The in silico screening and X-ray structure analysis of the inhibitor complex of Plasmodium falciparum orotidine 5'-monophosphate decarboxylase.

Orotidine 5'-monophosphate decarboxylase from Plasmodium falciparum (PfOMPDC) catalyses the final step in the de novo synthesis of uridine 5'-monophosphate (UMP) from orotidine 5'-monophosphate (OMP). A defective PfOMPDC enzyme is lethal to the parasite. Novel in silico screening methods were performed to select 14 inhibitors against PfOMPDC, with a high hit rate of 9%. X-ray structure analysis of PfOMPDC in complex with one of the inhibitors, 4-(2-hydroxy-4-methoxyphenyl)-4-oxobutanoic acid, was carried out to at 2.1 Å resolution. The crystal structure revealed that the inhibitor molecule occupied a part of the active site that overlaps with the phosphate-binding region in the OMP- or UMP-bound complexes. Space occupied by the pyrimidine and ribose rings of OMP or UMP was not occupied by this inhibitor. The carboxyl group of the inhibitor caused a dramatic movement of the L1 and L2 loops that play a role in the recognition of the substrate and product molecules. Combining part of the inhibitor molecule with moieties of the pyrimidine and ribose rings of OMP and UMP represents a suitable avenue for further development of anti-malarial drugs.

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes.

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely ß(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.

Selection of in silico drug screening results by using universal active probes (UAPs).

We developed a new method that uses a set of drug-like compounds to select reliable in silico drug screening results. If some active compounds are known, the screening results that rank these active compounds at the top should be reliable. If no active compound is known, how to select the result is in question. We propose a concept of a set of "universal active probes" (UAPs), which is a set of small active compounds that bind to different kinds of proteins. We found that the hit ratio of the true active compounds in in silico screening shows positive correlation to that of the UAPs, probably because UAPs form a set of drug-like compounds. Thus, if the UAPs were added to the compound library, the screening result that shows a high hit ratio of the UAPs could give reliable actual hit compounds for the target protein. We examined this method for several targets and found this idea useful.

Structural ensemble in computational drug screening.

IMPORTANCE OF THE FIELD: Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient. AREAS COVERED IN THIS REVIEW: This review highlights the recent progress of the post-processing of protein-compound complexes after docking. WHAT THE READER WILL GAIN: These methods utilize ensembles of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the ensemble of docking poses. TAKE HOME MESSAGE: The protein-compound docking program provides an arbitral rather than a canonical ensemble of docking poses. When the ensemble of docking poses satisfies the canonical ensemble, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined ensembles of docking poses.

Development of Chemical Compound Libraries for In Silico Drug Screening.

Chemical compound libraries are the basic database for virtual (in silico) drug screening, and the number of entries has reached 20 million. Many drug-like compound libraries for virtual drug screening have been developed and released. In this review, the process of constructing a database for virtual screening is reviewed, and several popular databases are introduced. Several kinds of focused libraries have been developed. The author has developed databases for metalloproteases, and the details of the libraries are described. The library for metalloproteases was developed by improving the generation of the dominant-ion forms. For instance, the SH group is treated as S- in this library while all SH groups are protonated in the conventional libraries. In addition, metal complexes were examined as new candidates of drug-like compounds. Finally, a method for generating chemical space is introduced, and the diversity of compound libraries is discussed.

A similarity search using molecular topological graphs.

A molecular similarity measure has been developed using molecular topological graphs and atomic partial charges. Two kinds of topological graphs were used. One is the ordinary adjacency matrix and the other is a matrix which represents the minimum path length between two atoms of the molecule. The ordinary adjacency matrix is suitable to compare the local structures of molecules such as functional groups, and the other matrix is suitable to compare the global structures of molecules. The combination of these two matrices gave a similarity measure. This method was applied to in silico drug screening, and the results showed that it was effective as a similarity measure.

Structure-based drug screening and ligand-based drug screening with machine learning.

The initial stage of drug development is the hit (active) compound search from a pool of millions of compounds; for this process, in silico (virtual) screening has been successfully applied. One of the problems of in silico screening, however, is the low hit ratio in relation to the high computational cost and the long CPU time. This problem becomes serious in structure-based in silico screening. The major reason is the low accuracy of the estimation of protein-compound binding free energy. The problem of ligand-based in silico screening is that the conventional quantitative structure-activity relationship (QSAR) approach is not effective at predicting new hit compounds with new scaffolds. Recently, machine-learning approaches have been applied to in silico drug screening to overcome the above problems. We review here machine-learning approaches for both structure-based and ligand-based drug screening. Machine learning is used to improve database enrichment in two ways, namely by improving the docking score calculated by the protein-compound docking program and by calculating the optimal distance between the feature vectors of active and inactive compounds. Both approaches require compounds that are known to be active with respect to the target protein. In structure-based screening, the former approach is mainly used with a protein-compound affinity matrix. In ligand-based screening, both the former and latter approaches are used, and the latter approach can be applied to various kinds of descriptors, such as 1D/2D descriptors/fingerprints and the affinity fingerprint given by the protein-compound affinity matrix.

