New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Considerations in compound database preparation--"hidden" impact on virtual screening results

Structure-based virtual screening (SBVS) utilizing docking algorithms has become an essential tool in the drug discovery process, and significant progress has been made in successfully applying the technique to a wide range of receptor targets. In silico validation of virtual screening protocols before application to a receptor target using a corporate or commercially available compound collection is key to establishing a successful process. Ultimately, retrieval of a set of active compounds from a database of inactives is required, and the metric of enrichment (E) is habitually used to discern the quality of separation of the two. Numerous reports have addressed the performance of docking algorithms with regard to the quality of binding mode prediction and the issue of postprocessing "hit lists" of docked ligands. However, the impact of ligand database preprocessing has yet to be examined in the context of virtual screening and prioritization of compounds for biological evaluation. We provide an insight into the implications of cheminformatic preprocessing of a validation database of compounds where multiple protonated, tautomeric, stereochemical, and conformational states have been enumerated. Several commonly used methods for the generation of ligand conformations and conformational ensembles are examined, paired with an exhaustive rigid-body algorithm for the docking of different "multimeric" compound representations to the ligand binding site of the human estrogen receptor alpha. Chemgauss, a shapegaussian scoring function with intrinsic chemical knowledge, was combined with PLP as a consensus-scoring scheme to rank output from the docking protocol and enrichment rates calculated for each screen. The overheads of CPU consumption and the effect on relative database size (disk requirement) for each of the protocols employed are considered. Assessment of these parameters indicates that SBVS enrichments are highly dependent on the initial cheminformatic treatment(s) used in database construction. The interplay of SMILES representations, stereochemical information, protonation state enumeration, and ligand conformation ensembles are critical in achieving optimum enrichment rates in such screening.     

Functionally orthogonal ligand-receptor pairs for the selective regulation of gene expression generated by manipulation of charged residues at the ligand-receptor interface of ER alpha and ER beta

The reengineering of protein-small molecule interfaces represents a powerful tool of chemical biology. For many applications it is necessary to engineer receptors so that they do not interact with their endogenous ligands but are highly responsive to designed ligand analogues, which in turn do not interact with endogenous proteins. The chemical design strategy used to reengineer protein-small molecule interfaces is particularly challenging for interfaces involving relatively plastic receptor binding sites and therefore presents a unique challenge in molecular design. In this study we explore the scope and limitations of a new strategy for manipulating polar/charged residues across the ligand receptor interface of estradiol (E2) and the estrogen receptor (ER). Carboxylate-functionalized E2 analogues can activate ER alpha(Glu353-->Ala) and ER beta(Glu305-->Ala) with very large selectivites, demonstrating that this design strategy is extendable to other members of the steroid hormone receptor family. Neutral E2 analogues were found to complement ER alpha(E353A) with similar potencies but with generally lower selectivities. This suggests that the high selectivity observed with ligand-receptor pairs generated by exchanging charged residues across ligand-receptor interfaces is only due in part to their complementary shapes and that appropriate introduction of charged functionality on the ligand can provide substantial enhancement of selectivity by decreasing the engineered ligands affinity for the endogenous receptor. Attempts to modify the cationic residues by complementing Arg394-->Ala or Arg394-->Glu were not successful.     

Recognizing five molecular ligand-binding sites with similar chemical structure

Accurate identification of ligand-binding sites and discovering the protein–ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research are two grand challenges in proteomics. In this article, ligand-binding residues of five ligands (ATP, ADP, GTP, GDP, and NAD) are predicted as a group, due to their similar chemical structures and close biological function relations. The data set of binding sites by five ligands (ATP, ADP, GTP, GDP, and NAD) are collated from Biolip database. Then, five features, containing increment of diversity value, matrix scoring value, auto-covariance, secondary structure information, and surface accessibility information are used in binding site predictions. The support vector machine (SVM) model is used with the five features to predict ligand-binding sites. Finally, prediction results are tested by fivefold cross validation. Accuracy (Acc) of five ligands (ATP, ADP, GTP, GDP, and NAD) achieves 77.4%, 71.2%, 82.1%, 82.9%, and 85.3%, respectively; and Matthew correlation coefficient (MCC) of the above five ligands achieves 0.549, 0.424, 0.643, 0.659, and 0.702, respectively. The research result shows that for ligands with similar chemical structures, microenvironment of their binding sites and their sensitivities to features are similar, while, differences of their ligand-binding properties exist at the same time. © 2019 Wiley Periodicals, Inc.     

Virtual screening of organic molecule databases. Design of focused libraries of potential ligands of NMDA and AMPA receptors

A system of virtual screening of organic molecule databases is designed, which permits preprocessing of databases, molecular docking to a three-dimensional model of receptor, and post-processing of the results obtained. Using this screening system, it is possible to reproduce positions of the known ligands in the glutamate sites of the NMDA and AMPA receptors and in the glycine site of the NMDA receptor, to substantially enrich the database with potentially active compounds, and to distinguish between the agonistic and antagonistic character of the action of these compounds in the case of docking to the open and closed forms of the binding sites. Based on the results of screening of a database of low-molecular-weight organic compounds (total of 135,000 structures) using models of the open and closed forms of the glutamate and glycine sites of the NMDA receptor and of the glutamate site of the AMPA receptor, focused libraries of potential agonists and antagonists of these sites were designed.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

Assessment of programs for ligand binding affinity prediction

The prediction of the binding free energy between a ligand and a protein is an important component in the virtual screening and lead optimization of ligands for drug discovery. To determine the quality of current binding free energy estimation programs, we examined FlexX, X-Score, AutoDock, and BLEEP for their performance in binding free energy prediction in various situations including cocrystallized complex structures, cross docking of ligands to their non-cocrystallized receptors, docking of thermally unfolded receptor decoys to their ligands, and complex structures with "randomized" ligand decoys. In no case was there a satisfactory correlation between the experimental and estimated binding free energies over all the datasets tested. Meanwhile, a strong correlation between ligand molecular weight-binding affinity correlation and experimental predicted binding affinity correlation was found. Sometimes the programs also correctly ranked ligands' binding affinities even though native interactions between the ligands and their receptors were essentially lost because of receptor deformation or ligand randomization, and the programs could not decisively discriminate randomized ligand decoys from their native ligands; this suggested that the tested programs miss important components for the accurate capture of specific ligand binding interactions.