Core trees and consensus fragment sequences for molecular representation and similarity analysis

A new type of molecular representation is introduced that is based on activity class characteristic substructures extracted from random fragment populations. Mapping of characteristic substructures is used to determine atom match rates in active molecules. Comparison of match rates of bonded atoms defines a hierarchical molecular fragmentation scheme. Active compounds are encoded as fragmentation pathways isolated from core trees. These paths are amenable to biological sequence alignment methods in combination with substructure-based scoring functions. From multiple core path alignments, consensus fragment sequences are derived that represent compound activity classes. Consensus fragment sequences weighted by increasing structural specificity can also be used to map molecules and search databases for active compounds.     

Mining of randomly generated molecular fragment populations uncovers activity-specific fragment hierarchies

We introduce a methodology to analyze random molecular fragment populations and determine conditional probability relationships between fragments. Random fragment profiles are generated for an arbitrary set of molecules, and each observed fragment is assigned a frequency vector. An algorithm is designed to compare frequency vectors and derive dependencies of fragment occurrence. Using calculated dependency values, random fragment populations can be organized in graphs that capture their relationships and make it possible to map fragment pathways of biologically active molecules. For sets of molecules having similar activity, unique fragment signatures are identified. The analysis reveals that random fragment profiles contain compound class-specific information and provides evidence for the existence of activity-specific fragment hierarchies.     

Exploring fragment spaces under multiple physicochemical constraints

We present a new algorithm for the enumeration of chemical fragment spaces under constraints. Fragment spaces consist of a set of molecular fragments and a set of rules that specifies how fragments can be combined. Although fragment spaces typically cover an infinite number of molecules, they can be enumerated in case that a physicochemical profile of the requested compounds is given. By using min-max ranges for a number of corresponding properties, our algorithm is able to enumerate all molecules which obey these properties. To speed up the calculation, the given ranges are used directly during the build-up process to guide the selection of fragments. Furthermore, a topology based fragment filter is used to skip most of the redundant fragment combinations. We applied the algorithm to 40 different target classes. For each of these, we generated tailored fragment spaces from sets of known inhibitors and additionally derived ranges for several physicochemical properties. We characterized the target-specific fragment spaces and were able to enumerate the complete chemical subspaces for most of the targets.     

Methods and algorithms for predicting the properties of chemical compounds by common fragments of molecular graphs

Methods and algorithms for predicting the properties of chemical compounds by common fragments of their molecular graphs are described. The prediction algorithms are based on determination of a measure of structural proximity (distance) between molecular graphs, which depends on the size of their common fragment. The prediction procedure involves the following steps: partitioning the property classes of the training sample compounds into subclasses of structurally similar compounds; seeking structurally typical compounds and their fragments in each subclass; classifying control compounds according to their distances from the training sample compounds or fragments of classes; forming a set of essential fragments of samples potentially responsible for the properties exhibited by the compounds. The algorithms were successfully tested in the BACC system for analyzing and classifying biologically active compounds designed at the Institute of Mathematics, Siberian Branch, Russian Academy of Sciences. S. L. Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences. Translated fromZhurnal Strukturnoi Khimii, Vol. 39, No. 1, pp. 113–125, January–February, 1998.     

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 Å for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.     

Chemical fragment spaces for de novo design

Chemical fragment spaces are combinations of molecular fragments and connection rules. They offer the possibility to encode an enormously large number of chemical structures in a very compact format. Fragment spaces are useful both in similarity-based (2D) and structure-based (3D) de novo design applications. We present disconnection and filtering rules leading to several thousand unique, medium size fragments when applied to databases of druglike molecules. We evaluate alternative strategies to select subsets of these fragments, with the aim of maximizing the coverage of known druglike chemical space with a strongly reduced set of fragments. For these evaluations, we use the Ftrees fragment space method. We assess a diversity-oriented selection method based on maximum common substructures and a method biased toward high frequency of occurrence of fragments and find that they are complementary to each other.     

