26 research outputs found
MONA 2: A Light Cheminformatics Platform for Interactive Compound Library Processing
Because
of the availability of large compound collections on the Web, elementary
cheminformatics tasks such as chemical library browsing, analyzing,
filtering, or unifying have become widespread in the life science
community. Furthermore, the high performance of desktop hardware allows
an interactive, problem-driven approach to these tasks, avoiding rigid
processing scripts and workflows. Here, we present MONA 2, which is
the second major release of our cheminformatics desktop application
addressing this need. Using MONA requires neither complex database
setups nor expert knowledge of cheminformatics. A new molecular set
concept purely based on structural entities rather than individual
compounds has allowed the development of an intuitive user interface.
Based on a chemically precise, high-performance software library,
typical tasks on chemical libraries with up to one million compounds
can be performed mostly interactively. This paper describes the functionality
of MONA, its fundamental concepts, and a collection of application
scenarios ranging from file conversion, compound library curation,
and management to the post-processing of large-scale experiments
ASCONA: Rapid Detection and Alignment of Protein Binding Site Conformations
The
usage of conformational ensembles constitutes a widespread
technique for the consideration of protein flexibility in computational
biology. When experimental structures are applied for this purpose,
alignment techniques are usually required in dealing with structural
deviations and annotation inconsistencies. Moreover, many application
scenarios focus on protein ligand binding sites. Here, we introduce
our new alignment algorithm ASCONA that has been specially geared
to the problem of aligning multiple conformations of sequentially
similar binding sites. Intense efforts have been directed to an accurate
detection of highly flexible backbone deviations, multiple binding
site matches within a single structure, and a reliable, but at the
same time highly efficient, search algorithm. In contrast, most available
alignment methods rather target other issues, e.g., the global alignment
of distantly related proteins that share structurally conserved regions.
For conformational ensembles, this might not only result in an overhead
of computation time but could also affect the achieved accuracy, especially
for more complicated cases as highly flexible proteins. ASCONA was
evaluated on a test set containing 1107 structures of 65 diverse proteins.
In all cases, ASCONA was able to correctly align the binding site
at an average alignment computation time of 4 ms per target. Furthermore,
no false positive matches were observed when searching the same query
sites in the structures of other proteins. ASCONA proved to cope with
highly deviating backbone structures and to tolerate structural gaps
and moderate mutation rates. ASCONA is available free of charge for
academic use at http://www.zbh.uni-hamburg.de/ascona
FSees: Customized Enumeration of Chemical Subspaces with Limited Main Memory Consumption
In the search for
new marketable drugs, new ideas are required
constantly. Particularly with regard to challenging targets and previously
patented chemical space, designing novel molecules is crucial. This
demands efficient and innovative computational tools to generate libraries
of promising molecules. Here we present an efficient method to generate
such libraries by systematically enumerating all molecules in a specific
chemical space. This space is defined by a fragment space and a set
of user-defined physicochemical properties (e.g., molecular weight,
tPSA, number of H-bond donors and acceptors, or predicted logP). In
order to enumerate a very large number of molecules, our algorithm
uses file-based data structures instead of memory-based ones, thus
overcoming the limitations of computer main memory. The resulting
chemical library can be used as a starting point for computational
lead-finding technologies, like similarity searching, pharmacophore
mapping, docking, or virtual screening. We applied the algorithm in
different scenarios, thus creating numerous target-specific libraries.
Furthermore, we generated a fragment space from all approved drugs
in DrugBank and enumerated it with lead-like constraints, thus generating
0.5 billion molecules in the molecular weight range 250–350
SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles
Structural
flexibility of proteins has an important influence on
molecular recognition and enzymatic function. In modeling, structure
ensembles are therefore often applied as a valuable source of alternative
protein conformations. However, their usage is often complicated by
structural artifacts and inconsistent data annotation. Here, we present
SIENA, a new computational approach for the automated assembly and
preprocessing of protein binding site ensembles. Starting with an
arbitrarily defined binding site in a single protein structure, SIENA
searches for alternative conformations of the same or sequentially
closely related binding sites. The method is based on an indexed database
for identifying perfect <i>k</i>-mer matches and a recently
published algorithm for the alignment of protein binding site conformations.
