Volume 12 Supplement 1
Potent bace-1 inhibitor design using pharmacophore modeling, in silico screening and molecular docking studies
© John et al; licensee BioMed Central Ltd. 2011
Published: 15 February 2011
Beta-site amyloid precursor protein cleaving enzyme (BACE-1) is a single-membrane protein belongs to the aspartyl protease class of catabolic enzymes. This enzyme involved in the processing of the amyloid precursor protein (APP). The cleavage of APP by BACE-1 is the rate-limiting step in the amyloid cascade leading to the production of two peptide fragments Aβ40 and Aβ42. Among two peptide fragments Aβ42 is the primary species thought to be responsible for the neurotoxicity and amyloid plaque formation that lead to memory and cognitive defects in Alzheimer’s disease (AD). AD is a ravaging neurodegenerative disorder for which no disease-modifying treatment is currently available. Inhibition of BACE-1 is expected to stop amyloid plaque formation and emerged as an interesting and attractive therapeutic target for AD.
Ligand-based computational approach was used to identify the molecular chemical features required for the inhibition of BACE-1 enzyme. A training set of 20 compounds with known experimental activity was used to generate pharmacophore hypotheses using 3D QSAR Pharmacophore Generation module available in Discovery studio. The hypothesis was validated by four different methods and the best hypothesis was utilized in database screening of four chemical databases like Maybridge, Chembridge, NCI and Asinex. The retrieved hit compounds were subjected to molecular docking study using GOLD 4.1 program.
Among ten generated pharmacophore hypotheses, Hypo 1 was chosen as best pharmacophore hypothesis. Hypo 1 consists of one hydrogen bond donor, one positive ionizable, one ring aromatic and two hydrophobic features with high correlation coefficient of 0.977, highest cost difference of 121.98 bits and lowest RMSD value of 0.804. Hypo 1 was validated using Fischer randomization method, test set with a correlation coefficient of 0.917, leave-one-out method and decoy set with a goodness of hit score of 0.76. The validated Hypo 1 was used as a 3D query in database screening and retrieved 773 compounds with the estimated activity value <100 nM. These hits were docked into the active site of BACE-1 and further refined based on molecular interactions with the essential amino acids and good GOLD fitness score.
The best pharmacophore hypothesis, Hypo 1, with high predictive ability contains chemical features required for the effective inhibition of BACE-1. Using Hypo 1, we have identified two compounds with diverse chemical scaffolds as potential virtual leads which, as such or upon further optimization, can be used in the designing of new BACE-1 inhibitors.
Beta-site amyloid precursor protein cleaving enzyme (BACE-1), also known as β-secretase, memapsin-2, or Aspartyl protease-2, is a single-membrane protein belongs to the aspartyl protease class of catabolic enzyme. This is one of the enzymes responsible for the sequential proteolysis of amyloid precursor protein (APP) . The cleavage of APP by BACE-1, which is the rate-limiting step in the amyloid cascade, results in the generation of two peptide fragments Aβ40 and Aβ42. Among two peptide fragments, Aβ42 is the primary species and thought to be causal for the neurotoxicity and amyloid plaque formation that lead to memory and cognitive defects in Alzheimer’s disease (AD) . The AD is a debilitating neurodegenerative disease that results in the irreversible loss of neurons, particularly in the cortex and hippocampus . It is characterized by progressive decline in cognitive function that inevitably leading to incapacitation and death. It also histopathologically characterized by the presence of amyloid plaques and neurofibrillar tangles in the brain. Regardless of the increasing demand for medication, no truly disease-modifying treatment is currently available [4, 5]. The BACE knockout study in mice shows a complete absence of Aβ production with no reported side effects [6–8]. Since gene knockout study showed a reduction in AD-like pathology, inhibition of BACE-1 the key enzyme in the production of Aβ peptide has emerged as an attractive therapeutic target for AD . Therefore extensive efforts have been followed in the discovery of potential inhibitors of BACE-1. Most of the designing of BACE-1 inhibitors are based on the transition state mimetic approach, which depends mainly on replacing the scissile amide bond of an appropriate substrate with a stable mimetic of the putative transition-state structure .
The main aim of our approach, which is discussed in this study is different than the transition state mimetic approach, is to develop an accurate and efficient method for discovering potent BACE-1 inhibitors. A pharmacophore hypothesis was generated based on key structural features of compounds with BACE-1 inhibitory activity. It provides a rational hypothetical representation of the most important chemical features responsible for activity. Herein, a ligand-based 3D pharmacophore hypothesis for BACE-1 inhibitors was constructed based on the structure-activity relationship observed in a set of known BACE-1 inhibitors. The resulted pharmacophore hypotheses were validated by test set, Fischer randomization, leave-one-out, and decoy set methods. The validated pharmacophore hypothesis has been used in in silico screening to identify hits that are highly varied in chemical nature. The retrieved hits were subsequently subjected to a well-defined refining procedure based on estimated activity values, drug-likeness prediction and further by molecular docking study. The identified hits can further be utilized in designing novel and potent BACE-1 inhibitors.
