Alignmentindependent technique for 3D QSAR analys

Authors: Jon G Wilkes Iva B StoyanovaSlavova Dan A Buzatu

Publish Date: 2016/03/30

Volume: 30, Issue: 4, Pages: 331-345

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Abstract

Molecular biochemistry is controlled by 3D phenomena but structure–activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3Dquantitative spectral data activity relationship models Molecular conformations tested 1 global minimum of molecules’ potential energy surface 2 alignmenttotemplates using equal electronic and steric force field contributions 3 alignment using contributions “BestforEach” template 4 nonenergy optimized nonaligned 2D  3D Aggregate predictions from models were compared Highest average coefficients of determination ranged from R Test 2  = 056 to 061 The best model using 2D  3D imported directly from ChemSpider produced R Test 2  = 061 It was superior to energyminimized and conformationaligned models and was achieved in only 3–7  of the time required using the other conformation strategies Predictions averaged from models built on different conformations achieved a consensus R Test 2  = 065 The best 2D  3D model was analyzed for underlying structure–activity relationships For the compound strongest binding to the androgen receptor 10 substructural features contributing to binding were flagged Utility of 2D  3D was compared for two other activity endpoints each modeling a medium sized data set Results suggested that large scale accurate predictions using 2D  3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates3Dimensional spectral dataactivity relationship 3DSDAR modeling is a gridbased in silico technique which belongs to a group of methods collectively known as Structure–Activity Relationships SARs In 3DSDAR each compound is represented by a unique “fingerprint” constructed from the NMR chemical shifts δ of all carbon atom pairs placed on the X and Yaxes joined with the interatomic distances between each pair on the Zaxis 1 For details see the subsection below on the 3DQSDAR fingerprint The atomspecific nature of chemical shifts and the use of interatomic distances enable representation of interaction potential with receptor active sites in terms of electronic and steric qualities respectively 2 3DSDAR can produce models that facilitate identification of 3D pharmacophores and toxicophoresThis work models quantitative data and so exemplifies 3DQSDAR In this project information about the presence of atoms other than carbon was not explicitly included We have found in several of our previous SDAR modeling projects that the high sensitivity of 13C δs to their environment is often sufficient for useful reflection of chemical structure in the vicinity including the presence of nearby heteroatoms 1 3 4 A tessellation of the 3DSDAR space into regular grids “binning” is further used to convert the information contained in a fingerprint into a set of 3DSDAR descriptors For a particular molecule in addition to the 3D coordinates from carbon atoms each descriptor includes the number of fingerprint elements belonging to each bin Depending on the granularity of the grid thousands of such descriptors can be generated but most bins have zero occupancy These are further handled by an ensemble modeling PLS algorithm performing multiple training/holdout test randomization cycles producing averaged “composite” modelsThe 3DSDAR parametric space with a quantitative measure of each compound’s biological affinity appended can be explored by comparing the predictive power of models derived from grids of different density/granularity thus determining an optimal grid size As a 3Dmodeling technique conceptually similar to Comparative Molecular Field Analysis CoMFA and Comparative Molecular Similarity Analysis CoMSIA 3DQSDAR depends on the specific conformation chosen for fingerprint generation Unlike CoMFA and CoMSIA 3DQSDAR is an alignment independent techniqueOur earlier studies indicated that 3DQSDAR models based on lowest energy conformations perform well 1 3 4 However we hypothesized that use of substrates internally aligned with respect to molecular template molecules rather than energyminimized conformations might prove beneficial In other words we asked whether the adoption of a biologically more appropriate conformation for flexible compounds significantly increases overall predictive accuracy of the models by using 3D descriptors Addressing this was the point of an experimental design intended to explore a variety of ways to establish 3D conformations As will be shown below the hypothesis was contradicted for modeling androgen receptor binding the endpoint studied here and another similar challengeWhether substratetemplate alignment or energy minimization generates optimal 3DSDAR models has not been previously determined By definition of the 3DSDAR fingerprints substrate conformational changes would affect the position of the fingerprint elements only along the Zaxis Because the distance between first and second order atom neighbors does not change with conformation only fingerprint elements associated with more widely separated atom pairs in flexible molecules would vary significantly

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