J Chem Inf Model - Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome.

Tópicos

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{ model(2656) set(1616) predict(1553) }
{ compound(1573) activ(1297) structur(1058) }
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Resumo

Reliable in silico prediction methods promise many advantages over experimental high-throughput screening (HTS): vastly lower time and cost, affinity magnitude estimates, no requirement for a physical sample, and a knowledge-driven exploration of chemical space. For the specific case of kinases, given several hundred experimental IC(50) training measurements, the empirically parametrized profile-quantitative structure-activity relationship (profile-QSAR) and surrogate AutoShim methods developed at Novartis can predict IC(50) with a reliability approaching experimental HTS. However, in the absence of training data, prediction is much harder. The most common a priori prediction method is docking, which suffers from many limitations: It requires a protein structure, is slow, and cannot predict affinity. (1) Highly accurate profile-QSAR (2) models have now been built for roughly 100 kinases covering most of the kinome. Analyzing correlations among neighboring kinases shows that near neighbors share a high degree of SAR similarity. The novel chemogenomic kinase-kernel method reported here predicts activity for new kinases as a weighted average of predicted activities from profile-QSAR models for nearby neighbor kinases. Three different factors for weighting the neighbors were evaluated: binding site sequence identity to the kinase neighbors, similarity of the training set for each neighbor model to the compound being predicted, and accuracy of each neighbor model. Binding site sequence identity was by far most important, followed by chemical similarity. Model quality had almost no relevance. The median R(2) = 0.55 for kinase-kernel interpolations on 25% of the data of each set held out from method optimization for 51 kinase assays, approached the accuracy of median R(2) = 0.61 for the trained profile-QSAR predictions on the same held out 25% data of each set, far faster and far more accurate than docking. Validation on the full data sets from 18 additional kinase assays not part of method optimization studies also showed strong performance with median R(2) = 0.48. Genetic algorithm optimization of the binding site residues used to compute binding site sequence identity identified 16 privileged residues from a larger set of 46. These 16 are consistent with the kinase selectivity literature and structural biology, further supporting the scientific validity of the approach. A priori kinase-kernel predictions for 4 million compounds were interpolated from 51 existing profile-QSAR models for the remaining >400 novel kinases, totaling 2 billion activity predictions covering the entire kinome. The method has been successfully applied in two therapeutic projects to generate predictions and select compounds for activity testing.

Resumo Limpo

reliabl silico predict method promis mani advantag experiment highthroughput screen hts vast lower time cost affin magnitud estim requir physic sampl knowledgedriven explor chemic space specif case kinas given sever hundr experiment ic train measur empir parametr profilequantit structureact relationship profileqsar surrog autoshim method develop novarti can predict ic reliabl approach experiment hts howev absenc train data predict much harder common priori predict method dock suffer mani limit requir protein structur slow predict affin high accur profileqsar model now built rough kinas cover kinom analyz correl among neighbor kinas show near neighbor share high degre sar similar novel chemogenom kinasekernel method report predict activ new kinas weight averag predict activ profileqsar model nearbi neighbor kinas three differ factor weight neighbor evalu bind site sequenc ident kinas neighbor similar train set neighbor model compound predict accuraci neighbor model bind site sequenc ident far import follow chemic similar model qualiti almost relev median r kinasekernel interpol data set held method optim kinas assay approach accuraci median r train profileqsar predict held data set far faster far accur dock valid full data set addit kinas assay part method optim studi also show strong perform median r genet algorithm optim bind site residu use comput bind site sequenc ident identifi privileg residu larger set consist kinas select literatur structur biolog support scientif valid approach priori kinasekernel predict million compound interpol exist profileqsar model remain novel kinas total billion activ predict cover entir kinom method success appli two therapeut project generat predict select compound activ test

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