In a computerized pharmacophore generation process the accurate choice of the training set is a key issue. The built pharmacophore hypothesis can be as good as the input data information. The following criteria should be considered during the selection of data set in order to achieve a significant pharmacophore hypothesis. (1) All compounds used in the training set have to bind to the same receptor in roughly the same fashion. Compounds having more binding interaction with the receptor are more active than those with fewer; (2) the data set must be widely populated covering an activity range of at least 4 orders of magnitude; (3) the most active compounds should inevitably be included in the training set and (4) all biologically relevant data should be obtained by homogenous procedures . Every individual feature in the resulting hypotheses will invade a certain weight that is proportional to its relative contribution to biological activity.
Diverse conformation generation
Prior to the generation of pharmacophore hypotheses, the training set compounds, which were converted to 3D structure, were used to generate diverse conformations. Diverse Conformation Generation protocol implemented in DS was used to generate conformations using the Best conformation model generation method with CHARMM force field and Poling algorithm to ensure the energy-minimized conformation for each compound. The parameters like maximum number of 250 conformers, the ‘best conformational analysis’ method, and an energy threshold of 20 kcal/mol above the global energy minimum were chosen during conformation generation.
The training set comprises of 20 compounds was used in pharmacophore hypothesis generation. The HypoGen algorithm available in 3D QSAR Pharmacophore Generation protocol of DS tries to generate hypotheses with features common amongst active molecules and do not reflect the inactive molecules of the training set. The training set compounds were predicted for their inherent chemical features using Feature Mapping protocol implemented in DS. During pharmacophore hypothesis generation a minimum of 4 and a maximum of 5 pharmacophoric features like hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), positive ionizable (PI), ring aromatic (RA) and hydrophobic (HY) were included. These features were selected based on the feature mapping results. All parameters were set to their default values except uncertainty value, which has been changed to 2 instead of 3. An uncertainty value of 2 was more convenient for our dataset because the activity values of the training set spanned exactly the required 4 orders of magnitude; this choice has been further confirmed by preliminary calculations and by other literature evidence . The uncertainty value represents the ratio of the uncertainty range of the actual activity against measured biology activity for each compound.
The HypoGen algorithm
With the full range of training set compounds from active to inactive the pharmacophore hypotheses were generated by HypoGen algorithm implemented in DS. This algorithm constructs and ranks the pharmacophore hypotheses that correlate best between 3D spatial arrangement of features in a given training set compounds and their respective experimental activities. This process is accomplished in three steps: the constructive phase, the subtractive phase and the optimization phase .
The constructive phase identifies the hypotheses that are common amongst the active compounds. HypoGen enumerates all possible pharmacophore configurations using all combinations of pharmacophore features for each of the conformations of the most active compound. In order to consider the left over most active compounds the hypotheses must fit a minimum subset of its features. Hence, a large database of pharmacophore configurations will be generated at the end of the constructive phase.
The subtractive phase will remove the pharmacophore configurations that are present in the least active compounds. All compounds whose activity is by default 3.5 orders of magnitude less than that of the most active compound are considered to represent the least active compounds. The value 3.5 is adjustable depending on the activity range of the training set. The optimization phase improves the hypothesis score. These scores of the generated hypotheses depend on the errors in activity estimation from regression and complexity. The optimization involves a variation of features and/or locations to optimize activity prediction via a simulated annealing approach. The total cost parameter will be calculated for every new hypothesis. The HypoGen will quit and reports the 10 top-scoring hypotheses when there is no improvement in the hypothesis score.
The quality of a pharmacophore hypothesis is best determined by two theoretical cost calculations, which are represented in bit units . One is the ‘fixed cost’ also known as cost of an ideal hypothesis, which represents the simplest model that fits all the data perfectly. The second cost is the ‘null cost’, which represents the highest cost of a pharmacophore with no features that estimates every activity to be the average of the activity data of the training set compounds.
The total cost of any pharmacophore hypothesis should always be close to the fixed cost and away from the null cost to be the significant model. The cost difference between fixed and null cost values should be larger for a meaningful pharmacophore hypothesis. A value of 40-60 bits in a pharmacophore hypothesis indicates that it has 75-90% probability of representing a true correlation in the data.
