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.

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters.

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome.

Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory and degenerative diseases, and many more. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help explain and predict off-targets among the kinase family. Here, we present the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. We could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib's known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group), although belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as a conda package: https://github.com/volkamerlab/kissim.

Mechanism of praziquantel action at a parasitic flatworm ion channel.

Praziquantel (PZQ) is an essential medicine for treating parasitic flatworm infections such as schistosomiasis, which afflicts over 250 million people. However, PZQ is not universally effective, lacking activity against liver flukes of the Fasciola genus. The reason for this insensitivity is unclear, as the mechanism of PZQ action is unknown. Here, we use ligand- and target-based methods to demonstrate that PZQ activates a transient receptor potential melastatin ion channel (TRPMPZQ) in schistosomes by engaging a hydrophobic ligand binding pocket within the voltage sensor­like domain of the channel to cause calcium entry and worm paralysis. PZQ activates TRPMPZQ homologs in other PZQ-sensitive flukes, but not Fasciola hepatica. However, a single amino acid change in the F. hepatica TRPMPZQ binding pocket, to mimic schistosome TRPMPZQ, confers PZQ sensitivity. After decades of clinical use, the molecular basis of PZQ action at a druggable TRP channel is resolved.

Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies.

Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.

Short linear motif candidates in the cell entry system used by SARS-CoV-2 and their potential therapeutic implications.

The first reported receptor for SARS-CoV-2 on host cells was the angiotensin-converting enzyme 2 (ACE2). However, the viral spike protein also has an RGD motif, suggesting that cell surface integrins may be co-receptors. We examined the sequences of ACE2 and integrins with the Eukaryotic Linear Motif (ELM) resource and identified candidate short linear motifs (SLiMs) in their short, unstructured, cytosolic tails with potential roles in endocytosis, membrane dynamics, autophagy, cytoskeleton, and cell signaling. These SLiM candidates are highly conserved in vertebrates and may interact with the µ2 subunit of the endocytosis-associated AP2 adaptor complex, as well as with various protein domains (namely, I-BAR, LC3, PDZ, PTB, and SH2) found in human signaling and regulatory proteins. Several motifs overlap in the tail sequences, suggesting that they may act as molecular switches, such as in response to tyrosine phosphorylation status. Candidate LC3-interacting region (LIR) motifs are present in the tails of integrin ß3 and ACE2, suggesting that these proteins could directly recruit autophagy components. Our findings identify several molecular links and testable hypotheses that could uncover mechanisms of SARS-CoV-2 attachment, entry, and replication against which it may be possible to develop host-directed therapies that dampen viral infection and disease progression. Several of these SLiMs have now been validated to mediate the predicted peptide interactions.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting.

In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

KinMap: a web-based tool for interactive navigation through human kinome data.

BACKGROUND: Annotations of the phylogenetic tree of the human kinome is an intuitive way to visualize compound profiling data, structural features of kinases or functional relationships within this important class of proteins. The increasing volume and complexity of kinase-related data underlines the need for a tool that enables complex queries pertaining to kinase disease involvement and potential therapeutic uses of kinase inhibitors. RESULTS: Here, we present KinMap, a user-friendly online tool that facilitates the interactive navigation through kinase knowledge by linking biochemical, structural, and disease association data to the human kinome tree. To this end, preprocessed data from freely-available sources, such as ChEMBL, the Protein Data Bank, and the Center for Therapeutic Target Validation platform are integrated into KinMap and can easily be complemented by proprietary data. The value of KinMap will be exemplarily demonstrated for uncovering new therapeutic indications of known kinase inhibitors and for prioritizing kinases for drug development efforts. CONCLUSION: KinMap represents a new generation of kinome tree viewers which facilitates interactive exploration of the human kinome. KinMap enables generation of high-quality annotated images of the human kinome tree as well as exchange of kinome-related data in scientific communications. Furthermore, KinMap supports multiple input and output formats and recognizes alternative kinase names and links them to a unified naming scheme, which makes it a useful tool across different disciplines and applications. A web-service of KinMap is freely available at http://www.kinhub.org/kinmap/ .

Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay.

Kinome-wide screening would have the advantage of providing structure-activity relationships against hundreds of targets simultaneously. Here, we report the generation of ligand-based activity prediction models for over 280 kinases by employing Machine Learning methods on an extensive data set of proprietary bioactivity data combined with open data. High quality (AUC > 0.7) was achieved for ∼200 kinases by (1) combining open with proprietary data, (2) choosing Random Forest over alternative tested Machine Learning methods, and (3) balancing the training data sets. Tests on left-out and external data indicate a high value for virtual screening projects. Importantly, the derived models are evenly distributed across the kinome tree, allowing reliable profiling prediction for all kinase branches. The prediction quality was further improved by employing experimental bioactivity fingerprints of a small kinase subset. Overall, the generated models can support various hit identification tasks, including virtual screening, compound repurposing, and the detection of potential off-targets.

Perturbation Approaches for Exploring Protein Binding Site Flexibility to Predict Transient Binding Pockets.

