blood mononuclear cells. A restricted T lymphocyte activation was also reflected by a reduced expression of the surface markers CD25, CD45RO, CD54, CD71 and HLA DR. Anti-interleukin-10 neutralizing antibody could not completely block the suppressive effect of cordycepin on production of interleukin-2. Cordycepin in the effective concentration presented slight cytotoxicity but did not increase apoptosis. These results indicate that cordycepin exerts immunoregulative effects. Further research on it may provide an approach for the development of novel immunomodulatory drugs which directly alter the secretion of cytokines.

Surface expression and genotypes of Toll-like receptors 2 and 4 in patients with juvenile idiopathic arthritis and systemic lupus erythematosus.

BACKGROUND: Chronic arthritis is a common feature of juvenile idiopathic arthritis (JIA) and systemic lupus erythematosus (SLE). It was subsequently discovered that Toll-like receptors (TLRs) are able to upregulate cytokine production in response to endogenous ligands released after tissue damage, suggesting that TLRs can maintain an inflammatory response even in absence of pathogen. Thus, TLRs may contribute to increased inflammation in JIA and SLE patients. The aim of this study was to investigate the role of TLRs in JIA and SLE. We examined the in vivo expression and polymorphisms of TLR2 and TLR4 in peripheral monocytes of patients with JIA and SLE during active and inactive disease phases. METHODS: This single center cohort study consisted of JIA and SLE affected children and control subjects. TLR2 and TLR4 protein expression on CD14+ monocytes was examined by flow cytometry. TLR2 and TLR4 genotypes were determined using the polymerase chain reaction-restriction fragment length polymorphism method (RFLP-PCR). RESULTS: A significant reduction in the level of TLR4 expression (p ≤ 0.001) was observed on monocytes of patients with JIA and SLE compared with that of healthy control subjects. There was no correlation between the TLR2 or TLR4 genotypes and the observed differential TLR protein expression on monocytes. CONCLUSIONS: To conclude, our observations suggest involvement of investigated TLRs in the pathogenesis of JIA and SLE. It still remains to be elucidated whether reduced TLR4 expression is cause of chronic arthritis or a result of some feedback loop.

Understanding and predicting druggability. A high-throughput method for detection of drug binding sites.

Druggability predictions are important to avoid intractable targets and to focus drug discovery efforts on sites offering better prospects. However, few druggability prediction tools have been released and none has been extensively tested. Here, a set of druggable and nondruggable cavities has been compiled in a collaborative platform ( http://fpocket.sourceforge.net/dcd ) that can be used, contributed, and curated by the community. Druggable binding sites are often oversimplified as closed, hydrophobic cavities, but data set analysis reveals that polar groups in druggable binding sites have properties that enable them to play a decisive role in ligand recognition. Finally, the data set has been used in conjunction with the open source fpocket suite to train and validate a logistic model. State of the art performance was achieved for predicting druggability on known binding sites and on virtual screening experiments where druggable pockets are retrieved from a pool of decoys. The algorithm is free, extremely fast, and can effectively be used to automatically sieve through massive collections of structures ( http://fpocket.sourceforge.net ).