Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge

We continued prospective assessments of the Wilma–solvated interaction energy (SIE) platform for pose prediction, binding affinity prediction, and virtual screening on the challenging SAMPL4 data sets including the HIV-integrase inhibitor and two host–guest systems. New features of the docking algorithm and scoring function are tested here prospectively for the first time. Wilma–SIE provides good correlations with actual binding affinities over a wide range of binding affinities that includes strong binders as in the case of SAMPL4 host–guest systems. Absolute binding affinities are also reproduced with appropriate training of the scoring function on available data sets or from comparative estimation of the change in target’s vibrational entropy. Even when binding modes are known, SIE predictions lack correlation with experimental affinities within dynamic ranges below 2 kcal/mol as in the case of HIV-integrase ligands, but they correctly signaled the narrowness of the dynamic range. Using a common protein structure for all ligands can reduce the noise, while incorporating a more sophisticated solvation treatment improves absolute predictions. The HIV-integrase virtual screening data set consists of promiscuous weak binders with relatively high flexibility and thus it falls outside of the applicability domain of the Wilma–SIE docking platform. Despite these difficulties, unbiased docking around three known binding sites of the enzyme resulted in over a third of ligands being docked within 2 Å from their actual poses and over half of the ligands docked in the correct site, leading to better-than-random virtual screening results.     

Ligand binding affinity prediction by linear interaction energy methods

A recent method for estimating ligand binding affinities is extended. This method employs averages of interaction potential energy terms from molecular dynamics simulations or other thermal conformational sampling techniques. Incorporation of systematic deviations from electrostatic linear response, derived from free energy perturbation studies, into the absolute binding free energy expression significantly enhances the accuracy of the approach. This type of method may be useful for computational prediction of ligand binding strengths, e.g., in drug design applications.     

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 1. Exploring the parameter space

We present a binding free energy function that consists of force field terms supplemented by solvation terms. We used this function to calibrate the solvation model along with the binding interaction terms in a self-consistent manner. The motivation for this approach was that the solute dielectric-constant dependence of calculated hydration gas-to-water transfer free energies is markedly different from that of binding free energies (J. Comput. Chem. 2003, 24, 954). Hence, we sought to calibrate directly the solvation terms in the context of a binding calculation. The five parameters of the model were systematically scanned to best reproduce the absolute binding free energies for a set of 99 protein-ligand complexes. We obtained a mean unsigned error of 1.29 kcal/mol for the predicted absolute binding affinity in a parameter space that was fairly shallow near the optimum. The lowest errors were obtained with solute dielectric values of Din = 20 or higher and scaling of the intermolecular van der Waals interaction energy by factors ranging from 0.03 to 0.15. The high apparent Din and strong van der Waals scaling may reflect the anticorrelation of the change in solvated potential energy and configurational entropy, that is, enthalpy-entropy compensation in ligand binding (Biophys. J. 2004, 87, 3035-3049). Five variations of preparing the protein-ligand data set were explored in order to examine the effect of energy refinement and the presence of bound water on the calculated results. We find that retaining water in the final protein structure used for calculating the binding free energy is not necessary to obtain good results; that is the continuum solvation model is sufficient. Virtual screening enrichment studies on estrogen receptor and thymidine kinase showed a good ability of the binding free energy function to recover true hits in a collection of decoys.     

