Rapid and accurate prediction and scoring of water molecules in protein binding sites

HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures

Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures remains a significant challenge, making it a vibrant research area. In this paper, we introduce HydraProt (deep Hydration of Proteins), a novel methodology for predicting precise positions of water molecule oxygen atoms around protein structures, leveraging two interconnected deep learning architectures: a 3D U-net and a Multi-Layer Perceptron (MLP). Our approach starts by introducing a coarse voxel-based representation of the protein, which allows for rapid sampling of candidate water positions via the 3D U-net. These water positions are then assessed by embedding the water-protein relationship in the Euclidean space by means of an MLP. Finally, a postprocessing step is applied to further refine the MLP predictions. HydraProt surpasses existing state-of-the-art approaches in terms of precision and recall and has been validated on large data sets of protein structures. Notably, our method offers rapid inference runtime and should constitute the method of choice for protein structure studies and drug discovery applications. Our pretrained models, data, and the source code required to reproduce these results are accessible at https://github.com/azamanos/HydraProt.

In-silico lead identification of the pan-mutant IDH1 and IDH2 inhibitors to target glioblastoma

Glioblastoma (GBM) is an aggressive malignant type of brain tumor. Targeting one single intracellular pathway might not alleviate the disease, rather it activates the other molecular pathways that lead to the worsening of the disease condition. Therefore, in this study, we attempted to target both isocitrate dehydrogenase 1 (IDH1) and IDH2, which are one of the most commonly mutated proteins in GBM and other cancer types. Here, standard precision and extra precision docking, IFD, MM-GBSA, QikProp, and molecular dynamics (MD) simulation were performed to identify the potential dual inhibitor for IDH1 and IDH2 from the enamine database containing 59,161 ligands. Upon docking the ligands with IDH1 (PDB: 6VEI) and IDH2 (PDB: 6VFZ), the top eight ligands were selected, based on the XP Glide score. These ligands produced favourable MMGBSA scores and ADME characteristics. Finally, the top four ligands 12953, 44825, 51295, and 53210 were subjected to MD analysis. Interestingly, 53210 showed maximum interaction with Gln 277 for 99% in IDH1 and Gln 316 for 100% in IDH2, which are the crucial amino acids for the inhibitory function of IDH1 and IDH2 to target GBM. Therefore, the present study attempts to identify the novel molecules which could possess a pan-inhibitory action on both IDH1 and IDH that could be crucial in the management of GBM. Yet further evaluation involving in vitro and in vivo studies is warranted to support the data in our current study.Communicated by Ramaswamy H. Sarma.

The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering

Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.

Elucidation of a Thermodynamical Feature Attributed to Substrate Binding to the Prokaryotic H+/Oligopeptide Cotransporter YdgR with Calorimetric Analysis: The Substrate Binding Driven by the Change in Entropy Implies the Release of Bound Water Molecules from the Binding Pocket

Here, we have elucidated the substrate recognition mechanism by a prokaryotic H⁺/oligopeptide cotransporter, YdgR, using isothermal titration calorimetry. Under acidic conditions (pH 6.0), the binding of a dipeptide, Val-Ala, to YdgR elicited endothermic enthalpy, which compensated for the increase in entropy due to dipeptide binding. A series of dipeptides were used in the binding titration. The dipeptides represent Val-X and X-Val, where X is Ala, Ser, Val, Tyr, or Phe. Most dipeptides revealed endothermic enthalpy, which was completely compensated by the increase in entropy due to dipeptide binding. The change in enthalpy due to binding correlated well with the change in entropy, whereas the Gibbs free energy involved in the binding of the dipeptide to YdgR remained unchanged irrespective of dipeptide sequences, implying that the binding reaction was driven by entropy, that is, the release of bound water molecules in the binding pocket. It is also important to clarify that, based on the prediction of water molecules in the ligand-binding pocket of YdgR, the release of three bound water molecules in the putative substrate binding pocket occurred through binding to YdgR. In the comparison of Val-X and X-Val dipeptides, the N-terminal region of the binding pocket might contain more bound water molecules than the C-terminal region. In light of these findings, we suggest that bound water molecules might play an important role in substrate recognition and binding by YdgR.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics

Developing a ligand with high affinity for a specific protein target is essential for drug design, and water molecules are well known to play a key role in protein-drug recognition. However, predicting the role of particularly ordered water molecules in drug binding remains challenging. Furthermore, hydration free energy contributed from the water network, including the second shell of water molecules, is far from being well studied. In this research we focused on these aspects to accurately and efficiently evaluate water effects in protein-ligand binding affinity. We developed a new strategy using a free-energy calculation method, VM2. We successfully predicted the stable ordered water molecules in a number of protein systems: PDE 10a, HSP90, tryptophan synthase (TRPS), CDK2 and Factor Xa. In some of these, the second shell of water molecules appeared to be critical in protein-ligand binding. We also applied the strategy to largely improve binding free energy calculation using the MM/PBSA method. When applying MM/PBSA alone for two systems, CDK2 and Factor Xa, the computed binding free energy resulted in poor to moderate R² values with experimental data. However, including water free energy correction greatly improved the free energy calculation. Furthermore, our work helped to explain how xk263 is a 1000 times faster binder to HIVp than ritonavir, a potentially useful tool for investigating binding kinetics. Our studies reveal the importance of fully considering water effects in therapeutic developments in pharmaceutical and biotechnology industries and for fundamental research in protein-ligand recognition.