Furthermore, SIENA provides a new algorithm for the interaction-based
selection of binding site conformations which aims at covering all
known ligand-binding geometries. Various experiments highlight that
SIENA is able to generate comprehensive and well selected binding
site ensembles improving the compatibility to both known and unconsidered
ligand molecules. Starting with the whole PDB as data source, the
computation time of the whole ensemble generation takes only a few
seconds. SIENA is available via a Web service at www.zbh.uni-hamburg.de/siena
Benchmark Data Sets for Structure-Based Computational Target Prediction
Structure-based
computational target prediction methods identify
potential targets for a bioactive compound. Methods based on protein–ligand
docking so far face many challenges, where the greatest probably is
the ranking of true targets in a large data set of protein structures.
Currently, no standard data sets for evaluation exist, rendering comparison
and demonstration of improvements of methods cumbersome. Therefore,
we propose two data sets and evaluation strategies for a meaningful
evaluation of new target prediction methods, i.e., a small data set
consisting of three target classes for detailed proof-of-concept and
selectivity studies and a large data set consisting of 7992 protein
structures and 72 drug-like ligands allowing statistical evaluation
with performance metrics on a drug-like chemical space. Both data
sets are built from openly available resources, and any information
needed to perform the described experiments is reported. We describe
the composition of the data sets, the setup of screening experiments,
and the evaluation strategy. Performance metrics capable to measure
the early recognition of enrichments like AUC, BEDROC, and NSLR are
proposed. We apply a sequence-based target prediction method to the
large data set to analyze its content of nontrivial evaluation cases.
The proposed data sets are used for method evaluation of our new inverse
screening method <i>i</i>RAISE. The small data set reveals
the method’s capability and limitations to selectively distinguish
between rather similar protein structures. The large data set simulates
real target identification scenarios. <i>i</i>RAISE achieves
in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs
below 2.0 Ă… for 74% and was able to predict the first true target
in 59 out of 72 cases in the top 2% of the protein data set of about
8000 structures
Enhanced Calculation of Property Distributions in Chemical Fragment Spaces
Chemical fragment spaces exceed traditional virtual compound
libraries
by orders of magnitude, making them ideal search spaces for drug design
projects. However, due to their immense size, they are not compatible
with traditional analysis and search algorithms that rely on the enumeration
of molecules. In this paper, we present SpaceProp2, an evolution of
the SpaceProp algorithm, which enables the calculation of exact property
distributions for chemical fragment spaces without enumerating them.
We extend the original algorithm by the capabilities to compute distributions
for the TPSA, the number of rotatable bonds, and the occurrence of
user-defined molecular structures in the form of SMARTS patterns.
Furthermore, SpaceProp2 produces example molecules for every property
bin, enabling a detailed interpretation of the distributions. We demonstrate
SpaceProp2 on six established make-on-demand chemical fragment spaces
as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim.
The possibility to search multiple SMARTS patterns simultaneously
as well as the produced example molecules offers previously impossible
insights into the composition of these vast combinatorial molecule
collections, making it an ideal tool for the analysis and design of
chemical fragment spaces
The Valence State Combination Model: A Generic Framework for Handling Tautomers and Protonation States
The consistent handling of molecules
is probably the most basic
and important requirement in the field of cheminformatics. Reliable
results can only be obtained if the underlying calculations are independent
of the specific way molecules are represented in the input data. However,
ensuring consistency is a complex task with many pitfalls, an important
one being the fact that the same molecule can be represented by different
valence bond structures. In order to achieve reliability, a cheminformatics
system needs to solve two fundamental problems. First, different choices
of valence bond structures must be identified as the same molecule.
Second, for each molecule all valence bond structures relevant to
the context must be taken into consideration. The latter is especially
important with regard to tautomers and protonation states, as these
have considerable influence on physicochemical properties of molecules.