De novo design by pharmacophore-based searches in fragment spaces

De novo ligand design supports the search for novel molecular scaffolds in medicinal chemistry projects. This search can either be based on structural information of the targeted active site (structure-based approach) or on similarity to known binders (ligand-based approach). In the absence of structural information on the target, pharmacophores provide a way to find topologically novel scaffolds. Fragment spaces have proven to be a valuable source for molecular structures in de novo design that are both diverse and synthetically accessible. They also offer a simple way to formulate custom chemical spaces. We have implemented a new method which stochastically constructs new molecules from fragment spaces under consideration of a three dimensional pharmacophore. The program has been tested on several published pharmacophores and is shown to be able to reproduce scaffold hops from the literature, which resulted in new chemical entities.     

Structure-based fragment hopping for lead optimization using predocked fragment database

In this work, we describe a structure-based de novo optimization process, called "LeadOp" (short for lead optimization), that decomposes a compound into fragments of different molecular components either by chemical or user-defined rules. Each fragment is evaluated through a predocked fragment database that ranks fragments according to specific fragment-receptor binding interactions, replacing fragments that contribution the least to binding and finally reassembling the fragments to form a new ligand. The fundamental idea is to replace "bad" fragments of a ligand with "good" fragments while leaving the core of the ligand intact, thus improving the compound's activity. The molecular fragments were selected from a collection of 27,417 conformers that are the fragments of compounds in the DrugBank database. The collection of molecular fragments are docked to the target's binding site and evaluated using group efficiency (calculated binding affinity divided by the number of heavy atoms), and the "strongest" binder is selected. The LeadOp method was tested with two biomolecular systems: mutant B-Raf kinase and human 5-lipoxygenase. The LeadOp methodology was able to optimize the query molecules and systematically developed improved analogs for each of our example systems.     

Virtual fragment screening: exploration of MM-PBSA re-scoring

An NMR fragment screening dataset with known binders and decoys was used to evaluate the ability of docking and re-scoring methods to identify fragment binders. Re-scoring docked poses using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) implicit solvent model identifies additional active fragments relative to either docking or random fragment screening alone. Early enrichment, which is clearly most important in practice for selecting relatively small sets of compounds for experimental testing, is improved by MM-PBSA re-scoring. In addition, the value in MM-PBSA re-scoring of docked poses for virtual screening may be in lessening the effect of the variation in the protein complex structure used.     

Fuzzy definition of molecular fragments in chemical structures

This paper presents a methodology for seeking the relationships between chemical substructures (molecular fragments) and spectral parameters using a computer collection data of molecular spectra. To establish the spectrum-structure correlations, the program has to search the chemical structure base in order to find compounds containing a given molecular fragment in the molecule. There exists no sole definition of a substructure, as it always depends on the type of problem dealt with. In the problem of structural identification, fuzzy definitions of substructures are applied, and their forms are imposed by the spectral methods used.     

Use of a double resonance electron capture dissociation experiment to probe fragment intermediate lifetimes

The relative abundances of fragment ions in electron capture dissociation (ECD) are often greatly affected by the secondary and tertiary structures of the precursor ion, and have been used to derive the gas-phase conformations of the protein ions. In this study, it is found that resonance ejection of the charge reduced molecular ion during ECD resulted in significant changes in many fragment ion populations. The ratio of the ion peak intensities in the double resonance (DR)-ECD to that in the normal ECD experiment can be used to calculate the lifetime of the radical intermediates from which these fragments are derived. These lifetimes are often in the ms range, a time sufficiently long to facilitate multiple free radical rearrangements. These ratios correlate with the intramolecular noncovalent interactions in the precursor ion, and can be used to deduce information about the gas-phase conformation of peptide ions. DR-ECD experiments can also provide valuable information on ECD mechanisms, such as the importance of secondary electron capture and the origin of c./z ions.     

Chemoinformatics Analyses of Tau Ligands Reveal Key Molecular Requirements for the Identification of Potential Drug Candidates against Tauopathies

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material.     

