Towards ligand docking including explicit interface water molecules

Molecular screening of phytocompounds targeting the interface between influenza A NS1 and TRIM25 to enhance host immune responses

BACKGROUND

Influenza A virus causes severe respiratory illnesses, especially in developing nations where most child deaths under 5 occur due to lower respiratory tract infections. The RIG-I protein acts as a sensor for viral dsRNA, triggering interferon production through K63-linked poly-ubiquitin chains synthesized by TRIM25. However, the influenza A virus's NS1 protein hinders this process by binding to TRIM25, disrupting its association with RIG-I and preventing downstream interferon signalling, contributing to the virus's evasion of the immune response.

METHODS

In our study we used structural-based drug designing, molecular simulation, and binding free energy approaches to identify the potent phytocompounds from various natural product databases (>100,000 compounds) able to inhibit the binding of NS1 with the TRIM25.

RESULTS

The molecular screening identified EA-8411902 and EA-19951545 from East African Natural Products Database, NA-390261 and NA-71 from North African Natural Products Database, SA-65230 and SA- 4477104 from South African Natural Compounds Database, NEA- 361 and NEA- 4524784 from North-East African Natural Products Database, TCM-4444713 and TCM-6056 from Traditional Chinese Medicines Database as top hits. The molecular docking and binding free energies results revealed that these compounds have high affinity with the specific active site residues (Leu95, Ser99, and Tyr89) involved in the interaction with TRIM25. Additionally, analysis of structural dynamics, binding free energy, and dissociation constants demonstrates a notably stronger binding affinity of these compounds with the NS1 protein. Moreover, all selected compounds exhibit exceptional ADMET properties, including high water solubility, gastrointestinal absorption, and an absence of hepatotoxicity, while adhering to Lipinski's rule.

CONCLUSION

Our molecular simulation findings highlight that the identified compounds demonstrate high affinity for specific active site residues involved in the NS1-TRIM25 interaction, exhibit exceptional ADMET properties, and adhere to drug-likeness criteria, thus presenting promising candidates for further development as antiviral agents against influenza A virus infections.

Comparative modeling and enzymatic affinity of novel haloacid dehalogenase from Bacillus megaterium strain BHS1 isolated from alkaline Blue Lake in Turkey

This study presents the initial structural model of L-haloacid dehalogenase (DehLBHS1) from Bacillus megaterium BHS1, an alkalotolerant bacterium known for its ability to degrade halogenated environmental pollutants. The model provides insights into the structural features of DehLBHS1 and expands our understanding of the enzymatic mechanisms involved in the degradation of these hazardous pollutants. Key amino acid residues (Arg40, Phe59, Asn118, Asn176, and Trp178) in DehLBHS1 were identified to play critical roles in catalysis and molecular recognition of haloalkanoic acid, essential for efficient binding and transformation of haloalkanoic acid molecules. DehLBHS1 was modeled using I-TASSER, yielding a best TM-score of 0.986 and an RMSD of 0.53 Å. Validation of the model using PROCHECK revealed that 89.2% of the residues were located in the most favored region, providing confidence in its structural accuracy. Molecular docking simulations showed that the non-simulated DehLBHS1 preferred 2,2DCP over other substrates, forming one hydrogen bond with Arg40 and exhibiting a minimum energy of -2.5 kJ/mol. The simulated DehLBHS1 exhibited a minimum energy of -4.3 kJ/mol and formed four hydrogen bonds with Arg40, Asn176, Asp9, and Tyr11, further confirming the preference for 2,2DCP. Molecular dynamics simulations supported this preference, based on various metrics, including RMSD, RMSF, gyration, hydrogen bonding, and molecular distance. MM-PBSA calculations showed that the DehLBHS1-2,2-DCP complex had a markedly lower binding energy (-21.363 ± 1.26 kcal/mol) than the DehLBHS1-3CP complex (-14.327 ± 1.738 kcal/mol). This finding has important implications for the substrate specificity and catalytic function of DehLBHS1, particularly in the bioremediation of 2,2-DCP in contaminated alkaline environments. These results provide a detailed view of the molecular interactions between the enzyme and its substrate and may aid in the development of more efficient biocatalytic strategies for the degradation of halogenated compounds.Communicated by Ramaswamy H. Sarma.