We present a comprehensive method for the rapid and consistent generation
of reasonable tautomers and protonation states for molecules relevant
in the context of drug design. This method is based on a generic scheme,
the Valence State Combination Model, which has been designed for the
enumeration and scoring of valence bond structures in large data sets.
In order to ensure our method’s consistency, we have developed
procedures which can serve as a general validation scheme for similar
approaches. The analysis of both the average number of generated structures
and the associated runtimes shows that our method is perfectly suited
for typical cheminformatics applications. By comparison with frequently
used and curated public data sets, we can demonstrate that the tautomers
and protonation state produced by our method are chemically reasonable
RingDecomposerLib: An Open-Source Implementation of Unique Ring Families and Other Cycle Bases
Many
cheminformatics applications like aromaticity detection, SMARTS matching,
or the calculation of atomic coordinates require a chemically meaningful
perception of the molecular ring topology. The unique ring families
(URFs) were recently introduced as a unique, polynomial, and chemically
meaningful description of the ring topology. Here we present the first
open-source implementation of the URF concept for ring perception.
The C library RingDecomposerLib is easy to use, portable, well-documented,
and thoroughly tested. Aside from the URFs, other related ring topology
descriptions like the relevant cycles (RCs), relevant cycle prototypes
(RCPs), and a smallest set of smallest rings (SSSR) can be calculated.
We demonstrate the runtime efficiency of the RingDecomposerLib with
computing time benchmarks for the complete PubChem Compound Database
and thereby show the applicability in large-scale and interactive
applications
Unique Ring Families: A Chemically Meaningful Description of Molecular Ring Topologies
The perception of a set of rings forms the basis for
a number of chemoinformatics applications, e.g. the systematic naming
of compounds, the calculation of molecular descriptors, the matching
of SMARTS expressions, and the generation of atomic coordinates. We
introduce the concept of unique ring families (URFs) as an extension
of the concept of relevant cycles (RCs)., URFs are consistent
for different atom orders and represent an intuitive description of
the rings of a molecular graph. Furthermore, in contrast to RCs, URFs
are polynomial in number. We provide an algorithm to efficiently calculate
URFs in polynomial time and demonstrate their suitability for real-time
applications by providing computing time benchmarks for the PubChem
Database. URFs combine three important
properties of chemical ring descriptions, for the first time, namely
being unique, chemically meaningful, and efficient to compute. Therefore,
URFs are a valuable alternative to the commonly used concept of the
smallest set of smallest rings (SSSR) and would be suited to become
the standard measure for ring topologies of small molecules
Searching for Recursively Defined Generic Chemical Patterns in Nonenumerated Fragment Spaces
Retrieving molecules with specific
structural features is a fundamental
requirement of today’s molecular database technologies. Estimates
claim the chemical space relevant for drug discovery to be around
10<sup>60</sup> molecules. This figure is many orders of magnitude
larger than the amount of molecules conventional databases retain
today and will store in the future. An elegant description of such
a large chemical space is provided by the concept of fragment spaces.
A fragment space comprises fragments that are molecules with open
valences and describes rules how to connect these fragments to products.
Due to the combinatorial nature of fragment spaces, a complete enumeration
of its products is intractable. We present an algorithm to search
fragment spaces for generic chemical patterns as present in the SMARTS
chemical pattern language. Our method allows specification of the
chemical surrounding of an atom in a query and, therefore, enables
a chemically intuitive search. During the search, the costly enumeration
of products is avoided. The result is a fragment space that exactly
describes all possible molecules that contain the user-defined pattern.
We evaluated the algorithm in three different drug development use-cases
and performed a large scale statistical analysis with 738 SMARTS patterns
on three public available fragment spaces. Our results show the ability
of the algorithm to explore the chemical space around known active
molecules, to analyze fragment spaces for the presence of likely toxic
molecules, and to identify complex macromolecular structures under
additional structural constraints. By searching the fragment space
in its nonenumerated form, spaces covering up to 10<sup>19</sup> molecules
can be examined in times ranging between 47 s and 19 min depending
on the complexity of the query pattern