Protein-water hydrogen-bond networks of G protein-coupled receptors: Graph-based analyses of static structures and molecular dynamics

Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether GPCR structures and GPCR conformational dynamics could be described in terms of conserved hydrogen-bond networks, and alterations of these hydrogen-bond networks along the reaction coordinate of the GPCR. To enable efficient analyses of the hydrogen-bond networks of GPCRs we implemented graph-based algorithms, and applied these algorithms to static GPCR structures from structural biology, and from molecular dynamics simulations of two opioid receptors. We find that static GPCR structures tend to have a conserved, core hydrogen-bond network which, when protein and water dynamics are included with simulations, extends to comprise most of the interior of an inactive receptor. In an active receptor, the dynamic protein-water hydrogen-bond network spans the entire receptor, bridging all functional motifs. Such an extensive, dynamic hydrogen-bond network might contribute to the activation mechanism of the GPCR

The Use of Yeast Saccharomyces Cerevisiae as a Biorecognition element in the Development of a Model Impedimetric Biosensor for Caffeine Detection

In the present study, an electrochemical-impedimetric biosensor using Saccharomyces cerevisiae as an effective biorecognition element was designed to detect caffeine. The presented biosensor consists of a previously developed stainless steel electrochemical cell constructed as a three-electrode system in the RCW side-by-side configuration. The electrochemical stability of the sensing electrode was evaluated by measuring the open circuit potential (OCP), and electrochemical impedance spectroscopy (EIS) was applied to determine the impedimetric response of the biosensor with Saccharomyces cerevisiae cells attached to the working electrode (WE) in the absence (0.9% NaCl) and presence (10 mg/mL in 0.9% NaCl) of caffeine. Moreover, the limit of detection (LOD) was determined. In this way, a new approach in biosensor development has been established, which involves assembling a low-cost and disposable electrochemical system to detect alkaloids such as caffeine. The developed biosensor represents a good candidate for detecting caffeine in beverages, foods, and drugs with the merits of time-saving, robustness, low cost, and low detection limit

The Effect of Growth Medium Strength on Minimum Inhibitory Concentrations of Tannins and Tannin Extracts against E. coli

In this study the effect of growth medium strength on the minimum inhibitory concentration (MIC) of different tannins and tannin extracts against Escherichia coli was systematically investigated for the first time. Three pure compounds (vescalagin, castalagin and gallic acid) and five extracts (chestnut, quebracho, mimosa, Colistizer and tannic acid) were studied. Broth microdilution was assayed and bacteria were grown using different growth medium strengths varying from half to double the concentration recommended by the producer. MICs were determined using the iodonitrotetrazolium chloride (INT) dye or turbidity measurements. It was observed that MIC values depend on the growth medium strength. With an increase in the growth medium concentration MIC values rose roughly linearly for all samples, while their relative order remained unchanged, indicating that a direct interaction of tannins with growth medium nutrients represents the likely source of their antimicrobial activity. Understanding the effect of growth medium strength can finally yield a plausible explanation for the observed variation in MIC values reported in the scientific literature as well as provide help in planning proper applications of tannins in the livestock production

Terminologija v poklicnem vsakdanu: stanje in potrebe

Terminologija v poklicnem vsakdanu: stanje in potreb

Ensemble Docking Coupled to Linear Interaction Energy Calculations for Identification of Coronavirus Main Protease (3CLpro) Non-Covalent Small-Molecule Inhibitors

SARS-CoV-2, or severe acute respiratory syndrome coronavirus 2, represents a new strain of Coronaviridae. In the closing 2019 to early 2020 months, the virus caused a global pandemic of COVID-19 disease. We performed a virtual screening study in order to identify potential inhibitors of the SARS-CoV-2 main viral protease (3CLpro or Mpro). For this purpose, we developed a novel approach using ensemble docking high-throughput virtual screening directly coupled with subsequent Linear Interaction Energy (LIE) calculations to maximize the conformational space sampling and to assess the binding affinity of identified inhibitors. A large database of small commercial compounds was prepared, and top-scoring hits were identified with two compounds singled out, namely 1-[(R)-2-(1,3-benzimidazol-2-yl)-1-pyrrolidinyl]-2-(4-methyl-1,4-diazepan-1-yl)-1-ethanone and [({(S)-1-[(1H-indol-2-yl)methyl]-3-pyrrolidinyl}methyl)amino](5-methyl-2H-pyrazol-3-yl)formaldehyde. Moreover, we obtained a favorable binding free energy of the identified compounds, and using contact analysis we confirmed their stable binding modes in the 3CLpro active site. These compounds will facilitate further 3CLpro inhibitor design

Effects of translational and rotational degrees of freedom on the hydration of ionic solutes as seen by the popular water models

We employed molecular dynamics simulations with separate thermostats for translational and rotational temperatures in order to study the effects of these degrees of freedom on the hydration of ions. In this work we examine how water models, differing in charge distribution, respond to the rise of rotational temperature. The study shows that, with respect to the distribution of negative charge, popular water models lead to different responses upon an increase of the rotational temperature. The differences arise in hydration of cations, as the negative charge distribution on the model solvent represents the determining factor in such cases. The cation-water correlation increases with the increasing rotational temperature if negative charge is placed in (or close to) the centre of the water molecule (a typical example is the SPC water model) and decreases, when the negative charge is shifted from the centre (as in the TIP5P model of water). Because all the water models examined here have similar distributions of positive charge, they all exhibit similar trends in solvation of anions. In contrast to above, the effect of translational temperature variation is similar for all water-solute pairsany increase in translational temperature decreases the solute-water correlations.S pomočjo simulacije gibanja molekul v sistemu, kjer smo posameznim prostostnim stopnjam (translacija, rotacija) predpisali različne temperature, smo raziskali vpliv teh prostostnih stopenj na hidratacijo enostavnih ionov. Zanimalo nas je, kako se, glede na izbrani model vode, hidratacija enostavnih ionov odziva na povišanje rotacijske temperature. Pokazali smo, da se modeli vode, skladno s porazdelitvijo negativnega naboja na molekuli, ločijo v dve skupini. Do različnega odziva zato prihaja predvsem pri hidrataciji kationa. Za 3- in 4-točkovne modele vode (npr. SPC model), kjer je negativni naboj v središču molekule vode, hidratacija kationov z naraščujočo temperaturo rotacije narašča. V nasprotju s tem pa pri 5-točkovnih modelih vode (npr. TIP5P), hidratacija kationov z višanjem temperature rotacije slabi. Pri vseh obravnavanih modelih vode se hidratacija anionov zmanjšuje s povišanjem rotacijske temperature. Razlog je podobna porazdelitev pozitivnega naboja pri vseh modelih vode. Vpliv translacijske temperature je pri vseh topljencih in za vse modele vode kvalitativno enak – vsako povišanje temperature slabi korelacijo med topljencem in vodo

Naive prediction of protein backbone phi and psi dihedral angles using deep learning

Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening

Abstract Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries’ display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing