Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Application of Molecular Dynamics Simulations to Determine Interactions between Canary Seed (Phalaris canariensis L.) Bioactive Peptides and Skin-Aging Enzymes

Food bioactive peptides are well recognized for their health benefits such as antimicrobial, antioxidant, and antihypertensive benefits, among others. Their drug-like behavior has led to their potential use in targeting skin-related aging factors like the inhibition of enzymes related with the skin-aging process. In this study, canary seed peptides (CSP) after simulated gastrointestinal digestion (<3 kDa) were fractioned by RP-HPLC and their enzyme-inhibition activity towards elastase and tyrosinase was evaluated in vitro. CSP inhibited elastase (IC50 = 6.2 mg/mL) and tyrosinase (IC50 = 6.1 mg/mL), while the hydrophobic fraction-VI (0.2 mg/mL) showed the highest inhibition towards elastase (93%) and tyrosinase (67%). The peptide fraction with the highest inhibition was further characterized by a multilevel in silico workflow, including physicochemical descriptor calculations, antioxidant activity predictions, and molecular dynamics-ensemble docking towards elastase and tyrosinase. To gain insights into the skin permeation process during molecular dynamics simulations, based on their docking scores, five peptides (GGWH, VPPH, EGLEPNHRVE, FLPH, and RPVNKYTPPQ) were identified to have favorable intermolecular interactions, such as hydrogen bonding of polar residues (W, H, and K) to lipid polar groups and 2-3 Å van der Waals close contact of hydrophobic aliphatic residues (P, V, and L). These interactions can play a critical role for the passive insertion of peptides into stratum corneum model skin-membranes, suggesting a promising application of CSP for skin-aging treatments.

In Silico Design of a Chimeric Humanized L-asparaginase

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in plasma, leading to starvation of leukemic cells. The ASNase formulations of E. coli and E. chrysanthemi present notorious adverse effects, especially the immunogenicity they generate, which undermine both their effectiveness as drugs and patient safety. In this study, we developed a humanized chimeric enzyme from E. coli L-asparaginase which would reduce the immunological problems associated with current L-asparaginase therapy. For these, the immunogenic epitopes of E. coli L-asparaginase (PDB: 3ECA) were determined and replaced with those of the less immunogenic Homo sapiens asparaginase (PDB:4O0H). The structures were modeled using the Pymol software and the chimeric enzyme was modeled using the SWISS-MODEL service. A humanized chimeric enzyme with four subunits similar to the template structure was obtained, and the presence of asparaginase enzymatic activity was predicted by protein-ligand docking.

Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery

The lack of biologically relevant protein structures can hinder rational design of small molecules to target G protein-coupled receptors (GPCRs). While ensemble docking using multiple models of the protein target is a promising technique for structure-based drug discovery, model clustering and selection still need further investigations to achieve both high accuracy and efficiency. In this work, we have developed an original ensemble docking approach, which identifies the most relevant conformations based on the essential dynamics of the protein pocket. This approach is applied to the study of small-molecule antagonists for the PAC1 receptor, a class B GPCR and a regulator of stress. As few as four representative PAC1 models are selected from simulations of a homology model and then used to screen three million compounds from the ZINC database and 23 experimentally validated compounds for PAC1 targeting. Our essential dynamics ensemble docking (EDED) approach can effectively reduce the number of false negatives in virtual screening and improve the accuracy to seek potent compounds. Given the cost and difficulties to determine membrane protein structures for all the relevant states, our methodology can be useful for future discovery of small molecules to target more other GPCRs, either with or without experimental structures.

Molecular Simulations and Drug Discovery of Adenosine Receptors

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Prospecting for new catechol-O-methyltransferase (COMT) inhibitors as a potential treatment for Parkinson's disease: a study by molecular dynamics and structure-based virtual screening

Parkinson's disease (PD) is a neurodegenerative, chronic, and progressive disease, common in the elderly. The catechol-O-methyltransferase (COMT) is a monomeric enzyme involved in dopamine (DA) degradation, the neurotransmitter in deficit in patients with PD. The reference treatment of PD consists of levodopa (L-dopa) administration, which is the precursor of DA. The inhibition of COMT is an adjuvant treatment in PD since it keeps DA levels constant. The goal of this study was to identify drug candidates capable of inhibiting COMT for the treatment of PD and identify important fragments of these molecules. Initially, we analyzed the flexibility of COMT and defined its main conformations in solution regarding the absence (system I) and presence of the S-adenosyl-L-methionine (SAM) cofactor (system II) through molecular dynamics (MD) simulations. Two regions in these structures were selected for molecular docking, firstly the entire cavity where the cofactor and substrates are bound and secondly the specific biding region of the enzyme substrates. Based on the conformations of the MD, the virtual screening (VS) was performed against FDA Approved and Zinc Natural Products databases aiming at the selection of the best compounds. Subsequently, the absorption, distribution, metabolization, excretion, and toxicity (ADMET) properties, as well as drug-score and drug-likeness indexes of the most promising compounds were analyzed. After a detailed analysis of the compounds selected by structure-based VS, it was possible to highlight the fragments most frequently involved in their stability: 2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole, 9H-Benz(c)indole(3,2,1-ij)(1,5)naphthyridin-9-one and (10R,13S)-10,13-dimethyl-1,2,6,7,8,9,11,12,14,15,16,17dodecahydrocyclopenta[a]phenanthren-3-one. The identification of these potential fragments is essential for the prospection of more specific inhibitors against COMT using the technique of Fragment-based lead discovery (FBLD). Besides, this study allowed us to identify the potential COMT inhibitors through a complete understanding of molecular-level interactions based on the flexibility of this protein.Communicated by Ramaswamy H. Sarma.