A Review on Molecular Docking on HDAC Isoforms: Novel Tool for Designing Selective Inhibitors

Research into histone deacetylases (HDACs) has experienced a remarkable surge in recent years. These enzymes are key regulators of several fundamental biological processes, often associated with severe and potentially fatal diseases. Inhibition of their activity represents a promising therapeutic approach and a prospective strategy for the development of new therapeutic agents. A critical aspect of their inhibition is to achieve selectivity in terms of enzyme isoforms, which is essential to improve treatment efficacy while reducing undesirable pleiotropic effects. The development of computational chemistry tools, particularly molecular docking, is greatly enhancing the precision of designing molecules with inherent potential for specific activity. Therefore, it was considered necessary to review the molecular docking studies conducted on the major isozymes of the enzyme in order to identify the specific interactions associated with each selective HDAC inhibitor. In particular, the most critical isozymes of HDAC (1, 2, 3, 6, and 8) have been thoroughly investigated within the scope of this review.

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.

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

Computational Study of Bis-(1-(Benzoyl)-3-Methyl Thiourea) Platinum (II) Complex Derivatives as Anticancer Candidates

Background

The increasing incidence of cancer every year has resulted in cancer becoming one of the most common causes of death in the world. The most common types of cancer are breast cancer, lung cancer and prostate cancer. Thiourea is one of the compounds that have anticancer effects, and its activity can be increased by structural modifications, one of which involves making a Bis-(1-(benzoyl)-3-methyl thiourea) platinum (II) metal complex.

Purpose

This study aims to obtain platinum (II)-thiourea complex compounds that have a more stable interaction as an anticancer agent compared to cisplatin.

Methods

The methods used are computational studies with molecular docking, simulation of molecular dynamics, and prediction of pharmacokinetics and toxicity.

Results

Based on the molecular docking of the platinum (II)-thiourea complex which has the most stable interaction with lower binding energy than the native ligand and the cisplatin, namely Bis-(3-methyl-1-(naphthalene-2-carbonyl)thiourea)) Platinum (II) against breast cancer receptors (3ERT) and lung cancer (2ITO) and compounds Bis-(1-(3-chlorobenzoyl)-3-methylthiourea) Platinum (II) against prostate cancer receptors (1Z95). The evaluation results of the stability of the interaction using a 50 ns molecular dynamic simulation showed that the Bis-(1-benzoyl-3-methylthiourea) Platinum (II) which binds to the prostate cancer receptor (1Z95) has the most stable interaction. Pharmacokinetic prediction results show that the platinum (II)-thiourea complex has a good pharmacokinetic profile, but there are several compounds that are mutagenic and hepatotoxic.

Conclusion

The Bis-(1-(3,4-dichlorobenzoyl)-3-methyl thiourea) platinum (II) compounds could be a suitable anticancer agent for the lungs.

In silico analysis of a putative dehalogenase from the genome of halophilic bacterium Halomonas smyrnensis AAD6T

