A fast and efficient method for 2D and 3D molecular shape description

iCDI-W2vCom: Identifying the Ion Channel-Drug Interaction in Cellular Networking Based on word2vec and node2vec

Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer's disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel-drug, computational approaches are effective and efficient compared with the costly, labor-intensive, and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channels of knowing 3D structures; however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge, while most of their results need to be improved, or predicting web servers are missing. In this paper, a sequence-based classifier, called "iCDI-W2vCom," was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILES-word2vec, FP2-word2vec, SMILES-node2vec, and ECFPs via a 1184D vector, ion channel was represented by the word2vec via a 64D vector, and the prediction engine was operated by the LightGBM classifier. The accuracy and AUC achieved by iCDI-W2vCom via the fivefold cross validation were 91.95% and 0.9703, which outperformed other existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be a potential method for predicting target-drug interaction.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

3DMolNavi: a web-based retrieval and navigation tool for flexible molecular shape comparison

BACKGROUND

Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison treat them as rigid shapes, which may lead to incorrect measure of the shape similarity of flexible molecules. Currently, there still is a limited effort in retrieval and navigation for flexible molecular shape comparison, which would improve data retrieval by helping users locate the desirable molecule in a convenient way.

RESULTS

To address this issue, we develop a web-based retrieval and navigation tool, named 3DMolNavi, for flexible molecular shape comparison. This tool is based on the histogram of Inner Distance Shape Signature (IDSS) for fast retrieving molecules that are similar to a query molecule, and uses dimensionality reduction to navigate the retrieved results in 2D and 3D spaces. We tested 3DMolNavi in the Database of Macromolecular Movements (MolMovDB) and CATH. Compared to other shape descriptors, it achieves good performance and retrieval results for different classes of flexible molecules.

CONCLUSIONS

The advantages of 3DMolNavi, over other existing softwares, are to integrate retrieval for flexible molecular shape comparison and enhance navigation for user's interaction. 3DMolNavi can be accessed via https://engineering.purdue.edu/PRECISE/3dmolnavi/index.html.

Analysis of HIV protease binding pockets based on 3D shape and electrostatic potential descriptors

Using a shape analysis technique, a Dataset of 79 (19 wild-type and 60 mutated) HIV-1 protease crystal structures was analyzed for the shape changes in their binding pockets. The structures are reported with different bound inhibitors and consist of a variety of mutations. Several 3D-shape descriptors based on the volumetric shape function are calculated, in addition to the electrostatic potential (EP)-based descriptors. A cluster analysis is performed using the calculated descriptors of the binding pocket of the proteins to investigate the effect of mutations or bound inhibitors on the shape of the binding pocket of proteins. We observed that mutations and/or bound ligands influence the 3D shape of the binding pocket of these HIV proteases. This study shows that the shape changes in the binding pocket caused by mutations or bound ligands can be quantitatively captured using 3D shape- and EP-based descriptors.

Spatial chemical distance based on atomic property fields

Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well. Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of 115 protein–ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening.

Extraction and analysis of chemical modification patterns in drug development

Most drugs have been continuously modified from prototypic compounds in the drug development process. Such chemical modifications in the history of drug development are expected to contain a wealth of medicinal chemists' knowledge, and the KEGG DRUG structure maps have been compiled to capture this knowledge. Here we attempted to extract the information on the chemical modification patterns from 3745 approved drugs in the KEGG DRUG database and 255 drug pairs in the KEGG DRUG structure maps. We first identified 236 core structures and 506 peripheral fragments from the KEGG DRUG database using bit-represented fingerprints and hierarchical clustering of similar structures. We then examined position-dependent relationships between core structures and peripheral fragments, which revealed the tendency of specific fragments connected to specific modification sites on the core structures. Next we converted the drug pairs into 204 peripheral fragment changes at the modification sites. Each change was represented by the transformation profile defined as a difference of fingerprint bit patterns, and the hierarchical clustering of similar transformation profiles was performed. We thus identified 125 chemical modification patterns that characterize the KEGG DRUG structure maps. These patterns were further applied to the reconstruction of a new structure map. The approach presented here may be applicable to systematic in silico drug modifications.

