Bullard McKenzie (brasssteven67)

The massive amount of data generated from genome sequencing brings tons of newly identified mutations, whose pathogenic/non-pathogenic effects need to be evaluated. This has given rise to several mutation predictor tools that, in general, do not consider the specificities of the various protein groups. find more We aimed to develop a predictor tool dedicated to membrane proteins, under the premise that their specific structural features and environment would give different responses to mutations compared to globular proteins. For this purpose, we created TMSNP, a database that currently contains information from 2624 pathogenic and 196 705 non-pathogenic reported mutations located in the transmembrane region of membrane proteins. By computing various conservation parameters on these mutations in combination with annotations, we trained a machine-learning model able to classify mutations as pathogenic or not. TMSNP (freely available at http//lmc.uab.es/tmsnp/) improves considerably the prediction power of commonly used mutation predictors trained with globular proteins.Single-stranded DNA-binding proteins (SSBs) play crucial roles in DNA replication, recombination and repair, and serve as key players in the maintenance of genomic stability. While a number of SSBs bind single-stranded DNA (ssDNA) non-specifically, the others recognize and bind specific ssDNA sequences. The mechanisms underlying this binding discrepancy, however, are largely unknown. Here, we present a comparative study of protein-ssDNA interactions by annotating specific and non-specific SSBs and comparing structural features such as DNA-binding propensities and secondary structure types of residues in SSB-ssDNA interactions, protein-ssDNA hydrogen bonding and π-π interactions between specific and non-specific SSBs. Our results suggest that protein side chain-DNA base hydrogen bonds are the major contributors to protein-ssDNA binding specificity, while π-π interactions may mainly contribute to binding affinity. We also found the enrichment of aspartate in the specific SSBs, a key feature in specific protein-double-stranded DNA (dsDNA) interactions as reported in our previous study. In addition, no significant differences between specific and non-specific groups with respect of conformational changes upon ssDNA binding were found, suggesting that the flexibility of SSBs plays a lesser role than that of dsDNA-binding proteins in conferring binding specificity. Risk stratification of COVID-19 patients upon hospital admission is key for their successful treatment and efficient utilization of hospital resources. We sought to evaluate the risk factors on admission (including comorbidities, vital signs, and initial laboratory assessment) associated with ventilation need and in-hospital mortality in COVID-19. We established a retrospective cohort of COVID-19 patients from Mass General Brigham hospitals. Demographic, clinical, and admission laboratory data were obtained from electronic medical records of patients admitted to the hospital with laboratory-confirmed COVID-19 before May 19, 2020. Multivariable logistic regression analyses were used to construct and validate the Ventilation in COVID Estimator (VICE) and Death in COVID Estimator (DICE) risk scores. The entire cohort included 1042 patients (median age, 64 years; 56.8% male). The derivation and validation cohorts for the risk scores included 578 and 464 patients, respectively. We found four factors to be ints.org). Birth is undisputedly one of the most painful experiences many women endure in their lives. This study aimed to compare the effects of mechanical and warm mechanical massage application in reducing labor pain and enhancing childbirth satisfaction in primipara women. A randomized-controlled trial was conducted on 210 primipara women. Subjects were randomly divided into three groups (each group comprised 70 women) to receive either a mechanical massage