Microbial-assisted removal of natural or synthetic pollutants is the prevailing green, low-cost technology to treat polluted environments. However, the challenge with enzyme-assisted bioremediation is the laborious nature of dehalogenase-producing microorganisms' bioprospecting. This bottleneck could be circumvented by in-silico analysis of certain microorganisms' whole-genome sequences to predict their protein functions and enzyme versatility for improved biotechnological applications. Herein, this study performed structural analysis on a dehalogenase (DehHsAAD6) from the genome of Halomonas smyrnensis AAD6 by molecular docking and molecular dynamic (MD) simulations. Other bioinformatics tools were also employed to identify substrate preference (haloacids and haloacetates) of the DehHsAAD6. The DehHsAAD6 preferentially degraded haloacids and haloacetates (-3.2-4.8 kcal/mol) and which formed three hydrogen bonds with Tyr12, Lys46, and Asp182. MD simulations data revealed the higher stability of DehHsAAD6-haloacid- (RMSD 0.22-0.3 nm) and DehHsAAD6-haloacetates (RMSF 0.05-0.14 nm) complexes, with the DehHsAAD6-L-2CP complex being the most stable. The detail of molecular docking calculations ranked complexes with the lowest binding free energies as: DehHsAAD6-L-2CP complex (-4.8 kcal/mol) = DehHsAAD6-MCA (-4.8 kcal/mol) < DehHsAAD6-TCA (-4.5 kcal/mol) < DehHsAAD6-2,3-DCP (-4.1 kcal/mol) < DehHsAAD6-D-2CP (-3.9 kcal/mol) < DehHsAAD6-2,2-DCP (-3.5 kcal/mol) < DehHsAAD6-3CP (-3.2 kcal/mol). In a nutshell, the study findings offer valuable perceptions into the elucidation of possible reaction mechanisms of dehalogenases for extended substrate specificity and higher catalytic activity.Communicated by Ramaswamy H. Sarma.

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules

Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.

PlaceWaters: Real-time, explicit interface water sampling during Rosetta ligand docking

Water molecules at the protein-small molecule interface often form hydrogen bonds with both the small molecule ligand and the protein, affecting the structural integrity and energetics of a binding event. The inclusion of these 'bridging waters' has been shown to improve the accuracy of predicted docked structures; however, due to increased computational costs, this step is typically omitted in ligand docking simulations. In this study, we introduce a resource-efficient, Rosetta-based protocol named "PlaceWaters" to predict the location of explicit interface bridging waters during a ligand docking simulation. In contrast to other explicit water methods, this protocol is independent of knowledge of number and location of crystallographic waters in homologous structures. We test this method on a diverse protein-small molecule benchmark set in comparison to other Rosetta-based protocols. Our results suggest that this coarse-grained, structure-based approach quickly and accurately predicts the location of bridging waters, improving our ability to computationally screen drug candidates.

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.

Comparative molecular docking and molecular-dynamic simulation of wild-type- and mutant carboxylesterase with BTA-hydrolase for enhanced binding to plastic

According to the literature review, microbial degradation of polyethylene terephthalate by PETases has been detected effective and eco-friendly. However, the number of microorganisms capable of such feats is limited with some undesirable bioprospecting results. BTA-hydrolase has been already reported capable of degrading polyethylene terephthalate. Therefore, mutation by in silico site-directed mutagenesis means to introduce current isomer of PETase for polyethylene terephthalate degradative capability as a better approach to resolve this issue. This study aimed to use in silico site-directed mutagenesis to convert a carboxylesterase from Archaeoglobus fulgidus to BTA-hydrolase from Thermobifida fusca by replacing six amino acids in specific locations. This work was followed by molecular docking analysis with polyethylene terephthalate and polypropylene to compare their interactions. The best-docked enzyme-substrate complex was further subjected to molecular dynamics simulation to gauge the binding quality of the BTA-hydrolase, wild-type and mutant-carboxylesterase with only polyethylene terephthalate as a substrate. Results of molecular docking revealed lowest binding energy for the wild-type carboxylesterase-polypropylene complex (-7.5 kcal/mol). The root-mean-square deviation value was observed stable for BTA-hydrolase. Meanwhile, root-mean-square fluctuation was assessed with higher fluctuation for the mutated residue Lys178. Consequently, the Rg value for BTA-hydrolase-ligand complex (∼1.68 nm) was the lowest compared to the mutant and wild-type carboxylesterase. The collective data conveyed that mutations imparted a minimal change in the ability of the mutant carboxylesterase to bind to polyethylene terephthalate.

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.

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.

