Protein folding and unfolding: proline cis - trans isomerization at the c subunits of F 1 F O -ATPase might open a high conductance ion channel

The c subunits, which constitutes the c-ring apparatus of the F F -ATPase, could be the main components of the mitochondrial permeability transition pore (mPTP). The well-known modulator of the mPTP formation and opening is the cyclophilin D (CyPD), a peptidyl-prolyl cis- trans isomerase. On the loop, which connects the two hairpin α-helix of c subunit, is present the unique proline residue (Pro ) that could be a biological target of CyPD. Indeed, the proline cis- trans isomerization might provide the switch that interconverts the open/closed states of the pore by pulling out the c-ring lipid plug.

An Original Approach to Measure Ligand/receptor Binding Affinity in Non-purified Samples

Several biochemical and biophysical methods are available to determine dissociation constants between a biological target and its ligands. Most of them require purification, labelling or surface immobilisation. However, these measurements remain challenging concerning membrane proteins because purification requires their extraction from the native lipid environment using different approaches, a process that may impact receptor conformation and functionality. We have developed a novel experimental procedure to determine binding affinities of a ligand to a membrane protein, the dopamine D2 receptor (D2R), directly from cell membrane fragments, using microscale thermophoresis (MST). Two main challenges had to be overcome: to determine the concentration of dopamine D2R in the crude sample; to find ways to minimize or account for non-specific binding of the ligand to cell fragments. Using MST, we were able to determine the D2R concentration in cell membrane fragments to be about 36.8 ± 2.6 pmol/mg. Then titration curves allowed the determination of a KD about 5.3 ± 1.7 nM, that is very close to the reported value. Important details of the experimental procedure are detailed to allow the transposition of this novel method to various membrane proteins.

Control of Cytoskeletal Dynamics in Cancer through a Combination of Cytoskeletal Components

The dynamic alterations in the cytoskeletal components actin and intermediate, etc. filaments are required for cell invasion and migration. The actin cytoskeleton is a highly dynamic structure that is governed by a delicate balance of actin filament formation and disassembly. To controlling the activities of key components of the epithelial mesenchymal transition (EMT) could be a viable solution to metastasis. Bioinformatics technologies also allow researchers to investigate the consequences of synthetic mutations or naturally occurring variations of these cytoskeletal proteins. S100A4 is S100 protein family member that interact with a variety of biological target. In study has shown that S100A4 interacts with the tumor suppressor protein p53, indicating that S100A4 may have additional roles in tumor development. The S100A4 and p53 interaction increases after inhibition of MDM2-dependent p53 degradation using Nutlin-3A. The main goal of this research was control of cytoskeletal dynamics in cancer through a combination of, actin and S100A4 protein. The investigate the molecular mechanism behind S100A4 function in (EMT) and indicating that S100A4 is promoting p53 degradation. Understanding the signaling pathways involved would provide a better understanding of the changes that occur during metastasis, which will eventually lead to the identification of proteins that can be targeted for treatment, resulting in lower mortality.

A versatile active learning workflow for optimization of genetic and metabolic networks

The study, engineering and application of biological networks require practical and efficient approaches. Current optimization efforts of these systems are often limited by wet lab labor and cost, as well as the lack of convenient, easily adoptable computational tools. Aimed at democratization and standardization, we describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets. We demonstrate our workflow for various applications, from simple to complex gene circuits and metabolic networks, including several cell-free transcription and translation systems, a LacI-based multi-level controller and a 27-variable synthetic CO2-fixation cycle (CETCH cycle). Using METIS, we could improve above systems between one and two orders of magnitude compared to their original setup with minimal experimental efforts. For the CETCH cycle, we explored the combinatorial space of ~1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system. This allows to identify so far unknown interactions and bottlenecks in complex systems, which paves the way for their hypothesis-driven improvement, which we demonstrate for the LacI multi-level controller that we were able to improve by 100-fold after having identified resource competition as limiting factor. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Thienopyrimidine: A Promising Scaffold to Access Anti-Infective Agents

Thienopyrimidines are widely represented in the literature, mainly due to their structural relationship with purine base such as adenine and guanine. This current review presents three isomers—thieno[2,3-d]pyrimidines, thieno[3,2-d]pyrimidines and thieno[3,4-d]pyrimidines—and their anti-infective properties. Broad-spectrum thienopyrimidines with biological properties such as antibacterial, antifungal, antiparasitic and antiviral inspired us to analyze and compile their structure–activity relationship (SAR) and classify their synthetic pathways. This review explains the main access route to synthesize thienopyrimidines from thiophene derivatives or from pyrimidine analogs. In addition, SAR study and promising anti-infective activity of these scaffolds are summarized in figures and explanatory diagrams. Ligand–receptor interactions were modeled when the biological target was identified and the crystal structure was solved.