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise

Solvated interaction energy (SIE) is an end-point physics-based scoring function for predicting binding affinities from force-field nonbonded interaction terms, continuum solvation, and configurational entropy linear compensation. We tested the SIE function in the Community Structure-Activity Resource (CSAR) scoring challenge consisting of high-resolution cocrystal structures for 343 protein-ligand complexes with high-quality binding affinity data and high diversity with respect to protein targets. Particular emphasis was placed on the sensitivity of SIE predictions to the assignment of protonation and tautomeric states in the complex and the treatment of metal ions near the protein-ligand interface. These were manually curated from an originally distributed CSAR-HiQ data set version, leading to the currently distributed CSAR-NRC-HiQ version. We found that this manual curation was a critical step for accurately testing the performance of the SIE function. The standard SIE parametrization, previously calibrated on an independent data set, predicted absolute binding affinities with a mean-unsigned-error (MUE) of 2.41 kcal/mol for the CSAR-HiQ version, which improved to 1.98 kcal/mol for the upgraded CSAR-NRC-HiQ version. Half-half retraining-testing of SIE parameters on two predefined subsets of CSAR-NRC-HiQ led to only marginal further improvements to an MUE of 1.83 kcal/mol. Hence, we do not recommend altering the current default parameters of SIE at this time. For a sample of SIE outliers, additional calculations by molecular dynamics-based SIE averaging with or without incorporation of ligand strain, by MM-PB(GB)/SA methods with or without entropic estimates, or even by the linear interaction energy (LIE) formalism with an explicit solvent model, did not further improve predictions.     

An energy decomposition analysis for intramolecular non-covalent interaction in solvated environment

In this work, the intra-EDA method, which is a recently developed energy decomposition analysis scheme for intramolecular non-covalent interaction is extended from gas phase to solvated environment. It is the first analysis scheme that performs analysis for intramolecular interaction in solution. By fragmentation scheme, a molecule is divided into intramolecular interacting fragments and environmental fragments via single bond homolysis breaking. The solvent effect is taken into account by implicit solvation model. Intramolecular interaction free energy is estimated as the separated treatment of inter-fragment interactions in dielectric environment. The analysis results highlight the importance of solvent effects to intramolecular non-covalent interaction.     

FRED pose prediction and virtual screening accuracy

Results of a previous docking study are reanalyzed and extended to include results from the docking program FRED and a detailed statistical analysis of both structure reproduction and virtual screening results. FRED is run both in a traditional docking mode and in a hybrid mode that makes use of the structure of a bound ligand in addition to the protein structure to screen molecules. This analysis shows that most docking programs are effective overall but highly inconsistent, tending to do well on one system and poorly on the next. Comparing methods, the difference in mean performance on DUD is found to be statistically significant (95% confidence) 61% of the time when using a global enrichment metric (AUC). Early enrichment metrics are found to have relatively poor statistical power, with 0.5% early enrichment only able to distinguish methods to 95% confidence 14% of the time.     

Biospecific interaction (affinity) CEC and affinity nano-LC

This review article is aimed at assessing the recent progress made in affinity nano-LC and affinity CEC performed in capillaries and microchips. A variety of biospecific interactions is covered including lectin affinity, immunoaffinity, immobilized metal affinity, sugar-based affinity, protein A affinity, protein G affinity, aptamer affinity, enzyme affinity, and other miscellanea. ACE involving affinity interaction in free solution is not covered in this review article. Also, affinity-based separations involving chiral recognition are not the subject of this review article because they are the topic of a more specialized review article on chiral separations in this special issue. A total of 31 papers published in the period 1998-2006 have been discussed in this review article.     

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.     

A novel search engine for virtual screening of very large databases

Virtual screening of large chemical databases using the structure of the receptor can be computationally very demanding. We present a novel strategy that combines exhaustive similarity searches directly in SMILES format with the docking of flexible ligands, whose 3D structure is generated on the fly from the SMILES representation. Our strategy makes use of the recently developed LINGO tools to extract implicit chemical information from SMILES strings and integrates LINGO similarities into a pseudo-evolutionary algorithm. The algorithm represents a combination of a fast target-independent similarity method with a slower but information richer target-focused method. A virtual search of FactorXa ligands provided 62% of the potential hits after docking only 6.5% of a database of nearly 1 million molecules. The set of solutions showed good diversity, indicating that the method shows good scaffold hopping capabilities.     

