Humans benefit from a vast community of microorganisms in their gastrointestinal tract, known as the gut microbiota, numbering in the tens of trillions. An imbalance in the gut microbiota known as dysbiosis, can lead to changes in the metabolite profile, elevating the levels of toxins like Bacteroides fragilis toxin (BFT), colibactin, and cytolethal distending toxin. These toxins are implicated in the process of oncogenesis. However, a significant portion of the Bacteroides fragilis genome consists of functionally uncharacterized and hypothetical proteins. This study delves into the functional characterization of hypothetical proteins (HPs) encoded by the Bacteroides fragilis genome, employing a systematic in silico approach. A total of 379 HPs were subjected to a BlastP homology search against the NCBI non-redundant protein sequence database, resulting in 162 HPs devoid of identity to known proteins. CDD-Blast identified 106 HPs with functional domains, which were then annotated using Pfam, InterPro, SUPERFAMILY, SCANPROSITE, SMART, and CATH. Physicochemical properties, such as molecular weight, isoelectric point, and stability indices, were assessed for 60 HPs whose functional domains were identified by at least three of the aforementioned bioinformatic tools. Subsequently, subcellular localization analysis was examined and the gene ontology analysis revealed diverse biological processes, cellular components, and molecular functions. Remarkably, E1WPR3 was identified as a virulent and essential gene among the HPs. This study presents a comprehensive exploration of B. fragilis HPs, shedding light on their potential roles and contributing to a deeper understanding of this organism\'s functional landscape.

A comprehensive pangenomic approach was employed to analyze the genomes of 75 type II methylotrophs spanning various genera. Our investigation revealed 256 exact core gene families shared by all 75 organisms, emphasizing their crucial role in the survival and adaptability of these organisms. Additionally, we predicted the functionality of 12 hypothetical proteins. The analysis unveiled a diverse array of genes associated with key metabolic pathways, including methane, serine, glyoxylate, and ethylmalonyl-CoA (EMC) metabolic pathways. While all selected organisms possessed essential genes for the serine pathway, Methylooceanibacter marginalis lacked serine hydroxymethyltransferase (SHMT), and Methylobacterium variabile exhibited both isozymes of SHMT, suggesting its potential to utilize a broader range of carbon sources. Notably, Methylobrevis sp. displayed a unique serine-glyoxylate transaminase isozyme not found in other organisms. Only nine organisms featured anaplerotic enzymes (isocitrate lyase and malate synthase) for the glyoxylate pathway, with the rest following the EMC pathway. Methylovirgula sp. 4MZ18 stood out by acquiring genes from both glyoxylate and EMC pathways, and Methylocapsa sp. S129 featured an A-form malate synthase, unlike the G-form found in the remaining organisms. Our findings also revealed distinct phylogenetic relationships and clustering patterns among type II methylotrophs, leading to the proposal of a separate genus for Methylovirgula sp. 4M-Z18 and Methylocapsa sp. S129. This pangenomic study unveils remarkable metabolic diversity, unique gene characteristics, and distinct clustering patterns of type II methylotrophs, providing valuable insights for future carbon sequestration and biotechnological applications.
OBJECTIVE: Methylotrophs have played a significant role in methane-based product production for many years. However, a comprehensive investigation into the diverse genetic architectures across different genera of methylotrophs has been lacking. This study fills this knowledge gap by enhancing our understanding of core hypothetical proteins and unique enzymes involved in methane oxidation, serine, glyoxylate, and ethylmalonyl-CoA pathways. These findings provide a valuable reference for researchers working with other methylotrophic species. Furthermore, this study not only unveils distinctive gene characteristics and phylogenetic relationships but also suggests a reclassification for Methylovirgula sp. 4M-Z18 and Methylocapsa sp. S129 into separate genera due to their unique attributes within their respective genus. Leveraging the synergies among various methylotrophic organisms, the scientific community can potentially optimize metabolite production, increasing the yield of desired end products and overall productivity.

