https://www.selleckchem.com/products/pirtobrutinib-loxo-305.html 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. 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,