Water Networks in Complexes between Proteins and FDA-Approved Drugs

Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.

Anti-Alopecia Activity of Alkaloids Group from Noni Fruit against Dihydrotestosterone-Induced Male Rabbits and Its Molecular Mechanism: In Vivo and In Silico Studies

Androgenic alopecia (AA) is a condition that most commonly affects adult men and is caused by an increase in the hormone dihydrotestosterone (DHT) in the hair follicles. Anti-alopecia drugs should be discovered for hair follicles to enter the anagen growth phase. Therefore, this study evaluated the hair growth-promoting activity of Noni fruit’s water, ethyl acetate, n-hexane fractions, and sub-fractions from the active fraction in the alopecia male white rabbit model. The Matias method was modified by inducing rabbits using DHT for 17 days, followed by topical application of Noni fruit solution for 21 days. Meanwhile, hair growth was evaluated by histological observation of the follicular density and the anagen/telogen (A/T) ratio in skin tissue. In the first stage, five groups of male white rabbits were studied to obtain the active fraction; DHT+Minoxidil as standard, DHT+vehicle (NaCMC 1%), DHT+FW, DHT+FEA, and DHT+FH. The FEA as the active fraction was followed by open-column chromatography separation (DCM:Methanol) with a gradient of 10% to produce sub-fractions. In the second stage, the six main sub-fraction groups of male rabbits studied were DHT+FEA-1 to DHT+FEA-6. The follicular density of groups FEA-3 was 78.00 ± 1.52 compared with 31.55 ± 1.64 and 80.12 ± 1.02 in the Vehicle and Minoxidil groups. Additionally, group FEA-3 showed large numbers of anagen follicles with an A/T ratio of 1.64/1 compared to the vehicle group of 1/1.50 and 1.39/1 for Minoxidil control. Group FEA-3 was identified by LC-MS/MS-QTOF, followed by molecular docking to the androgen receptor (PDB: 4K7A), causing alopecia. The results showed that three alkaloid compounds with skeleton piperazine and piperidine, namely (compounds 2 (−4.99 Kcal/mol), 3 (−4.60 Kcal/mol), and 4 (−4.57 Kcal/mol)) had a binding affinity similar to Minoxidil, with also has alkaloid skeleton piperidine−pyrimidine (−4.83 Kcal/mol). The dynamic behavior showed the stability of all androgen receptor compounds with good RMSD, SMSF, and SASA values after being studied with 100 ns molecular dynamics (MD) simulations. This study produced a common thread in discovering a class of alkaloid compounds as inhibitors of androgen receptors that cause alopecia.

Conserved Water Networks Identification for Drug Design Using Density Clustering Approaches on Positional and Orientational Data

This work describes the development and testing of a method for the identification and classification of conserved water molecules and their networks from molecular dynamics (MD) simulations. The conserved waters in the active sites of proteins influence protein-ligand binding. Recently, several groups have argued that a water network formed from conserved waters can be used to interpret the thermodynamic signature of the binding site. We implemented a novel methodology in which we apply the complex approach to categorize water molecules extracted from the MD simulation trajectories using clustering approaches. The main advantage of our methodology as compared to current state of the art approaches is the inclusion of the information on the orientation of hydrogen atoms to further inform the clustering algorithm and to classify the conserved waters into different subtypes depending on how strongly certain orientations are preferred. This information is vital for assessing the stability of water networks. The newly developed approach is described in detail as well as validated against known results from the scientific literature including comparisons with the experimental data on thermolysin, thrombin, and Haemophilus influenzae virulence protein SiaP as well as with the previous computational results on thermolysin. We observed excellent agreement with the literature and were also able to provide additional insights into the orientations of the conserved water molecules, highlighting the key interactions which stabilize them. The source code of our approach, as well as the utility tools used for visualization, are freely available on GitHub.

Virtual structure-based docking and molecular dynamics of FDA-approved drugs for the identification of potential IKKB inhibitors possessing dopaminergic activity in Alzheimer’s disease