Quality Assessment of Protein Docking Models Based on Graph Neural Network

Protein docking provides a structural basis for the design of drugs and vaccines. Among the processes of protein docking, quality assessment (QA) is utilized to pick near-native models from numerous protein docking candidate conformations, and it directly determines the final docking results. Although extensive efforts have been made to improve QA accuracy, it is still the bottleneck of current protein docking systems. In this paper, we presented a Deep Graph Attention Neural Network (DGANN) to evaluate and rank protein docking candidate models. DGANN learns inter-residue physio-chemical properties and structural fitness across the two protein monomers in a docking model and generates their probabilities of near-native models. On the ZDOCK decoy benchmark, our DGANN outperformed the ranking provided by ZDOCK in terms of ranking good models into the top selections. Furthermore, we conducted comparative experiments on an independent testing dataset, and the results also demonstrated the superiority and generalization of our proposed method.

Implications of Oxidative Stress in Glioblastoma Multiforme Following Treatment with Purine Derivatives

Recently, small compound-based therapies have provided new insights into the treatment of glioblastoma multiforme (GBM) by inducing oxidative impairment. Kinetin riboside (KR) and newly designed derivatives (8-azaKR, 7-deazaKR) selectively affect the molecular pathways crucial for cell growth by interfering with the redox status of cancer cells. Thus, these compounds might serve as potential alternatives in the oxidative therapy of GBM. The increased basal levels of reactive oxygen species (ROS) in GBM support the survival of cancer cells and cause drug resistance. The simplest approach to induce cell death is to achieve the redox threshold and circumvent the antioxidant defense mechanisms. Consequently, cells become more sensitive to oxidative stress (OS) caused by exogenous agents. Here, we investigated the effect of KR and its derivatives on the redox status of T98G cells in 2D and 3D cell culture. The use of spheroids of T98G cells enabled the selection of one derivative-7-deazaKR-with comparable antitumor activity to KR. Both compounds induced ROS generation and genotoxic OS, resulting in lipid peroxidation and leading to apoptosis. Taken together, these results demonstrated that KR and 7-deazaKR modulate the cellular redox environment of T98G cells, and vulnerability of these cells is dependent on their antioxidant capacity.

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.

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.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

Molecular docking and molecular dynamics simulation of Bacillus thuringiensis dehalogenase against haloacids, haloacetates and chlorpyrifos

The high dependency and surplus use of agrochemical products have liberated enormous quantities of toxic halogenated pollutants into the environment and threaten the well-being of humankind. Herein, this study performed molecular docking, molecular dynamic (MD) simulations, molecular mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculations on the DehH2 from Bacillus thuringiensis, to identify the order of which the enzyme degrades different substrates, haloacids, haloacetate and chlorpyrifos. The study discovered that the DehH2 favored the degradation of haloacids and haloacetates (-3.3 - 4.6 kcal/mol) and formed three hydrogen bonds with Asp125, Arg201 and Lys202. Despite the inconclusive molecular docking result, chlorpyrifos was consistently shown to be the least favored substrate of the DehH2 in MD simulations and MM-PBSA calculations. Results of MD simulations revealed the DehH2-haloacid- (RMSD 0.15 - 0.25 nm) and DehH2-haloacetates (RMSF 0.05 - 0.25 nm) were more stable, with the DehH2-L-2CP complex being the most stable while the least was the DehH2-chlorpyrifos (RMSD 0.295 nm; RMSF 0.05 - 0.59 nm). The Molecular Mechanics Poisson-Boltzmann Surface Area calculations showed the DehH2-L-2CP complex (-24.27 kcal/mol) having the lowest binding energy followed by DehH2-MCA (-22.78 kcal/mol), DehH2-D-2CP (-21.82 kcal/mol), DehH2-3CP (-21.11 kcal/mol), DehH2-2,2-DCP (-18.34 kcal/mol), DehH2-2,3-DCP (-8.34 kcal/mol), DehH2-TCA (-7.62 kcal/mol), while chlorpyrifos was unable to spontaneously bind to DehH2 (+127.16 kcal/mol). In a nutshell, the findings of this study offer valuable insights into the rational tailoring of the DehH2 for expanding its substrate specificity and catalytic activity in the near future.Communicated by Ramaswamy H. Sarma.

