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Molecular Interaction Potentials (MIP) are frequently used for the comparison of series of compounds displaying related biological behaviors. These potentials are interaction energies between the considered compounds and relevant probes. The interaction energies are computed in the nodes of grids defined around the compounds. There is a need of detailed and objective comparative analyses of MIP distributions in the framework of structure-activity studies. On the other hand, MIP-based studies do not have to be restricted to series of small ligands, since such studies present also interesting possibilities for the analysis and comparison of biological macromolecules. Such analyses can benefit from the application of new methods and computational approaches. The new software MIPSim (Molecular Interaction Potentials Similarity analysis) has recently been introduced with the purpose of analyzing and comparing MIP distributions of series of biomolecules. This program is transparently integrated with other programs, like GAMESS or GRID, which can be used for the computation of the potentials to be analyzed or compared. MIPSim incorporates several definitions of similarity coefficients, and is capable of combining several similarity measures into a single one. On the other hand, MIPSim can perform automatic explorations of the maximum similarity alignments between pairs of molecules.

Abstract Background Large chemical databases require fast, efficient, and simple ways of looking for similar structures. Although such tasks are now fairly well resolved for graph-based similarity queries, they remain an issue for 3D approaches, particularly for those based on 3D shape overlays. Inspired by a recent technique developed to compare molecular shapes, we designed a hybrid methodology, alignment-recycling, that enables efficient retrieval and alignment of structures with similar 3D shapes. Results Using a dataset of more than one million PubChem compounds of limited size (< 28 heavy atoms) and flexibility (< 6 rotatable bonds), we obtained a set of a few thousand diverse structures covering entirely the 3D shape space of the conformers of the dataset. Transformation matrices gathered from the overlays between these diverse structures and the 3D conformer dataset allowed us to drastically (100-fold) reduce the CPU time required for shape overlay. The alignment-recycling heuristic produces results consistent with de novo alignment calculation, with better than 80% hit list overlap on average. Conclusion Overlay-based 3D methods are computationally demanding when searching large databases. Alignment-recycling reduces the CPU time to perform shape similarity searches by breaking the alignment problem into three steps: selection of diverse shapes to describe the database shape-space; overlay of the database conformers to the diverse shapes; and non-optimized overlay of query and database conformers using common reference shapes. The precomputation, required by the first two steps, is a significant cost of the method; however, once performed, querying is two orders of magnitude faster. Extensions and variations of this methodology, for example, to handle more flexible and larger small-molecules are discussed.

Summary More than 150 peptide therapeutics are globally in clinical development. Many enzymatic barriers should be crossed by a successful drug to be prosperous in such a process. Therefore, the new peptide drugs must be designed preventing the potential protease cleavage to make the compound less susceptible to protease reaction. We present a new data analysis tool developed in WebMetabase, an approach that stores the information from liquid chromatography mass spectrometry-based experimental data or from external sources such as the MEROPS database. The tool is a chemically aware system where each peptide substrate is presented as a sequence of structural blocks (SBs) connected by amide bonds and not being limited to the natural amino acids. Each SB is characterized by its pharmacophoric and physicochemical properties including a similarity score that describes likelihood between a SB and each one of the other SBs in the database. This methodology can be used to perform a frequency analysis to discover the most frequent cleavage sites for similar amide bonds, defined based on the similarity of the SB that participate in such a bond within the experimentally derived and/or public database. Availability and implementation http://webmetabase.com:8182/WebMetabaseBioinformatics/ Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]