ProbResist: a database for drug-resistant probiotic bacteria.

Drug resistance remains a global threat, and the rising trend of consuming probiotic-containing foods, many of which harbor antibiotic resistant determinants, has raised serious health concerns. Currently, the lack of accessibility to location-, drug- and species-specific information of drug-resistant probiotics has hampered efforts to combat the global spread of drug resistance. Here, we describe the development of ProbResist, which is a manually curated online database that catalogs reports of probiotic bacteria that have been experimentally proven to be resistant to antibiotics. ProbResist allows users to search for information of drug resistance in probiotics by querying with the names of the bacteria, antibiotic or location. Retrieved results are presented in a downloadable table format containing the names of the antibiotic, probiotic species, resistant determinants, region where the study was conducted and digital article identifiers (PubMed Identifier and Digital Object Identifier) hyperlinked to the original sources. The webserver also presents a simple analysis of information stored in the database. Given the increasing reports of drug-resistant probiotics, an exclusive database is necessary to catalog them in one platform. It will enable medical practitioners and experts involved in policy making to access this information quickly and conveniently, thus contributing toward the broader goal of combating drug resistance. DATABASE URL: https://probresist.com.

Computational Identification of Functional Centers in Complex Proteins: A Step-by-Step Guide With Examples.

In proteins, functional centers consist of the key amino acids required to perform molecular functions such as catalysis, ligand-binding, hormone- and gas-sensing. These centers are often embedded within complex multi-domain proteins and can perform important cellular signaling functions that enable fine-tuning of temporal and spatial regulation of signaling molecules and networks. To discover hidden functional centers, we have developed a protocol that consists of the following sequential steps. The first is the assembly of a search motif based on the key amino acids in the functional center followed by querying proteomes of interest with the assembled motif. The second consists of a structural assessment of proteins that harbor the motif. This approach, that relies on the application of computational tools for the analysis of data in public repositories and the biological interpretation of the search results, has to-date uncovered several novel functional centers in complex proteins. Here, we use recent examples to describe a step-by-step guide that details the workflow of this approach and supplement with notes, recommendations and cautions to make this protocol robust and widely applicable for the discovery of hidden functional centers.