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.

Ligand binding free-energy calculations with funnel metadynamics

The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.

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.

Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors

Molecular dynamics simulations provide valuable insights into the behavior of molecular systems. Extending the recent trend of using machine learning techniques to predict physicochemical properties from molecular dynamics data, we propose to consider the trajectories as multidimensional time series represented by 2D tensors containing the ligand-protein interaction descriptor values for each time step. Similar in structure to the time series encountered in modern approaches for signal, speech, and natural language processing, these time series can be directly analyzed using long short-term memory (LSTM) recurrent neural networks or convolutional neural networks (CNNs). The predictive regression models for the ligand-protein affinity were built for a subset of the PDBbind v.2017 database and applied to inhibitors of tankyrase, an enzyme of the poly(ADP-ribose)-polymerase (PARP) family that can be used in the treatment of colorectal cancer. As an additional test set, a subset of the Community Structure-Activity Resource (CSAR) data set was used. For comparison, the random forest and simple neural network models based on the crystal pose or the trajectory-averaged descriptors were used, as well as the commonly employed docking and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) scores. Convolutional neural networks based on the 2D tensors of ligand-protein interaction descriptors for short (2 ns) trajectories provide the best accuracy and predictive power, reaching the Spearman rank correlation coefficient of 0.73 and Pearson correlation coefficient of 0.70 for the tankyrase test set. Taking into account the recent increase in computational power of modern GPUs and relatively low computational complexity of the proposed approach, it can be used as an advanced virtual screening filter for compound prioritization.

Interaction of metals from group 10 (Ni, Pd, and Pt) and 11 (Cu, Ag, and Au) with human blood δ-ALA-D: in vitro and in silico studies

Mammalian δ-aminolevulinate dehydratase (δ-ALA-D) is a metalloenzyme, which requires Zn(II) and reduced thiol groups for catalytic activity, and is an important molecular target for the widespread environmental toxic metals. The δ-ALA-D inhibition mechanism by metals of Group 10 (Ni, Pd, and Pt) and 11 (Cu, Ag, and Au) of the periodic table has not yet been determined. The objective of this study was to characterize the molecular mechanism of δ-ALA-D inhibition caused by the elements of groups 10 and 11 using in vitro (δ-ALA-D activity from human erythrocytes) and in silico (docking simulations) methods. Our results showed that Ni(II) and Pd(II) caused a small inhibition (~ 10%) of the δ-ALA-D. Pt(II) and Pt(IV) significantly inhibited the enzyme (75% and 44%, respectively), but this inhibition was attenuated by Zn(II) and dithiothreitol (DTT). In group 11, all metals inhibited δ-ALA-D with great potency (~ 70-90%). In the presence of Zn(II) and DTT, the enzyme activity was restored to the control levels. The in silico molecular docking data suggest that the coordination of the ions Pt(II), Pt(IV), Cu(II), Ag(I), and Au(III) with thiolates groups from C135 and C143 residues from the δ-ALA-D active site are crucial to the enzyme inhibition. The results indicate that a possible mechanism of inhibition of δ-ALA-D by these metals may involve the replacement of the Zn(II) from the active site and/or the cysteinyl residue oxidation.