Liquid water simulations with the density fragment interaction approach

We reformulate the density fragment interaction (DFI) approach [Fujimoto and Yang, J. Chem. Phys., 2008, 129, 054102.] to achieve linear-scaling quantum mechanical calculations for large molecular systems. Two key approximations are developed to improve the efficiency of the DFI approach and thus enable the calculations for large molecules: the electrostatic interactions between fragments are computed efficiently by means of polarizable electrostatic-potential-fitted atomic charges; and frozen fragment pseudopotentials, similar to the effective fragment potentials that can be fitted from interactions between small molecules, are employed to take into account the Pauli repulsion effect among fragments. Our reformulated and parallelized DFI method demonstrates excellent parallel performance based on the benchmarks for the system of 256 water molecules. Molecular dynamics simulations for the structural properties of liquid water also show a qualitatively good agreement with experimental measurements including the heat capacity, binding energy per water molecule, and the radial distribution functions of atomic pairs of O-O, O-H, and H-H. With this approach, large-scale quantum mechanical simulations for water and other liquids become feasible.     

Finding multiactivity substructures by mining databases of drug-like compounds

We have developed a method, given a database of molecules and associated activities, to identify molecular substructures that are associated with many different biological activities. These may be therapeutic areas (e.g. antihypertensive) and/or mechanism-based activities (e.g. renin inhibitor). This information helps us avoid chemical classes that are likely to have unanticipated side effects and also can suggest combinatorial libraries that might have activity on a variety of receptor targets. The method was applied to the USPDI and MDDR databases. There are clearly substructures in each database that occur in many compounds and span a variety of therapeutic categories. Some of these are expected, but some are not.     

Unravelling ionization and fragmentation pathways of carotenoids using orbitrap technology: a first step towards identification of unknowns

Vegetables are a major source of carotenoids and carotenoids are identified as potentially important natural antioxidants that may aid in the prevention of several human chronic degenerative diseases. Characterization of carotenoids in organic biological matrices is a crucial step in any research valorization trajectory. This study reports for the first time the use of high mass resolution and exact mass orbitrap technology for the elucidation of carotenoid fragmentation pathways. This contributes to the generation of new tools for identifying unknown carotenoids based on fragmentation patterns. Two different chromatographic methods making use of different mobile phases resulted in the generation of different ion species because of the large influence of the mobile phase solvent composition on ionization. It was shown that depending on the molecular ion species that are generated (protonated ions or radical molecular ions), different fragments are formed when applying higher energy collisional dissociation. Fragmentation and the abundance of fragments provide valuable structural information on the type of functional groups, the polyene backbone and the location of double bonds in ring structures of carotenoids. Furthermore, coherence between specific substructures in the molecules and characteristic fragmentation patterns was observed allowing the assignment of fragmentation patterns for carotenoid substructures that can theoretically be extrapolated to carotenoids with similar (sub)structures. Differentiation between isomeric carotenoids by compound specific fragments could however not be made for all the isomeric groups under study. As a wide variety of isomeric forms of carotenoids exist in nature, the combination of good chromatographic separation with high resolution mass spectrometry and other complementary qualitative structure elucidation techniques such as a photo diode array detector and/or nuclear magnetic resonance spectroscopy are indispensable for unambiguous identification of unknown carotenoids. Copyright © 2013 John Wiley & Sons, Ltd.     

Computational simulations of the conformational behaviour of the adhesive proteins RGDS fragment

Summary Many adhesive proteins present in extracellular matrices and in blood contain the tetrapeptide sequence -Arg-Gly-Asp-Ser- (or RGDS) at their cell recognition site. Since this sequence, or similar ones, was found in many proteins involved in major biological mechanisms, conformational investigations were performed on the RGDS fragment. A preliminary review of available crystal structures indicates that the RxDy sequences exhibit 3 well-defined structural patterns: one corresponding to a strong interaction between the Arg and Asp ionic side chains which are only about 4 Å apart, one with the ions separated by about 8 Å, and another in which the side chains are further apart (about 11 Å).The conformational behaviour of the isolated RGDS fragment was next tackled using sequential building, Monte Carlo and molecular dynamics computational techniques. Analysis of the RGDS sequence conformational possibilities, as simulated in vacuum and in water solution, indicates that they can be classified into several conformational classes, which correspond roughly to the behaviour of the RGDS fragment as observed in protein matrices. This suggests the possibility of understanding the biological role of the RGDS or parent sequences in recognition processes.