Physical binding pocket induction for affinity prediction

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.

Regiodivergent Stereoselective Access to Fused Alkylideneazetidines

Following recent advances in the generalization and simplification of 2H-azetine synthesis, a regiodivergent approach to fused 2- and 3-alkylideneazetines was designed via the intermediate formation of unprecedented vinylazetine structures. Concise sequences to the latter are described from which an expected unsaturated fused ring system was isolated with very high yields and regio- and stereoselectivities by [4 + 2] cycloadditions.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Extrapolative prediction using physically-based QSAR

Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction . Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automated flexible molecular alignment, with the model itself defining the optimal pose for each ligand. QMOD performance was compared to that of four approaches that depended on manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed comparable performance to the other methods on a challenging, but structurally limited, test set. The QMOD models were also applied to test a large and structurally diverse dataset of ligands from ChEMBL, nearly all of which were synthesized years after those used for model construction. Extrapolation across diverse chemical structures was possible because the method addresses the ligand pose problem and provides structural and geometric means to quantitatively identify ligands within a model’s applicability domain. Predictions for such ligands for the four tested targets were highly statistically significant based on rank correlation. Those molecules predicted to be highly active (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {pK}_i \ge 7.5$$\end{document}pKi≥7.5) had a mean experimental \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {pK}_i$$\end{document}pKi of 7.5, with potent and structurally novel ligands being identified by QMOD for each target.

A structure-guided approach for protein pocket modeling and affinity prediction

Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure-activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction.

Iterative refinement of a binding pocket model: active computational steering of lead optimization

Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for "synthesis." Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection.

Human serum albumin stability and toxicity of anthraquinone dye alizarin complexone: an albumin-dye model

The complexation between the primary vector of ligands in blood plasma, human serum albumin (HSA) and a toxic anthraquinone dye alizarin complexone, was unmasked by means of circular dichroism (CD), molecular modeling, steady state and time-resolved fluorescence, and UV/vis absorption measurements. The structural investigation of the complexed HSA through far-UV CD, three-dimensional and synchronous fluorescence shown the polypeptide chain of HSA partially destabilizing with a reduction of α-helix upon conjugation. From molecular modeling and competitive ligand binding results, Sudlow's site I, which was the same as that of warfarin-azapropazone site, was appointed to retain high-affinity for alizarin complexone. Moreover, steady state fluorescence displayed that static type and Förster energy transfer is the operational mechanism for the vanish in the tryptophan (Trp)-214 fluorescence, this corroborates time-resolved fluorescence that HSA-alizarin complexone adduct formation has an affinity of 10(5) M(-1), and the driving forces were found to be chiefly π-π, hydrophobic, and hydrogen bonds, associated with an exothermic free energy change. These data should be utilized to illustrate the mechanism by which the toxicological action of anthraquinone dyes is mitigated by transporter HSA.

Does your model weigh the same as a duck?

Computer-aided drug design is a mature field by some measures, and it has produced notable successes that underpin the study of interactions between small molecules and living systems. However, unlike a truly mature field, fallacies of logic lie at the heart of the arguments in support of major lines of research on methodology and validation thereof. Two particularly pernicious ones are cum hoc ergo propter hoc (with this, therefore because of this) and confirmation bias (seeking evidence that is confirmatory of the hypothesis at hand). These fallacies will be discussed in the context of off-target predictive modeling, QSAR, molecular similarity computations, and docking. Examples will be shown that avoid these problems.

Surface-based protein binding pocket similarity

Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three-dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all-by-all comparisons two sets of human protein kinases, a very diverse set of ATP-bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence-based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation.

QMOD: physically meaningful QSAR

Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have utility in retrospective rationalization of activity patterns of substituents on a common scaffold, but are limited when either multiple scaffolds are present or when ligand alignment varies significantly based on structural changes. In addition, such methods generally assume independence and additivity of effect from scaffold substituents. Collectively, these non-physical modeling assumptions sharply limit the utility of widely used QSAR approaches for prospective prediction of ligand activity. The recently introduced Surflex-QMOD approach, by virtue of constructing physical models of binding sites, comes closer to a modeling approach that is congruent with protein ligand binding events. A set of congeneric CDK2 inhibitors showed that induced binding pockets can be quite congruent with the enzyme's active site but that model predictivity within a chemical series does not necessarily depend on congruence. Muscarinic antagonists were used to show that the QMOD approach is capable of making accurate predictions in cases where highly non-additive structure activity effects exist. The QMOD method offers a means to go beyond non-causative correlations in QSAR analysis.

Combining 3-D quantitative structure-activity relationship with ligand based and structure based alignment procedures for in silico screening of new hepatitis C virus NS5B polymerase inhibitors

The viral NS5B RNA-dependent RNA-polymerase (RdRp) is one of the best-studied and promising targets for the development of novel therapeutics against hepatitis C virus (HCV). Allosteric inhibition of this enzyme has emerged as a viable strategy toward blocking replication of viral RNA in cell based systems. Herein, we describe how the combination of a complete computational procedure together with biological studies led to the identification of novel molecular scaffolds, hitherto untested toward NS5B polymerase. Structure based 3-D quantitative structure-activity relationship (QSAR) models were generated employing NS5B non-nucleoside inhibitors (NNIs), whose bound conformations were readily available from the protein database (PDB). These were grouped into two training sets of structurally diverse NS5B NNIs, based on their binding to the enzyme thumb (15 NNIs) or palm (10 NNIs) domains. Ligand based (LB) and structure based (SB) alignments were rigorously investigated to assess the reliability on the correct molecular alignment for unknown binding mode modeled compounds. Both Surflex and Autodock programs were able to reproduce with minimal errors the experimental binding conformations of 24 experimental NS5B allosteric inhibitors. Eighty-one (thumb) and 223 (palm) modeled compounds taken from literature were LB and SB aligned and used as external validation sets for the development of 3-D QSAR models. Low error of prediction proved the 3-D QSARs to be useful scoring functions for the in silico screening procedure. Finally, the virtual screening of the NCI Diversity Set led to the selection for enzymatic assays of 20 top-scoring molecules for each final model. Among the 40 selected molecules, preliminary data yielded four derivatives exhibiting IC(50) values ranging between 45 and 75 microM. Binding mode analysis of hit compounds within the NS5B polymerase thumb domain showed that one of them, NSC 123526, exhibited a docked conformation which was in good agreement with the thumb training set most active compound (6).