scholarly journals Correlated motions are a fundamental property of β-sheets

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
R. Bryn Fenwick ◽  
Laura Orellana ◽  
Santi Esteban-Martín ◽  
Modesto Orozco ◽  
Xavier Salvatella
Keyword(s):  
Fundamental Property ◽  
Correlated Motions ◽  
Β Sheets

The Role of Hydrophobicity in the Stability and pH-Switchability of (RXDX)4 and Coumarin-RXDX Conjugate β-Sheets

2020 ◽  
Author(s):  
Ryan Weber ◽  
Martin McCullagh
Keyword(s):  
Biological Imaging ◽  
Ph Sensing ◽  
Hydrophobic Residue ◽  
Ideal Candidate ◽  
Self Assembling ◽  
Sensing Applications ◽  
Β Sheets ◽  
The Stability ◽  
Dynamics Simulations

<p>pH-switchable, self-assembling materials are of interest in biological imaging and sensing applications. Here we propose that combining the pH-switchability of RXDX (X=Ala, Val, Leu, Ile, Phe) peptides and the optical properties of coumarin creates an ideal candidate for these materials. This suggestion is tested with a thorough set of all-atom molecular dynamics simulations. We first investigate the dependence of pH-switchabiliy on the identity of the hydrophobic residue, X, in the bare (RXDX)<sub>4</sub> systems. Increasing the hydrophobicity stabilizes the fiber which, in turn, reduces the pH-switchabilty of the system. This behavior is found to be somewhat transferable to systems in which a single hydrophobic residue is replaced with a coumarin containing amino acid. In this case, conjugates with X=Ala are found to be unstable and both pHs while conjugates with X=Val, Leu, Ile and Phe are found to form stable β-sheets at least at neutral pH. The (RFDF)<sub>4</sub>-coumarin conjugate is found to have the largest relative entropy value of 0.884 +/- 0.001 between neutral and acidic coumarin ordering distributions. Thus, we posit that coumarin-(RFDF)<sub>4</sub> containing peptide sequences are ideal candidates for pH-sensing bioelectronic materials.</p>

Surfactant Impurities Can Explain the Jones-Ray Effect

2018 ◽  
Author(s):  
Timothy Duignan ◽  
Marcel Baer ◽  
Christopher Mundy
Keyword(s):  
Surface Tension ◽  
Electrostatic Potential ◽  
Driving Forces ◽  
Salt Water ◽  
Fundamental Property ◽  
Apparent Contradiction ◽  
Low Salt ◽  
Air Water Interface ◽  
Added Salt ◽  
Effect Of Surface

<div> <p> </p><div> <div> <div> <p>The surface tension of dilute salt water is a fundamental property that is crucial to understanding the complexity of many aqueous phase processes. Small ions are known to be repelled from the air-water surface leading to an increase in the surface tension in accordance with the Gibbs adsorption isotherm. The Jones-Ray effect refers to the observation that at extremely low salt concentration the surface tension decreases in apparent contradiction with thermodynamics. Determining the mechanism that is responsible for this Jones-Ray effect is important for theoretically predicting the distribution of ions near surfaces. Here we show that this surface tension decrease can be explained by surfactant impurities in water that create a substantial negative electrostatic potential at the air-water interface. This potential strongly attracts positive cations in water to the interface lowering the surface tension and thus explaining the signature of the Jones-Ray effect. At higher salt concentrations, this electrostatic potential is screened by the added salt reducing the magnitude of this effect. The effect of surface curvature on this behavior is also examined and the implications for unexplained bubble phenomena is discussed. This work suggests that the purity standards for water may be inadequate and that the interactions between ions with background impurities are important to incorporate into our understanding of the driving forces that give rise to the speciation of ions at interfaces. </p> </div> </div> </div> </div>

On the Measurement of Cooperativity and the Physico-Chemical Meaning of the Hill Coefficient

2019 ◽  
Vol 20 (9) ◽  
pp. 861-872 ◽  
Author(s):  
Andrea Bellelli ◽  
Emanuele Caglioti
Keyword(s):  
Ligand Binding ◽  
Hormone Receptors ◽  
Fundamental Property ◽  
Statistical Correlation ◽  
Hill Coefficient ◽  
Biological Macromolecules ◽  
Physico Chemical ◽  
Physiological Relevance ◽  
Minimum Estimate ◽  
The Hill

Cooperative ligand binding is a fundamental property of many biological macromolecules, notably transport proteins, hormone receptors, and enzymes. Positive homotropic cooperativity, the form of cooperativity that has greatest physiological relevance, causes the ligand affinity to increase as ligation proceeds, thus increasing the steepness of the ligand-binding isotherm. The measurement of the extent of cooperativity has proven difficult, and the most commonly employed marker of cooperativity, the Hill coefficient, originates from a structural hypothesis that has long been disproved. However, a wealth of relevant biochemical data has been interpreted using the Hill coefficient and is being used in studies on evolution and comparative physiology. Even a cursory analysis of the pertinent literature shows that several authors tried to derive more sound biochemical information from the Hill coefficient, often unaware of each other. As a result, a perplexing array of equations interpreting the Hill coefficient is available in the literature, each responding to specific simplifications or assumptions. In this work, we summarize and try to order these attempts, and demonstrate that the Hill coefficient (i) provides a minimum estimate of the free energy of interaction, the other parameter used to measure cooperativity, and (ii) bears a robust statistical correlation to the population of incompletely saturated ligation intermediates. Our aim is to critically evaluate the different analyses that have been advanced to provide a physical meaning to the Hill coefficient, and possibly to select the most reliable ones to be used in comparative studies that may make use of the extensive but elusive information available in the literature.

