Optimizing fragment and scaffold docking by use of molecular interaction fingerprints

Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.     

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.     

Molecular modelling prediction of ligand binding site flexibility

We have investigated the efficacy of generating multiple sidechain conformations using a rotamer library in order to find the experimentally observed ligand binding site conformation of a protein in the presence of a bound ligand. We made use of a recently published algorithm that performs an exhaustive conformational search using a rotamer library to enumerate all possible sidechain conformations in a binding site. This approach was applied to a dataset of proteins whose structures were determined by X-ray and NMR methods. All chosen proteins had two or more structures, generally involving different bound ligands. By taking one of these structures as a reference, we were able in most cases to successfully reproduce the experimentally determined conformations of the other structures, as well as to suggest alternative low-energy conformations of the binding site. In those few cases where this procedure failed, we observed that the bound ligand had induced a high-energy conformation of the binding site. These results suggest that for most proteins that exhibit limited backbone motion, ligands tend to bind to low energy conformations of their binding sites. Our results also reveal that it is possible in most cases to use a rotamer search-based approach to predict alternative low-energy protein binding site conformations that can be used by different ligands. This opens the possibility of incorporating alternative binding site conformations to improve the efficacy of docking and structure-based drug design algorithms.     

Ab initio prediction of protein-ligand binding structures by replica-exchange umbrella sampling simulations

We have developed a prediction method for the binding structures of ligands with proteins. Our method consists of three steps. First, replica-exchange umbrella sampling simulations are performed along the distance between a putative binding site of a protein and a ligand as the reaction coordinate. Second, we obtain the potential of mean force (PMF) of the unbiased system using the weighted histogram analysis method and determine the distance that corresponds to the global minimum of PMF. Third, structures that have this global-minimum distance and energy values around the average potential energy are collected and analyzed using the principal component analysis. We predict the binding structure as the global-minimum free energy state on the free energy landscapes along the two major principal component axes. As test cases, we applied our method to five protein-ligand complex systems. Starting from the configuration in which the protein and the ligand are far away from each other in each system, our method predicted the ligand binding structures in excellent agreement with the experimental data from Protein Data Bank.     

Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor

D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.     

Estimation of important binding sites in compounds that interact with proteins

Proteins are one of the important substances in understanding biological activity, and many of them express the function by binding to other proteins or small molecules (ligands) on the molecular surface. This interaction often occurs in the hollows (pockets) on the molecular surface of the protein. It is known that when pockets are similar in structure and physical properties, they are likely to express similar functions and to bind similar ligands. Therefore, exploring the similarity of the structure and physical properties in pockets is very useful because it leads to the discovery of new ligands that are likely to bind. In addition, exploring the important structure when binding to the protein significant spot in the ligand will provide useful knowledge for the development of new ligands.In this study, we propose a method to search for proteins containing pockets that are structurally and physically similar to significant spot in the pocket of the analyzed protein, and to extract significant spots in the ligands that bind to them. We use feature points as data. Feature points are the 3-dimensional points that are extracted from 3D structure data of proteins with feature values quantifying hydrophobicity and electrostatic potential. The corresponding feature points are extracted by comparing structurally and physically the pockets of the search target proteins with the significant spot of the analyzed protein. By evaluating the similarity based on the comparison results of the feature values given to the extracted feature points, we search for proteins that are similar to the analyzed protein. From the ligands that bind to the searched proteins, atoms that are near the protein pocket and similar to the atoms in ligand binding to the analyzed protein are extracted. The site constituted by the extracted atoms is defined as a significant spot in the ligand.As a result of classifying ligands binding to the protein by using the extracted significant spot in the ligand, the effectiveness of the proposed method was confirmed.     

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein–ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein–ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein–ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein–ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein–ligand complex structures available to improve predictions on binding.     