In Alzheimer's disease (AD), neuroinflammation is detrimental in causing neurodegeneration. In the central nervous system, inhibitor of nuclear factor kappa B kinase subunit beta (IKK2/IKKβ/IKKB/IKBKB) signaling is linked to neuroinflammation-mediated learning and memory deficits through canonical pathway, while dopamine agonists have been known to reverse such effects. Our in silico analysis predicted if dopaminergic agonists could have IKKB inhibitory actions, to ameliorate neuroinflammation-associated learning and memory deficits. Here, the FDA-approved Zinc 15 database was screened with IKKB (PDB ID 4KIK). Potential molecules with IKKB inhibition were identified through docking, which also possessed dopaminergic activity. Molecular mechanics—generalized Born and surface area (MMGBSA), induced fit docking (IFD) and molecular dynamic (MD) studies of 100 ns simulation time were done. Apomorphine and rotigotine showed greater non-bonding and bonding interactions with amino acids of IKKB as compared to Aripiprazole in docking studies. The IFD studies predicted improved interactions with IKKB. MMGBSA scores indicated that the complex binding free energies were favorable, and MD studies showed an acceptable root mean square deviation between protein and ligands. The protein–ligand interactions showed hydrogen bonds, water and salt bridges necessary for IKKB inhibition, as well as solvent system stability. On the protein–ligand contact map, the varying color band intensities represented the ligand’s ability to bind with amino acids. Dopamine agonists apomorphine, rotigotine, and aripiprazole were predicted to bind and inhibit IKKB in in silico system.

Graphical Abstract

Bioinsecticidal activity of actinomycete secondary metabolites against the acetylcholinesterase of the legume’s insect pest Acyrthosiphon pisum: a computational study

Background

Acyrthosiphon pisum or pea aphid is an insect of the Aphididae family, which attacks various species of legumes such as beans and peas. This pest causes economically heavy crop losses around the world. The use of conventional chemical insecticides is the only way to control its development. However, the harmful consequences of these chemicals are well known. They pollute various compartments of the environment, thus constituting a major risk for human and environmental health. The search for a more ecological alternative, respectful of the environment is, therefore, a necessity. Actinomycetes represent a source of biologically active secondary metabolites, such as antibiotics and biopesticidal agents. In this study, 150 secondary metabolites of actinomycetes have made the objective of an in silico research by molecular docking, by screening their potential inhibitors against the enzyme acetylcholinesterase (AChE) of A. pisum.

Results

The 3D structure of AChE, unavailable in the PDB database, was first modeled using the Modeller program, then the stereochemical quality of the model was validated. The molecular docking performed by the Autodock Vina algorithm allowed the selection of two metabolites giving binding energy equal to or lower than that of the co-crystallized inhibitor tetrahydro-acridine (−10.3Kcal/mol). The top-two metabolites are diazepinomicine (−10.9 Kcal/mol), and hygromycin (−10.3 Kcal/mol). These components have shown numerous interactions with the key residues of the catalytic site of the AChE enzyme, indicating their potential to inhibit its biological activity. The environmental and health safety of these components, as well as their bioavailability, were also studied by the verification of several pharmacokinetic and ADMET criteria. Diazepinomicine has shown excellent results verifying most of the criteria studied. A 50-ns MD simulation was also performed in order to test the stability of the complexes formed.

Conclusions

In addition to its favorable pharmacokinetic properties, the special chemical structure of diazepinomicin allows this molecule to interact intensely with AChE notably through the involvement of its two groups farnesyl diphosphate and dibenzodiazepinone which ensure several hydrogen and hydrophobic interactions, that offers very high stability to the complex AChE diazepinomicin. In conclusion, diazepinomicin can be suggested as a potential bioinsecticidal agent against the pest A. pisum.

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.

Imidazole and nitroimidazole derivatives as NADH-fumarate reductase inhibitors: Density functional theory studies, homology modeling, and molecular docking

Chagas disease is caused by Trypanosoma cruzi. Benznidazole and nifurtimox are drugs used for its therapy; nevertheless, they have collateral effects. NADH-fumarate (FUM) reductase is a potential pharmacological target since it is essential for survival of parasite and is not found in humans. The objectives are to design and characterize the electronic structure of imidazole and nitroimidazole derivatives at DFT-M06-2X level in aqueous solution; also, to model the NADH-FUM reductase and analyze its intermolecular interactions by molecular docking. Quantum-chemical descriptors allowed to select the molecules with the best physicochemical properties and lowest toxicity. A high-quality three-dimensional structure of NADH-FUM reductase was obtained by homology modeling. Water molecules do not have influence in the interaction between FUM and NADH-FUM reductase. The main hydrogen-binding interactions for FUM were identified in NADH, Lys172, and Arg89; while hydrophobic interactions in Phe479, Thr174, Met63. The molecules S3-8, S2-8, and S1-8 could be inhibitors of NADH-FUM reductase.

Predicting Protein-Ligand Docking Structure with Graph Neural Network

Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.

GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network

Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein-protein interfaces, and protein-compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.

Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.

Point-specific interactions of isovitexin with the neighboring amino acid residues of the hACE2 receptor as a targeted therapeutic agent in suppressing the SARS-CoV-2 influx mechanism

Objective:

Despite the development of several vaccines against severe acute respiratory syndrome coronavirus-2, the need for an additional prophylactic agent is evident. In recent in silico studies, isovitexin exhibited a higher binding affinity against the human angiotensin converting-enzyme 2 (hACE2) receptor than existing antiviral drugs. The research aimed to find out the point specificity of isovitexin for the hACE2 receptor and to assess its therapeutic potential, depending on the stability of the isovitexin–hACE2 complex.