Charge-Separated Fmoc-Peptide β-Sheets: Sequence-Secondary Structure Relationship for Arranging Charged Side Chains on Both Sides

2014 ◽  
Vol 3 (11) ◽  
pp. 1182-1188 ◽  
Author(s):  
Toru Nakayama ◽  
Taro Sakuraba ◽  
Shunsuke Tomita ◽  
Akira Kaneko ◽  
Eisuke Takai ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Side Chains ◽  
Structure Relationship ◽  
Β Sheets

scholarly journals Implementing Zn2+ ion and pH-value control into artificial mussel glue proteins by abstracting a His-rich domain from preCollagen

Soft Matter ◽  
2021 ◽  
Author(s):  
Sandra Arias ◽  
Shahrouz Amini ◽  
Jana M. Krüger ◽  
Lukas D. Bangert ◽  
Hans G. Börner
Keyword(s):  
Ph Value ◽  
Control Mechanisms ◽  
Rich Domain ◽  
Glue Proteins ◽  
Β Sheets ◽  
Mussel Adhesive

A chemically activated mussel-inspired polymerization of a His-rich peptide, yielded artificial mussel glue proteins, where β-sheets can be triggered to mimic both adhesive motifs and cohesion control mechanisms of the mussel adhesive apparatus.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Antigen receptor repertoires of one of the smallest known vertebrates

2021 ◽  
Vol 7 (1) ◽  
pp. eabd8180
Author(s):  
Orlando B. Giorgetti ◽  
Prashant Shingate ◽  
Connor P. O’Meara ◽  
Vydianathan Ravi ◽  
Nisha E. Pillai ◽  
...  
Keyword(s):  
Immune Responses ◽  
Antigen Receptor ◽  
Fundamental Property ◽  
Cell Receptor ◽  
Immune Reactivity ◽  
Self Similarity ◽  
Frequency Distributions ◽  
Scale Free ◽  
Scale Free Networks ◽  
Incomplete Sampling

The rules underlying the structure of antigen receptor repertoires are not yet fully defined, despite their enormous importance for the understanding of adaptive immunity. With current technology, the large antigen receptor repertoires of mice and humans cannot be comprehensively studied. To circumvent the problems associated with incomplete sampling, we have studied the immunogenetic features of one of the smallest known vertebrates, the cyprinid fish Paedocypris sp. “Singkep” (“minifish”). Despite its small size, minifish has the key genetic facilities characterizing the principal vertebrate lymphocyte lineages. As described for mammals, the frequency distributions of immunoglobulin and T cell receptor clonotypes exhibit the features of fractal systems, demonstrating that self-similarity is a fundamental property of antigen receptor repertoires of vertebrates, irrespective of body size. Hence, minifish achieve immunocompetence via a few thousand lymphocytes organized in robust scale-free networks, thereby ensuring immune reactivity even when cells are lost or clone sizes fluctuate during immune responses.

(Dis)similarity Index To Compare Correlated Motions in Molecular Simulations

2015 ◽  
Vol 11 (9) ◽  
pp. 4404-4414 ◽  
Author(s):  
Matteo Tiberti ◽  
Gaetano Invernizzi ◽  
Elena Papaleo
Keyword(s):  
Molecular Simulations ◽  
Similarity Index ◽  
Correlated Motions

Can machine learning be used to forecast the future uncertainty of military teams?

2021 ◽  
pp. 154851292199911
Author(s):  
Ronald H Stevens ◽  
Trysha L Galloway
Keyword(s):  
Machine Learning ◽  
Sensory Information ◽  
Fundamental Property ◽  
Transition Matrices ◽  
Team Members ◽  
Student Teams ◽  
The Future ◽  
Network States ◽  
Uncertainty Models ◽  
Military Teams

Uncertainty is a fundamental property of neural computation that becomes amplified when sensory information does not match a person’s expectations of the world. Uncertainty and hesitation are often early indicators of potential disruption, and the ability to rapidly measure uncertainty would have implications for future educational and training efforts by targeting reflective discussions about past actions, supporting in-progress corrections, and generating forecasts about future disruptions. An approach is described combining neurodynamics and machine learning to provide quantitative measures of uncertainty. Models of neurodynamic information derived from electroencephalogram (EEG) brainwaves have provided detailed neurodynamic histories of US Navy submarine navigation team members. Persistent periods (25–30 s) of neurodynamic information were seen as discrete peaks when establishing the submarine’s position and were identified as periods of uncertainty by an artificial intelligence (AI) system previously trained to recognize the frequency, magnitude, and duration of different patterns of uncertainty in healthcare and student teams. Transition matrices of neural network states closely predicted the future uncertainty of the navigation team during the three minutes prior to a grounding event. These studies suggest that the dynamics of uncertainty may have common characteristics across teams and tasks and that forecasts of their short-term evolution can be estimated.

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