RosettaLigandEnsemble: A Small-Molecule Ensemble-Driven Docking Approach

RosettaLigand is a protein-small-molecule (ligand) docking software capable of predicting binding poses and is used for virtual screening of medium-sized ligand libraries. Structurally similar small molecules are generally found to bind in the same pose to one binding pocket, despite some prominent exceptions. To make use of this information, we have developed RosettaLigandEnsemble (RLE). RLE docks a superimposed ensemble of congeneric ligands simultaneously. The program determines a well-scoring overall pose for this superimposed ensemble before independently optimizing individual protein-small-molecule interfaces. In a cross-docking benchmark of 89 protein-small-molecule co-crystal structures across 20 biological systems, we found that RLE improved sampling efficiency in 62 cases, with an average change of 18%. In addition, RLE generated more consistent docking results within a congeneric series and was capable of rescuing the unsuccessful docking of individual ligands, identifying a nativelike top-scoring model in 10 additional cases. The improvement in RLE is driven by a balance between having a sizable common chemical scaffold and meaningful modifications to distal groups. The new ensemble docking algorithm will work well in conjunction with medicinal chemistry structure-activity relationship (SAR) studies to more accurately recapitulate protein-ligand interfaces. We also tested whether optimizing the rank correlation of RLE-binding scores to SAR data in the refinement step helps the high-resolution positioning of the ligand. However, no significant improvement was observed.

Enhancing Structure Prediction and Design of Soluble and Membrane Proteins with Explicit Solvent-Protein Interactions

Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug-binding sites in structurally uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Computational design of ligand-binding membrane receptors with high selectivity

Accurate modeling and design of protein-ligand interactions have broad applications in cell, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein homology modeling-ligand docking-protein design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced fit modes of ligand binding. In structure modeling tests, we blindly predicted near-atomic accuracy ligand conformations bound to G protein-coupled receptors (GPCRs) that were rarely identified by traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally-uncharacterized GPCRs. We then applied the technique to design functional human dopamine receptors with novel ligand binding selectivity. Most blindly predicted ligand binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand binding profile of membrane receptors that remain difficult to crystallize.

Structural Insights into the Atomistic Mechanisms of Action of Small Molecule Inhibitors Targeting the KCa3.1 Channel Pore

The intermediate-conductance Ca2+-activated K+ channel (KCa3.1) constitutes an attractive pharmacological target for immunosuppression, fibroproliferative disorders, atherosclerosis, and stroke. However, there currently is no available crystal structure of this medically relevant channel that could be used for structure-assisted drug design. Using the Rosetta molecular modeling suite we generated a molecular model of the KCa3.1 pore and tested the model by first confirming previously mapped binding sites and visualizing the mechanism of TRAM-34 (1-[(2-chlorophenyl)diphenylmethyl]-1H-pyrazole), senicapoc (2,2-bis-(4-fluorophenyl)-2-phenylacetamide), and NS6180 (4-[[3-(trifluoromethyl)phenyl]methyl]-2H-1,4-benzothiazin-3(4H)-one) inhibition at the atomistic level. All three compounds block ion conduction directly by fully or partially occupying the site that would normally be occupied by K+ before it enters the selectivity filter. We then challenged the model to predict the receptor sites and mechanisms of action of the dihydropyridine nifedipine and an isosteric 4-phenyl-pyran. Rosetta predicted receptor sites for nifedipine in the fenestration region and for the 4-phenyl-pyran in the pore lumen, which could both be confirmed by site-directed mutagenesis and electrophysiology. While nifedipine is thus not a pore blocker and might be stabilizing the channel in a nonconducting conformation or interfere with gating, the 4-phenyl-pyran was found to be a classical pore blocker that directly inhibits ion conduction similar to the triarylmethanes TRAM-34 and senicapoc. The Rosetta KCa3.1 pore model explains the mechanism of action of several KCa3.1 blockers at the molecular level and could be used for structure-assisted drug design.

Single Mutations Reshape the Structural Correlation Network of the DMXAA-Human STING Complex

