Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.

Efficient virtual high-content screening using a distance-aware transformer model.

Molecular similarity search is an often-used method in drug discovery, especially in virtual screening studies. While simple one- or two-dimensional similarity metrics can be applied to search databases containing billions of molecules in a reasonable amount of time, this is not the case for complex three-dimensional methods. In this work, we trained a transformer model to autoencode tokenized SMILES strings using a custom loss function developed to conserve similarities in latent space. This allows the direct sampling of molecules in the generated latent space based on their Euclidian distance. Reducing the similarity between molecules to their Euclidian distance in latent space allows the model to perform independent of the similarity metric it was trained on. While we test the method here using 2D similarity as proof-of-concept study, the algorithm will enable also high-content screening with time-consuming 3D similarity metrics. We show that the presence of a specific loss function for similarity conservation greatly improved the model's ability to predict highly similar molecules. When applying the model to a database containing 1.5 billion molecules, our model managed to reduce the relevant search space by 5 orders of magnitude. We also show that our model was able to generalize adequately when trained on a relatively small dataset of representative structures. The herein presented method thereby provides new means of substantially reducing the relevant search space in virtual screening approaches, thus highly increasing their throughput. Additionally, the distance awareness of the model causes the efficiency of this method to be independent of the underlying similarity metric.

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent developments in generative deep neural networks rendered possible the parallel, statistically independent sampling of molecular configurations. To be able to accurately generate samples of large molecular systems from a high-dimensional multimodal equilibrium distribution function, we developed a hierarchical approach based on expressive normalizing flows with rational quadratic neural splines and coarse-grained representation. Furthermore, system specific priors and adaptive and property-based controlled learning was designed to diminish the likelihood for the generation of high-energy structures during sampling. Finally, backmapping from a coarse-grained to fully atomistic representation is performed through an equivariant transformer model. We demonstrate the applicability of the method on the one-shot configurational sampling of a protein system with more than a hundred amino acids. The results show enhanced expressivity that diminish the invertibility constraints inherent in the normalizing flow framework. Moreover, the capacity of the hierarchical normalizing flow model was tested on a challenging case study of the folding/unfolding dynamics of the peptide chignolin.

Indolin-2-one derivatives as selective Aurora B kinase inhibitors targeting breast cancer.

Aurora B is a pivotal cell cycle regulator where errors in its function results in polyploidy, genetic instability, and tumorigenesis. It is overexpressed in many cancers, consequently, targeting Aurora B with small molecule inhibitors constitutes a promising approach for anticancer therapy. Guided by structure-based design and molecular hybridization approach we developed a series of fifteen indolin-2-one derivatives based on a previously reported indolin-2-one-based multikinase inhibitor (1). Seven derivatives, 5g, 6a, 6c-e, 7, and 8a showed preferential antiproliferative activity in NCI-60 cell line screening and out of these, carbamate 6e and cyclopropylurea 8a derivatives showed optimum activity against Aurora B (IC50 = 16.2 and 10.5 nM respectively) and MDA-MB-468 cells (IC50 = 32.6 ± 9.9 and 29.1 ± 7.3 nM respectively). Furthermore, 6e and 8a impaired the clonogenic potential of MDA-MB-468 cells. Mechanistic investigations indicated that 6e and 8a induced G2/M cell cycle arrest, apoptosis, and necrosis of MDA-MB-468 cells and western blot analysis of 8a effect on MDA-MB-468 cells revealed 8a's ability to reduce Aurora B and its downstream target, Histone H3 phosphorylation. 6e and 8a displayed better safety profiles than multikinase inhibitors such as sunitinib, showing no cytotoxic effects on normal rat cardiomyoblasts and murine hepatocytes. Finally, 8a demonstrated a more selective profile than 1 when screened against ten related kinases. Based on these findings, 8a represents a promising candidate for further development to target breast cancer via Aurora B selective inhibition.

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning.

Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. Despite being currently neglected in drug discovery, this hydration phenomenon could lead to new avenues in optimizing the free energy of ligand binding. Application of deep neural networks incorporating hydration to docking provides 89% accuracy in binding pose ranking, an essential step for rational structure-based drug design.

Instantaneous generation of protein hydration properties from static structures.

Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen.