In silico fragment screening by replica generation (FSRG) method for fragment-based drug design.

We developed a new in silico screening method, which is a structure-based virtual fragment screening with protein-compound docking. The structure-based in silico screening of small fragments is known to be difficult due to poor surface complementarity between protein surfaces and small compound (fragment) surfaces. In our method, several side chains were attached to the fragment in question to generate a set of replica molecules of different sizes. This chemical modification enabled us to select potentially active fragments more easily than basing the selection on the original form of the fragment. In addition, the Coulombic and hydrogen bonding interactions were ignored in the docking simulation to reduce the variety of chemical modifications. Namely, we focused on the sizes and the shapes of the side chains and could ignore the atomic charges and types of elements. This procedure was validated in the screenings of inhibitors of six target proteins using known active compounds, and the results revealed that our procedure was effective.

A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method.

We developed a new molecular dynamics simulation method for molecular overlapping (alignment) and ligand-based in-silico drug screening based on molecular similarity. The molecular system consists of the query compound and the other compound(s) selected from a compound library. The newly introduced intermolecular interaction between compounds is proportional to the molecular overlap instead of the van der Waals and Coulomb interactions between atoms of different molecules. This method was able to achieve both conformer generation of molecules and molecular overlapping (alignment) at the same time. After an energy minimization and following short-time MD simulation, the molecules in the system were overlapped with each other and the similarity between compounds was measured by the volume of the overlap. We applied this MD simulation method to ligand-based in-silico drug screening and found that it worked well for several targets.

[New progress in crystallization technology of membrane protein and introduction of pharamaceutical innovation value chain].

We have recently established a Pharamaceutical Innovation Value Chain in collaboration with the SOSHO project (http://www.so-sho.jp) and BioGrid Project (http://www.biogrid.jp/) to accelerate new drug development. The SOSHO project provides novel crystallization technology with laser-irradiation and stirring growth methods, and the BioGrid Project is developing the software necessary for the in silico screening of promising drugs and the simulation of biological responses to proteins. In this paper, we report the recent research work on the crystallization of membrane proteins and the development of a method for in silico drug discovery.

In-silico drug screening method based on the protein-compound affinity matrix using the factor selection technique.

We have developed a new in-silico drug screening method, a modified version of a docking score index (DSI) method, based on a protein-compound docking affinity matrix. By using this method, the docking scores are converted to the docking score indexes by the principal component analysis (PCA) method and each compound is projected into a PCA space. In this study, we propose a method to select a set of suitable principal component axes and evaluate the database enrichment for 12 target proteins. This method selects the new active compounds or hits, which are close to the known active compounds, thereby enhancing the database enrichment.

Finding ligands for G protein-coupled receptors based on the protein-compound affinity matrix.

We developed a novel method of identifying new active ligands based on information related to known active compounds using protein-compound docking simulations, even when the tertiary structure of the actual target receptor protein is unknown. This method was used to find ligands of G protein-coupled receptors (GPCRs), i.e., agonists and antagonists of histamine, adrenaline, serotonin and dopamine receptors. The principal component analysis (PCA) method was applied to the protein-compound affinity matrix, which was given by thorough docking calculations between sets of many protein pockets and chemical compounds. The set of protein pockets did not necessary include the target protein. Each compound was depicted as a point in the PCA space. Compounds in a sphere, whose center was set to the known active compound in the multi-dimensional PCA space or to the average position of several known active compounds, were selected as candidate-hit compounds. Our method was found to be effective for finding the ligands of GPCRs based on known native ligands, even when only the soluble protein structures were used in the docking simulations.

An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands.

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.

A virtual active compound produced from the negative image of a ligand-binding pocket, and its application to in-silico drug screening.

We developed a new structure-based in-silico screening method using a negative image of a ligand-binding pocket and a multi-protein-compound interaction matrix. Based on the structure of the ligand pocket of the target protein, we designed a negative image, which consists of virtual atoms whose radii are close to those of carbon atoms. The virtual atoms fit the pocket ideally and achieve an optimal Coulomb interaction. A protein-compound docking program calculates the protein-compound interaction matrix for many proteins and many compounds including the negative image, which can be treated as a virtual compound. With specific attention to a vector of docking scores for a single compound with many proteins, we selected a compound whose score vector was similar to that of the negative image as a candidate hit compound. This method was applied to representative target proteins and showed high database enrichment with a relatively quick procedure.

Multiple target screening method for robust and accurate in silico ligand screening.

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].