Breeding Cell Penetrating Peptides: Optimization of Cellular Uptake by a Function-Driven Evolutionary Process.

In nature, building block-based biopolymers can adapt to functional and environmental demands by recombination and mutation of the monomer sequence. We present here an analogous, artificial evolutionary optimization process which we have applied to improve the functionality of cell-penetrating peptide molecules. The "evolution" consisted of repeated rounds of in silico peptide sequence alterations using a genetic algorithm followed by in vitro peptide synthesis, experimental analysis, and ranking according to their "fitness" (i.e., their ability to carry the cargo carboxyfluorescein into cultured cells). The genetic algorithm-based optimization method was customized and adapted from former successful applications in the lab to realize an early convergence and a minimum number of in vitro and in silico processing steps by configured settings derived from empirical in silico simulation. We started out with 20 "lead peptides" which we had previously identified as top performers regarding their ability to enter cultured cells. Ten breeding rounds comprising 240 peptides each yielded a peptide population of which the top 10 candidates displayed a 6-fold (median values) increase in its cell-penetration capability compared with the top 10 lead peptides, and two consensus sequences emerged which represent local fitness optima. In addition, the cell-penetrating potential could be proven independently of the carboxyfluorescein cargo in an alternative setting. Our results demonstrate that we have established a powerful optimization technology that can be used to further improve peptides with known functionality and adapt them to specific applications.

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers.

BACKGROUND: Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs. RESULTS: We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies. CONCLUSIONS: Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.

Insights into the classification of small GTPases.

In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.

fMRI data visualization with BrainBlend and Blender.

The visualization and exploration of neuroimaging data is important for the analysis of anatomical and functional magnetic resonance (MR) images and thresholded statistical parametric maps. While two-dimensional orthogonal views of neuroimaging data are used to display statistical analyses, real three-dimensional (3d) depictions are helpful for showing the spatial distribution of a functional network, as well as its temporal evolution. However, viewers that are freely available on the internet offer only limited rendering capabilities and depictions of temporal changes of the blood oxygen level-dependent (BOLD) response. In this article, we present BrainBlend, a toolbox for the software package Statistical Parametric Mapping (SPM), that generates voxeldata files to be used with the open-source 3d-software "Blender". Our interface between SPM and Blender permits the use of any Analyze- and Nifti-file for the creation of images and animations of transparent volumetric objects. Different kinds of anatomical, functional and statistical data can be rendered as volumetric objects in order to convey an immediate understanding of the three-dimensional shape. Representations of functional networks can be animated using a time course extracted from the general linear model or the independent component analysis. Relative BOLD activations of functional MR-images can be calculated for a time-resolved depiction of hemodynamic changes. The resulting animation can be displayed along with its corresponding paradigm matrix and the presented stimuli. BrainBlend is particularly suitable for the visual exploration of interactions between functional networks, for time-resolved animations of BOLD changes and meets high demands on visual quality in images and animations.

Impact of working memory load on FMRI resting state pattern in subsequent resting phases.

BACKGROUND: The default-mode network (DMN) is a functional network with increasing relevance for psychiatric research, characterized by increased activation at rest and decreased activation during task performance. The degree of DMN deactivation during a cognitively demanding task depends on its difficulty. However, the relation of hemodynamic responses in the resting phase after a preceding cognitive challenge remains relatively unexplored. We test the hypothesis that the degree of activation of the DMN following cognitive challenge is influenced by the cognitive load of a preceding working-memory task. METHODOLOGY/PRINCIPAL FINDINGS: Twenty-five healthy subjects were investigated with functional MRI at 3 Tesla while performing a working-memory task with embedded short resting phases. Data were decomposed into statistically independent spatio-temporal components using Tensor Independent Component Analysis (TICA). The DMN was selected using a template-matching procedure. The spatial map contained rest-related activations in the medial frontal cortex, ventral anterior and posterior cingulate cortex. The time course of the DMN revealed increased activation at rest after 1-back and 2-back blocks compared to the activation after a 0-back block. CONCLUSION/SIGNIFICANCE: We present evidence that a cognitively challenging working-memory task is followed by greater activation of the DMN than a simple letter-matching task. This might be interpreted as a functional correlate of self-evaluation and reflection of the preceding task or as relocation of cerebral resources representing recovery from high cognitive demands. This finding is highly relevant for neuroimaging studies which include resting phases in cognitive tasks as stable baseline conditions. Further studies investigating the DMN should take possible interactions of tasks and subsequent resting phases into account.