Proliferating cell nuclear antigen (PCNA) is a central factor in DNA replication and repair pathways that plays an essential role in genome stability. The functional roles of PCNA are mediated through an extensive list of protein-protein interactions, each of which transmits specific information in protein assemblies. The flexibility at the PCNA-protein interaction interfaces offers opportunities for the discovery of functionally selective inhibitors of DNA repair pathways. Current fragment-based drug design methodologies can be limited by the flexibility of protein interfaces. These factors motivated an approach to defining compounds that could leverage previously identified subpockets on PCNA that are suitable for fragment-binding sites. Methodologies for screening multiple connected fragment-binding events in distinct subpockets are deployed to improve the selection of fragment combinations. A flexible backbone based on N-alkyl-glycine amides offers a scaffold to combinatorically link multiple fragments for in silico screening libraries that explore the diversity of subpockets at protein interfaces. This approach was applied to discover new potential inhibitors of DNA replication and repair that target PCNA in a multiprotein recognition site. The screens of the libraries were designed to computationally filter ligands based upon the fragments and positions to <1%, which were synthesized and tested for direct binding to PCNA. Molecular dynamics simulations also revealed distinct features of these novel molecules that block key PCNA-protein interactions. Furthermore, a Bayesian classifier predicted 15 of the 16 new inhibitors to be modulators of protein-protein interactions, demonstrating the method's utility as an effective screening filter. The cellular activities of example ligands with similar affinity for PCNA demonstrate unique properties for novel selective synergy with therapeutic DNA-damaging agents in drug-resistant contexts.

Improving Atom-Type Diversity and Sampling in Cosolvent Simulations Using λ-Dynamics.

Cosolvent molecular dynamics (MD) simulations perform MD simulations of the protein in explicit water mixed with cosolvent molecules that represent functional groups of ligands potentially binding to the protein. The competition between different probes and water molecules allows the identification of the energetic preference of functional groups in different binding site moieties including enthalpic and entropic contributions. Cosolvent MD simulations have recently been applied to a variety of different questions in structure-based drug design but still have significant shortcomings. Among those issues is the limited chemical diversity of probe molecules ignoring the chemical context of the pharmacophoric feature represented by a probe. Here we present a novel cosolvent MD simulation method based on the λ-dynamics simulation concept that significantly increases the chemical diversity of functional groups investigated during cosolvent simulations. Application to four different test cases highlights the utility of the new approach to identify binding preferences of different functional groups and to correctly rank ligand series that differ by their substitution patterns.

Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations.

Co-solvent molecular dynamics (MD) simulations have recently become successful approaches in structure-based drug design but neglect important interactions such as halogen bonding. To be able to successfully model compound libraries containing halogenated ligands using co-solvent simulations, we investigate the use of halogenated benzene probes in co-solvent simulations on the two test systems human cathepsin L (hCatL) and the Y220C mutant of the tumor suppressor p53 (p53-Y220C). Our studies demonstrate that halogenated benzene probes indeed can unambiguously identify halogen-bonding interaction sites in the binding pocket and show superior correlation and ranking performance compared to standard co-solvent approaches.

Rubiscolins are naturally occurring G protein-biased delta opioid receptor peptides.

The impact that ß-arrestin proteins have on G protein-coupled receptor trafficking, signaling and physiological behavior has gained much appreciation over the past decade. A number of studies have attributed the side effects associated with the use of naturally occurring and synthetic opioids, such as respiratory depression and constipation, to excessive recruitment of ß-arrestin. These findings have led to the development of biased opioid small molecule agonists that do not recruit ß-arrestin, activating only the canonical G protein pathway. Similar G protein-biased small molecule opioids have been found to occur in nature, particularly within kratom, and opioids within salvia have served as a template for the synthesis of other G protein-biased opioids. Here, we present the first report of naturally occurring peptides that selectively activate G protein signaling pathways at δ opioid receptors, but with minimal ß-arrestin recruitment. Specifically, we find that rubiscolin peptides, which are produced as cleavage products of the plant protein rubisco, bind to and activate G protein signaling at δ opioid receptors. However, unlike the naturally occurring δ opioid peptides leu-enkephalin and deltorphin II, the rubiscolin peptides only very weakly recruit ß-arrestin 2 and have undetectable recruitment of ß-arrestin 1 at the δ opioid receptor.

Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites.

Molecular dynamics (MD) simulations allow for accurate prediction of the thermodynamic profile of binding-site water molecules critical for protein-ligand association. Whereas this hydration-site profiling converges rapidly for solvent-exposed sites independent of the initial water placement, an accurate and reliable placement is required for water molecules in occluded binding sites. Here, we present an accurate and efficient hydration-site prediction method for occluded binding sites combining water placement based on 3D-RISM and MD simulations using WATsite.

Applying ligands profiling using multiple extended electron distribution based field templates and feature trees similarity searching in the discovery of new generation of urea-based antineoplastic kinase inhibitors.