IDSS: deformation invariant signatures for molecular shape comparison

Background

Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison (MSC) treat them as rigid bodies, which may lead to incorrect measure of the shape similarity of flexible molecules.

Results

To address the issue we introduce a new shape descriptor, called Inner Distance Shape Signature (IDSS), for describing the 3D shapes of flexible molecules. The inner distance is defined as the length of the shortest path between landmark points within the molecular shape, and it reflects well the molecular structure and deformation without explicit decomposition. Our IDSS is stored as a histogram which is a probability distribution of inner distances between all sample point pairs on the molecular surface. We show that IDSS is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. Our approach reduces the 3D shape comparison problem of flexible molecules to the comparison of IDSS histograms.

Conclusion

The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. We demonstrate the effectiveness of IDSS within a molecular search engine application for a benchmark containing abundant conformational changes of molecules. Such comparisons in several thousands per second can be carried out. The presented IDSS method can be considered as an alternative and complementary tool for the existing methods for rigid MSC. The binary executable program for Windows platform and database are available from .

Novel DOCK clique driven 3D similarity database search tools for molecule shape matching and beyond: Adding flexibility to the search for ligand kin

With readily available CPU power and copious disk storage, it is now possible to undertake rapid comparison of 3D properties derived from explicit ligand overlay experiments. With this in mind, shape software tools originally devised in the 1990s are revisited, modified and applied to the problem of ligand database shape comparison. The utility of Connolly surface data is highlighted using the program MAKESITE, which leverages surface normal data to a create ligand shape cast. This cast is applied directly within DOCK, allowing the program to be used unmodified as a shape searching tool. In addition, DOCK has undergone multiple modifications to create a dedicated ligand shape comparison tool KIN. Scoring has been altered to incorporate the original incarnation of Gaussian function derived shape description based on STO-3G atomic electron density. In addition, a tabu-like search refinement has been added to increase search speed by removing redundant starting orientations produced during clique matching. The ability to use exclusion regions, again based on Gaussian shape overlap, has also been integrated into the scoring function. The use of both DOCK with MAKESITE and KIN in database screening mode is illustrated using a published ligand shape virtual screening template. The advantages of using a clique-driven search paradigm are highlighted, including shape optimization within a pharmacophore constrained framework, and easy incorporation of additional scoring function modifications. The potential for further development of such methods is also discussed.

Surface similarity-based molecular query-retrieval

BACKGROUND

Discerning the similarity between molecules is a challenging problem in drug discovery as well as in molecular biology. The importance of this problem is due to the fact that the biochemical characteristics of a molecule are closely related to its structure. Therefore molecular similarity is a key notion in investigations targeting exploration of molecular structural space, query-retrieval in molecular databases, and structure-activity modelling. Determining molecular similarity is related to the choice of molecular representation. Currently, representations with high descriptive power and physical relevance like 3D surface-based descriptors are available. Information from such representations is both surface-based and volumetric. However, most techniques for determining molecular similarity tend to focus on idealized 2D graph-based descriptors due to the complexity that accompanies reasoning with more elaborate representations.

RESULTS

This paper addresses the problem of determining similarity when molecules are described using complex surface-based representations. It proposes an intrinsic, spherical representation that systematically maps points on a molecular surface to points on a standard coordinate system (a sphere). Molecular surface properties such as shape, field strengths, and effects due to field super-positioning can then be captured as distributions on the surface of the sphere. Surface-based molecular similarity is subsequently determined by computing the similarity of the surface-property distributions using a novel formulation of histogram-intersection. The similarity formulation is not only sensitive to the 3D distribution of the surface properties, but is also highly efficient to compute.

CONCLUSION

The proposed method obviates the computationally expensive step of molecular pose-optimisation, can incorporate conformational variations, and facilitates highly efficient determination of similarity by directly comparing molecular surfaces and surface-based properties. Retrieval performance, applications in structure-activity modeling of complex biological properties, and comparisons with existing research and commercial methods demonstrate the validity and effectiveness of the approach.

