Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase

COG database update 2024

The Clusters of Orthologous Genes (COG) database, originally created in 1997, has been updated to reflect the constantly growing collection of completely sequenced prokaryotic genomes. This update increased the genome coverage from 1309 to 2296 species, including 2103 bacteria and 193 archaea, in most cases, with a single representative genome per genus. This set covers all genera of bacteria and archaea that included organisms with 'complete genomes' as per NCBI databases in November 2023. The number of COGs has been expanded from 4877 to 4981, primarily by including protein families involved in bacterial protein secretion. Accordingly, COG pathways and functional groups now include secretion systems of types II through X, as well as Flp/Tad and type IV pili. These groupings allow straightforward identification and examination of the prokaryotic lineages that encompass-or lack-a particular secretion system. Other developments include improved annotations for the rRNA and tRNA modification proteins, multi-domain signal transduction proteins, and some previously uncharacterized protein families. The new version of COGs is available at https://www.ncbi.nlm.nih.gov/research/COG, as well as on the NCBI FTP site https://ftp.ncbi.nlm.nih.gov/pub/COG/, which also provides archived data from previous COG releases.

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknome". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for over 450 enzymes of unknown function from the model bacteria Escherichia coli uxgsing the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.

Article Summary

Many proteins in any genome, ranging from 30 to 70%, lack an assigned function. This knowledge gap limits the full use of the vast available genomic data. Machine learning has shown promise in transferring functional knowledge from proteins of known functions to similar ones, but largely fails to predict novel functions not seen in its training data. Understanding these failures can guide the development of better machine-learning methods to help experts make accurate functional predictions for uncharacterized proteins.

PII-like signaling proteins: a new paradigm in orchestrating cellular homeostasis

Members of the PII superfamily are versatile, multitasking signaling proteins ubiquitously found in all domains of life. They adeptly monitor and synchronize the cell's carbon, nitrogen, energy, redox, and diurnal states, primarily by binding interdependently to adenyl-nucleotides, including charged nucleotides (ATP, ADP, and AMP) and second messengers such as cyclic adenosine monophosphate (cAMP), cyclic di-adenosine monophosphate (c-di-AMP), and S-adenosylmethionine-AMP (SAM-AMP). These proteins also undergo a variety of posttranslational modifications, such as phosphorylation, adenylation, uridylation, carboxylation, and disulfide bond formation, which further provide cues on the metabolic state of the cell. Serving as precise metabolic sensors, PII superfamily proteins transmit this information to diverse cellular targets, establishing dynamic regulatory assemblies that fine-tune cellular homeostasis. Recently discovered, PII-like proteins are emerging families of signaling proteins that, while related to canonical PII proteins, have evolved to fulfill a diverse range of cellular functions, many of which remain elusive. In this review, we focus on the evolution of PII-like proteins and summarize the molecular mechanisms governing the assembly dynamics of PII complexes, with a special emphasis on the PII-like protein SbtB.

Metalloproteins and metalloproteomics in health and disease

Metalloproteins represents more than one third of human proteome, with huge variation in physiological functions and pathological implications, depending on the metal/metals involved and tissue context. Their functions range from catalysis, bioenergetics, redox, to DNA repair, cell proliferation, signaling, transport of vital elements, and immunity. The human metalloproteomic studies revealed that many families of metalloproteins along with individual metalloproteins are dysregulated under several clinical conditions. Also, several sorts of interaction between redox- active or redox- inert metalloproteins are observed in health and disease. Metalloproteins profiling shows distinct alterations in neurodegenerative diseases, cancer, inflammation, infection, diabetes mellitus, among other diseases. This makes metalloproteins -either individually or as families- a promising target for several therapeutic approaches. Inhibitors and activators of metalloenzymes, metal chelators, along with artificial metalloproteins could be versatile in diagnosis and treatment of several diseases, in addition to other biomedical and industrial applications.

Beyond blast: enabling microbiologists to better extract literature, taxonomic distributions and gene neighbourhood information for protein families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3) family, we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most information published on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighbourhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

Beyond Blast: Enabling Microbiologists to Better Extract Literature, Taxonomic Distributions and Gene Neighborhood Information for Protein Families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that said family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3), we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most published information on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighborhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

On the necessity to include multiple types of evidence when predicting molecular function of proteins

Machine learning-based platforms are currently revolutionizing many fields of molecular biology including structure prediction for monomers or complexes, predicting the consequences of mutations, or predicting the functions of proteins. However, these platforms use training sets based on currently available knowledge and, in essence, are not built to discover novelty. Hence, claims of discovering novel functions for protein families using artificial intelligence should be carefully dissected, as the dangers of overpredictions are real as we show in a detailed analysis of the prediction made by Kim et al 1 on the function of the YciO protein in the model organism Escherichia coli .

RNA-seq analysis identified glucose-responsive genes and YqfO as a global regulator in Bacillus subtilis

Objective

We observed that the addition of glucose enhanced the expression of sigX and sigM, encoding extra-cytoplasmic function sigma factors in Bacillus subtilis. Several regulatory factors were identified for this phenomenon, including YqfO, CshA (RNA helicase), and YlxR (nucleoid-associated protein). Subsequently, the relationships among these regulators were analyzed. Among them, YqfO is conserved in many bacterial genomes and may function as a metal ion insertase or metal chaperone, but has been poorly characterized. Thus, to further characterize YqfO, we performed RNA sequencing (RNA-seq) analysis of YqfO in addition to CshA and YlxR.

Results

We first performed comparative RNA-seq to detect the glucose-responsive genes. Next, to determine the regulatory effects of YqfO in addition to CshA and YlxR, three pairs of comparative RNA-seq analyses were performed (yqfO/wt, cshA/wt, and ylxR/wt). We observed relatively large regulons (approximately 420, 780, and 180 for YqfO, CshA, and YlxR, respectively) and significant overlaps, indicating close relationships among the three regulators. This study is the first to reveal that YqfO functions as a global regulator in B. subtilis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05869-1.