The hypotheses are also evaluated based on other cost components. The cost value for every individual hypothesis is the summation of three cost components: the error cost (E), the weight cost (W) and the configuration cost (C). The error cost is the value represents the root-mean-squared difference (RMSD) between experimental and estimated activity value of the training set compounds. The weight cost is a value that increases in a Gaussian form as this function weights in a model deviate from the ideal value of two. The configuration cost or entropy cost measures the entropy of the hypothesis space. If the input training set compounds are too multiplex, e.g. because of too flexible training set compounds, this will result in an effusive number of hypotheses as an outcome of the subtractive phase. This configuration cost should always be less than a maximum value of 17 . The correlation coefficient of the pharmacophore hypothesis should be close to 1.
The generated pharmacophore hypothesis was validated using test set, Fischer randomization, decoy set and leave-one-out methods.
Test set method
A total of 40 compounds with experimental activity data were selected as test set compounds. This method is used to elucidate whether the generated pharmacophore hypothesis is proficient to predict the activities of the compounds other than training set and classify them correctly in their activity scale. The conformation generation for test set compounds was carried out in a similar way like training set compounds using Diverse Conformation Generation protocol in DS. The compounds associated with their conformations were subsequently carried out for pharmacophore mapping using Ligand Pharmacophore Mapping protocol with Best/Flexible Search option available in DS.
Fischer randomization method
The main purpose of this validation is to verify whether there is a strong correlation existing between the chemical structure and biological activities of compounds. This generates pharmacophore hypotheses by randomizing the activity data of the training set compounds with the same features and parameters used to generate the original pharmacophore hypothesis. The statistical significance is calculated using the following formula: Significance = 100 (1-(1+x/y)), where x represents the total number of hypotheses having a total cost value lower than the original hypothesis, and y represents the total number of HypoGen runs i.e. initial and random runs. The confidence level was set to 99%, where 99 random spreadsheets (random hypotheses) were generated. During the pharmacophore generation, if the randomized data set results in similar or better cost values, RMSD and correlation, it means that the original hypothesis have been generated by chance.
Decoy set method
An external database containing BACE-1 active and inactive compounds was used to evaluate the discriminative ability of Hypo 1 in the separation of active compounds from the inactive compounds. The database was developed using a total of 453 compounds containing 206 actives and 247 inactives. All the compounds were collected from published literature including binding database [12–25, 31]. The database screening was carried out using Ligand Pharmacophore Mapping protocol available in DS. A set of statistical parameters  like Ht, % yield of actives, Enrichment factor (E), false positives, false negatives and Goodness of Hit (GH) score were calculated.
The pharmacophore hypothesis is cross validated by leave-one-out method. In this method, one compound is left in the generation of a new pharmacophore model and its affinity is predicted using that new model. The model building and estimation cycle is repeated until each compound was left out once . This test is performed to verify whether the correlation coefficient of the training set compounds is strongly depend on one particular compound or not .
The validated pharmacophore hypothesis, Hypo 1, was used as a 3D query for screening four different chemical databases. The purpose of this screening is to retrieve novel and potential leads suitable for further development. The chemical databases used were Maybridge, Chembridge, NCI and Asinex. Conformers were generated for each molecule in the database using best conformer generation method that allows a maximum energy of 15 kcal/mol above that of the most stable conformation. The database screening was carried out using Ligand Pharmacophore Mapping protocol implemented in DS with Best/Flexible Search option. The retrieved compounds were filtered by restricting the estimated activity value less than 100 nM and the obtained compounds were further refined using molecular docking study.
Pharmacophore modeling normally firmly associated with docking procedure, which in a first step flexibly aligns the ligand molecule into a rigid macromolecule environment and then estimates the tightness of the interaction by different scoring functions . The Docking takes all the information from a rigid protein environment and scores several possible interaction modes for different alignments. There are many docking programs available for molecular docking studies. In this study, we used GOLD (Genetic Optimisation for Ligand Docking), a docking program  that uses genetic algorithm for docking and performs automated docking with full acyclic ligand flexibility, partial cyclic ligand flexibility and partial protein flexibility in the neighborhood of the protein active site. The crystal structure of BACE-1 complexed with an inhibitor SC7 (PDB ID: 2QP8) was used in molecular docking studies. The inhibitor SC7 was extracted from the active site and the retrieved database hits were docked based on the ligand SC7 coordinates, in to the active site of BACE-1. The water molecules were removed prior to docking because they were not found to play any important roles in BACE1-ligand interaction. The early termination option parameter in GOLD was changed from 3 to 5 and the maximum save conformations was set to 10. All the other parameters were set at their default values.