Ultrafast shape recognition for similarity search in molecular databases

Molecular databases are routinely screened for compounds that most closely resemble a molecule of known biological activity to provide novel drug leads. It is widely believed that three-dimensional molecular shape is the most discriminating pattern for biological activity as it is directly related to the steep repulsive part of the interaction potential between the drug-like molecule and its macromolecular target. However, efficient comparison of molecular shape is currently a challenge. Here, we show that a new approach based on moments of distance distributions is able to recognize molecular shape at least three orders of magnitude faster than current methodologies. Such an ultrafast method permits the identification of similarly shaped compounds within the largest molecular databases. In addition, the problematic requirement of aligning molecules for comparison is circumvented, as the proposed distributions are independent of molecular orientation. Our methodology could be also adapted to tackle similar hard problems in other fields, such as designing content-based Internet search engines for three-dimensional geometrical objects or performing fast similarity comparisons between proteins. From a broader perspective, we anticipate that ultrafast pattern recognition will soon become not only useful, but also essential to address the data explosion currently experienced in most scientific disciplines.

Ultrafast shape recognition to search compound databases for similar molecular shapes

Finding a set of molecules, which closely resemble a given lead molecule, from a database containing potentially billions of chemical structures is an important but daunting problem. Similar molecular shapes are particularly important, given that in biology small organic molecules frequently act by binding into a defined and complex site on a macromolecule. Here, we present a new method for molecular shape comparison, named ultrafast shape recognition (USR), capable of screening billions of compounds for similar shapes using a single computer and without the need of aligning the molecules before testing for similarity. Despite its extremely fast comparison rate, USR will be shown to be highly accurate at describing, and hence comparing, molecular shapes.

Lingos, finite state machines, and fast similarity searching

We apply a recently published method of text-based molecular similarity searching (LINGO) to standard data sets for the purpose of quantifying the accuracy of the approach. Our implementation is based on a pattern-matching finite state machine (FSM) which results in fast search times. The accuracy of LINGO is demonstrated to be comparable to that of a path-based fingerprint and offers a simple yet effective method for similarity searching.

Reasoning about molecular similarity and properties

Ascertaining the similarity amongst molecules is a fundamental problem in biology and drug discovery. Since similar molecules tend to have similar biological properties, the notion of molecular similarity plays an important role in exploration of molecular structural space, query-retrieval in molecular databases, and in structure-activity modeling. This problem is related to the issue of molecular representation. Currently, approaches with high descriptive power like 3D surface-based representations are available. However, most techniques tend to focus on 2D graph-based molecular similarity due to the complexity that accompanies reasoning with more elaborate representations. This paper addresses the problem of determining similarity when molecules are described using complex surface-based representations. It proposes an intrinsic, spherical representation that systematically maps points on a molecular surface to points on a standard coordinate system (a sphere). Molecular geometry, molecular fields, and effects due to field super-positioning can then be captured as distributions on the surface of the sphere. Molecular similarity is obtained by computing the similarity of the corresponding property distributions using a novel formulation of histogram-intersection. This method is robust to noise, obviates molecular pose-optimization, can incorporate conformational variations, and facilitates highly efficient determination of similarity. Retrieval performance, applications in structure-activity modeling of complex biological properties, and comparisons with existing research and commercial methods demonstrate the validity and effectiveness of the approach.

e-molecular shapes and properties

Due to recent computer technology advances, shape analysis has gained importance in all domains. In drug design and proteomics, molecular surfaces (van der Waals surface, solvent accessible surface, solvent excluded surface, polar surface area, electron density surface, separating surface, etc.), buried surfaces (gap, cleft, cavity, etc.) as well as shape properties of these surfaces, can be easily computed and visualized via the Internet. Freely available resources from the Internet for academic use, are reviewed.