Neisseria gonorrhoeae, a World Health Organization (WHO) declared superbug and the second-most frequent cause of bacterial sexually transmitted infections worldwide is responsible for gonorrhea. Hypothetical proteins are gene products that are predicted to be encoded by a particular gene based on the DNA sequence, but their specific functions and characteristics have not been experimentally determined or verified. In the context of this research, annotating hypothetical proteins is crucial for identifying their potential as therapeutic targets. Without proper annotation, these proteins would remain vague, hindering efforts to understand their roles in disease. The methodology used aims to bridge this gap by employing algorithm-based tools and software to annotate hypothetical proteins and assess their suitability as therapeutic targets based on factors such as essentiality, virulence, subcellular localization, and druggability. Out of 716 N. gonorrhoeae hypothetical proteins reported in UniProt, assessment of crucial pathogenic factors, including essentiality, virulence, subcellular localization, and druggability, effectively filtered and prioritized the hypothetical proteins for further therapeutic exploration and lead to 5 proteins being chosen as targets. The molecular docking studies conducted identified 10 hits targeting the five targets. Conclusively, this study aided in identification of targets and hit compounds for therapeutic targeting of gonorrhea disease.
UNASSIGNED:
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40203-023-00186-w.

Mycoplasma synoviae infection rates in chickens are increasing worldwide. Genomic studies have considerably improved our understanding of M. synoviae biology and virulence. However, approximately 20% of the predicted proteins have unknown functions. In particular, the M. synoviae ATCC 25204 genome has 663 encoding DNA sequences, among which 155 are considered encoding hypothetical proteins (HPs). Several of these genes may encode unknown virulence factors. This study aims to reannotate all 155 proteins in M. synoviae ATCC 25204 to predict new potential virulence factors using currently available databases and bioinformatics tools. Finally, 125 proteins were reannotated, including enzymes (39%), lipoproteins (10%), DNA-binding proteins (6%), phase-variable hemagglutinin (19%), and other protein types (26%). Among 155 proteins, 28 proteins associated with virulence were detected, five of which were reannotated. Furthermore, HP expression was compared before and after the M. synoviae infection of cells to identify potential virulence-related proteins. The expression of 14 HP genes was upregulated, including that of five virulence-related genes. Our study improved the functional annotation of M. synoviae ATCC 25204 from 76% to 95% and enabled the discovery of potential virulence factors in the genome. Moreover, 14 proteins that may be involved in M. synoviae infection were identified, providing candidate proteins and facilitating the exploration of the infection mechanism of M. synoviae.

Fusobacterium nucleatum is a gram-negative bacteria associated with diverse infections like appendicitis and colorectal cancer. It mainly attacks the epithelial cells in the oral cavity and throat of the infected individual. It has a single circular genome of 2.7 Mb. Many proteins in F. nucleatum genome are listed as \"Uncharacterized.\" Annotation of these proteins is crucial for obtaining new facts about the pathogen and deciphering the gene regulation, functions, and pathways along with discovery of novel target proteins. In the light of new genomic information, an armoury of bioinformatic tools were used for predicting the physicochemical parameters, domain and motif search, pattern search, and localization of the uncharacterized proteins. The programs such as receiver operating characteristics determine the efficacy of the databases that have been employed for prediction of different parameters at 83.6%. Functions were successfully assigned to 46 uncharacterized proteins which included enzymes, transporter proteins, membrane proteins, binding proteins, etc. Apart from the function prediction, the proteins were also subjected to string analysis to reveal the interacting partners. The annotated proteins were also put through homology-based structure prediction and modeling using Swiss PDB and Phyre2 servers. Two probable virulent factors were also identified which could be investigated further for potential drug-related studies. The assigning of functions to uncharacterized proteins has shown that some of these proteins are important for cell survival inside the host and can act as effective drug targets.

