The effects of maturation on self-induced dynamic body sway frequencies of girls performing acrobatics or classical dance

We investigated the effects of maturation on the dynamic body sways of healthy girls. Prepubertal and postpubertal girls practising professional physical activities requiring a good ability to maintain equilibrium (acrobats and dancers) were asked to stand on a free seesaw platform and the results compared to those for untrained age-matched girls. This platform (stabilometer) allows self-induced body sways. Stabilograms were obtained by a double integration of the angular acceleration from the recordings of the platform sways made with an accelerometer. Fast Fourier transform processing of stabilograms allowed spectral frequency analysis. The total spectrum energy and the energies of three frequency bands (0–0.5 Hz, 0.5–2 Hz, 2–20 Hz) were determined. ANOVA showed that, for all groups of different equilibrium activity and independent of visual input, prepubertal girls had higher energy values than postpubertal girls in the 0- to 0.5-Hz band whereas the opposite was true for 0.5- to 2-Hz band. Ballet dancers were more dependent than acrobats on visual inputs for the regulation of their postural control but were less dependent than untrained girls at both ages. Maturation seemed to shift body sways towards higher frequencies and the utilization of the cues of postural control was different according to the type of equilibrium activity practised by the subjects. Accepted: 7 February 1997     

Towards a matrix mechanics framework for dynamic protein network

Protein–protein interaction networks are currently visualized by software generated interaction webs based upon static experimental data. Current state is limited to static, mostly non-compartmental network and non time resolved protein interactions. A satisfactory mathematical foundation for particle interactions within a viscous liquid state (situation within the cytoplasm) does not exist nor do current computer programs enable building dynamic interaction networks for time resolved interactions. Building mathematical foundation for intracellular protein interactions can be achieved in two increments (a) trigger and capture the dynamic molecular changes for a select subset of proteins using several model systems and high throughput time resolved proteomics and, (b) use this information to build the mathematical foundation and computational algorithm for a compartmentalized and dynamic protein interaction network. Such a foundation is expected to provide benefit in at least two spheres: (a) understanding physiology enabling explanation of phenomenon such as incomplete penetrance in genetic disorders and (b) enabling several fold increase in biopharmaceutical production using impure starting materials.     

Identification and synthesis of an efficient multivalent E. coli heat labile toxin inhibitor __ A dynamic combinatorial chemistry approach

A polymer based dynamic combinatorial library (DCL) was generated through condensation between aldehyde functionalized linear poly(glycidol) (APG) and galactose containing acylhydrazide derivatives. Pentameric E. coli heat labile enterotoxin B subunit (LTB) was subsequently applied to the DCL as external stimulus, resulting in amplification of a specific acylhydrazone side chain that was further used for the synthesis of a multivalent LTB inhibitor. In the in vitro biological evaluation, this inhibitor exhibited strong inhibition properties as well as low cytotoxicity.     

Monitoring the early aggregatory behaviour and size of Aβ1-42 in the absence & presence of metal ions using dynamic light scattering

Background and aimAβ₁₋₄₂ is an amyloidogenic peptide found within senile plaques extracted from those who died with a diagnosis of Alzheimer’s disease. The potent neurotoxicity of this peptide is related to its propensity to form aggregated conformations in vivo, a process that is influenced by the species and concentration of metal ions present within the local environment. This study examines the impact of different metals upon the early aggregatory behaviour and size of Aβ₁₋₄₂ under simulated physiological conditions.MethodsThe size and aggregatory behaviour of Aβ₁₋₄₂ in the presence and absence of metal ions was monitored during the initial 30 min of fibril formation in real-time using dynamic light scattering.ResultsIntensity scattering measurements showed a clear tendency towards aggregation with regards to Aβ₁₋₄₂ only solutions (10 μM). Both equimolar Al³⁺ & Cu²⁺ lowered and stabilised the dimensions of Aβ₁₋₄₂ aggregates; however, a diminutive but significant increase in size was still observed over a 30-min period. While excess Al³⁺ continued to supress the size of Aβ_1−42, a 10-fold increase in the concentration of Cu²⁺ accelerated peptide aggregation relative to that observed for equimolar metal but not compared to Aβ₁₋₄₂ alone.ConclusionThese results infer that Al³⁺ ions stabilise and aid in the maintenance of smaller, toxic intermediates while excess Cu²⁺ facilitates the formation of larger, more inert, amorphous species exceeding 1 μm in size. Furthermore, we propose that metal-induced toxicity of Aβ₁₋₄₂ is reflective of their ability to preserve smaller oligomeric species in vitro.     