Subtle changes in protein sequences are able to alter ligand-protein interactions. Unraveling the mechanism of such phenomena is important for understanding ligand-protein interactions, including the DMXAA-STING interaction. DMXAA specifically binds to mouse STING instead of human STING. However, the S162A mutation and a newly discovered E260I mutation endow human STING^AQ with DMXAA sensitivity. Through molecular dynamics simulations, we revealed how these single mutations alter the DMXAA-STING interaction. Compared to mutated systems, structural correlations in the interaction of STING^AQ with DMXAA are stronger, and the correlations are cross-protomers in the dimeric protein. Analyses on correlation coefficients lead to the identification of two key interactions that mediate the strong cross-protomer correlation in the DMXAA-STING^AQ interaction network: DMXAA-267T-162S* and 238R-260E*. These two interactions are partially and totally interrupted by the S162A and E260I mutations, respectively. Moreover, a smaller number of water molecules are displaced upon DMXAA binding to STING^AQ than that on binding to its mutants, leading to a larger entropic penalty for the former. Considering the sensitivity of STING^AQ and two of its mutants to DMXAA, a strong structural correlation appears to discourage DMXAA-STING binding. Such an observation suggests that DMXAA derivatives, which are deprived of hydrogen-bond interaction with both 162S* and 267T, are potential agonists of human STING.

Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.

Synergistic antibacterial effects of herbal extracts and antibiotics on methicillin-resistant Staphylococcus aureus: A computational and experimental study

Antibiotic resistance has become a serious global concern, and the discovery of antimicrobial herbal constituents may provide valuable solutions to overcome the problem. In this study, the effects of therapies combining antibiotics and four medicinal herbs on methicillin-resistant Staphylococcus aureus (MRSA) were investigated. Specifically, the synergistic effects of Magnolia officinalis, Verbena officinalis, Momordica charantia, and Daphne genkwa in combination with oxacillin or gentamicin against methicillin-resistant (ATCC43300) and methicillin-susceptible (ATCC25923) S. aureus were examined. In vitro susceptibility and synergistic testing were performed to measure the minimum inhibitory concentration and fractional inhibitory concentration (FIC) index of the antibiotics and medicinal herbs against MRSA and methicillin-susceptible S. aureus. To identify the active constituents in producing these synergistic effects, in silico molecular docking was used to investigate the binding affinities of 139 constituents of the four herbs to the two common MRSA inhibitory targets, penicillin binding proteins 2a (PBP2a) and 4 (PBP4). The physicochemical and absorption, distribution, metabolism, and excretion properties and drug safety profiles of these compounds were also analyzed. D. genkwa extract potentiated the antibacterial effects of oxacillin against MRSA, as indicated by an FIC index value of 0.375. M. officinalis and V. officinalis produced partial synergistic effects when combined with oxacillin, whereas M. charantia was found to have no beneficial effects in inhibiting MRSA. Overall, tiliroside, pinoresinol, magnatriol B, and momorcharaside B were predicted to be PBP2a or PBP4 inhibitors with good drug-like properties. This study identifies compounds that deserve further investigation with the aim of developing therapeutic agents to modulate the effect of antibiotics on MRSA. Impact statement Antibiotic resistant is a well-known threat to global health and methicillin-resistant Staphylococcus aureus is one of the most significant ones. These resistant bacteria kill thousands of people every year and therefore a new effective antimicrobial treatment is necessary. This study identified the herbs and their associated bioactive ingredients that can potential the effects of current antibiotics. These herbs have long history of human usage in China and have well-defined monograph in the Chinese Pharmacopeia. These indicate their relatively high clinical safety and may have a quicker drug development process than that of a new novel antibiotic. Based on the results of this study, the authors will perform further in vitro and animal studies, aiming to accumulate significant data for the application of clinical trial.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Rosetta and the Design of Ligand Binding Sites

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand. Starting with a protein structure and the structure of the ligand, Rosetta can optimize both the placement of the ligand in the pocket and the identity and conformation of the surrounding sidechains, yielding proteins that bind the target compound.

In silico prediction of prostaglandin D2 synthase inhibitors from herbal constituents for the treatment of hair loss

ETHNOPHARMACOLOGICAL RELEVANCE

Many herbal topical formulations have been marketed worldwide to prevent hair loss or promote hair growth. Certain in vivo studies have shown promising results among them; however, the effectiveness of their bioactive constituents remains unknown.