A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series

We introduce the "Prime-ligand" method for ranking ligands in congeneric series. The method employs a single scoring function, the OPLS-AA/GBSA molecular mechanics/implicit solvent model, for all stages of sampling and scoring. We evaluate the method using 12 test sets of congeneric series for which experimental binding data is available in the literature, as well as the structure of one member of the series bound to the protein. Ligands are "docked" by superimposing a common stem fragment among the compounds in the series using a crystal complex from the Protein Data Bank and sampling the conformational space of the variable region. Our results show good correlation between our predicted rankings and the experimental data for cases in which binding affinities differ by at least 1 order of magnitude. For 11 out of 12 cases, >90% of such ligand pairs could be correctly ranked, while for the remaining case, Factor Xa, 76% of such pairs were correctly ranked. A small number of compounds could not be docked using the current protocol because of the large size of functional groups that could not be accommodated by a rigid receptor. CPU requirements for the method, involving CPU minutes per ligand, are modest compared with more rigorous methods that use similar force fields, such as free energy perturbation. We also benchmark the scoring function using series of ligands bound to the same protein within the CSAR data set. We demonstrate that energy minimization of ligands in the crystal structures is critical to obtain any correlation with experimentally determined binding affinities.     

The atom assignment problem in automated de novo drug design. 3. Algorithms for optimization of fragment placement onto 3D molecular graphs

Summary Atom assignment onto 3D molecular graphs is a combinatoric problem in discrete space. If atoms are to be placed efficiently on molecular graphs produced in drug binding sites, the assignment must be optimized. An algorithm, based on simulated annealing, is presented for efficient optimization of fragment placement. Extensive tests of the method have been performed on five ligands taken from the Protein Data Bank. The algorithm is presented with the ligand graph and the electrostatic potential as input. Self placement of molecular fragments was monitored as an objective test. A hydrogen-bond option was also included, to enable the user to highlight specific needs. The algorithm performed well in the optimization, with successful replications. In some cases, a modification was necessary to reduce the tendency to give multiple halogenated structures. This optimization procedure should prove useful for automated de novo drug design.     

A novel method for enzyme design

Rational design of enzymes is a stringent test of our understanding of protein structure and function relationship, which also has numerous potential applications. We present a novel method for enzyme design that can find good candidate protein scaffolds in a protein-ligand database based on vector matching of key residues. Residues in the vicinity of the active site were also compared according to a similarity score between the scaffold protein and the target enzyme. Suitable scaffold proteins were selected, and the side chains of residues around the active sites were rebuilt using a previously developed side-chain packing program. Triose phosphate isomerase (TIM) was used as a validation test for enzyme design. Selected scaffold proteins were found to accommodate the enzyme active sites and successfully form a good transition state complex. This method overcomes the limitations of the current enzyme design methods that use limited number of protein scaffold and based on the position of ligands. As there are a large number of protein scaffolds available in the Protein Data Band, this method should be widely applicable for various types of enzyme design.     

Computational combinatorial ligand design: Application to human α-thrombin

Summary A new method is presented for computer-aided ligand design by combinatorial selection of fragments that bind favorably to a macromolecular target of known three-dimensional structure. Firstly, the multiple-copy simultaneous-search procedure (MCSS) is used to exhaustively search for optimal positions and orientations of functional groups on the surface of the macromolecule (enzyme or receptor fragment). The MCSS minima are then sorted according to an approximated binding free energy, whose solvation component is expressed as a sum of separate electrostatic and nonpolar contributions. The electrostatic solvation energy is calculated by the numerical solution of the linearized Poisson-Boltzmann equation, while the nonpolar contribution to the binding free energy is assumed to be proportional to the loss in solvent-accessible surface area. The program developed for computational combinatorial ligand design (CCLD) allows the fast and automatic generation of a multitude of highly diverse compounds, by connecting in a combinatorial fashion the functional groups in their minimized positions. The fragments are linked as two atoms may be either fused, or connected by a covalent bond or a small linker unit. To avoid the combinatorial explosion problem, pruning of the growing ligand is performed according to the average value of the approximated binding free energy of its fragments. The method is illustrated here by constructing candidate ligands for the active site of human -thrombin. The MCSS minima with favorable binding free energy reproduce the interaction patterns of known inhibitors. Starting from these fragments, CCLD generates a set of compounds that are closely related to high-affinity thrombin inhibitors. In addition, putative ligands with novel binding motifs are suggested. Probable implications of the MCSS-CCLD approach for the evolving scenario of drug discovery are discussed.     