Analyzing the effect of mutations in SARS-CoV2 papain-like protease from Saudi isolates on protein structure and drug-protein binding: Molecular modelling and dynamics studies

The continuous and rapid development of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus remains a health concern especially with the emergence of numerous variants and mutations worldwide. As with other RNA viruses, SARS-CoV-2 has a genetically high mutation rate. These mutations have an impact on the virus characteristics, including transmissibility, antigenicity and development of drug and vaccine resistance. This work was pursued to identify the differences that exist in the papain-like protease (PL^Pro) from 58 Saudi isolates in comparison to the first reported sequence from Wuhan, China and determine their implications on protein structure and the inhibitor binding. PL^pro is a key protease enzyme for the host cells invasion and viral proteolytic cleavage, hence, it emerges as a valuable antiviral therapeutic target. Two mutations were identified including D108G and A249V and shown to increase the molecular flexibility of PL^Pro protein and alter the protein stability, particularly with D108G mutation. The effect of these mutations on the stability and dynamic behavior of PL^Pro structures as well as their effect on the binding of a known inhibitor; GRL0617 were further investigated by molecular docking and dynamic simulation.     

Is GPR146 really the receptor for proinsulin C-peptide?

Proinsulin C-peptide has previously been proposed to interact with a G-protein coupled receptor (GPCR), specifically the orphan receptor GPR146. To investigate the potential of C-peptide in treating complications of diabetes, such as kidney damage, it is necessary to understand its mode of action. We used CHO-K1 cells expressing human GPR146 to study human and murine C-peptide in dynamic mass redistribution and GPCR β-arrestin assays, as well as with fluorescence confocal microscopy. Neither assay revealed any significant intracellular response to C-peptide at concentrations of up to 33 µM. We observed no internalisation of C-peptide by fluorescence microscopy. Our results do not support GPR146 as the receptor for C-peptide, but suggest that further investigations of the mode of action of C-peptide should be undertaken.     

Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process

We model the vague-to-crisp dynamics of forming percepts in the brain by combining two methodologies: dynamic logic (DL) and operant learning process. Forming percepts upon the presentation of visual inputs is likened to model selection based on sampled evidence. Our framework utilizes the DL in selecting the correct “percept” among competing ones, but uses an intrinsic reward mechanism to allow stochastic online update in lieu of performing the optimization step of the DL framework. We discuss the connection of our framework with cognitive processing and the intentional neurodynamic cycle.     

Dynamic combinatorial libraries of artificial repeat proteins

Repeat proteins are found in almost all cellular systems, where they are involved in diverse molecular recognition processes. Recent studies have suggested that de novo designed repeat proteins may serve as universal binders, and might potentially be used as practical alternative to antibodies. We describe here a novel chemical methodology for producing small libraries of repeat proteins, and screening in parallel the ligand binding of library members. The first stage of this research involved the total synthesis of a consensus-based three-repeat tetratricopeptide (TPR) protein (～14 kDa), via sequential attachment of the respective peptides. Despite the effectiveness of the synthesis and ligation steps, this method was found to be too demanding for the production of proteins containing variable number of repeats. Additionally, the analysis of binding of the individual proteins was time consuming. Therefore, we designed and prepared novel dynamic combinatorial libraries (DCLs), and show that their equilibration can facilitate the formation of TPR proteins containing up to eight repeating units. Interestingly, equilibration of the library building blocks in the presence of the biologically relevant ligands, Hsp90 and Hsp70, induced their oligomerization into forming more of the proteins with large recognition surfaces. We suggest that this work presents a novel simple and rapid tool for the simultaneous screening of protein mixtures with variable binding surfaces, and for identifying new binders for ligands of interest.     

Fitting dynamic growth models of biological phenomena from sample observations through Gaussian diffusion processes

This paper addresses the building of stochastic models that adequately describe dynamic phenomena and, in particular, those that occur in the Biosciences. In this context, the empirical fitting of a Gaussian diffusion process from sample data of a dynamic growth phenomenon is considered. In order to do this, a methodology based on approximations to its mean and variance functions is presented. Finally, several applications based on simulated and real data have been carried out.