This study provides a comprehensive computational procedure for the discovery of novel urea-based antineoplastic kinase inhibitors while focusing on diversification of both chemotype and selectivity pattern. It presents a systematic structural analysis of the different binding motifs of urea-based kinase inhibitors and the corresponding configurations of the kinase enzymes. The computational model depends on simultaneous application of two protocols. The first protocol applies multiple consecutive validated virtual screening filters including SMARTS, support vector-machine model (ROC = 0.98), Bayesian model (ROC = 0.86) and structure-based pharmacophore filters based on urea-based kinase inhibitors complexes retrieved from literature. This is followed by hits profiling against different extended electron distribution (XED) based field templates representing different kinase targets. The second protocol enables cancericidal activity verification by using the algorithm of feature trees (Ftrees) similarity searching against NCI database. Being a proof-of-concept study, this combined procedure was experimentally validated by its utilization in developing a novel series of urea-based derivatives of strong anticancer activity. This new series is based on 3-benzylbenzo[d]thiazol-2(3H)-one scaffold which has interesting chemical feasibility and wide diversification capability. Antineoplastic activity of this series was assayed in vitro against NCI 60 tumor-cell lines showing very strong inhibition of GI(50) as low as 0.9 uM. Additionally, its mechanism was unleashed using KINEX™ protein kinase microarray-based small molecule inhibitor profiling platform and cell cycle analysis showing a peculiar selectivity pattern against Zap70, c-src, Mink1, csk and MeKK2 kinases. Interestingly, it showed activity on syk kinase confirming the recent studies finding of the high activity of diphenyl urea containing compounds against this kinase. Allover, the new series, which is based on a new kinase scaffold with interesting chemical diversification capabilities, showed that it exhibits its "emergent" properties by perturbing multiple unexplored kinase pathways.

A highly selective structure-based virtual screening model of Palm I allosteric inhibitors of HCV Ns5b polymerase enzyme and its application in the discovery and optimization of new analogues.

First structure-based activity prediction model of topologically diverse inhibitors of Palm I allosteric site of HCV NS5b polymerase enzyme is reported here. The model is a workflow of structure-based pharmacophore followed by guided docking. The pharmacophore was constructed using a novel procedure which includes PLIF (protein ligand interaction fingerprint), Hypogen, contact-based pharmacophore and shape constraints. The guided docking was tweaked using both a scoring function of high correlation with activity (ChemPLP) and essential pharmacophore features. Statistically, ROC analysis for the workflow, deploying the novel technique of virtual decoys, yielded AUC of 0.947. Experimentally, the model was used to screen Asinex GOLD database yielding a new hit with a different scaffold which was further confirmed by synthesis and biological evaluation.

Integrated structure-based activity prediction model of benzothiadiazines on various genotypes of HCV NS5b polymerase (1a, 1b and 4) and its application in the discovery of new derivatives.

This work presents the first structure-based activity prediction model for benzothiadiazines against various genotypes of HCV NS5b polymerase (1a, 1b and 4).The model is a comprehensive workflow of structure-based field template followed by guided docking. The field template was used as a pre-filter and a tool to provide hits in good orientation and position. It was created based on detailed molecular interaction field analysis which includes Topomer CoMFA, grid independent analysis and Superstar. On the other hand, Guided docking was used as a refinement and assessment tool. It was actively directed by two scores: Moldock score as an interaction descriptor (r(2)=0.65) and a template similarity score as a measure for accurate binding-mode compliance. The docking template was based on energy-based pharmacophore analysis. The whole procedure was formulated and tweaked for both screening (ROC of AUC=0.91) and activity prediction (r(2) of 0.8) for the genotype 1a. In order to widen the model scope, linear interaction energy was used as a tool for predicting activities of other genotypes based on the docked ligand poses while mutation binding energy was used to investigate the effect of each amino acid mutation in genotype 4. The model was applied for structure-based fragment hopping by screening a library designed by reaction enumeration. A top scoring hit was used to generate a focused library such that it has lower TPSA than the original class ligands and thus better pharmacokinetic properties. After that, experimental validation was carried out by the synthesis of this library and its biological evaluation which yielded compounds that exhibit EC(50) ranging from 1.86 to 23 µM.

Synthesis, binding studies and molecular modeling of novel cannabinoid receptor ligands.

In the present work, we report upon the design, synthesis and biological evaluation of new anandamide derivatives obtained by modifications of the fatty acyl chain and/or of the ethanolamide 'tail'. The compounds are of the general formula: 6-(substituted-phenyl)/naphthyl-4-oxohex-5-enoic acid N-substituted amide and 7-naphthyl-5-oxohept-6-enoicacid N-substituted amide. The novel compounds had been evaluated for their binding affinity to CB1/CB2 cannabinoid receptors, binding studies showed that some of the newly developed compounds have measurable affinity and selectivity for the CB2 receptor. Compounds XI and XVIII showed the highest binding affinity for CB2 receptor. None of the compounds exhibited inhibitory activity towards anandamide hydrolysis, thus arguing in favor of their enzymatic stability. The structure-activity relationship has been extensively studied through a tailor-made homological model using constrained docking in addition to pharmacophore analysis, both feature and field based.