Biological spectra analysis: Linking biological activity profiles to molecular structure.
ABSTRACT: Establishing quantitative relationships between molecular structure and broad biological effects has been a longstanding challenge in science. Currently, no method exists for forecasting broad biological activity profiles of medicinal agents even within narrow boundaries of structurally similar molecules. Starting from the premise that biological activity results from the capacity of small organic molecules to modulate the activity of the proteome, we set out to investigate whether descriptor sets could be developed for measuring and quantifying this molecular property. Using a 1,567-compound database, we show that percent inhibition values, determined at single high drug concentration in a battery of in vitro assays representing a cross section of the proteome, provide precise molecular property descriptors that identify the structure of molecules. When broad biological activity of molecules is represented in spectra form, organic molecules can be sorted by quantifying differences between biological spectra. Unlike traditional structure-activity relationship methods, sorting of molecules by using biospectra comparisons does not require knowledge of a molecule's putative drug targets. To illustrate this finding, we selected as starting point the biological activity spectra of clotrimazole and tioconazole because their putative target, lanosterol demethylase (CYP51), was not included in the bioassay array. Spectra similarity obtained through profile similarity measurements and hierarchical clustering provided an unbiased means for establishing quantitative relationships between chemical structures and biological activity spectra. This methodology, which we have termed biological spectra analysis, provides the capability not only of sorting molecules on the basis of biospectra similarity but also of predicting simultaneous interactions of new molecules with multiple proteins.
Project description:Nonribosomal peptides (NRPs) are molecules produced by microorganisms that have a broad spectrum of biological activities and pharmaceutical applications (e.g., antibiotic, immunomodulating, and antitumor activities). One particularity of the NRPs is the biodiversity of their monomers, extending far beyond the 20 proteogenic amino acid residues. Norine, a comprehensive database of NRPs, allowed us to review for the first time the main characteristics of the NRPs and especially their monomer biodiversity. Our analysis highlighted a significant similarity relationship between NRPs synthesized by bacteria and those isolated from metazoa, especially from sponges, supporting the hypothesis that some NRPs isolated from sponges are actually synthesized by symbiotic bacteria rather than by the sponges themselves. A comparison of peptide monomeric compositions as a function of biological activity showed that some monomers are specific to a class of activities. An analysis of the monomer compositions of peptide products predicted from genomic information (metagenomics and high-throughput genome sequencing) or of new peptides detected by mass spectrometry analysis applied to a culture supernatant can provide indications of the origin of a peptide and/or its biological activity.
Project description:Photopharmacology relies on molecules that change their biological activity upon irradiation. Many of these are derived from known drugs by replacing their core with an isosteric azobenzene photoswitch (azologization). The question is how many of the known bioactive ligands could be addressed in such a way. Here, we systematically assess the space of molecules amenable to azologization from databases of bioactive molecules (DrugBank, PDB, CHEMBL) and the Cambridge Structural Database. Shape similarity scoring functions (3DAPfp) and analyses of dihedral angles are employed to quantify the structural homology between a bioactive molecule and the cis or trans isomer of its corresponding azolog ("azoster") and assess which isomer is likely to be active. Our analysis suggests that a very large number of bioactive ligands (>40?000) is amenable to azologization and that many new biological targets could be addressed with photopharmacology. N-Aryl benzamides, 1,2-diarylethanes, and benzyl phenyl ethers are particularly suited for this approach, while benzylanilines and sulfonamides appear to be less well-matched. On the basis of our analysis, the majority of azosters are expected to be active in their trans form. The broad applicability of our approach is demonstrated with photoswitches that target a nuclear hormone receptor (RAR) and a lipid processing enzyme (LTA4 hydrolase).
Project description:MOTIVATION: Although the integration and analysis of the activity of small molecules across multiple chemical screens is a common approach to determine the specificity and toxicity of hits, the suitability of these approaches to reveal novel biological information is less explored. Here, we test the hypothesis that assays sharing selective hits are biologically related. RESULTS: We annotated the biological activities (i.e. biological processes or molecular activities) measured in assays and constructed chemical hit profiles with sets of compounds differing on their selectivity level for 1640 assays of ChemBank repository. We compared the similarity of chemical hit profiles of pairs of assays with their biological relationships and observed that assay pairs sharing non-promiscuous chemical hits tend to be biologically related. A detailed analysis of a network containing assay pairs with the highest hit similarity confirmed biological meaningful relationships. Furthermore, the biological roles of predicted molecular targets of the shared hits reinforced the biological associations between assay pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Project description:This paper reports the MEPS-HPLC-DAD method for the simultaneous determination of 12 azole drugs (bifonazole, butoconazole, clotrimazole, econazole, itraconazole, ketoconazole, miconazole, posaconazole, ravuconazole, terconazole, tioconazole and voriconazole) administered to treat different systemic and topical fungal infections, in biological samples. Azole drugs separation was performed in 36?min. The analytical method was validated in the ranges as follows: 0.02-5??g mL-1 for ravuconazole; 0.2-5??g mL-1 for terconazole; 0.05-5??g mL-1 for the other compounds. Human plasma and urine were used as biological samples during the analysis, while benzyl-4-hydroxybenzoate was used as an internal standard. The precision (RSD%) and trueness (Bias%) values fulfill with International Guidelines requirements. To the best of our knowledge, this is the first HPLC-DAD procedure coupled to MEPS, which provides the simultaneous analysis of 12 azole drugs, available in the market, in human plasma and urine. Moreover, the method was successfully applied for the quantitative determination of two model drugs (itraconazole and miconazole) after oral administration in real samples.