Similarity metrics for ligands reflecting the similarity of the target proteins

In this study we evaluate how far the scope of similarity searching can be extended to identify not only ligands binding to the same target as the reference ligand(s) but also ligands of other homologous targets without initially known ligands. This "homology-based similarity searching" requires molecular representations reflecting the ability of a molecule to interact with target proteins. The Similog keys, which are introduced here as a new molecular representation, were designed to fulfill such requirements. They are based only on the molecular constitution and are counts of atom triplets. Each triplet is characterized by the graph distances and the types of its atoms. The atom-typing scheme classifies each atom by its function as H-bond donor or acceptor and by its electronegativity and bulkiness. In this study the Similog keys are investigated in retrospective in silico screening experiments and compared with other conformation independent molecular representations. Studied were molecules of the MDDR database for which the activity data was augmented by standardized target classification information from public protein classification databases. The MDDR molecule set was split randomly into two halves. The first half formed the candidate set. Ligands of four targets (dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease, and progesterone receptor) were taken from the second half to form the respective reference sets. Different similarity calculation methods are used to rank the molecules of the candidate set by their similarity to each of the four reference sets. The accumulated counts of molecules binding to the reference target and groups of targets with decreasing homology to it were examined as a function of the similarity rank for each reference set and similarity method. In summary, similarity searching based on Unity 2D-fingerprints or Similog keys are found to be equally effective in the identification of molecules binding to the same target as the reference set. However, the application of the Similog keys is more effective in comparison with the other investigated methods in the identification of ligands binding to any target belonging to the same family as the reference target. We attribute this superiority to the fact that the Similog keys provide a generalization of the chemical elements and that the keys are counted instead of merely noting their presence or absence in a binary form. The second most effective molecular representation are the occurrence counts of the public ISIS key fragments, which like the Similog method, incorporates key counting as well as a generalization of the chemical elements. The results obtained suggest that ligands for a new target can be identified by the following three-step procedure: 1. Select at least one target with known ligands which is homologous to the new target. 2. Combine the known ligands of the selected target(s) to a reference set. 3. Search candidate ligands for the new targets by their similarity to the reference set using the Similog method. This clearly enlarges the scope of similarity searching from the classical application for a single target to the identification of candidate ligands for whole target families and is expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

CLIP: similarity searching of 3D databases using clique detection

This paper describes a program for 3D similarity searching, called CLIP (for Candidate Ligand Identification Program), that uses the Bron-Kerbosch clique detection algorithm to find those structures in a file that have large structures in common with a target structure. Structures are characterized by the geometric arrangement of pharmacophore points and the similarity between two structures calculated using modifications of the Simpson and Tanimoto association coefficients. This modification takes into account the fact that a distance tolerance is required to ensure that pairs of interatomic distances can be regarded as equivalent during the clique-construction stage of the matching algorithm. Experiments with HIV assay data demonstrate the effectiveness and the efficiency of this approach to virtual screening.

A novel mechanism for inhibition of HIV-1 reverse transcriptase

The human immunodeficiency virus (HIV) epidemic is an important medical problem. Although combination drug regimens have produced dramatic decreases in viral load, current therapies do not provide a cure for HIV infection. We have used structure-based design and combinatorial medicinal chemistry to identify potent and selective HIV-1 reverse transcriptase (RT) inhibitors that may work by a mechanism distinct from that of current HIV drugs. The most potent of these compounds (compound 4, 2-naphthalenesulfonic acid, 4-hydroxy-7-[[[[5-hydroxy-6-[(4-cinnamylphenyl)azo]-7-sulfo-2-naphthalenyl]amino]carbonyl]amino]-3-[(4-cinnamylphenyl)azo], disodium salt) has an IC(50) of 90 nM for inhibition of polymerase chain extension, a K(d) of 40 nM for inhibition of DNA-RT binding, and an IC(50) of 25-100 nM for inhibition of RNaseH cleavage. The parent compound (1) was as effective against 10 nucleoside and non-nucleoside resistant HIV-1 RT mutants as it was against the wild-type enzyme. Compound 4 inhibited HIV-1 RT and murine leukemia virus (MLV) RT, but it did not inhibit T(4) DNA polymerase, T(7) DNA polymerase, or the Klenow fragment at concentrations up to 200 nM. Finally, compound 4 protected cells from HIV-1 infection at a concentration more than 40 times lower than the concentration at which it caused cellular toxicity.