Take your Positions and Shine: Effects of Positioning Aggregation-Induced Emission Luminophores within Sequence-Defined Macromolecules

A luminophore with aggregation-induced emission (AIE) is employed for the conjugation onto supramolecular ligands to allow for detection of ligand binding. Supramolecular ligands are based on the combination of sequence-defined oligo(amidoamine) scaffolds and guanidiniocarbonyl-pyrrole (GCP) as binding motif. We hypothesize that AIE properties are strongly affected by positioning of the luminophore within the ligand scaffold. Therefore, we systematically investigate the effects placing the AIE luminophore at different positions within the overall construct, for example, in the main or side chain of the olig(amidoamine). Indeed, we can show that the position within the ligand structure strongly affects AIE, both for the ligand itself as well as when applying the ligand for the detection of different biological and synthetic polyanions.     

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.     

Rapid access to unexplored chemical space by ligand scanning around a ruthenium center: discovery of potent and selective protein kinase inhibitors

An important objective for the discovery of compounds with unique biological activities is the development of methods for the synthesis of molecular scaffolds with defined three-dimensional shapes. We are currently investigating the scope of using metal complexes to accomplish this goal. In these compounds, the metal center has the role of organizing the orientation of the organic ligands, thus defining the overall shape of the molecule. A strategy is presented that allows a rapid scanning of ligands around a ruthenium center in the search for ligand spheres that are complementary in shape and functional group presentation to ATP binding sites of protein kinases. Following this approach, we have identified octahedral ruthenium complexes as potent inhibitors for the protein kinases Pim1, MSK1, and GSK3alpha.     

Use of MCSS to design small targeted libraries: application to picornavirus ligands.

Computational methods were used to design structure-based combinatorial libraries of antipicornaviral capsid-binding ligands. The multiple copy simultaneous search (MCSS) program was employed to calculate functionality maps for many diverse functional groups for both the poliovirus and rhinovirus capsid structures in the region of the known drug binding pocket. Based on the results of the MCSS calculations, small combinatorial libraries consisting of 10s or 100s of three-monomer compounds were designed and synthesized. Ligand binding was demonstrated by a noncell-based mass spectrometric assay, a functional immuno-precipitation assay, and crystallographic analysis of the complexes of the virus with two of the candidate ligands. The P1/Mahoney poliovirus strain was used in the experimental studies. A comparison showed that the MCSS calculations had correctly identified the observed binding site for all three monomer units in one ligand and for two out of three in the other ligand. The correct central monomer position in the second ligand was reproduced in calculations in which the several key residues lining the pocket were allowed to move. This study validates the computational methodology. It also illustrates that subtle changes in protein structure can lead to differences in docking results and points to the importance of including target flexibility, as well as ligand flexibility, in the design process.     

Lock-and-key motif as a concept for designing affinity adsorbents for protein purification

