WebMetabase: cleavage sites analysis tool for natural and unnatural substrates from diverse data source.

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.

Metabolite identification using an ion mobility enhanced data-independent acquisition strategy and automated data processing.

RATIONALE: Liquid chromatography/mass spectrometry is an essential tool for efficient and reliable quantitative and qualitative analysis and underpins much of contemporary drug metabolism and pharmacokinetics. Data-independent acquisition methods such as MSE have reduced the potential to miss metabolites, but do not formally generate quadrupole-resolved product ion spectra. The addition of ion mobility separation to these approaches, for example, in High-Definition MSE (HDMSE ) has the potential to reduce the time needed to set up an experiment and maximize the chance that all metabolites present can be resolved and characterized. We compared High-Definition Data-Dependent Acquisition (HD-DDA), MSE and HDMSE approaches using automated software processing with Mass-MetaSite and WebMetabase. METHODS: Metabolite identification was performed on incubations of glucagon-like peptide-1 (7-37) (GLP-1) and verapamil hydrochloride. The HD-DDA, MSE and HDMSE experiments were conducted on a Waters ACQUITY UPLC I-Class LC system with a VION IMS quadrupole time-of-flight (QTOF) mass spectrometer operating under UNIFI control. All acquired data were processed using MassMetaSite able to read data from UNIFI 1.9.4. WebMetabase was used to review the detected chromatographic peaks and the spectral data interpretations. RESULTS: A comparison of outcomes obtained for MSE and HDMSE data demonstrated that the same structures were proposed for metabolites of both verapamil and GLP-1. The ratio of structurally matched to mismatched product ions found by MassMetaSite was slightly greater for HDMSE than for MSE , and HD-DDA, thus improving confidence in the structures proposed through the addition of ion mobility based data acquisitions. CONCLUSIONS: HDMSE data acquisition is an effective approach for the elucidation of metabolite structures for both small molecules and peptides, with excellent accuracy and quality, requiring minimal tailoring for the compound under investigation.

Software-aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery.

RATIONALE: Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis. METHODS: Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review. RESULTS: CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes. CONCLUSIONS: This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

Post-acquisition analysis of untargeted accurate mass quadrupole time-of-flight MS(E) data for multiple collision-induced neutral losses and fragment ions of glutathione conjugates.

RATIONALE: Analytical methods to assess glutathione (GSH) conjugate formation based on mass spectrometry usually take advantage of the specific fragmentation behavior of the glutathione moiety. However, most methods used for GSH adduct screening monitor only one specific neutral loss or one fragment ion, even though the peptide moiety of GSH adducts shows a number of other specific neutral fragments and fragment ions which can be used for identification. METHODS: Nine reference drugs well known to form GSH adducts were incubated with human liver microsomes. Mass spectrometric analysis was performed with a quadrupole time-of-flight mass spectrometer in untargeted accurate mass MS(E) mode. The data analysis and evaluation was achieved in an automated approach with software to extract and identify GSH conjugates based on the presence of multiple collision-induced neutral losses and fragment ions specific for glutathione conjugates in the high-energy MS spectra. RESULTS: In total 42 GSH adducts were identified. Eight (18%) adducts did not show the neutral loss of 129 but were identified based on the appearance of other GSH-specific neutral losses or fragment ions. In high-energy MS(E) spectra the GSH-specific fragment ions of m/z 308 and 179 as well as the neutral loss of 275 Da were complementary to the commonly used neutral loss of 129 Da. Further, one abundant (yet unpublished) GSH conjugate of troglitazone formed in human liver microsomes was found. CONCLUSIONS: A software-aided approach was developed to reliably retrieve GSH adduct formation data out of untargeted complex full scan QTOFMS(E) data in a fast and efficient way. The present approach to detect and analyze multiple collision-induced neutral losses and fragment ions of glutathione conjugates in untargeted MS(E) data might be applicable to higher throughput to assess reactive metabolite formation in drug discovery.

High-throughput, computer assisted, specific MetID. A revolution for drug discovery.

One of the key factors in drug discovery is related to the metabolic properties of the lead compound, which may influence the bioavailability of the drug, its therapeutic window, and unwanted side-effects of its metabolites. Therefore, it is of critical importance to enable the fast translation of the experimentally determined metabolic information into design knowledge. The elucidation of the metabolite structure is the most structurally rich and informative end-point in the available range of metabolic assays. A methodology is presented to partially automate the analysis of this experimental information, making the process more efficient. The computer assisted method helps in the chromatographic peak selection and the metabolite structure assignment, enabling automatic data comparison for qualitative applications (kinetic analysis, cross species comparison).