Structure-based approach for the discovery of bis-benzamidines as novel inhibitors of matriptase

Matriptase, a trypsin-like serine protease, which may be involved in tissue remodeling, cancer invasion, and metastasis. Potent and selective matriptase inhibitors not only would be useful pharmacological tools for further elucidation of the role of matriptase in these processes but also could have therapeutic potential for the treatment and/or prevention of cancers. We report herein the structure-based approach for the discovery of bis-benzamidines as a novel class of potent matriptase inhibitors. The lead compound, hexamidine (1), inhibits not only the proteolytic activity of matriptase, (K(i) = 924 nM) but also of thrombin K(i) = 224 nM). By testing several available analogues, we identified a new analogue (7) that has a K(i) = 208 nM against matriptase and has only weak inhibitory activity against thrombin (K(i) = 2670 nM), thus displaying a 13-fold selectivity toward matriptase. Our results demonstrated that structure-based database screening is effective in the discovery of matriptase inhibitors and that bis-benzamidines represent a class of promising matriptase inhibitors that can be used for further drug design studies. Finally, our study suggested that there is sufficient structural differences between matriptase and its closely related serine proteases, such as thrombin, for the design of potent and selective matriptase inhibitors.

The structure of inosine 5'-monophosphate dehydrogenase and the design of novel inhibitors

The enzyme IMPDH is a homotetramer of approximately 55 kDa subunits and consists of a (beta/alpha)(8) barrel core domain and a smaller subdomain. The active site has binding pockets for the two substrates IMP and NAD. The enzymatic reaction of oxidation of IMP to XMP proceeds through a covalent mechanism involving an active site cysteine residue. This enzyme is a target for immunosuppressive agents because it catalyzes a key step in purine nucleotide biosynthesis which is important for the proliferation of lymphocytes. Several X-ray structures of inhibitors bound to IMPDH have been published. The uncompetitive IMPDH inhibitor MPA is the active metabolite of the immunosuppressive agent mycophenolate mofetil (CellCept(R)) which is approved for the prevention of acute rejection after kidney and heart transplantation. The bicyclic ring system of MPA packs underneath the hypoxanthine ring of XMP*, thereby trapping this covalent intermediate of the enzymatic reaction. Ribavirin monophosphate, the active metabolite of the antiviral agent ribavirin, is a substrate mimic of IMP. The structure of the two inhibitors 6-Cl-IMP and SAD binding in the IMP and NAD pockets of IMPDH, respectively, gives information for the binding mode of the di-nucleotide cofactor to the enzyme. At Vertex Pharmaceuticals a structure-based drug design program for the design of IMPDH inhibitors was initiated. Several new lead compound classes unrelated to other IMPDH inhibitors were found. Integrating structural information into an iterative drug-design process led to the design of VX-497. VX-497 is a potent uncompetitive enzyme inhibitor of IMPDH. The phenyl-oxazole moiety of the molecule packs underneath XMP*, analogous to MPA. VX-497 also makes several new interactions that are not observed in the binding of MPA. VX-497 is a potent immunosuppressive agent in vitro and in vivo. A Phase I clinical trial has been successfully concluded and the compound is currently in Phase II trials in psoriasis and hepatitis C. The rapid progress from initiation of the drug design program to a compound entering clinical trials illustrates the power of structure-based drug design to accelerate the drug discovery process. The structural information on IMPDH has also significantly increased our knowledge about the mechanistic details of this fascinating enzyme.

Quadratic shape descriptors. 1. Rapid superposition of dissimilar molecules using geometrically invariant surface descriptors

In this paper, we present a novel approach to shape-based molecular similarity searching. The method that we introduce is able to superimpose dissimilar molecules by using geometrically invariant molecular surface descriptors. The shape descriptors are calculated by least-squares fitting of a quadratic function to small sections of the molecular surface of a ligand. Invariant geometric properties of the approximated surface patch are then extracted from the fitted quadratic function. The extracted properties are used to quantify the shape and to obtain a canonical orientation for this section of surface. The superimposition algorithm uses these geometric invariants to recognize similar regions of surface shape existing on two molecules and to bring these regions (and consequently the molecules) into registration. Because these geometric descriptors are based upon local surface shape, the superimposing algorithm is insensitive to the connectivity and the relative sizes of the molecules being matched. The capabilities of our algorithm are demonstrated by superimposing dissimilar ligands known to inhibit the same enzyme system. In all cases examined the algorithm generates superpositions that are in agreement with crystallographic results. The algorithm is also applied to align the two different proteins on the basis of the shape of their active sites.

Similarity searching in files of three-dimensional chemical structures: analysis of the BIOSTER database using two-dimensional fingerprints and molecular field descriptors

This paper compares the effectiveness of similarity measures based on two-dimensional fingerprints and on molecular fields for identifying pairs of bioisosteric molecules in the BIOSTER database. The results suggest that the two types of descriptor are complementary in nature, each finding some bioisosteric pairs that are not found by the other. This conclusion is confirmed by studies of groups of BIOSTER molecules that share the same activity characteristics, and by experiments that involve combining the two types of similarity measure.