The lock-and-key (LAK) motif, a common structural moiety found in subunit interfaces of glutathione S-transferases (GSTs), plays an important role in biomolecular recognition and quaternary structure integrity. Inspection of the key structural features of the LAK motif prompted the de novo design and combinatorial synthesis of a 13-membered solid-phase ligand library, employing as a lead ligand the Phe-Trz-X structure, mimicking the LAK motif. 1,3,5-Triazine (Trz) was used as the scaffold for assembly, substituted with different LAK-mimetic amino acids. De novo ligand design was effected using bioinformatics and molecular modeling and based on mimicking the interactions of the LAK motif. The library of affinity adsorbents was assessed for binding corn and human serum proteomes and purified proteins of different structure and ligand binding specificity. The results showed remarkable differences in the binding specificity of LAK-mimetic adsorbents for a wide range of proteins, as a consequence of minor changes in ligand structure. One LAK-mimetic adsorbent was integrated in a single-step purification protocol for human monoclonal anti-human immunodeficiency virus 2F5 antibody (mAb 2F5) from spiked corn extract, affording high recovery and purity. The results demonstrate that the principle of natural recognition found in the lock-and-key motif, in combination with de novo combinatorial design, may lead to synthetic affinity ligands, useful in downstream processing and proteomic research.     

Geometries of functional group interactions in enzyme-ligand complexes: Guides for receptor modelling

Summary An approach is described which makes use of X-ray structural data from enzyme-ligand complexes in order to obtain information for application in receptor modelling. The atomic surroundings of five different ligand functional groups were determined for all complex structures recorded in the Brookhaven Protein Data Bank. These atomic surroundings were then superimposed with respect to the atoms of the functional groups of the ligands in order to obtain clouds of neighbouring atoms. General principles were sought to describe the orientiation or favoured position of groups or atoms around those functional groups when bound to a macromolecule. Some simple conclusions and leads for further modelling were thus derived.     

Wide-angle X-ray solution scattering as a probe of ligand-induced conformational changes in proteins

A chemical genetics approach to functional analysis of gene products utilizes high-throughput target-based screens of compound libraries to identify ligands that modulate the activity of proteins of interest. Candidates are further screened using functional assays designed specifically for the protein--and function--of interest, suffering from the need to customize the assay to each protein. An alternative strategy is to utilize a probe to detect the structural changes that usually accompany binding of a functional ligand. Wide-angle X-ray scattering from proteins provides a means to identify a broad range of ligand-induced changes in secondary, tertiary, and quaternary structure. The speed and accuracy of data acquisition, combined with the label-free targets and binding conditions achievable, indicate that WAXS is well suited as a moderate-throughput assay in the detection and analysis of protein-ligand interactions.     

Effect of the hyaluronidase microe nvironment on the enzyme structure–function relationship and computational study of the <Emphasis Type="Italic">in silico</Emphasis> molecular docking of chondroitin sulfate and heparin short fragments to hyaluronidase

The review addresses the biochemical interactions of hyaluronidases with components of the natural microenvironment. The effect of subtle structural differences between ligands on the enzyme structure–function relationship regulation is noted. Docking of chondroitin sulfate (CS) trimers (hexasaccharides) and heparin tetramers (octasaccharides) to the 3D model of the bovine testicular hyaluronidase (BTH) was performed by computational chemistry methods in order to elucidate the mechanism of regulation of the enzyme functioning in the body (using virtual screening, molecular dynamics, and calculation of surface electrostatic potential of protein complexes). Several binding sites for glycosaminoglycan (GAG) ligands were found to occur on the hyaluronidase surface. They are identical for CS trimers and heparin tetramers. The calculations showed the possibility of both reversible and irreversible conformational changes of the 3D structure of BTH, depending on the arrangement of negatively charged ligands on its globule. When the changes are irreversible, Glu-149 and Asp-147, which are key amino acid residues for the catalytic activity of BTH, can migrate from the vicinity of the native enzyme active site to the periphery of the protein molecule, thus inducing enzyme inactivation. The interaction of the GAG ligands with the BTH active site is mainly caused by electrostatic forces. Four or five binding sites of the chondroitin sulfate trimer proved to be critical for stabilization of the enzyme structure. Their occupation was sufficient for preventing irreversible deformation of the BTH molecule upon the insertion of the heparin ligand into the active site cavity. Protein stabilization is accompanied by the formation of a particular form of the surface electrostatic potential.