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.

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.

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.

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).

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.

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.

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.

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.

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

The impact that β-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 β-arrestin. These findings have led to the development of biased opioid small molecule agonists that do not recruit β-arrestin, activating only the canonical G protein pathway.

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.

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.

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.

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.

Optimization of Benzoisothiazole dioxide inhibitory activity of the NS5B polymerase of HCV genotype 4 using ligand-steered homological modeling, reaction-driven scaffold-hopping and Enovo workflow.

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. The virus is highly prevalent in the Middle East and Africa particularly Egypt with more than 90% of infections due to genotype 4. Nonstructural (NS5B) viral proteins have emerged as an attractive target for HCV antivirals discovery.

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.