Molecular modeling of G-protein coupled receptor kinase 2: docking and biochemical evaluation of inhibitors

G-protein coupled receptor kinase 2 (GRK2) regulates the activity of many receptors. Because potent inhibitors of GRK2 are thus far limited to polyanionic compounds like heparin, we searched for new inhibitors with the aid of a molecular model of GRK2. We used the available crystal structure of cAMP dependent protein kinase (cAPK) as a template to construct a 3D homology model of GRK2. Known cAPK and GRK2 inhibitors were docked into the active sites of GRK2 and cAPK using DOCK v3.5. H8 docked into the hydrophobic pocket of the adenosine 5'-triphosphate (ATP) binding site of cAPK, consistent with its known competitive cAPK inhibition relative to ATP. Similarly, 3 of 4 known GRK2 inhibitors docked into the ATP binding pocket of GRK2 with good scores. Screening the Fine Chemicals Directory (FCD, containing the 3D structures of 13,000 compounds) for docking into the active sites of GRK2 identified H8 and the known GRK2 inhibitor trifluoperazine as candidates. Whereas H8 indeed inhibited light-dependent phosphorylation of rhodopsin by GRK2, but with low potency, 3 additional FCD compounds with promising GRK2 scores failed to inhibit GRK2. This result demonstrates limitations of the GRK2 model in predicting activity among diverse chemical structures. Docking suramin, an inhibitor of protein kinase C (not present in FCD) yielded a good fit into the ATP binding site of GRK2 over cAPK. Suramin did inhibit GRK2 with IC50 32 microM (pA26.39 for competitive inhibition of ATP). Suramin congeners with fewer sulfonic acid residues (NF062, NF503 [IC50 14 microM]) or representing half of the suramin molecule (NF520) also inhibited GRK2 as predicted by docking. In conclusion, suramin and analogues are lead compounds in the development of more potent and selective inhibitors of GRK2.

X-ray structures of small ligand-FKBP complexes provide an estimate for hydrophobic interaction energies

A new crystal form of native FK506 binding protein (FKBP) has been obtained which has proved useful in ligand binding studies. Three different small molecule ligand complexes and the native enzyme have been determined at higher resolution than 2.0 A. Dissociation constants of the related small molecule ligands vary from 20 mM for dimethylsulphoxide to 200 microM for tetrahydrothiophene 1-oxide. Comparison of the four available crystal structures shows that the protein structures are identical to within experimental error, but there are differences in the water structure in the active site. Analysis of the calculated buried surface areas of these related ligands provides an estimated van der Waals contribution to the binding energy of -0.5 kJ/A(2) for non-polar interactions between ligand and protein.

Feature trees: a new molecular similarity measure based on tree matching

In this paper we present a new method for evaluating molecular similarity between small organic compounds. Instead of a linear representation like fingerprints, a more complex description, a feature tree, is calculated for a molecule. A feature tree represents hydrophobic fragments and functional groups of the molecule and the way these groups are linked together. Each node in the tree is labeled with a set of features representing chemical properties of the part of the molecule corresponding to the node. The comparison of feature trees is based on matching subtrees of two feature trees onto each other. Two algorithms for tackling the matching problem are described throughout this paper. On a dataset of about 1000 molecules, we demonstrate the ability of our approach to identify molecules belonging to the same class of inhibitors. With a second dataset of 58 molecules with known binding modes taken from the Brookhaven Protein Data Bank, we show that the matchings produced by our algorithms are compatible with the relative orientation of the molecules in the active site in 61% of the test cases. The average computation time for a pair comparison is about 50 ms on a current workstation.