Results and discussion
Results of the top 10 pharmacophore hypotheses generated by the HypoGen algorithm
Test set correlation coefficient
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY
HBD, PI, RA, HY, HY
HBD, PI, RA, HY, HY
Statistical data analysis
Activity prediction and mapping of training set compound on Hypo1
Experimental and estimated IC50 values of the training set compounds based on the pharmacophore hypothesis ‘Hypo 1’.
Validation of Hypo 1
Hypo 1 was further validated by test set, Fischer randomization test, leave-one-out and decoy set methods.
Test set method
Fisher randomization method
Fischer’s randomization test results of the pharmacophore hypothesis Hypo 1.
Decoy set method
Statistical parameters of GH score validation for Hypo 1.
Total molecules in database (D)
Total no. of actives in database (A)
Total hits (Ht)
Active hits (Ha)
% Yield of actives [(Ha/Ht) X 100]
% Ratio of actives [(Ha/A) X 100]
Enrichment factor (E) [(Ha X D)/(Ht X A)]
False negatives [A - Ha]
False positives [Ht - Ha]
Goodness of hit (GH)*
The cross validation of the model was done using the leave-one-out method. This method is progressed by recomputing the pharmacophore hypotheses by leaving one compound at a time from the training set compounds. The importance of this validation is to prove that the correlation of the original pharmacophore hypothesis (Hypo 1) is not depending only on one particular compound. If the activity of each left-out compound is correctly estimated by the corresponding one-missing hypothesis then the test is positive. The feature composition of the pharmacophore, the value of correlation coefficient and the quality of the estimated activity of the left-out compound were used as measures for the assessment of the statistical test. By leaving each one of the 20 training set compounds according to this method, 20 new hypotheses were generated. As a result we did not obtain any meaningful differences between Hypo1 and each hypothesis resulting from the leave-one-out method. This result gives more confidence on Hypo 1 that it does not depend on one particular compound in the training set.
The validated pharmacophore hypothesis, Hypo1, was used as a 3D structural query for retrieving compounds from chemical databases including MayBridge (59 652 compounds), Chembridge (50 000 compounds), NCI (238 819 compounds) and Asinex (213 462 compounds). As a result of first screening 11 578, 590, 5096 and 63 265 compounds were retrieved from Maybridge, Chembridge, NCI and Asinex respectively. Since the active site of BACE-1 is larger in size, the experimentally known most active inhibitors are also larger in size and violate the first rule of Lipinski’s rule of five. Hence, the retrieved hit compounds were filtered based only on the estimated activity values calculated by Hypo 1. The activity range for the most active compounds is <100 nM. Finally 773 compounds were selected by restricting the minimum estimated activity to <100 nM.
The hydrophobic interactions of the final hits compounds were observed using Ligplot program . The novelty of the two hits compounds were confirmed using SciFinder search  and PubChem search .
A chemical feature based 3D pharmacophore hypotheses of BACE-1 inhibitors have been developed using 3D QSAR Pharmacophore Generation protocol available in DS 2.5. The best quantitative pharmacophore model, Hypo 1, was characterized by the highest cost difference (121.98), best correlation coefficient (0.977), lowest total cost value (81.24) and lowest RMSD (0.804). The fixed cost and null cost values were 74.77 and 203.22 bits, respectively. Hypo1 consisted of one HBD, one PI, one RA and two HY features. Hypo1 was further validated by test set, Fischer randomization test, leave-one-out, and decoy set methods. The test set containing 40 compounds was used in investigating the predictive ability of Hypo1 and resulted with a correlation coefficient of 0.917. Other validation methods also have provided reliable results on the strength of Hypo 1. This validated Hypo1 was used as a 3D query in database screening. The database hit compounds were subsequently subjected to filtering by estimated activity value. To further refine the retrieved hits the 793 compounds along with training set were carried out for molecular docking studies. The molecular docking result of all compounds was analyzed based on the GOLD fitness score, binding modes and molecular interactions with essential active site residues. Finally, two hits, namely, RJC01726 and Asnx-2 of different scaffolds with GOLD fitness score of 68.362 and 63.053, respectively, and interactions with important active site residues were chosen as lead candidates. These compounds as such and on further optimization can be used as potential leads in designing new BACE-1 inhibitors.
SJ and ST equally involved in designing the work, analyzing the results and writing the manuscript. SS formatted and corrected the manuscript. KWL supervised the work and edited the manuscript. All four authors have read and approved the manuscript.
This research was supported by Basic Science Research Program (2009-0073267), Pioneer Research Center Program (2009-0081539), and Environmental Biotechnology National Core Research Center program (20090091489) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST). And all students were recipients of fellowship from the BK21 Program of MEST.
This article has been published as part of BMC Bioinformatics Volume 12 Supplement 1, 2011: Selected articles from the Ninth Asia Pacific Bioinformatics Conference (APBC 2011). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/12?issue=S1.
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