Human adenoviruses (HAdVs) are non-enveloped, small double stranded DNA (dsDNA) viruses that cause asymptomatic infections, clinical syndromes and significant susceptibility to infections in immunocompromised people. The aim of the present study was to identify critical host proteins and HAdV hypothetical proteins that could be developed as potential host-viral targets for antiHAdV therapy. Here, the function of selected hypothetical proteins of HAdV based on phylogenetic relationship with the therapeutic targets of antiretroviral drugs of human immunodeficiency virus (HIV) was predicted computationally, and characterized the molecular dynamics and binding affinity of DNA polymerase of HAdV. Thirty-eight hypothetical proteins (HPs) of human adenovirus (HAdV) were used in this study. The results showed that HAdV DNA polymerase (P03261) is related to Human TERT (O14746) and HLA-B (P01889) genes. The protein-protein interaction of human five molecular targets (PNP, TERT, CCR5, HLA-B, and NR1I2) of ARVDs are well-coordinated/networked with CD4, AHR, FKBP4, NR3C1, HSP90AA1, and STUB1 proteins in the anti-HIV infection mechanism. The results showed that the free energy score of abacavir and zidovudine binding to HAdV DNA polymerase are -5.8 and -5.4 kcal mol-1 respectively. Also, the control drug, cidofovir and ganciclovir have less binding affinity for DNA polymerase of HAdV when compare to that of abacavir and zidovudine. Similarity was observed in the binding of abacavir and zidovudine to HAdV DNA polymerase (ASP742, ALA743, LEU772, ARG773 and VAL776). In conclusion, combination of abacavir and zidovudine was predicted to be potential therapy for controlling HAdV infection targeting HAdV DNA polymerase.

BACKGROUND: Monkeypox virus is a small, double-stranded DNA virus that causes a zoonotic disease called Monkeypox. The disease has spread from Central and West Africa to Europe and North America and created havoc in some countries all around the world. The complete genome of the Monkeypox virus Zaire-96-I-16 has been sequenced. The viral strain contains 191 protein-coding genes with 30 hypothetical proteins whose structure and function are still unknown. Hence, it is imperative to functionally and structurally annotate the hypothetical proteins to get a clear understanding of novel drug and vaccine targets. The purpose of the study was to characterize the 30 hypothetical proteins through the determination of physicochemical properties, subcellular characterization, function prediction, functional domain prediction, structure prediction, structure validation, structural analysis, and ligand binding sites using Bioinformatics tools.
RESULTS: The structural and functional analysis of 30 hypothetical proteins was carried out in this research. Out of these, 3 hypothetical functions (Q8V547, Q8V4S4, Q8V4Q4) could be assigned a structure and function confidently. Q8V547 protein in Monkeypox virus Zaire-96-I-16 is predicted as an apoptosis regulator which promotes viral replication in the infected host cell. Q8V4S4 is predicted as a nuclease responsible for viral evasion in the host. The function of Q8V4Q4 is to prevent host NF-kappa-B activation in response to pro-inflammatory cytokines like TNF alpha or interleukin 1 beta.
CONCLUSIONS: Out of the 30 hypothetical proteins of Monkeypox virus Zaire-96-I-16, 3 were annotated using various bioinformatics tools. These proteins function as apoptosis regulators, nuclease, and inhibitors of NF-Kappa-B activator. The functional and structural annotation of the proteins can be used to perform a docking with potential leads to discover novel drugs and vaccines against the Monkeypox. In vivo research can be carried out to identify the complete potential of the annotated proteins.

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of \'omics\' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available \'Big Data\' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.

Orientia tsutsugamushi(O. tsutsugamushi) is an intracellular bacterial pathogen which causes zoonosis scrub typhus in humans. Genome of O. tsutsugamushi strain Ikeda contains 214 hypothetical proteins (HPs) which is nearly 20% of the total proteins. Domain and family based functional analysis of HPs results in the annotation of 44 hypothetical proteins. The annotated HPs were classified in to five main classes namely, gene expression and regulation, transport, metabolism, cell signaling and proteolysis. Thus, computational analysis of HPs helps to understand their putative roles in various biological and cellular processes, including pathogenesis for further consideration as potential therapeutic targets.

Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.