Simulations of the long-time scale motions of a ligand binding pocket in a protein may open up new perspectives for the design of compounds with steric or chemical properties differing from those of known binders. However, slow motions of proteins are difficult to access using standard molecular dynamics (MD) simulations and are thus usually neglected in computational drug design. Here, we introduce two nonequilibrium MD approaches to identify conformational changes of a binding site and detect transient pockets associated with these motions. The methods proposed are based on the rotamerically induced perturbation (RIP) MD approach, which employs perturbation of side-chain torsional motion for initiating large-scale protein movement. The first approach, Langevin-RIP (L-RIP), entails a series of short Langevin MD simulations, each starting with perturbation of one of the side-chains lining the binding site of interest. L-RIP provides extensive sampling of conformational changes of the binding site. In less than 1 ns of MD simulation with L-RIP, we observed distortions of the α-helix in the ATP binding site of HSP90 and flipping of the DFG loop in Src kinase. In the second approach, RIPlig, a perturbation is applied to a pseudoligand placed in different parts of a binding pocket, which enables flexible regions of the binding site to be identified in a small number of 10 ps MD simulations. The methods were evaluated for four test proteins displaying different types and degrees of binding site flexibility. Both methods reveal all transient pocket regions in less than a total of 10 ns of simulations, even though many of these regions remained closed in 100 ns conventional MD. The proposed methods provide computationally efficient tools to explore binding site flexibility and can aid in the functional characterization of protein pockets, and the identification of transient pockets for ligand design.

Identification and Visualization of Kinase-Specific Subpockets.

The identification and design of selective compounds is important for the reduction of unwanted side effects as well as for the development of tool compounds for target validation studies. This is, in particular, true for therapeutically important protein families that possess conserved folds and have numerous members such as kinases. To support the design of selective kinase inhibitors, we developed a novel approach that allows identification of specificity determining subpockets between closely related kinases solely based on their three-dimensional structures. To account for the intrinsic flexibility of the proteins, multiple X-ray structures of the target protein of interest as well as of unwanted off-target(s) are taken into account. The binding pockets of these protein structures are calculated and fused to a combined target and off-target pocket, respectively. Subsequently, shape differences between these two combined pockets are identified via fusion rules. The approach provides a user-friendly visualization of target-specific areas in a binding pocket which should be explored when designing selective compounds. Furthermore, the approach can be easily combined with in silico alanine mutation studies to identify selectivity determining residues. The potential impact of the approach is demonstrated in four retrospective experiments on closely related kinases, i.e., p38α vs Erk2, PAK1 vs PAK4, ITK vs AurA, and BRAF vs VEGFR2. Overall, the presented approach does not require any profiling data for training purposes, provides an intuitive visualization of a large number of protein structures at once, and could also be applied to other target classes.

Pocketome of human kinases: prioritizing the ATP binding sites of (yet) untapped protein kinases for drug discovery.

Protein kinases are involved in a variety of diseases including cancer, inflammation, and autoimmune disorders. Although the development of new kinase inhibitors is a major focus in pharmaceutical research, a large number of kinases remained so far unexplored in drug discovery projects. The selection and assessment of targets is an essential but challenging area. Today, a few thousands of experimentally determined kinase structures are available, covering about half of the human kinome. This large structural source allows guiding the target selection via structure-based druggability prediction approaches such as DoGSiteScorer. Here, a thorough analysis of the ATP pockets of the entire human kinome in the DFG-in state is presented in order to prioritize novel kinase structures for drug discovery projects. For this, all human kinase X-ray structures available in the PDB were collected, and homology models were generated for the missing part of the kinome. DoGSiteScorer was used to calculate geometrical and physicochemical properties of the ATP pockets and to predict the potential of each kinase to be druggable. The results indicate that about 75% of the kinome are in principle druggable. Top ranking structures comprise kinases that are primary targets of known approved drugs but additionally point to so far less explored kinases. The presented analysis provides new insights into the druggability of ATP binding pockets of the entire kinome. We anticipate this comprehensive druggability assessment of protein kinases to be helpful for the community to prioritize so far untapped kinases for drug discovery efforts.

Protein pocket and ligand shape comparison and its application in virtual screening.

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

TRAPP: a tool for analysis of transient binding pockets in proteins.

We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.

Predicting enzymatic function from global binding site descriptors.

Due to the rising number of solved protein structures, computer-based techniques for automatic protein functional annotation and classification into families are of high scientific interest. DoGSiteScorer automatically calculates global descriptors for self-predicted pockets based on the 3D structure of a protein. Protein function predictors on three levels with increasing granularity are built by use of a support vector machine (SVM), based on descriptors of 26632 pockets from enzymes with known structure and enzyme classification. The SVM models represent a generalization of the available descriptor space for each enzyme class, subclass, and substrate-specific sub-subclass. Cross-validation studies show accuracies of 68.2% for predicting the correct main class and accuracies between 62.8% and 80.9% for the six subclasses. Substrate-specific recall rates for a kinase subset are 53.8%. Furthermore, application studies show the ability of the method for predicting the function of unknown proteins and gaining valuable information for the function prediction field.

DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment.

MOTIVATION: Many drug discovery projects fail because the underlying target is finally found to be undruggable. Progress in structure elucidation of proteins now opens up a route to automatic structure-based target assessment. DoGSiteScorer is a newly developed automatic tool combining pocket prediction, characterization and druggability estimation and is now available through a web server. AVAILABILITY: The DoGSiteScorer web server is freely available for academic use at http://dogsite.zbh.uni-hamburg.de CONTACT: rarey@zbh.uni-hamburg.de.