An example of a protein ligand found by database mining: description of the docking method and its verification by a 2.3 A X-ray structure of a thrombin-ligand complex

A computer program (SANDOCK) has been developed for the automated docking of small ligands to a target protein. It uses a guided matching algorithm to fit ligand atoms into the protein binding pocket. The protein is described by a modified Lee-Richard's dotted surface with each dot coded by chemical property and accessibility. Orientations of the ligand in the active site are generated such that a chemical and a shape complementary between the ligand and the active site cavity have to be fulfilled. The generated fits are evaluated with scoring functions which account for van der Waals, hydrophobic and hydrogen bonding interactions. This newly developed docking program can efficiently screen very large databases in a reasonable time and has been used to successfully identify novel ligands. The X-ray structure of a thrombin-ligand complex predicted by SANDOCK is described. The ligand binds to thrombin with a Kd of 65 microM and has an rmsd of 0.7 A for all ligand atoms from the predicted binding mode by SANDOCK.

Checking the projection display of multivariate data with colored graphs

Projection methods such as principal component analysis (PCA), nonlinear mapping (NLM), and the self-organizing map (SOM) are valuable algorithms for visualizing multidimensional data in a two-dimensional plane. Unfortunately, the reduction of the dimensionality involves distortions. In an attempt to graphically localize the distortions of the projected data, we suggest superposing colored graphs onto the 2D plots. The color of the edges of these graphs encodes the original high-dimensional distances between the connected points. The method is applied to a cluster analysis of 37 biologically active compounds and 471 molecules represented by a structural 3D descriptor.

Bile pigments as HIV-1 protease inhibitors and their effects on HIV-1 viral maturation and infectivity in vitro

Using recently developed molecular-shape description algorithms, we searched the Available Chemical Directory for known compounds similar in shape to the potent HIV-1 protease inhibitor Merck L-700,417; 15 compounds most similar in shape to the inhibitor were selected for testing in vitro. Four of these inhibited the protease at 100 microM or less and the most active of the four were the naturally occurring pigments biliverdin and bilirubin. Biliverdin and bilirubin inhibited recombinant HIV-1 protease in vitro at pH 7.8 with K1 values of approx. 1 microM, and also inhibited HIV-2 and simian immunodeficiency virus proteases. The related pyrrolic pigments stercobilin, urobilin, biliverdin dimethyl ester and xanthobilirubic acid showed similar inhibitory activity at low micromolar concentrations. Biliverdin, bilirubin and xanthobilirubic acid did not inhibit viral polyprotein processing in cultured cells, but they reduced viral infectivity significantly. At 100 microM, xanthobilirubic acid affected viral assembly, resulting in a 50% decrease in the generation of infectious particles. In contrast, at the same concentrations biliverdin and bilirubin exerted little or no effect on viral assembly but blocked infection of HeLaT4 cells by 50%. These results suggest that bile pigments might be a new class of potential lead compounds for developing protease inhibitors and they raise the question of whether hyperbilirubinaemia can influence the course of HIV infection.

Molecular similarity based on DOCK-generated fingerprints

An alternative method for defining molecular similarity is presented. By using the docking program DOCK and a reference panel of protein binding sites, fingerprints for a set of molecules have been generated, based on calculated interaction energies. These binding patterns allowed us to calculate matrices of similarity coefficients which subsequently were used for nearest-neighbor searches within the database. Our results indicate that the method is suitable for finding significant similarities of compounds of the same biological activity. Although the overall performance of a traditional 2D similarity method is better in the test systems investigated, our 3D approach can be regarded as complementary since it is able to detect similarities independent of the covalent structure of the compounds. Thus it should be a useful 3D database-searching tool for rational lead discovery.

The properties of known drugs. 1. Molecular frameworks

In order to better understand the common features present in drug molecules, we use shape description methods to analyze a database of commercially available drugs and prepare a list of common drug shapes. A useful way of organizing this structural data is to group the atoms of each drug molecule into ring, linker, framework, and side chain atoms. On the basis of the two-dimensional molecular structures (without regard to atom type, hybridization, and bond order), there are 1179 different frameworks among the 5120 compounds analyzed. However, the shapes of half of the drugs in the database are described by the 32 most frequently occurring frameworks. This suggests that the diversity of shapes in the set of known drugs is extremely low. In our second method of analysis, in which atom type, hybridization, and bond order are considered, more diversity is seen; there are 2506 different frameworks among the 5120 compounds in the database, and the most frequently occurring 42 frameworks account for only one-fourth of the drugs. We discuss the possible interpretations of these findings and the way they may be used to guide future drug discovery research.