SUMMARY: The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development. AVAILABILITY AND IMPLEMENTATION: The NERDD web server is available at https://nerdd.zbh.uni-hamburg.de. Most tools are also available as software packages for local installation.

Virtual screening (VS) methods, which became a common complement to the in vitro approaches in drug discovery projects, are naturally restricted by the compound libraries at hand while ignoring the wealth of compounds hidden in general chemical space. To close this gap, various methods for the exploration of chemical space have been proposed. One such approach is Molpher, a software framework that uses the technique of molecular morphing. Molecular morphing generates a series of compounds called morphs that represent a gradual structural transition between two given compounds. Because the exploration is driven solely by structural information, it disregards structurally diverse but possibly active compounds. Thus, we introduce the improvement of the algorithm where the exploration is driven by ligand biological activity rather than by its structure. On its input, the method takes a set of known active and inactive compounds. In the preparatory phase, feature selection is applied to choose descriptors that likely discriminate between active and inactive compounds. These features are then used to define a reference point towards which the exploration is directed. In the exploration phase, morphs are generated from all active compounds and Pareto-ranking scheme is applied to accept morphs for the next generation of molecular morphing. This iterative process results in structurally diverse molecules that share characteristic features of actives that separate them from inactives. The method was tested on four datasets from the PubChem BioAssay database. The results indicate that an activity-based exploration technique is able to generate structurally diverse compounds close to the selected point in the activity space. Thus, this technique is suitable for the generation of virtual libraries that can be further optimized and subsequently screened.

BACKGROUND: Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm. RESULTS: In this paper, we present the updated version of the SETTER web server that implements a user friendly interface to the MultiSETTER algorithm. The server accepts RNA structures either as the list of PDB IDs or as user-defined PDB files. After the superposition is computed, structures are visualized in 3D and several reports and statistics are generated. CONCLUSION: To the best of our knowledge, the MultiSETTER web server is the first publicly available tool for a multiple RNA structure alignment. The MultiSETTER server offers the visual inspection of an alignment in 3D space which may reveal structural and functional relationships not captured by other multiple alignment methods based either on a sequence or on secondary structure motifs.

Recent advances in RNA research and the steady growth of available RNA structures call for bioinformatics methods for handling and analyzing RNA structural data. Recently, we introduced SETTER-a fast and accurate method for RNA pairwise structure alignment. In this paper, we describe MultiSETTER, SETTER extension for multiple RNA structure alignment. MultiSETTER combines SETTER's decomposition of RNA structures into non-overlapping structural subunits with the multiple sequence alignment algorithm ClustalW adapted for the structure alignment. The accuracy of MultiSETTER was assessed by the automatic classification of RNA structures and its comparison to SCOR annotations. In addition, MultiSETTER classification was also compared to multiple sequence alignment-based and secondary structure alignment-based classifications provided by LocARNA and RNADistance tools, respectively. MultiSETTER precompiled Windows libraries, as well as the C++ source code, are freely available from http://siret.cz/multisetter.

BACKGROUND: A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism. RESULTS: A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a non-hierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures. CONCLUSIONS: In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations.

MOTIVATION: Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their 3D structures. Although a structural alignment of short RNAs is achievable in a reasonable amount of time, large structures represent much bigger challenge. However, the growth of the number of large RNAs deposited in the PDB database calls for the development of fast and accurate methods for analyzing their structures, as well as for rapid similarity searches in databases. RESULTS: In this article a novel algorithm for an RNA structural comparison SETTER (SEcondary sTructure-based TERtiary Structure Similarity Algorithm) is introduced. SETTER uses a pairwise comparison method based on 3D similarity of the so-called generalized secondary structure units. For each pair of structures, SETTER produces a distance score and an indication of its statistical significance. SETTER can be used both for the structural alignments of structures that are already known to be homologous, as well as for 3D structure similarity searches and functional annotation. The algorithm presented is both accurate and fast and does not impose limits on the size of aligned RNA structures. AVAILABILITY: The SETTER program, as well as all datasets, is freely available from http://siret.cz/hoksza/projects/setter/.

Many areas of chemical biology, such as drug discovery, rely heavily on chemical libraries offering compounds usable in the industrial processes. However, the "universe" containing all possible compounds, the so-called chemical space, is vast, and therefore, the libraries store only its representative parts. Thus, to explore the whole chemical space and to identify all its promising parts containing e.g., drug-like molecules computational methods have to be developed and employed. In this paper, we propose a method for traveling in the chemical space called Molpher. Given two molecules, Molpher is intended to find a sequence of related compounds, called path in the chemical space, leading from the starting molecule to the target one. The path is generated by iterative application of the so-called morphing operators corresponding to simple chemical operations such as adding or removing an atom or a bond. The molecules on the resulting path represent a focused library that can be used as a starting point for other experiments. We also propose a testbed for examining qualities of algorithms such as Molpher. The testbed is used to describe Molpher's qualities in terms of ability of finding a path in the space in the given time.