Checking the projection display of multivariate data with colored graphs

A collaborative visual analytics suite for protein folding research

Molecular dynamics (MD) simulation is a crucial tool for understanding principles behind important biochemical processes such as protein folding and molecular interaction. With the rapidly increasing power of modern computers, large-scale MD simulation experiments can be performed regularly, generating huge amounts of MD data. An important question is how to analyze and interpret such massive and complex data. One of the (many) challenges involved in analyzing MD simulation data computationally is the high-dimensionality of such data. Given a massive collection of molecular conformations, researchers typically need to rely on their expertise and prior domain knowledge in order to retrieve certain conformations of interest. It is not easy to make and test hypotheses as the data set as a whole is somewhat "invisible" due to its high dimensionality. In other words, it is hard to directly access and examine individual conformations from a sea of molecular structures, and to further explore the entire data set. There is also no easy and convenient way to obtain a global view of the data or its various modalities of biochemical information. To this end, we present an interactive, collaborative visual analytics tool for exploring massive, high-dimensional molecular dynamics simulation data sets. The most important utility of our tool is to provide a platform where researchers can easily and effectively navigate through the otherwise "invisible" simulation data sets, exploring and examining molecular conformations both as a whole and at individual levels. The visualization is based on the concept of a topological landscape, which is a 2D terrain metaphor preserving certain topological and geometric properties of the high dimensional protein energy landscape. In addition to facilitating easy exploration of conformations, this 2D terrain metaphor also provides a platform where researchers can visualize and analyze various properties (such as contact density) overlayed on the top of the 2D terrain. Finally, the software provides a collaborative environment where multiple researchers can assemble observations and biochemical events into storyboards and share them in real time over the Internet via a client-server architecture. The software is written in Scala and runs on the cross-platform Java Virtual Machine. Binaries and source code are available at http://www.aylasoftware.org and have been released under the GNU General Public License.

Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery

Visualization of 'chemical space' and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection.

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors

BACKGROUND

De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis.

METHODS

We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro.

RESULTS

The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested.

CONCLUSIONS

Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

Applications of integrated data mining methods to exploring natural product space for acetylcholinesterase inhibitors

Nature, especially the plant kingdom, is a rich source for novel bioactive compounds that can be used as lead compounds for drug development. In order to exploit this resource, the two neural network-based virtual screening techniques novelty detection with self-organizing maps (SOMs) and counterpropagation neural network were evaluated as tools for efficient lead structure discovery. As application scenario, significant descriptors for acetylcholinesterase (AChE) inhibitors were determined and used for model building, theoretical model validation, and virtual screening. Top-ranked virtual hits from both approaches were docked into the AChE binding site to approve the initial hits. Finally, in vitro testing of selected compounds led to the identification of forsythoside A and (+)-sesamolin as novel AChE inhibitors.

PharmaGist: a webserver for ligand-based pharmacophore detection

Predicting molecular interactions is a major goal in rational drug design. Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is an important model for achieving this goal. We present a freely available web server, named PharmaGist, for pharmacophore detection. The employed method is ligand based. Namely, it does not require the structure of the target receptor. Instead, the input is a set of structures of drug-like molecules that are known to bind to the receptor. The output consists of candidate pharmacophores that are computed by multiple flexible alignment of the input ligands. The method handles the flexibility of the input ligands explicitly and in deterministic manner within the alignment process. PharmaGist is also highly efficient, where a typical run with up to 32 drug-like molecules takes seconds to a few minutes on a stardard PC. Another important characteristic is the capability of detecting pharmacophores shared by different subsets of input molecules. This capability is a key advantage when the ligands belong to different binding modes or when the input contains outliers. The webserver has a user-friendly interface available at http://bioinfo3d.cs.tau.ac.il/PharmaGist.

Network models in drug discovery and regenerative medicine

Network motifs and modelling paradigms are attracting increasing attention as modelling tools in drug design and development, and in regenerative medicine. There is a gradual but inexorable convergence between these hitherto disparate disciplines. This review summarizes some very recent work in these areas, leading to an understanding of the complementary roles networks play and factors driving this convergence: network paradigms can be excellent ways of modelling and understanding drug molecules and their action, an understanding of the robustness and vulnerabilities of biological targets may improve the efficacy of drug design and discovery, drug design has an increasingly large role to play in directing stem cell properties, stem cell regulatory networks can be modelled in useful ways using network models at a reasonable level of scale, and the network tools of drug design are also very useful for the design of biomaterials used in regenerative medicine.

TMACC: interpretable correlation descriptors for quantitative structure-activity relationships

Highly predictive topological maximum cross correlation (TMACC) descriptors for the derivation of quantitative structure-activity relationships (QSARs) are presented, based on the widely used autocorrelation method. They require neither the calculation of three-dimensional conformations nor an alignment of structures. We have validated the TMACC descriptors across eight literature data sets, ranging in size from 66 to 361 molecules. In combination with partial least-squares regression, they perform competitively with a current state-of-the-art 2D QSAR methodology, hologram QSAR (HQSAR), yielding larger leave-one-out cross-validated coefficient of determination values (LOO q2) for five data sets. Like HQSAR, these descriptors are also interpretable but do not require hashing. The interpretation both enables the automated extraction of SARs and can give a description in qualitative agreement with more time-consuming 3D and 4D QSAR methods. Open source software for generating the TMACC descriptors is freely available from our Web site.

The acidic domain of hnRNPQ (NSAP1) has structural similarity to Barstar and binds to Apobec1

Apobec1 edits the ApoB mRNA by deaminating nucleotide C(6666), which results in a codon change from Glutamate to stop, and subsequent expression of a truncated protein. Apobec1 is regulated by ACF (Apobec1 complementation factor) and hnRNPQ, which contains an N-terminal "acidic domain" (AcD) of unknown function, three RNA recognition motifs, and an Arg/Gly-rich region. Here, we modeled the structure of AcD using the bacterial protein Barstar as a template. Furthermore, we demonstrated by in vitro pull-down assays that 6xHis-AcD alone is able to interact with GST-Apobec1. Finally, we performed in silico phosphorylation of AcD and molecular dynamics studies, which indicate conformational changes in the phosphorylated form. The results of the latter studies were confirmed by in vitro phosphorylation of 6xHis-AcD by protein kinase C, mass spectrometry, and spectroscopic analyses. Our data suggest hnRNPQ interactions via its AcD with Apobec1 and that this interaction is regulated by the AcD phosphorylation.

Mapping of protein surface cavities and prediction of enzyme class by a self-organizing neural network

An automated computer-based method for mapping of protein surface cavities was developed and applied to a set of 176 metalloproteinases containing zinc cations in their active sites. With very few exceptions, the cavity search routine detected the active site among the five largest cavities and produced reasonable active site surfaces. Cavities were described by means of solvent-accessible surface patches. For a given protein, these patches were calculated in three steps: (i) definition of cavity atoms forming surface cavities by a grid-based technique; (ii) generation of solvent accessible surfaces; (iii) assignment of an accessibility value and a generalized atom type to each surface point. Topological correlation vectors were generated from the set of surface points forming the cavities, and projected onto the plane by a self-organizing network. The resulting map of 865 enzyme cavities displays clusters of active sites that are clearly separated from the other cavities. It is demonstrated that both fully automated recognition of active sites, and prediction of enzyme class can be performed for novel protein structures at high accuracy.

Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a "seed peptide" with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-beta1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human beta1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.