Comparison of the Meta-Active Machine Learning Model Applied to Biological Data-Driven Experiments with Other Models

Currently, many methods that could estimate the effects of conditions on a given biological target require either strong modelling assumptions or separate screens. Traditionally, many conditions and targets, without doing all possible experiments, could be achieved by driven experimentation or several mathematical methods, especially conversational machine learning methods. However, these methods still could not avoid and replace manual labels completely. This paper presented a meta-active machine learning method to resolve this problem. This project has used nine traditional machine learning methods to compare their accuracy and running time. In addition, this paper analyzes the meta-active machine learning method (MAML) compared with a classical screening method and progressive experiments. The obtained results show that applying this method yields the best experimental results on the current dataset.

Animal Migration Patterns Extraction Based on Atrous-Gated CNN Deep Learning Model

Weather radar data can capture large-scale bird migration information, helping solve a series of migratory ecological problems. However, extracting and identifying bird information from weather radar data remains one of the challenges of radar aeroecology. In recent years, deep learning was applied to the field of radar data processing and proved to be an effective strategy. This paper describes a deep learning method for extracting biological target echoes from weather radar images. This model uses a two-stream CNN (Atrous-Gated CNN) architecture to generate fine-scale predictions by combining the key modules such as squeeze-and-excitation (SE), and atrous spatial pyramid pooling (ASPP). The SE block can enhance the attention on the feature map, while ASPP block can expand the receptive field, helping the network understand the global shape information. The experiments show that in the typical historical data of China next generation weather radar (CINRAD), the precision of the network in identifying biological targets reaches up to 99.6%. Our network can cope with complex weather conditions, realizing long-term and automated monitoring of weather radar data to extract biological target information and provide feasible technical support for bird migration research.

A Simple Queuing Model for Molecular Communications Receivers

The complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A typical scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in the diseased area. In this paper, we propose a simple although reliable receiver model for diffusion-based molecular communication systems tackling the time needed for analyzing such a system. The proposed model consists of using an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. It takes into account not only the fact that the absorption of molecules can occur only through receptors, but also that absorption is not an instantaneous process, and may require a significant time during which the receptor is not available to bind to other molecules. Our results, expressed in terms of number of absorbed molecules and average number of busy receptors, demonstrate that the proposed approach is in good agreement with results obtained through particle-based simulations of a large number of receptors, although the time taken for obtaining the results with the proposed model is order of magnitudes lower than the simulation time. We believe that this model can be the precursor of novel class of models based on similar principles that allow realizing reliable simulations of much larger systems.

Mechanism-based design of bioactive cyclopropanated sugars

<p>Carbohydrates are important feed stocks in synthesis of natural products and so attract the interest of many organic researchers throughout the world, most notably in the last 10 years. The work described within explores the manipulation of the glucose-derived glucal. The addition of a reactive substituted cyclopropane across the alkene has been employed synthetically for many years, the subsequent ring breaking/expansion has been identified in the lab as slow and needing the support of catalysts. We ask the question, “Will cyclopropanated carbohydrates undergo the slow ring breaking/expansion in the presence of proteins, and are we able to identify which of the two types of mechanisms the reaction is going through?” The cyclopropane will act as a warhead to bind to proteins through Ferrier like rearrangements, resulting in irreversible inhibition. To identify the potential of such compounds, a combination of techniques are used to identify potential pathways, protein targets and reactivity through structure activity relationships. The key steps involved in finding out the potential of cyclopropanated carbohydrates are to determine biological activities through bio-assays, structure activity relationships, selective binding, chemical genetics and chemical proteomics. The bio-assays together with structure activity relationships provides evidence on which chemical mechanism is occurring when the biological target is interacting with the bioactive cyclopropanated carbohydrates. The most active compound, benzose (7), was subjected to chemical genetic analysis to determine the pathways and processes that are involved with the mode of action. The chemical genetic analysis was complimented by chemical proteomics to identify the direct biological target. Analogues of benzose were synthesised by the addition of azide groups to undergo a Huisgen Cyclisation within a cell lysate to facilitate binding to an alkyne-substituted matrix.</p>