Binding affinity prediction with different force fields: examination of the linear interaction energy method

A systematic study of the linear interaction energy (LIE) method and the possible dependence of its parameterization on the force field and system (receptor binding site) is reported. We have calculated the binding free energy for nine different ligands in complex with P450cam using three different force fields (Amber95, Gromos87, and OPLS-AA). The results from these LIE calculations using our earlier parameterization give relative free energies of binding that agree remarkably well with the experimental data. However, the absolute energies are too positive for all three force fields, and it is clear that an additional constant term (gamma) is required in this case. Out of five examined LIE models, the same one emerges as the best for all three force fields, and this, in fact, corresponds to our earlier one apart from the addition of the constant gamma, which is almost identical for the three force fields. Thus, the present free energy calculations clearly indicate that the coefficients of the LIE method are independent of the force field used. Their relation to solvation free energies is also demonstrated. The only free parameter of the best model is gamma, which is found to depend on the hydrophobicity of the binding site. We also attempt to quantify the binding site hydrophobicity of four different proteins which shows that the ordering of gamma's for these sites reflects the fraction of hydrophobic surface area.     

Drug search for leishmaniasis: a virtual screening approach by grid computing

The trypanosomatid protozoa Leishmania is endemic in ~100 countries, with infections causing ~2 million new cases of leishmaniasis annually. Disease symptoms can include severe skin and mucosal ulcers, fever, anemia, splenomegaly, and death. Unfortunately, therapeutics approved to treat leishmaniasis are associated with potentially severe side effects, including death. Furthermore, drug-resistant Leishmania parasites have developed in most endemic countries. To address an urgent need for new, safe and inexpensive anti-leishmanial drugs, we utilized the IBM World Community Grid to complete computer-based drug discovery screens (Drug Search for Leishmaniasis) using unique leishmanial proteins and a database of 600,000 drug-like small molecules. Protein structures from different Leishmania species were selected for molecular dynamics (MD) simulations, and a series of conformational “snapshots” were chosen from each MD trajectory to simulate the protein’s flexibility. A Relaxed Complex Scheme methodology was used to screen ~2000 MD conformations against the small molecule database, producing >1 billion protein-ligand structures. For each protein target, a binding spectrum was calculated to identify compounds predicted to bind with highest average affinity to all protein conformations. Significantly, four different Leishmania protein targets were predicted to strongly bind small molecules, with the strongest binding interactions predicted to occur for dihydroorotate dehydrogenase (LmDHODH; PDB:3MJY). A number of predicted tight-binding LmDHODH inhibitors were tested in vitro and potent selective inhibitors of Leishmania panamensis were identified. These promising small molecules are suitable for further development using iterative structure-based optimization and in vitro/in vivo validation assays.     

Similarity search profiles as a diagnostic tool for the analysis of virtual screening calculations

An analysis method termed similarity search profiling has been developed to evaluate fingerprint-based virtual screening calculations. The analysis is based on systematic similarity search calculations using multiple template compounds over the entire value range of a similarity coefficient. In graphical representations, numbers of correctly identified hits and other detected database compounds are separately monitored. The resulting profiles make it possible to determine whether a virtual screening trial can in principle succeed for a given compound class, search tool, similarity metric, and selection criterion. As a test case, we have analyzed virtual screening calculations using a recently designed fingerprint on 23 different biological activity classes in a compound source database containing approximately 1.3 million molecules. Based on our predefined selection criteria, we found that virtual screening analysis was successful for 19 of 23 compound classes. Profile analysis also makes it possible to determine compound class-specific similarity threshold values for similarity searching.     

Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.     

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.     

Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques

Summary A linear quantitative–structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity. For the selection of the best variables the Elimination Selection-Stepwise Regression Method (ES-SWR) is utilized. The predictive ability of the model is evaluated against a set of 13 compounds. Based on the produced QSAR model and an analysis on the domain of its applicability, the effects of various structural modifications on biological activity are investigated. The study leads to a number of guanidine derivatives with significantly improved predicted activities.     

A neural network based virtual high throughput screening test for the prediction of CNS activity

A virtual high throughput screening test to identify potentially CNS-active drugs has been developed. Discrimination was based on the knowledge available in databases containing CNS-active (Cipsline from Prous Science) and inactive compounds (Chemical Directory from Sigma-Aldrich). Molecular structures were represented using 2D Unit y fingerprints and a feedforward neural network was trained to classify molecules regarding their CNS activity. The parameterized network was validated by reclassification of the training set elements, by the classification of a test set preselected from the Prous database, and also by the prediction of activity for known CNS drugs not used in the training set but available in the Medchem database (Daylight). These tests revealed that our neural net recognized at least 89% of CNS-active compounds and would be suitable for use in our virtual screening protocol.     

