Similarity searching in databases of three-dimensional molecules and macromolecules.

This paper discusses algorithmic techniques for measuring the degree of similarity between pairs of three-dimensional (3-D) chemical molecules represented by interatomic distance matrices. A comparison of four methods for the calculation of 3-D structural similarity suggests that the most effective one is a procedure that identifies pairs of atoms, one from each of the molecules that are being compared, that lie at the center of geometrically-related volumes of 3-D space. This atom mapping method enables the calculation of a wide range of types of intermolecular similarity coefficient, including measures that are based on physicochemical data. Massively-parallel implementations of the method are discussed, using the AMT Distributed Array Processor, that achieve a substantial increase in performance when compared with a sequential implementation on a UNIX workstation. Current work involves the use of angular information and the extension of the method to field-based similarity searching. Similarity searching in 3-D macromolecules is effected by the use of a maximal common subgraph (MCS) isomorphism algorithm with a novel, graph-based representation of the tertiary structures of proteins. This algorithm is being used to identify similarities between the 3-D structures of proteins in the Brookhaven Protein Data Bank; its use is exemplified by searches involving the NAD-binding fold motif.     

Searching for conformers of nine- to twelve-ring hydrocarbons on the MM2 and MM3 energy surfaces: Stochastic search for interconversion pathways

The stochastic search method was employed to find as many conformers on the MM2 and MM3 energy surfaces as possible for cyclic saturated hydrocarbons with ring sizes from 9 through 12. The number found was 8 MM2 (8 MM3) for 9 rings, 18 MM2 (16 MM3) for 10 rings, 40 MM2 (29 MM3) for 11 rings, and 111 MM2 (90 MM3) for 12 rings. A measure of similarity between pairs of conformers of a compound, called conformational distance, is described. It was used to correlate similar MM2 and MM3 conformers. It was discovered that some conformers on each energy surface are not close to minima on the other surface in rings larger than 9. On refinement with the other optimizer, they changed considerably—going downhill to other previously found minima on the other energy surface or (in a few cases) going to minima which had not been found by direct searches. Conformational distance was also employed as an indication of which pairs of MM2 (or MM3) conformers are likely to interconvert rapidly. A new stochastic procedure of using small kicks was used to search for the most likely interconversion processes among the conformers. There is fairly good agreement between the most facile pathways located by it and unusually short conformational distances. Several additional 12-ring conformers (not found with previous methods) were located through application of this small kick procedure.     

Calculating the knowledge-based similarity of functional groups using crystallographic data

A knowledge-based method for calculating the similarity of functional groups is described and validated. The method is based on experimental information derived from small molecule crystal structures. These data are used in the form of scatterplots that show the likelihood of a non-bonded interaction being formed between functional group A (the `central group') and functional group B (the `contact group' or `probe'). The scatterplots are converted into three-dimensional maps that show the propensity of the probe at different positions around the central group. Here we describe how to calculate the similarity of a pair of central groups based on these maps. The similarity method is validated using bioisosteric functional group pairs identified in the Bioster database and Relibase. The Bioster database is a critical compilation of thousands of bioisosteric molecule pairs, including drugs, enzyme inhibitors and agrochemicals. Relibase is an object-oriented database containing structural data about protein-ligand interactions. The distributions of the similarities of the bioisosteric functional group pairs are compared with similarities for all the possible pairs in IsoStar, and are found to be significantly different. Enrichment factors are also calculated showing the similarity method is statistically significantly better than random in predicting bioisosteric functional group pairs.     

Techniques for the calculation of three-dimensional structural similarity using inter-atomic distances

Summary This paper reports a comparison of several methods for measuring the degree of similarity between pairs of 3-D chemical structures that are represented by inter-atomic distance matrices. The methods that have been tested use the distance information in very different ways and have very different computational requirements. Experiments with 10 small datasets, for which both structural and biological activity data are available, suggest that the most cost-effective technique is based on a mapping procedure that tries to match pairs of atoms, one from each of the molecules that are being compared, that have neighbouring atoms at approximately the same distances.     

Method for clustering proteins by use of all possible pairs of amino acids as structural descriptors

Proteins were represented as vectors, of which components were all possible pairs of amino acids. From a distance matrix between any pairs of proteins thus represented, several clusters corresponding to connected components were generated. Application of this method to three different sets of proteins showed that it was suitable for clustering closely related proteins with respect to the sequential similarity defined by Dayhoff.     

Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens

In this work, we calculated the pair wise chemical similarity for a subset of small molecules screened against the NCI60 cancer cell line panel. Four different compound similarity calculation methods were used: Brutus, GRIND, Daylight and UNITY. The chemical similarity scores of each method were related to the biological similarity data set. The same was done also for combinations of methods. In the end, we had an estimate of biological similarity for a given chemical similarity score or combinations thereof. The data from above was used to identify chemical similarity ranges where combining two or more methods (data fusion) led to synergy. The results were also applied in ligand-based virtual screening using the DUD data set. In respect to their ability to enrich biologically similar compound pairs, the ranking of the four methods in descending performance is UNITY, Daylight, Brutus and GRIND. Combining methods resulted always in positive synergy within a restricted range of chemical similarity scores. We observed no negative synergy. We also noted that combining three or four methods had only limited added advantage compared to combining just two. In the virtual screening, using the estimated biological similarity for ranking compounds produced more consistent results than using the methods in isolation.     

4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment

A training set of 55 antifungal p450 analogue inhibitors was used to construct receptor-independent four-dimensional quantitative structure-activity relationship (RI 4D-QSAR) models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. One method compares the residual of fit between pairs of models using the cross-correlation coefficient of their residuals of fit as a similarity measure. The other method compares the spatial distributions of the IPE types (3D-pharmacophores) of pairs of 4D-QSAR models from different alignments. Optimum models from several different alignments have nearly the same correlation coefficients, r(2), and cross-validation correlation coefficients, xv-r(2), yet the 3D-pharmacophores of these models are very different from one another. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common pharmacorphore sites. A test set of 10 compounds was used to validate the predictivity of the best 4D-QSAR models of each alignment. The "best" model from the 10 alignments has the highest predictivity. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed.     

A steroids QSAR approach based on approximate similarity measurements

A new QSAR method based on approximate similarity measurements is described in this paper. Approximate similarity is calculated using both the classical similarity based on the graph isomorphism and a distance computation between nonisomorphic subgraphs. The latter is carried out through a parametric function where different topological invariants can be considered. After optimizing the contribution of nonisomorphic distance to the new graph similarity, predictive models built with approximate similarity matrixes show higher predictive ability than those using traditional similarity matrixes. The new method has been applied to the prediction of steroids binding to the corticosteroid globulin receptor. The proposed model allows us to obtain valuable external predictions (r=0.82 and SEP=0.30) after training the model by cross-validation (Q2=0.84 and SECV=0.47). Slope and bias parameters are also given.     

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.     

Automated detection of differently expressed fragments in mRNA differential display

We present a novel method for the automated detection of fragments showing dissimilar expression in mRNA differential display. The analysis is based on aligning the numerical electrophoretic lane data in respect of a given distance function defined on a set of fragments, or signal peaks in general. We presume that significant dissimilarities between peaks result in extreme score values computed for aligned peak pairs. Whereas in sequence comparison, an overall sequence similarity score is conventionally used, the current method defines a special dissimilarity score for searching the peak pairs showing the largest relative differences between the lanes. The output of the analysis is a highly reduced list of peak pairs, along with a set of associated features extracted from the lanes. Only the peaks of this list need to be visually confirmed instead of the vast amount of peaks in the original electrophoretic results. The results obtained by the algorithm correlate well with results of visual evaluation of the same electropherograms. The current algorithm may be applied to the study of complex expression patterns in multiple lanes and, in general, to automated recognition of variously defined patterns of quantitative electrophoretic data.     

空间距离在三维分子相似度计算中的应用

采用Tripos公司的MOPAC模块计算分子的空间距离,所得距离矩阵用带有约束条件的空间距离方法计算分子的相似度,同时通过4组化合物的计算,与纯空间距离方法进行比较,得到了较为满意的结果。     

Molecular surface-volume and property matching to superpose flexible dissimilar molecules

Summary Steric complementarity is a prerequisite for ligand-receptor recognition; this implies that drugs with a common receptor binding site should possess sterically similar binding surfaces. This principle is used as the basis for an automatic and unbiased method that superposes molecules. One molecule is rotated and translated to maximize the overlap between the two molecular surface volumes. A fast grid-based method is used to determine the extent of this overlap, and this is optimized using simulated annealing. Matches with high steric similarity scores are then sorted on the basis of both hydrogen-bond and electrostatic similarity between the matched molecules. Flexible molecules are treated as a set of rigid representative conformers. The algorithm has correctly predicted superpositions between a number of pairs of molecules, according to crystallographic data from ligands that have been co-crystallized at common enzyme binding sites.     

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.     

Aromatic-aromatic interactions in crystal structures of helical peptide scaffolds containing projecting phenylalanine residues

Aromatic-aromatic interactions between phenylalanine side chains in peptides have been probed by the structure determination in crystals of three peptides: Boc-Val-Ala-Phe-Aib-Val-Ala-Phe-Aib-OMe, I; Boc-Val-Ala-Phe-Aib-Val-Ala-Phe-Aib-Val-Ala-Phe-Aib-OMe, II; Boc-Aib-Ala-Phe-Aib-Phe-Ala-Val-Aib-OMe, III. X-ray diffraction studies reveal that all three peptides adopt helical conformations in the solid state with the Phe side chains projecting outward. Interhelix association in the crystals is promoted by Phe-Phe interactions. A total of 15 unique aromatic pairs have been characterized in the three independent crystal structures. In peptides I and II, the aromatic side chains lie on the same face of the helix at i/i + 4 positions resulting in both intrahelix and interhelix aromatic interactions. In peptide III, the Phe side chains are placed on the opposite faces of the helix, resulting in exclusive intermolecular aromatic interactions. The distances between the centroids of aromatic pair ranges from 5.11 to 6.86 A, while the distance of closest approach of ring carbon atoms ranges from 3.27 to 4.59 A. Examples of T-shaped and parallel-displaced arrangements of aromatic pairs are observed, in addition to several examples of inclined arrangements. The results support the view that the interaction potential for a pair of aromatic rings is relatively broad and rugged with several minima of similar energies, separated by small activation barriers.     

Efficient overlay of small organic molecules using 3D pharmacophores

Aligning and overlaying two or more bio-active molecules is one of the key tasks in computational drug discovery and bio-activity prediction. Especially chemical-functional molecule characteristics from the view point of a macromolecular target represented as a 3D pharmacophore are the most interesting similarity measure when describing and analyzing macromolecule-ligand interaction. In this study, a novel approach for aligning rigid three-dimensional molecules according to their chemical-functional pharmacophoric features is presented and compared to the overlay of experimentally determined poses in a comparable macromolecule coordinate frame. The presented approach identifies optimal chemical feature pairs using distance and density characteristics obtained by correlating pharmacophoric geometries and thus proves to be faster than existing combinatorial alignment methods and creates more reasonable alignments than pure atom-based methods. Examples will be provided to demonstrate the feasibility, speed and intuitiveness of this method.     

Principle of nonlinear chemical fingerprint by using dissipative components in samples as well as calculation and evaluation of similarity

Under the conditions of constant temperature and pressure,different influences of samples with different chemical components on the mechanism of nonlinear chemical reaction will cause different changes of the potential-time relationship curve of the nonlinear chemical reaction system.Using it as the character,and using the B-Z nonlinear chemical system to use acetone and substrates in samples as main dissipative substances qua an example,the principle of nonlinear chemical fingerprint has been researched and discussed in detail.At the same time,the general method for calculating the system similarity about nonlinear chemical fingerprint was also put forward,and similarities of nonlinear chemistry fingerprints of different batches of Guhan Yangshengjing and 18 sorts of other samples were calculated by Euclidean distance,correlation coefficient,included angle cosine and system similarity,at the same time,the various similarities were analyzed.The results showed that,both of correlation coefficient and included angle cosine are unable to be used as the criterion for quantitatively evaluating the similarity of nonlinear chemistry fingerprint;as non-parametric similarity,Euclidean distance can accurately reflect the feature differences in the fingerprints,but as parametric similarity,sometimes,Euclidean distance can not accurately reflect the relative extent of characteristic difference in the nonlinear chemical fingerprints;system similarity can most truthfully reflect the characteristic difference in the nonlinear chemical fingerprints,and is the best evaluating method among the four ones.Therefore,system similarity can be used to quantitatively calculate the similar extent between the nonlinear chemical fingerprints.An economical,simple and convenient,easy pushing and effective method for identifying and evaluating complicated samples has successfully been put forward.