Protein folding, stability, and function are usually influenced by pH. And free energy plays a fundamental role in analysis of such pH-dependent properties. Electrostatics-based theoretical framework using dielectric solvent continuum model and solving Poisson-Boltzmann equation numerically has been shown to be very successful in understanding the pH-dependent properties. However, in this approach the exact computation of pH-dependent free energy becomes impractical for proteins possessing more than several tens of ionizable sites (e.g. > 30), because exact evaluation of the partition function requires a summation over a vast number of possible protonation microstates. Here we present a method which computes the free energy using the average energy and the protonation probabilities of ionizable sites obtained by the well-established Monte Carlo sampling procedure. The key feature is to calculate the entropy by using the protonation probabilities. We used this method to examine a well-studied protein (lysozyme) and produced results which agree very well with the exact calculations. Applications to the optimum pH of maximal stability of proteins and protein-DNA interactions have also resulted in good agreement with experimental data. These examples recommend our method for application to the elucidation of the pH-dependent properties of proteins.
Cathepsin B ; chemistry ; metabolism ; DNA ; metabolism ; Hydrogen-Ion Concentration ; Molecular Dynamics Simulation ; Monte Carlo Method ; Muramidase ; chemistry ; metabolism ; Probability ; Protein Binding ; Proteins ; chemistry ; metabolism ; Protons ; Thermodynamics

Background and Objectives: The physiological oxygen tension in fetal brains (∼3%, physioxia) is beneficial for the maintenance of neural stem cells (NSCs). Sensitivity to oxygen varies between NSCs from different fetal brain regions, with midbrain NSCs showing selective susceptibility. Data on Hif-1α/Notch regulatory interactions as well as our observations that Hif-1α and oxygen affect midbrain NSCs survival and proliferation prompted our investigations on involvement of Notch signalling in physioxia-dependent midbrain NSCs performance. Methods: and Results: Here we found that physioxia (3% O2 ) compared to normoxia (21% O 2 ) increased proliferation, maintained stemness by suppression of spontaneous differentiation and supported cell cycle progression. Microarray and qRT-PCR analyses identified significant changes of Notch related genes in midbrain NSCs after long-term (13 days), but not after short-term physioxia (48 hours). Consistently, inhibition of Notch signalling with DAPT increased, but its stimulation with Dll4 decreased spontaneous differentiation into neurons solely under normoxic but not under physioxic conditions. Conclusions Notch signalling does not influence the fate decision of midbrain NSCs cultured in vitro in physioxia, where other factors like Hif-1α might be involved. Our findings on how physioxia effects in midbrain NSCs are transduced by alternative signalling might, at least in part, explain their selective susceptibility to oxygen.

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.
Acinetobacter baumannii ; genetics ; isolation & purification ; Bacteria ; genetics ; isolation & purification ; Cell Culture Techniques ; methods ; Drug Resistance, Microbial ; genetics ; Escherichia coli ; genetics ; isolation & purification ; Genome, Bacterial ; Genotype ; Humans ; Internet ; Microbial Sensitivity Tests ; Phenotype ; Whole Genome Sequencing