Project description:Ophiobolins (Ophs) are a group of tricarbocyclic sesterterpenoids whose structures contain a tricyclic 5-8-5 carbotricyclic skeleton. Thus far, 49 natural Ophs have been reported and assigned into A-W subgroups in order of discovery. While these sesterterpenoids were first characterized as highly effective phytotoxins, later investigations demonstrated that they display a broad spectrum of biological and pharmacological characteristics such as phytotoxic, antimicrobial, nematocidal, cytotoxic, anti-influenza and inflammation-promoting activities. These bioactive molecules are promising drug candidates due to the developments of their anti-proliferative activities against a vast number of cancer cell lines, multidrug resistance (MDR) cells and cancer stem cells (CSCs). Despite numerous studies on the biological functions of Ophs, their pharmacological mechanism still requires further research. This review summarizes the chemical structures, sources, and biological activities of the oph family and discusses its mechanisms and structure-activity relationship to lay the foundation for the future developments and applications of these promising molecules.
Project description:Surface enhanced Raman scattering (SERS) spectroscopy becomes increasingly used in biosensors for its capacity to detect and identify single molecules. In practice, a large number of SERS spectra are acquired and reliable ranking methods are thus essential for analysing all these data. Supervised classification strategies, which are the most effective methods, are usually applied but they require pre-determined models or classes. In this work, we propose to sort SERS spectra in unknown groups with an alternative strategy called Fourier polar representation. This non-fitting method based on simple Fourier sine and cosine transforms produces a fast and graphical representation for sorting SERS spectra with quantitative information. The reliability of this method was first investigated theoretically and numerically. Then, its performances were tested on two concrete biological examples: first with single amino-acid molecule (cysteine) and then with a mixture of three distinct odorous molecules. The benefits of this Fourier polar representation were highlighted and compared to the well-established statistical principal component analysis method.
Project description:Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.
Project description:Background: Tumor cells require proficient autophagy to meet high metabolic demands and resist chemotherapy, which suggests that reducing autophagic flux might be an attractive route for cancer therapy. However, this theory in clinical cancer research remains controversial due to the limited number of drugs that specifically inhibit autophagy-related (ATG) proteins. Methods: We screened FDA-approved drugs using a novel platform that integrates computational docking and simulations as well as biochemical and cellular reporter assays to identify potential drugs that inhibit autophagy-required cysteine proteases of the ATG4 family. The effects of ATG4 inhibitors on autophagy and tumor suppression were examined using cell culture and a tumor xenograft mouse model. Results: Tioconazole was found to inhibit activities of ATG4A and ATG4B with an IC50 of 1.3 µM and 1.8 µM, respectively. Further studies based on docking and molecular dynamics (MD) simulations supported that tioconazole can stably occupy the active site of ATG4 in its open form and transiently interact with the allosteric regulation site in LC3, which explained the experimentally observed obstruction of substrate binding and reduced autophagic flux in cells in the presence of tioconazole. Moreover, tioconazole diminished tumor cell viability and sensitized cancer cells to autophagy-inducing conditions, including starvation and treatment with chemotherapeutic agents. Conclusion: Tioconazole inhibited ATG4 and autophagy to enhance chemotherapeutic drug-induced cytotoxicity in cancer cell culture and tumor xenografts. These results suggest that the antifungal drug tioconazole might be repositioned as an anticancer drug or chemosensitizer.
Project description:Quantifying the similarity of spectra is an important task in various areas of spectroscopy, for example, to identify a compound by comparing sample spectra to those of reference standards. In mass spectrometry based discovery proteomics, spectral comparisons are used to infer the amino acid sequence of peptides. In targeted proteomics by selected reaction monitoring (SRM) or SWATH MS, predetermined sets of fragment ion signals integrated over chromatographic time are used to identify target peptides in complex samples. In both cases, confidence in peptide identification is directly related to the quality of spectral matches. In this study, we used sets of simulated spectra of well-controlled dissimilarity to benchmark different spectral comparison measures and to develop a robust scoring scheme that quantifies the similarity of fragment ion spectra. We applied the normalized spectral contrast angle score to quantify the similarity of spectra to objectively assess fragment ion variability of tandem mass spectrometric datasets, to evaluate portability of peptide fragment ion spectra for targeted mass spectrometry across different types of mass spectrometers and to discriminate target assays from decoys in targeted proteomics. Altogether, this study validates the use of the normalized spectral contrast angle as a sensitive spectral similarity measure for targeted proteomics, and more generally provides a methodology to assess the performance of spectral comparisons and to support the rational selection of the most appropriate similarity measure. The algorithms used in this study are made publicly available as an open source toolset with a graphical user interface.
Project description:The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.