Time-dependent density functional theory (TDDFT) modelling of Pechmann dyes: from accurate absorption maximum prediction to virtual dye screening

The red Pechmann dye (λ(max) = 550 nm) is the exo-dimer of 4-phenyl-3-butenolide connected at the α-carbon by a double bond in a trans-fashion. The ring system is easily rearranged to the trans-endo-fused bicyclic 6-membered lactone dimer (yellow). Both lactones can be singly or doubly amidated with primary amines leading to further colour changes. The nature of the core heterocycle (exo- vs. endo-; 5- or 6-membered ring), core heteroatom (O vs. N) and additional substituents on the phenyl ring allows for exquisite control over colour achievable within a single dye family. Herein we present a detailed investigation of the modelling of the electronic spectra of the Pechmann dye family by time-dependent density functional theory (TDDFT). Whereas pure Hartree-Fock (TDHF) ab-initio calculation underestimates the UV/Vis absorption maximum, pure TDDFT leads to a large overestimation. The accuracy of the prediction is highly dependent on the mix of HF and DFT, with BMK (42% HF) and M06-2X (54% HF) giving the closest match with the experimental value. Among all basis sets evaluated, the computationally-efficient, DFT-optimized DGDZVP showed the best chemical accuracy/size profile. Finally, the dispersion interaction-corrected (SMD) implicit solvation model was found to be advantageous compared to the original IEFPCM. The absorption maxima of substituted Pechmann dyes and their rearranged lactone counterparts can be predicted with excellent accuracy (±6 nm) at the optimal SMD(toluene)/TD-BMK/DGDZVP//SMD(toluene)B3LYP/DGDZVP level of theory. Using this procedure, a small virtual library of novel, heterocycle-substituted Pechmann dyes were screened. Such substitution was shown to be a viable strategy for colour tuning, giving λ(max) from 522 (4-pyridyl) to 627 (2-indolyl) nm.     

Identification of potential inhibitors of human methionine aminopeptidase (type II) for cancer therapy: Structure-based virtual screening,ADMET prediction and molecular dynamics studies

Methionine Aminopeptidases MetAPs are divalent-cofactor dependent enzymes that are responsible for the cleavage of the initiator Methionine from the nascent polypeptides. MetAPs are classified into two isoforms: namely, MetAP1 and MetAP2. Several studies have revealed that MetAP2 is upregulated in various cancers, and its inhibition has shown to suppress abnormal or excessive blood vessel formation and tumor growth in model organisms. Clinical studies show that the natural product fumagillin, and its analogs are potential inhibitors of MetAP2. However, due to their poor pharmacokinetic properties and neurotoxicities in clinical studies, their further developments have received a great setback. Here, we apply structure-based virtual screening and molecular dynamics methods to identify a new class of potential inhibitors for MetAP2. We screened Otava’s Chemical Library, which consists of about 3 200 000 tangible-chemical compounds, and meticulously selected the top 10 of these compounds based on their inhibitory potentials against MetAP2. The top hit compounds subjected to ADMET predictor using 3 independent ADMET prediction programs, were found to be drug-like. To examine the stability of ligand binding mode, and efficacy, the unbound form of MetAP2, its complexes with fumagillin, spiroepoxytriazole, and the best promising compounds compound-3369841 and compound-3368818 were submitted to 100 ns molecular dynamics simulation. Like fumagillin, spiroepoxytriazole, and both compound-3369841 and compound-3368818 showed stable binding mode over time during the simulations. Taken together, these uninherited-fumagillin compounds may serve as new class of inhibitors or provide scaffolds for further optimization towards the design of more potent MetAP2 inhibitors -development of such inhibitors would be essential strategy against various cancer types.