Materials and Methods:

The pharmacokinetic profile of isovitexin was analyzed. The crystal structure of the hACE2 receptor and the ligand isovitexin were docked to form a ligand–protein complex following molecular optimization. To determine the isovitexin–hACE2 complex stability, their binding affinity, hydrogen bonding, and hydrophobic interactions were studied. Lastly, the root mean square deviation (RMSD), root mean square fluctuation, solvent accessible surface area, molecular surface area, radius of gyration (Rg), polar surface area, and principal component analysis values were found by simulating the complex with molecular dynamic (MD).

Results:

The predicted Lethal dose₅₀ for isovitexin was 2.56 mol/kg, with an acceptable maximum tolerated dose and no hepatotoxicity or AMES toxicity. Interactions with the amino acid residues Thr371, Asp367, Glu406, Pro346, His345, Phe274, Tyr515, Glu375, Thr347, Glu402, and His374 of the hACE2 protein were required for the high binding affinity and specificity of isovitexin. Based on what was learned from the MD simulation, the hACE2 receptor-blocking properties of isovitexin were looked at.

Conclusions:

Isovitexin is a phytochemical with a reasonable bioactivity and safety profile for use in humans, and it can potentially be used as a hACE2-specific therapeutic to inhibit COVID-19 infection.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.

Prediction of Water Distributions and Displacement at Protein-Ligand Interfaces

The retention and displacement of water molecules during formation of ligand-protein interfaces play a major role in determining ligand binding. Understanding these effects requires a method for positioning of water molecules in the bound and unbound proteins and for defining water displacement upon ligand binding. We describe an algorithm for water placement and a calculation of ligand-driven water displacement in >9000 protein-ligand complexes. The algorithm predicts approximately 38% of experimental water positions within 1.0 Å and about 83% within 1.5 Å. We further show that the predicted water molecules can complete water networks not detected in crystallographic structures of the protein-ligand complexes. The algorithm was also applied to solvation of the corresponding unbound proteins, and this allowed calculation of water displacement upon ligand binding based on differences in the water network between the bound and unbound structures. We illustrate use of this approach through comparison of water displacement by structurally related ligands at the same binding site. This method for evaluation of water displacement upon ligand binding may be of value for prediction of the effects of ligand modification in drug design.

Secondary Structure and Conformational Stability of the Antigen Residues Making Contact with Antibodies

Antibodies are crucial biomolecules that bring high therapeutic efficacy in medicine and accurate molecular detection in diagnosis. Many studies have been devoted to analyzing the antigen-antibody interaction from the importance of understanding the antibody recognition mechanism. However, most of the previous studies examined the characteristic of the antibody for interaction. It is also informative to clarify the significant antigen residues contributing to the binding. To characterize the molecular interaction of antigens, we computationally analyzed 350 antigen-antibody complex structures by molecular mechanics (MM) calculations and molecular dynamics (MD) simulations. Based on the MM calculations, the antigen residues contributing to the binding were extracted from all the 350 complexes. The extracted residues are located at the antigen-antibody interface and are responsible for making contact with the antibody. The appearances of the charged polar residues, Asp, Glu, Arg, and Lys, were noticeably large. In contrast, the populations of the hydrophobic residues, Leu, Val, and Ala, were relatively low. The appearance frequencies of the other amino acid residues were almost close to the abundance of general proteins of eukaryotes. The binding score indicated that the hydrophilic interaction was dominant at the antigen-antibody contact instead of the hydrophobic one. The positively charged residues, Arg and Lys, remarkably contributed to the binding compared to the negatively charged ones, Asp and Glu. Considerable contributions were also observed for the noncharged polar residues, Asn and Gln. The analysis of the secondary structures of the extracted antigen residues suggested that there was no marked difference in recognition by antibodies among helix, sheet, turn, and coil. A long helix of the antigen sometimes made contact with antibody complementarity-determining regions, and a large sheet also frequently covered the antibody heavy and light chains. The turn structure was the most popularly observed at the contact with antibody among 350 complexes. Three typical complexes were picked up for each of the four secondary structures. MD simulations were performed to examine the stability of the interfacial structures of the antigens for these 12 complex models. The alterations of secondary structures were monitored through the simulations. The structural fluctuations of the contact residues were low compared with the other domains of antigen molecules. No drastic conversion was observed for every model during the 100 ns simulation. The motions of the interfacial antigen residues were small compared to the other residues on the protein surface. Therefore, diverse molecular conformations are possible for antibody recognition as long as the target areas are polar, nonflexible, and protruding on the protein surface.

Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.

Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking

Molecular docking is one of the most widely used computational tools in structure-based drug design and is critically dependent on accuracy and robustness of the scoring function. In this work, we introduce a new scoring function Lin_F9, which is a linear combination of nine empirical terms, including a unified metal bond term to specifically describe metal-ligand interactions. Parameters in Lin_F9 are obtained with a multistage fitting protocol using explicit water-included structures. For the CASF-2016 benchmark test set, Lin_F9 achieves the top scoring power among all 34 classical scoring functions for both original crystal poses and locally optimized poses with Pearson correlation coefficients (R) of 0.680 and 0.687, respectively. Meanwhile, in comparison with Vina, Lin_F9 achieves consistently better scoring power and ranking power with various types of protein-ligand complex structures that mimic real docking applications, including end-to-end flexible docking for the CASF-2016 benchmark test set using a single or an ensemble of protein receptor structures, as well as for D3R Grand Challenge (GC4) test sets. Lin_F9 has been implemented in a fork of Smina as an optional built-in scoring function that can be used for docking applications as well as for further improvement of scoring functions and docking protocols. Lin_F9 is accessible through https://yzhang.hpc.nyu.edu/Lin_F9/.

Water molecules at protein-drug interfaces: computational prediction and analysis methods

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Residue interaction networks of K-Ras protein with water molecules identifies the potential role of switch II and P-loop

The mutant K-Ras with aberrant signaling is the primary cause of several cancers. The proposed study investigated the influence of water molecules in K-Ras crystal structure, where they have a significant function by understanding their residue interaction networks (RINs). We analyzed the RINs of K-Ras with and without water molecules and determined their interaction properties. RINs were developed with the help of StructureViz2 and RINspector; further, the changes in K-Ras backbone flexibility were predicted with the DynaMine. We found that the residues K42, I142, and L159 are the hotspots from water, including the K-Ras-GTP complex with the highest residue centrality analysis (RCA) Z-score. The DynaMine prediction calculated the NMR S² value for the frequently mutated positions G12, G13, and Q61 showing a minor shift in flexibility, which make up the P-Loop and switch II of the K-Ras protein. This flexibility shift can account for changes in conformational activity and the protein's GTPase activity, making it difficult to recognize by the effectors and exchange factors. Taken together, our study helps in understanding the functional importance of the water molecules in K-Ras protein and the impact of mutation that modulate the conformational state of the protein.

Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé-Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.

GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential

Proteins fold and function in water, and protein-water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein-water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom-water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom-water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein-water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at http://galaxy.seoklab.org/wkgb.

Analysis of Binding Modes of Antigen-Antibody Complexes by Molecular Mechanics Calculation

Antibodies are one of the most important protein molecules in biopharmaceutics. Due to the recent advance in technology for producing monoclonal antibodies, many structural data are available on the antigen-antibody complexes. To characterize the molecular interaction in antigen-antibody recognition, we computationally analyzed 500 complex structures by molecular mechanics calculations. The presence of Ser and Tyr is markedly large in the complementarity-determining regions (CDRs). Although Ser is abundant in CDRs, its contribution to the binding score is not large. Instead, Tyr, Asp, Glu, and Arg significantly contribute to the molecular interaction from the viewpoint of the binding score. The decomposition of the binding score suggests that the hydrophilic interaction is predominant in all CDRs compared with the hydrophobic one. The contribution of the heavy chain is larger than that of the light chain. In particular, H2 and H3 largely contribute to the binding interaction. Tyr is a main contributing residue both in H2 and H3. The positively charged residue Arg also significantly contributes to the binding score in H3, while the contribution of Lys is small. The appearance of Ser is remarkable in H2, and Asp is abundant in H3. The non-charged polar residues, Thr, Asn, and Gln, appear much in H2, compared to appearing in H3. The negatively charged residues Asp and Glu significantly contribute to the binding score in H3. The contributions of Phe and Trp are not large in spite that the aromatic residues are capable of making the π-π or CH-π interaction. Gly is commonly abundant both in H2 and H3. The average distance of the shortest direct hydrogen bond between the antigen and antibody is longer than that of the hydrogen bonds observed in the complexes between compounds and their target proteins. Therefore, the antigen-antibody interface is not so tight as the compound-target protein interface. The calculation of shape complementarity is consistent with the result of the hydrogen bonds in that the fitness of the antigen-antibody contact is not so high as that of the compound-target protein contact. There exist many water molecules at the antigen-antibody interface. These findings suggest that Tyr, Asp, Glu, and Arg are rich in H3 and work as major contributors for the interaction with the antigen. Ser, Thr, Asn, and Gln are rich in H2 and support the interaction with enhancing molecular fitness. Gly is helpful in increasing flexibility and geometrical diversity. Because the antigen-antibody binding is fundamentally hydrophilic-driven, the non-polar residues are unfavorable for mediating the contact even for the aromatic residues such as Phe and Trp.

GridSolvate: A Web Server for the Prediction of Biomolecular Hydration Properties

We present a novel web server, named gridSolvate, dedicated to the prediction of biomolecular hydration properties. Given a solute in atomic representation, such as a protein or protein–ligand complex, the server determines positions and excess chemical potential of buried and first hydration shell water molecules. Calculations are based on our semiexplicit hydration model that provides computational efficiency close to implicit solvent approaches, yet captures a number of physical effects unique to explicit solvent representation. The model was introduced and validated before in the context of bulk hydration of drug-like solutes and determination of protein hydration sites. Current methodological developments merge those two avenues into a single, easily accessible tool. Here, we focus on the server’s ability to predict water distribution and affinity within protein–ligand interfaces. We demonstrate that with possibly minimal user intervention the server correctly predicts the locations of 77% of interface water molecules in an external set of test structures. The server is freely available at https://gsolvate.biomod.cent.uw.edu.pl.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.

Comprehensive 3D-RISM analysis of the hydration of small molecule binding sites in ligand-free protein structures

Hydration is a critical factor in the ligand binding process. Herein, to examine the hydration states of ligand binding sites, the three‐dimensional distribution function for the water oxygen site, g_O(r), is computed for 3,706 ligand‐free protein structures based on the corresponding small molecule–protein complexes using the 3D‐RISM theory. For crystallographic waters (CWs) close to the ligand, g_O(r) reveals that several CWs are stabilized by interaction networks formed between the ligand, CW, and protein. Based on the g_O(r) for the crystallographic binding pose of the ligand, hydrogen bond interactions are dominant in the highly hydrated regions while weak interactions such as CH‐O are dominant in the moderately hydrated regions. The polar heteroatoms of the ligand occupy the highly hydrated and moderately hydrated regions in the crystallographic (correct) and wrongly docked (incorrect) poses, respectively. Thus, the g_O(r) of polar heteroatoms may be used to distinguish the correct binding poses.

Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks

The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.

Comparing Fragment Binding Poses Prediction Using HSP90 as a Key Study: When Bound Water Makes the Difference

Fragment-Based Drug Discovery (FBDD) approaches have gained popularitynot only in industry but also in academic research institutes. However, the computational prediction of the binding mode adopted by fragment-like molecules within a protein binding site is still a very challenging task. One of the most crucial aspects of fragment binding is related to the large amounts of bound waters in the targeted binding pocket. The binding affinity of fragmentsmay not be sufficientto displace the bound water molecules. In the present work, we confirmed the importance of the bound water molecules in the correct prediction of the fragment binding mode. Moreover, we investigate whether the use of methods based on explicit solvent molecular dynamics simulations can improve the accuracy of fragment posing. The protein chosen for this study is HSP-90.

Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of protein-ligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water.

Efficient consideration of coordinated water molecules improves computational protein-protein and protein-ligand docking discrimination

Highly coordinated water molecules are frequently an integral part of protein-protein and protein-ligand interfaces. We introduce an updated energy model that efficiently captures the energetic effects of these ordered water molecules on the surfaces of proteins. A two-stage method is developed in which polar groups arranged in geometries suitable for water placement are first identified, then a modified Monte Carlo simulation allows highly coordinated waters to be placed on the surface of a protein while simultaneously sampling amino acid side chain orientations. This “semi-explicit” water model is implemented in Rosetta and is suitable for both structure prediction and protein design. We show that our new approach and energy model yield significant improvements in native structure recovery of protein-protein and protein-ligand docking discrimination tests.

Well-coordinated water molecules—those forming multiple hydrogen bonds with nearby polar groups—play an important role in the structure of biomolecular systems, yet the effect of these waters is often not considered in molecular energy computations. In this paper, we describe a method to efficiently consider these water molecules both implicitly and explicitly at the interfaces formed by two polar molecules. In computations related to determining how a protein interacts with binding partners, we show that the use of this new method significantly improves results. Future application of this approach may improve the design of new protein and small molecule drugs.

An assessment of water placement algorithms in quantum mechanics/molecular mechanics modeling: the case of rhodopsins' first spectral absorption band maxima

Quantum mechanics/molecular mechanics (QM/MM) models are a widely used tool to obtain detailed insight into the properties and functioning of proteins. The outcome of QM/MM studies heavily depends on the quality of the applied QM/MM model. Prediction and right placement of internal water molecules in protein cavities is one of the critical parts of any QM/MM model construction. Herein, we performed a systematic study of four protein hydration algorithms. We tested these algorithms for their ability to predict X-ray-resolved water molecules for a set of membrane photosensitive rhodopsin proteins, as well as the influence of the applied water placement algorithms on the QM/MM calculated absorption maxima (λ_max) of these proteins. We used 49 rhodopsins and their intermediates with available X-ray structures as the test set. We found that a proper choice of hydration algorithms and setups is needed to predict functionally important water molecules in the chromophore-binding cavity of rhodopsins, such as the water cluster in the N-H region of bacteriorhodopsin or two water molecules in the binding pocket of bovine visual rhodopsin. The QM/MM calculated λ_max of rhodopsins is also quite sensitive to the applied protein hydration protocols. The best methodology allows obtaining an 18.0 nm average value for the absolute deviation of the calculated λ_max from the experimental λ_max. Although the major effect of water molecules on λ_max originates from the water molecules located in the binding pocket, the water molecules outside the binding pocket also affect the calculated λ_max mainly by causing a reorganization of the protein structure. The results reported in this study can be used for the evaluation and further development of hydration methodologies, in general, and rhodopsin QM/MM models, in particular.

Distinct binding of cetirizine enantiomers to human serum albumin and the human histamine receptor H1

Cetirizine, a major metabolite of hydroxyzine, became a marketed second-generation H₁ antihistamine that is orally active and has a rapid onset of action, long duration of effects and a very good safety record at recommended doses. The approved drug is a racemic mixture of (S)-cetirizine and (R)-cetirizine, the latter being the levorotary enantiomer that also exists in the market as a third-generation, non-sedating and highly selective antihistamine. Both enantiomers bind tightly to the human histamine H₁ receptor (hH₁R) and behave as inverse agonists but the affinity and residence time of (R)-cetirizine are greater than those of (S)-cetirizine. In blood plasma, cetirizine exists in the zwitterionic form and more than 90% of the circulating drug is bound to human serum albumin (HSA), which acts as an inactive reservoir. Independent X-ray crystallographic work has solved the structure of the hH₁R:doxepin complex and has identified two drug-binding sites for cetirizine on equine serum albumin (ESA). Given this background, we decided to model a membrane-embedded hH₁R in complex with either (R)- or (S)-cetirizine and also the complexes of both ESA and HSA with these two enantiomeric drugs to analyze possible differences in binding modes between enantiomers and also among targets. The ensuing molecular dynamics simulations in explicit solvent and additional computational chemistry calculations provided structural and energetic information about all of these complexes that is normally beyond current experimental possibilities. Overall, we found very good agreement between our binding energy estimates and extant biochemical and pharmacological evidence. A much higher degree of solvent exposure in the cetirizine-binding site(s) of HSA and ESA relative to the more occluded orthosteric binding site in hH₁R is translated into larger positional fluctuations and considerably lower affinities for these two nonspecific targets. Whereas it is demonstrated that the two known pockets in ESA provide enough stability for cetirizine binding, only one such site does so in HSA due to a number of amino acid replacements. At the histamine-binding site in hH₁R, the distinct interactions established between the phenyl and chlorophenyl moieties of the two enantiomers with the amino acids lining up the pocket and between their free carboxylates and Lys179 in the second extracellular loop account for the improved pharmacological profile of (R)-cetirizine.

Validation of ligands targeting metacaspase-2 (MCA2) from Trypanosoma brucei brucei and their application to MCA5 from T. congolense as possible trypanocides

Metacaspases (MCAs) are ideal drug and diagnostic targets for animal and human African trypanosomiasis, as these cysteine peptidases are absent from the metazoan kingdom and have been implicated in the parasite cell cycle and cell death. Tsetse fly-transmitted trypanosomes that live free in the bloodstream and/or cerebrospinal fluid of the mammalian host cause animal and human African trypanosomiasis (nagana or sleeping sickness respectively). Chemotherapy and chemoprophylaxis are the main forms of control, but in contrast to human trypanocides, the veterinary drugs are old and drug resistance is on the increase. A peptidomimetic library targeting the MCA2 from Trypanosoma brucei brucei has ligands with low IC₅₀ values, some of which were antiparasitic. This study validates the inhibitory activity of these ligands using the protein structure solved by X-ray diffraction after the ligand library was published. Water molecules were shown to be important in substrate binding and strategies to improve the efficacy of these ligands are highlighted. These ligands appear to be pan-specific as they were docked into the active site of the homology modelled MCA5 of animal infective Trypanosoma congolense with similar binding energies and conformations.

Donepezil Inhibits Acetylcholinesterase via Multiple Binding Modes at Room Temperature

Donepezil is a second generation acetylcholinesterase (AChE) inhibitor for treatment of Alzheimer's disease (AD). AChE is important for neurotransmission at neuromuscular junctions and cholinergic brain synapses by hydrolyzing acetylcholine into acetate and choline. In vitro data support that donepezil is a reversible, mixed competitive and noncompetitive inhibitor of AChE. The experimental fact then suggests a more complex binding mechanism beyond the molecular view in X-ray models resolved at cryogenic temperatures that show a unique binding mode of donepezil in the active site of the enzyme. Aiming at clarifying the mechanism behind that mixed competitive and noncompetitive nature of the inhibitor, we have applied molecular dynamics (MD) simulations and docking and free-energy calculations to investigate microscopic details and energetics of donepezil association for conditions of substrate-free and -bound states of the enzyme. Liquid-phase MD simulation at room temperature shows AChE transits between "open" and "closed" conformations to control accessibility to the active site and ligand binding. As shown by docking and free-energy calculations, association of donepezil involves its reversible axial displacement and reorientation in the active site of the enzyme, assisted by water molecules. Donepezil binds equally well the main-door anionic binding site PAS, the acyl pocket, and the catalytic site CAS by respectively adopting outward-inward-inward orientations regardless of substrate occupancy-the overall stability of that reaction process depends however on co-occupancy of the enzyme being preferential for its substrate-free state. All together, our findings support a physiologically relevant mechanism of AChE inhibition by donepezil involving multistable interactions modes at the molecular origin of the inhibitor's activity.

Applications of water molecules for analysis of macromolecule properties

Water molecules maintain proteins' structures, functions, stabilities and dynamics. They can occupy certain positions or pass quickly via a protein's interior. Regardless of their behaviour, water molecules can be used for the analysis of proteins' structural features and biochemical properties. Here, we present a list of several software programs that use the information provided by water molecules to: i) analyse protein structures and provide the optimal positions of water molecules for protein hydration, ii) identify high-occupancy water sites in order to analyse ligand binding modes, and iii) detect and describe tunnels and cavities. The analysis of water molecules' distribution and trajectories sheds a light on proteins' interactions with small molecules, on the dynamics of tunnels and cavities, on protein composition and also on the functionality, transportation network and location of functionally relevant residues. Finally, the correct placement of water molecules in protein crystal structures can significantly improve the reliability of molecular dynamics simulations.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Bxy-fuca encoding α-L-fucosidase plays crucial roles in development and reproduction of the pathogenic pinewood nematode, Bursaphelenchus xylophilus

BACKGROUND

The pine wood nematode (PWN) Bursaphelenchus xylophilus is the causal agent of pine wilt disease (PWD). This disease is a serious threat to pine forests globally. The fuca gene encodes α-L-fucosidase, which plays crucial roles in numerous biological and pathological processes in bacteria, fungi, plants and animals. To find promising control strategies against PWD, we investigated the expression and functions of Bxy-fuca in B. xylophilus.

RESULTS

Bxy-fuca encoding α-L-fucosidase is highly conserved within the deduced functional domains and key residues. It is expressed continuously across all developmental stages of B. xylophilus. mRNA in situ hybridization demonstrated that Bxy-fuca was mainly localized in the body wall muscles and intestine. RNA interference indicated that the zygotic expression of Bxy-fuca was indispensable for embryogenesis. The rate of B. xylophilus egg hatch was significantly decreased after Bxy-fuca was interfered. Postembryonic silence of Bxy-fuca resulted in a dramatic decrease in the longevity of and the total number of eggs produced by B. xylophilus. In addition, the motility of the nematode was greatly hampered with a significant drop in head thrashing frequency.

CONCLUSION

Bxy-fuca plays crucial roles in development, lifespan and reproduction of B. xylophilus. Our results provide promising hints for control of PWD by blocking embryogenesis and ontogenesis, decreasing nematode fecundity, and disrupting the connection between B. xylophilus and its vector beetle by preventing nematode movement into the tracheal system. © 2019 Society of Chemical Industry.

Solvation Free Energy as a Measure of Hydrophobicity: Application to Serine Protease Binding Interfaces

Solvation and hydrophobicity play a key role in a variety of biological mechanisms. In substrate binding, but also in structure-based drug design, the thermodynamic properties of water molecules surrounding a given protein are of high interest. One of the main algorithms devised in recent years to quantify thermodynamic properties of water is the grid inhomogeneous solvation theory (GIST), which calculates these features on a grid surrounding the protein. Despite the inherent advantages of GIST, the computational demand is a major drawback, as calculations for larger systems can take days or even weeks. Here, we present a GPU accelerated version of the GIST algorithm, which facilitates efficient estimates of solvation free energy even of large biomolecular interfaces. Furthermore, we show that GIST can be used as a reliable tool to evaluate protein surface hydrophobicity. We apply the approach on a set of nine different proteases calculating localized solvation free energies on the surface of the binding interfaces as a measure of their hydrophobicity. We find a compelling agreement with the hydrophobicity of their substrates, i.e., peptides, binding into the binding cleft, and thus our approach provides a reliable description of hydrophobicity characteristics of these biological interfaces.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions

Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the training set, exploring new features related to explicit mediating water molecules as well as ligand conformation stability, and applying extreme gradient boosting (XGBoost) with Δ-Vina parametrization, we have improved robustness and applicability of machine-learning scoring functions. The new scoring function Δ_vinaXGB can not only perform consistently among the top compared to classical scoring functions for the CASF-2016 benchmark but also achieves significantly better prediction accuracy in different types of structures that mimic real docking applications.

Role of Displacing Confined Solvent in the Conformational Equilibrium of β-Cyclodextrin

This study investigates the role of hydration and its relationship to the conformational equilibrium of the host molecule β-cyclodextrin. Molecular dynamics simulations indicate that the unbound β-cyclodextrin exhibits two state behavior in explicit solvent due to the opening and closing of its cavity. In implicit solvent, these transitions are not observed, and there is one dominant conformation of β-cyclodextrin with an open cavity. Based on these observations, we investigate the hypothesis that the expulsion of thermodynamically unfavorable water molecules into the bulk plays an important role in controlling the accessibility of the closed macrostate at room temperature. We compare the results of the molecular mechanics analytical generalized Born plus nonpolar solvation approach to those obtained through grid inhomogeneous solvation theory analysis with explicit solvation to elucidate the thermodynamic forces at play. The work illustrates the use of continuum solvent models to tease out solvation effects related to the inhomogeneity and the molecular nature of water and demonstrates the key role of the thermodynamics of enclosed hydration in driving the conformational equilibrium of molecules in solution.