Enhanced metabolite identification with MS(E) and a semi-automated software for structural elucidation.

The identification of metabolites is almost exclusively done with liquid chromatography/tandem mass spectrometry (LC/MSMS) and despite the enormous progress in the development of these techniques and software for handling of data this is a time-consuming task. In this study the use of quadrupole time-of-flight (QTOF)-generated MS(E) and MS/MS data were compared with respect to rationalization of metabolites. In addition Mass-MetaSite, a semi-automated software for metabolite identification, was evaluated. The program combines the information from MS raw data, in the form of collision-induced dissociation spectra, with a prediction of the site of metabolism in order to assign the structure of a metabolite. The aim of the software is to mimic the rationalization of fragment ions performed by a biotransformation scientist in the process of structural elucidation. For this evaluation, metabolite identification in human liver microsomes was accomplished for 19 commercially available compounds and 15 in-house compounds. The results were very encouraging and for 96% of the metabolites the same structures were assigned using MS(E) compared with MSMS acquired data. The possibility of using MS(E) could considerably reduce the analysis time. Moreover, Mass-MetaSite performed well and the correct assigned structure, compared to manual inspection of the data, was picked in the first rank in ∼80% of the cases. In conclusion MS(E) could be successfully used for metabolite identification in order to reduce time of analysis and Mass-MetaSite could alleviate the work of a biotransformation scientist and decrease the workload by assigning the structure for a majority of the metabolites.

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery.

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

Correction: Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry.

[This corrects the article DOI: 10.1371/journal.pone.0186461.].

Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry.

Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example. We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids). This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids) of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The main advantages of the developed approach are the abilities to elucidate metabolite structure of cyclic peptides and those containing unnatural amino acids, store processed information in a searchable format within a database leading to frequency analysis of the labile sites for the analyzed peptides. The presented algorithm may be useful to optimize peptide drug properties with regards to cleavage sites, stability, metabolism and degradation products in drug discovery.

Anchor-GRIND: filling the gap between standard 3D QSAR and the GRid-INdependent descriptors.

The aim of this work is to present the anchor-GRIND methodology. Anchor-GRIND efficiently combines a priori chemical and biological knowledge about the studied compounds with alignment-independent molecular descriptors derived from molecular interaction fields. Such descriptors are particularly useful for series of ligands sharing a common scaffold but with very diverse substituents. The method uses a specific position of the molecular structure (the "anchor point") to compare the spatial distribution of the molecular interaction fields of the substituents. The descriptors produced are more detailed and specific than the original GRIND while still avoiding the bias introduced by the alignment. Three data sets have been studied to demonstrate the usefulness of the anchor-GRIND methodology for 3D-QSAR modeling. The two first data sets respectively include congeneric series of the hepatitis C virus NS3 protease and of the acetylcholinesterase inhibitors. The third data set discriminates between factor Xa inhibitors of high and low affinity. In all the series presented, the models obtained with the anchor-GRIND are statistically sound and easy to interpret.

Incorporating molecular shape into the alignment-free Grid-Independent Descriptors.

The recently introduced GRid-INdependent Descriptors (GRIND) were designed to provide a suitable description of a series of ligands for 3D-QSAR studies not requiring the spatial superimposition of their structures. Despite the proven usefulness of the method, it was recognized that the original GRIND failed to describe appropriately the shape of the ligand molecules, which in some cases plays a major role in ligand-receptor binding. For this reason, the original descriptors have been enhanced with the addition of a molecular shape description based on the local curvature of the molecular surface. The integration of this description into the GRIND allows the generation of 3D-QSAR models able to identify both favorable and unfavorable shape complementarity in a simple and alignment-independent way. The usefulness of the new GRIND-shape description in 3D-QSAR is illustrated using two structure-activity studies: one performed on a set of xanthine-like antagonists of the A(1) adenosine receptor; another performed on a series of Plasmodium falciparum plasmepsin II inhibitors.

Conformationally constrained butyrophenones as new pharmacological tools to study 5-HT 2A and 5-HT 2C receptor behaviours.

