Probing the Dynamic Nature of Water Molecules and Their Influences on Ligand Binding in a Model Binding Site

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features

Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it is difficult to identify the conserved water molecules (CWMs) from free water molecules (FWMs) directly as CWMs are normally deeply embedded in proteins and form strong hydrogen bonds with surrounding polar groups. To circumvent this difficulty, in this work, the abundance of spatial structure information and physicochemical properties of water molecules in proteins inspires us to adopt machine learning methods for identifying the CWMs. Therefore, in this study, a machine learning framework to identify the CWMs in the binding sites of the proteins was presented. First, by analyzing water molecules' physicochemical properties and spatial structure information, six features (i.e., atom density, hydrophilicity, hydrophobicity, solvent-accessible surface area, temperature B-factors, and mobility) were extracted. Those features were further analyzed and combined to reach a higher CWM identification rate. As a result, an optimal feature combination was determined. Based on this optimal combination, seven different machine learning models (including support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), discriminant analysis (DA), naïve Bayes (NB), and ensemble learning (EL)) were evaluated for their abilities in identifying two categories of water molecules, i.e., CWMs and FWMs. It showed that the EL model was the desired prediction model due to its comprehensive advantages. Furthermore, the presented methodology was validated through a case study of crystal 3skh and extensively compared with Dowser++. The prediction performance showed that the optimal feature combination and the desired EL model in our method could achieve satisfactory prediction accuracy in identifying CWMs from FWMs in the proteins' binding sites.

Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening

The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.

Molecular Dynamics Simulations of Adenosine Receptors: Advances, Applications and Trends

:

Adenosine receptors (ARs) are transmembrane proteins that belong to the G protein-coupled receptors (GPCRs) superfamily and mediate the biological functions of adenosine. To date, four AR subtypes are known, namely A1, A2A, A2B and A3 that exhibit different signaling pathways, tissue localization, and mechanisms of activation. Moreover, the widespread ARs and their implication in numerous physiological and pathophysiological conditions had made them pivotal therapeutic targets for developing clinically effective agents.

:

The crystallographic success in identifying the 3D crystal structures of A2A and A1 ARs has dramatically enriched our understanding of their structural and functional properties such as ligand binding and signal transduction. This, in turn, has provided a structural basis for a larger contribution of computational methods, particularly molecular dynamics (MD) simulations, toward further investigation of their molecular properties and designing bioactive ligands with therapeutic potential. MD simulation has been proved to be an invaluable tool in investigating ARs and providing answers to some critical questions. For example, MD has been applied in studying ARs in terms of ligand-receptor interactions, molecular recognition, allosteric modulations, dimerization, and mechanisms of activation, collectively aiding in the design of subtype selective ligands.

:

In this review, we focused on the advances and different applications of MD simulations utilized to study the structural and functional aspects of ARs that can foster the structure-based design of drug candidates. In addition, relevant literature was briefly discussed which establishes a starting point for future advances in the field of drug discovery to this pivotal group of drug targets.

Interactions of carbon nanotubes and/or graphene with manganese peroxidase during biodegradation of endocrine disruptors and triclosan

Molecular-level biodegradation processes of bisphenol A (BPA), nonylphenol (NP) and triclosan (TCS) mediated by manganese peroxidase (MnP) were investigated with and without single-walled carbon nanotube (SWCNT) and/or graphene (GRA). Although the incorporation of SWCNT, GRA or their combination (SWCNT+GRA) did not break up the complexes composed of manganese peroxidase (MnP) and these substrates, they had different effects on the native contacts between the substrates and MnP. GRA tended to decrease the overall stability of the binding between MnP and its substrates. SWCNT or SWCNT+GRA generally had a minor impact on the mean binding energy between MnP and its substrates. We detected some sensitive residues from MnP that were dramatically disturbed by the GRA, SWCNT or SWCNT+GRA. Nanomaterials changed the number and behavior of water molecules adjacent to both MnP and its substrates, which was not due to the destruction of H-bond network formed by sensitive regions and water molecules. The present results are useful for understanding the molecular basis of pollutant biodegradation affected by the nanomaterials in the environment, and are also helpful in assessing the risks of these materials to the environment.

Combining properties of different classes of PI3Kα inhibitors to understand the molecular features that confer selectivity

Phosphoinositide 3-kinases (PI3Ks) are major regulators of many cellular functions, and hyperactivation of PI3K cell signalling pathways is a major target for anticancer drug discovery. PI3Kα is the isoform most implicated in cancer, and our aim is to selectively inhibit this isoform, which may be more beneficial than concurrent inhibition of all Class I PI3Ks. We have used structure-guided design to merge high-selectivity and high-affinity characteristics found in existing compounds. Molecular docking, including the prediction of water-mediated interactions, was used to model interactions between the ligands and the PI3Kα affinity pocket. Inhibition was tested using lipid kinase assays, and active compounds were tested for effects on PI3K cell signalling. The first-generation compounds synthesized had IC₅₀ (half maximal inhibitory concentration) values >4 μM for PI3Kα yet were selective for PI3Kα over the other Class I isoforms (β, δ and γ). The second-generation compounds explored were predicted to better engage the affinity pocket through direct and water-mediated interactions with the enzyme, and the IC₅₀ values decreased by ∼30-fold. Cell signalling analysis showed that some of the new PI3Kα inhibitors were more active in the H1047R mutant bearing cell lines SK-OV-3 and T47D, compared with the E545K mutant harbouring MCF-7 cell line. In conclusion, we have used a structure-based design approach to combine features from two different compound classes to create new PI3Kα-selective inhibitors. This provides new insights into the contribution of different chemical units and interactions with different parts of the active site to the selectivity and potency of PI3Kα inhibitors.

Single-walled carbon nanotube release affects the microbial enzyme-catalyzed oxidation processes of organic pollutants and lignin model compounds in nature

The question how microbial enzyme-catalyzed oxidation processes of organic pollutants and lignin model compounds (LMCs) are affected by the release of single-walled carbon nanotube (SWCNT) into the environment remains to be addressed at the molecular level. We have, therefore concentrated the effects of SWCNT on some important properties associated with enzyme activity and function during microbial oxidation of polycyclic aromatic hydrocarbons (benzo(a)pyrene, acenaphthene and anthracene), LMCs (2,6-dimethoxyphenol, guaiacol and veratryl alcohol) and β-hexachlorocyclohexane, including the behaviour of water molecules, hydrogen bonds (HBs) and hydrophobic interactions (HYs) between ligand and the enzyme, and conformational dynamics in N- and C-terminus. Our study revealed that SWCNT significantly affected the behaviour of water molecules within 5 Å of both these substrates and their respective enzymes during oxidation (p < 0.01), by increasing or decreasing the water number near them. SWCNT tended to significantly enhance or reduce the stability of atom pairs that formed the HBs and HYs (p < 0.01). N- and C-terminus conformations underwent transitions between positive and negative states or between positive state or between negative state in all analyzed complexes. Significant conformational transitions were found for all C-terminus, but only for a part of N-terminus after the inclusion of the SWCNT. These results showed that SWCNT release would significantly affect the microbial enzyme-catalyzed processes of organic pollutants and LMCs in nature.

Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study

Recent advances in structural biology revealed that water molecules play a crucial structural role in the protein architecture and ligand binding of G protein-coupled receptors. In this work, we present an alternative approach to monitor the time-dependent organization of water molecules during the final stage of the ligand-receptor recognition process by means of membrane molecular dynamics simulations. We inspect the variation of fluid dynamics properties of water molecules upon ligand binding with the aim to correlate the results with the binding affinities. The outcomes of this analysis are transferred into a bidimensional graph called water fluid dynamics maps, that allow a fast graphical identification of protein "hot-spots" characterized by peculiar shape and electrostatic properties that can play a critical role in ligand binding. We hopefully believe that the proposed approach might represent a valuable tool for structure-based drug discovery that can be extended to cases where crystal structures are not yet available, or have not been solved at high resolution.

Strategies to calculate water binding free energies in protein-ligand complexes

Water molecules are commonplace in protein binding pockets, where they can typically form a complex between the protein and a ligand or become displaced upon ligand binding. As a result, it is often of great interest to establish both the binding free energy and location of such molecules. Several approaches to predicting the location and affinity of water molecules to proteins have been proposed and utilized in the literature, although it is often unclear which method should be used under what circumstances. We report here a comparison between three such methodologies, Just Add Water Molecules (JAWS), Grand Canonical Monte Carlo (GCMC), and double-decoupling, in the hope of understanding the advantages and limitations of each method when applied to enclosed binding sites. As a result, we have adapted the JAWS scoring procedure, allowing the binding free energies of strongly bound water molecules to be calculated to a high degree of accuracy, requiring significantly less computational effort than more rigorous approaches. The combination of JAWS and GCMC offers a route to a rapid scheme capable of both locating and scoring water molecules for rational drug design.

Blind prediction of interfacial water positions in CAPRI

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

A combination strategy to inhibit Pim-1: synergism between noncompetitive and ATP-competitive inhibitors

Pim-1 is a serine/threonine kinase critically involved in the initiation and progression of various types of cancer, especially leukemia, lymphomas and solid tumors such as prostate, pancreas and colon, and is considered a potential drug target against these malignancies. In an effort to discover new potent Pim-1 inhibitors, a previously identified ATP-competitive indolyl-pyrrolone scaffold was expanded to derive structure-activity relationship data. A virtual screening campaign was also performed, which led to the discovery of additional ATP-competitive inhibitors as well as a series of 2-aminothiazole derivatives, which are noncompetitive with respect to both ATP and peptide substrate. This mechanism of action, which resembles allosteric inhibition, has not previously been characterized for Pim-1. Notably, further evaluation of the 2-aminothiazoles indicated a synergistic inhibitory effect in enzymatic assays when tested in combination with ATP-competitive inhibitors. A synergistic effect in the inhibition of cell proliferation by ATP-competitive and ATP-noncompetitive compounds was also observed in prostate cancer cell lines (PC3), where all Pim-1 inhibitors tested in showed synergism with the known anticancer agent, paclitaxel. These results further establish Pim-1 as a target in cancer therapy, and highlight the potential of these agents for use as adjuvant agents in the treatment of cancer diseases in which Pim-1 is associated with chemotherapeutic resistance.

Quantifying the molecular origins of opposite solvent effects on protein-protein interactions

Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments.