Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.

Targeting a conserved pocket (n-octyl-β-D-glucoside) on the dengue virus envelope protein by small bioactive molecule inhibitors

Dengue virus enters the cell by receptor-mediated endocytosis followed by a viral envelope (DENVE) protein-mediated membrane fusion. A small detergent molecule n-octyl-β-D-glucoside (βOG) occupies the hydrophobic pocket which is located in the hinge region plays a major role in the rearrangement. It has been reported that mutations occurred in this binding pocket lead to the alterations of pH threshold for fusion. In addition to this event, the protonation of histidine residues present in the hydrophobic pocket would also impart the conformational change of the E protein evidence this pocket as a promising target. The present study identified novel cinnamic acid analogs as significant blockers of the hydrophobic pocket through molecular modeling studies against DENVE. A library of seventy-two analogs of cinnamic acid was undertaken for the discovery process of DENV inhibitors. A Molecular docking study was used to analyze the binding mechanism between these compounds and DENV followed by ADMET prediction. Binding energies were predicted by the MMGBSA study. The Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. The compounds CA and SCA derivatives have been tested against HSV-1 & 2 viruses. From the computational results, the compounds CA1, CA2, SCA 60, SCA 57, SCA 37, SCA 58, and SCA 14 exhibited favorable interaction energy. The compounds have in-vitro antiviral activity; the results clearly indicate that the compounds showed the activity against both the viruses (HSV-1 & HSV-2). Our study provides valuable information on the discovery of small molecules DENVE inhibitors.Communicated by Ramaswamy H. Sarma.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.

Design, Synthesis, and Antifungal Activities of 3-Acyl Thiotetronic Acid Derivatives: New Fatty Acid Synthase Inhibitors

Emerging fungal phytodiseases are increasingly becoming a food security threat. Twenty-six new 3-acylthiotetronic acid derivatives were designed, synthesized, characterized, and evaluated for activities against Valsa mali, Curvularia lunata, Fusarium graminearum, and Fusarium oxysporum f. sp. lycopersici. Among the 26 compounds, 6f was the most effective against V. mali, C. lunata, F. graminearum, and F. oxysporum f. sp. lycopersici with median effective concentrations (EC₅₀) of 4.1, 3.1, 3.6, and 4.1 μg/mL, respectively, while the corresponding EC₅₀ were 0.14, 6.7, 22.4, and 4.3 μg/mL of the fungicide azoxystrobin; 4.2, 41.7, 0.42, and 0.12 μg/mL of the fungicide carbendazim; and >50, 0.19, 0.43, and BS > 50 μg/mL of the fungicide fluopyram. The inhibitory potency against V. mali fatty acid synthase agreed well with the in vitro antifungal activity. The molecular docking suggested that the 3-acylthiotetronic acid derivatives targeted the C171Q KasA complex. The findings help understanding the mode of action and design and synthesis of novel potent fungicides.

A combinatorial approach for the discovery of cytochrome P450 2D6 inhibitors from nature

The human cytochrome P450 2D6 (CYP2D6) enzyme is part of phase-I metabolism and metabolizes at least 20% of all clinically relevant drugs. Therefore, it is an important target for drug-drug interaction (DDI) studies. High-throughput screening (HTS) assays are commonly used tools to examine DDI, but show certain drawbacks with regard to their applicability to natural products. We propose an in silico - in vitro workflow for the reliable identification of natural products with CYP2D6 inhibitory potential. In order to identify candidates from natural product-based databases that share similar structural features with established inhibitors, a pharmacophore model was applied. The virtual hits were tested for the inhibition of recombinant human CYP2D6 in a bioluminescence-based assay. By controlling for unspecific interferences of the test compounds with the detection reaction, the number of false positives were reduced. The success rate of the reported workflow was 76%, as most of the candidates identified in the in silico approach were able to inhibit CYP2D6 activity. In summary, the workflow presented here is a suitable and cost-efficient strategy for the discovery of new CYP2D6 inhibitors with natural product libraries.

Mechanism of host substrate acetylation by a YopJ family effector

The Yersinia outer protein J (YopJ) family of bacterial effectors depends on a novel acetyltransferase domain to acetylate signalling proteins from plant and animal hosts. However, the underlying mechanism is unclear. Here, we report the crystal structures of PopP2, a YopJ effector produced by the plant pathogen Ralstonia solanacearum, in complex with inositol hexaphosphate (InsP₆), acetyl-coenzyme A (AcCoA) and/or substrate Resistance to Ralstonia solanacearum 1 (RRS1-R)_WRKY. PopP2 recognizes the WRKYGQK motif of RRS1-R_WRKY to position a targeted lysine in the active site for acetylation. Importantly, the PopP2-RRS1-R_WRKY association is allosterically regulated by InsP₆ binding, suggesting a previously unidentified role of the eukaryote-specific cofactor in substrate interaction. Furthermore, we provide evidence for the reaction intermediate of PopP2-mediated acetylation, an acetyl-cysteine covalent adduct, lending direct support to the 'ping-pong'-like catalytic mechanism proposed for YopJ effectors. Our study provides critical mechanistic insights into the virulence activity of YopJ class of acetyltransferases.

In Silico Prediction of the Toxic Potential of Lupeol

Lupeol is a natural triterpenoid found in many plant species such as mango. This compound is the principal active component of many traditional herbal medicines. In the past decade, a considerable number of publications dealt with lupeol and its analogues due to the interest in their pharmacological activities against cancer, inflammation, arthritis, diabetes, and heart disease. To identify further potential applications of lupeol and its analogues, it is necessary to investigate their mechanisms of action, particularly their interaction with off-target proteins that may trigger adverse effects or toxicity. In this study, we simulated and quantified the interaction of lupeol and 11 of its analogues toward a series of 16 proteins known or suspected to trigger adverse effects employing the VirtualToxLab. This software provides a thermodynamic estimate of the binding affinity, and the results were challenged by molecular-dynamics simulations, which allow probing the kinetic stability of the underlying protein-ligand complexes. Our results indicate that there is a moderate toxic potential for lupeol and some of its analogues, by targeting and binding to nuclear receptors involved in fertility, which could trigger undesired adverse effects.

Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives

The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology. Moreover, the underlying "ab-initio" protocol is independent from any training data and makes the approach universal with respect to the applicability domain. The VirtualToxLab runs in client-server mode and is freely available to academic and non-profit organizations. As the underlying technology yields a thermodynamic estimate of the binding affinity, the associated ligand-protein complexes have been challenged by molecular-dynamics simulations to probe their kinetic stability. Human African trypanosomiasis is a neglected tropical disease caused by two subspecies of Trypanosoma brucei. The control of this parasitic infection relies on a few chemotherapeutic agents, most of which were discovered decades ago and pose many challenges including adverse side effects, poor efficacy, and the occurrence of drug resistances. Natural products, on the other hand, offer a high potential for the discovery of new drug leads due to their chemical diversity. In this in silico study, we analyze a series of 89 natural products and derivatives displaying anti-trypanosomal activity for their potential to trigger adverse effects. Our results indicate a moderate potential for a number of those compounds to bind to nuclear receptors and thereby ease the development of endocrine disregulation. A few others would seem to inhibit enzymes of the cytochrome P450 family and, hence, sustain drug-drug interactions.

Discovery of N-(3-((1-Isonicotinoylpiperidin-4-yl)oxy)-4-methylphenyl)-3-(trifluoromethyl)benzamide (CHMFL-KIT-110) as a Selective, Potent, and Orally Available Type II c-KIT Kinase Inhibitor for Gastrointestinal Stromal Tumors (GISTs)

c-KIT kinase is a validated drug discovery target for gastrointestinal stromal tumors (GISTs). Clinically used c-KIT kinase inhibitors, i.e., Imatinib and Sunitinib, bear other important targets such as ABL or FLT3 kinases. Here we report our discovery of a more selective c-KIT inhibitor, compound 13 (CHMFL-KIT-110), which completely abolished ABL and FLT3 kinase activity. KinomeScan selectivity profiling (468 kinases) of 13 exhibited a high selectivity (S score (1) = 0.01). 13 displayed great antiproliferative efficacy against GISTs cell lines GIST-T1 and GIST-882 (GI50: 0.021 and 0.043 μM, respectively). In the cellular context, it effectively affected c-KIT-mediated signaling pathways and induced apoptosis as well as cell cycle arrest. In addition, 13 possessed acceptable bioavailability (36%) and effectively suppressed the tumor growth in GIST-T1 cell inoculated xenograft model without apparent toxicity. 13 currently is undergoing extensive preclinical evaluation and might be a potential drug candidate for GISTs.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6

MOTIVATION

Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery.

RESULTS

We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

Persistence and dioxin-like toxicity of carbazole and chlorocarbazoles in soil

Halogenated carbazoles have recently been detected in soil and water samples, but their environmental effects and fate are unknown. Eighty-four soil samples obtained from a site with no recorded history of pollution were used to assess the persistence and dioxin-like toxicity of carbazole and chlorocarbazoles in soil under controlled conditions for 15 months. Soil samples were divided into two temperature conditions, 15 and 20 °C, both under fluctuating soil moisture conditions comprising 19 and 44 drying-rewetting cycles, respectively. This was characterized by natural water loss by evaporation and rewetting to -15 kPa. Accelerated solvent extraction (ASE) and cleanup were performed after incubation. Identification and quantification were done using high-resolution gas chromatogram/mass spectrometer (HRGC/MS), while dioxin-like toxicity was determined by ethoxyresorufin-O-deethylase (EROD) induction in H4IIA rat hepatoma cells assay and multidimensional quantitative structure-activity relationships (mQSAR) modelling. Carbazole, 3-chlorocarbazole and 3,6-dichlorocarbazole were detected including trichlorocarbazole not previously reported in soils. Carbazole and 3-chlorocarbazole showed significant dissipation at 15 °C but not at 20 °C incubating conditions indicating that low temperature could be suitable for dissipation of carbazole and chlorocarbazoles. 3,6-Dichlorocarbazole was resistant at both conditions. Trichlorocarbazole however exhibited a tendency to increase in concentration with time. 3-Chlorocarbazole, 3,6-dibromocarbazole and selected soil extracts exhibited EROD activity. Dioxin-like toxicity did not decrease significantly with time, whereas the sum chlorocarbazole toxic equivalence concentrations (∑TEQ) did not contribute significantly to the soil assay dioxin-like toxicity equivalent concentrations (TCDD-EQ). Carbazole and chlorocarbazoles are persistent with the latter also toxic in natural conditions.

OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Structure-based ligand design to overcome CYP inhibition in drug discovery projects

Cytochrome P450 (CYP) enzymes are key players in xenobiotic metabolism, and inhibition of CYPs can therefore result in unwanted drug-drug interactions. Within drug discovery, CYP inhibition can cause delays in the progression of candidate drugs, or even premature closure of projects. During the past decade, a massive effort in the pharmaceutical industry and academic research has produced a wealth of structural information in the CYP field. In this short review, we will describe how structure-based approaches can be used in the pharmaceutical industry to work away from CYP inhibition, with a focus on the opportunities and challenges. We will show two examples from our own work where structural information on CYP2C9 and CYP3A4 inhibitor complexes have been successfully exploited in ongoing drug discovery projects.

Advances in molecular modeling of human cytochrome P450 polymorphism

Cytochrome P450 (CYP) is a supergene family of metabolizing enzymes involved in the phase I metabolism of drugs and endogenous compounds. CYP oxidation often leads to inactive drug metabolites or to highly toxic or carcinogenic metabolites involved in adverse drug reactions (ADR). During the last decade, the impact of CYP polymorphism in various drug responses and ADR has been demonstrated. Of the drugs involved in ADR, 56% are metabolized by polymorphic phase I metabolizing enzymes, 86% among them being CYP. Here, we review the major CYP polymorphic forms, their impact for drug response and current advances in molecular modeling of CYP polymorphism. We focus on recent studies exploring CYP polymorphism performed by the use of sequence-based and/or protein-structure-based computational approaches. The importance of understanding the molecular mechanisms related to CYP polymorphism and drug response at the atomic level is outlined.

A molecular-modeling toolbox aimed at bridging the gap between medicinal chemistry and computational sciences

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand–protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

VirtualToxLab - a platform for estimating the toxic potential of drugs, chemicals and natural products

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on http://www.virtualtoxlab.org. The free platform - the OpenVirtualToxLab - is accessible (in client-server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

AcquaAlta: a directional approach to the solvation of ligand-protein complexes

Water molecules mediating polar interactions in ligand-protein complexes can substantially contribute to binding affinity and specificity. To account for such water molecules in computer-aided drug design, we performed an extensive search in the Cambridge Structural Database (CSD) to identify the geometrical criteria defining interactions of water molecules with ligand and protein. In addition, with ab initio calculations the propensity of ligand hydration was evaluated. Based on this information, we developed an algorithm (AcquaAlta) to reproduce water molecules bridging polar interactions between ligand and protein moieties. This approach was validated with 20 crystal structures and yielded a match of 76% between experimental and calculated water positions. When water molecules establishing only weak interactions with the protein were neglected, the match could be improved to 88%. Supported by a pharmacophore-based alignment tool, the solvation algorithm was then applied to the docking of oligopeptides to the periplasmic oligopeptide binding protein A (OppA). Calculated waters based on the crystal poses matched an average of 66% of the experimental waters. With water molecules calculated based on the docked ligands, the average match with the experimental waters dropped to 53%.