AIM OF THE STUDY

Recently, prostaglandin D2 (PGD2) inhibition has been discovered as a pharmacological mechanism for treating androgenic alopecia (AGA). This present study was aimed to identify prostaglandin D2 synthase (PTGDS) inhibitors in traditional Chinese medicines (TCMs) for treating AGA.

MATERIALS AND METHODS

In this study, 389 constituents of 12 selected herbs were docked into 6 different crystal structures of PTGDS. The accuracy of the docking methods was successfully validated with experimental data from the ZINC In Man (Zim) database using receiver operating characteristic (ROC) studies. Seven essential drug properties were predicted for topical formulation: skin permeability, sensitisation, irritation, corrosion, mutagenicity, tumorigenicity and reproductive effects.

RESULTS

Many constituents of the twelve herbs were found to have more advanced binding energies than the experimentally proved PTGDS inhibitors, but many of them were indicative of at least one type of skin adverse reactions, and exhibited poor skin permeability.

CONCLUSIONS

Overall, ricinoleic acid, acteoside, amentoflavone, quercetin-3-O-rutinoside and hinokiflavone were predicted to be PTGDS inhibitors with good pharmacokinetic properties and minimal adverse skin reactions. These compounds have the highest potential for further in vitro and in vivo investigation with the aim of developing safe and high-efficacy hair loss treatment.

Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand

RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial 'low-resolution' docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the 'high-resolution' full atom refinement step.

Docking small peptides remains a great challenge: an assessment using AutoDock Vina

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Molecular docking and site-directed mutagenesis of a Bacillus thuringiensis chitinase to improve chitinolytic, synergistic lepidopteran-larvicidal and nematicidal activities

Bacterial chitinases are useful in the biocontrol of agriculturally important pests and fungal pathogens. However, the utility of naturally occurring bacterial chitinases is often limited by their low enzyme activity. In this study, we constructed mutants of a Bacillus thuringiensis chitinase with enhanced activity based on homology modeling, molecular docking, and the site-directed mutagenesis of target residues to modify spatial positions, steric hindrances, or hydrophilicity/hydrophobicity. We first identified a gene from B. thuringiensis YBT-9602 that encodes a chitinase (Chi9602) belonging to glycosyl hydrolase family 18 with conserved substrate-binding and substrate-catalytic motifs. We constructed a structural model of a truncated version of Chi9602 (Chi9602_35-459) containing the substrate-binding domain using the homologous 1ITX protein of Bacillus circulans as the template. We performed molecular docking analysis of Chi9602_35-459 using di-N-acetyl-D-glucosamine as the ligand. We then selected 10 residues of interest from the docking area for the site-directed mutagenesis experiments and expression in Escherichia coli. Assays of the chitinolytic activity of the purified chitinases revealed that the three mutants exhibited increased chitinolytic activity. The ChiW50A mutant exhibited a greater than 60 % increase in chitinolytic activity, with similar pH, temperature and metal ion requirements, compared to wild-type Chi9602. Furthermore, ChiW50A exhibited pest-controlling activity and antifungal activity. Remarkable synergistic effects of this mutant with B. thuringiensis spore-crystal preparations against Helicoverpa armigera and Caenorhabditis elegans larvae and obvious activity against several plant-pathogenic fungi were observed.

Practical Considerations in Virtual Screening and Molecular Docking

Molecular docking has become an important common component of the drug discovery toolbox, and its relative low-cost implications and perceived simplicity of use has stimulated an everincreasing popularity within academic communities. The inherent “garbage-in-garbage-out” defect of molecular docking, however, leads a lot of researchers to dedicate countless hours to the identification of hit compounds that later prove to be inactive. Several considerations that can greatly improve the success and enrichment of true bioactive hit compounds are commonly overlooked at the initial stages of a molecular docking study. This chapter will cover several of these considerations, including protonation states, active site waters, separating actives from decoys, consensus docking and molecular mechanics generalized-Born/surface area (MM-GBSA) rescoring, and incorporation of pharmacophoric constraints, in an attempt to clarify what is, in fact, very complicated and inherent difficulties of a structure-based drug design study.