This study presents new pharmacological and molecular modelling studies on a recently described series of conformationally constrained butyrophenones. Alignment-free three-dimensional quantitative structure-activity relationship models developed on the basis of GRid Independent descriptors and partial least squares regression analysis, allow feasible predictions of activity of new compounds and reveal structural requirements for optimal affinity, particularly in the case of the 5-HT(2A) receptor. The requirements for the 5-HT(2A) affinity consist in a precise distance between hydrogen bond donor (protonated amino group) and hydrogen bond acceptor groups, as well as an optimal distance between the protonated amino group and the farthest extreme of the compounds. Another significant result has been the characterisation of two structurally similar compounds as interesting pharmacological tools (1-[(4-Oxo-4,5,6,7-tetrahydrobenzo[b]furan-5-yl)ethyl]-4-(6-fluorobenzisoxazol-3-yl)piperidine and 1-[(4-Oxo-4,5,6,7-tetrahydrobenzo[b]furan-6-yl)methyl]-4-(6-fluorobenzisoxazol-3-yl)piperidine). In spite of their structural similarity, the first compound shows clearly higher affinity for the 5-HT(2C) receptor (about 100 fold) and higher Meltzer ratio (1.17 vs. 0.99) than the second. Moreover, the first compound inhibits arachidonic acid release in a biphasic concentration-dependent way in functional experiments at the 5-HT(2A) receptor and it acts as inverse agonist at the 5-HT(2C) receptor, behaviours that are not shown by the second compound.

SHOP: a method for structure-based fragment and scaffold hopping.

A new method for fragment and scaffold replacement is presented that generates new families of compounds with biological activity, using GRID molecular interaction fields (MIFs) and the crystal structure of the targets. In contrast to virtual screening strategies, this methodology aims only to replace a fragment of the original molecule, maintaining the other structural elements that are known or suspected to have a critical role in ligand binding. First, we report a validation of the method, recovering up to 95% of the original fragments searched among the top-five proposed solutions, using 164 fragment queries from 11 diverse targets. Second, six key customizable parameters are investigated, concluding that filtering the receptor MIF using the co-crystallized ligand atom type has the greatest impact on the ranking of the proposed solutions. Finally, 11 examples using more realistic scenarios have been performed; diverse chemotypes are returned, including some that are similar to compounds that are known to bind to similar targets.

Assessment of conformational ensemble sizes necessary for specific resolutions of coverage of conformational space.

The size of conformational ensembles required for regular coverage of the conformational space of druglike molecules was examined. Using the conformer generation program Omega, the number of regularly distributed conformers (NRC) of flexible compounds was determined as a function of the root-mean-square deviation (RMSD) resolution of coverage. A regression equation was developed predicting the NRC of a molecule as a function of RMSD. The model yielded R(2) of 0.91 for both training and test sets, which consisted of 3414 and 3352 compounds, respectively. Utilizing 14 504 ligands from the Protein Data Bank with experimentally determined 3-D conformations, the regression equation was applied to the estimation of the NRC and the success rate of reproduction of experimental conformations from a theoretical conformation ensemble as a function of RMSD and flexibility was explored.

Fast 3D shape screening of large chemical databases through alignment-recycling.

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.

Use of alignment-free molecular descriptors in diversity analysis and optimal sampling of molecular libraries.

The selection of a sample of diverse compounds is a common strategy for exploring large molecular libraries. However, the success of such approach depends on the selection of relevant molecular descriptors and the use of appropriate sampling methods. In the context of pharmaceutical research, the molecular descriptors should be based on physicochemical properties related with the pharmacological behaviour of the compounds. In this sense, the alignment-free GRIND and VolSurf molecular descriptors are promising candidates since they have been successfully used in the modelling of both pharmacodynamic and pharmacokinetic properties of drugs. This work describes the use of such descriptors in the diversity sampling of a library of primary amines and compares the results with those obtained in a previous study that used quantum-mechanical descriptors. As in the previous work, principal component (PC) analysis was applied to reduce the dimensionality and remove redundant information of the original descriptors, and the compounds were sampled on the basis of k-means clustering on the space of the selected PCs. The results of the present study show that VolSurf and GRIND provide similar quality sampling regarding global features of the molecules such as hydrophilicity, however the topology of the compounds is considered differently. The similarity between particular compounds strongly depends on the original descriptors used. However all the sample selections done in the PC space after k-means clustering provide the same apparent diversity in comparison to the whole dataset. The results indicate that there is no best set of descriptors on a diversity basis. The selection of descriptors must be based on the drug features to be investigated.