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Results for “Protein HMM profile”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

BMC Caller: a webtool to identify and analyze bacterial microcompartment types in sequence data

Bacterial microcompartments (BMCs) are protein-based organelles found across the bacterial tree of life. They consist of a shell, made of proteins that oligomerize into hexagonally and pentagonally shaped building blocks, that surrounds enzymes constituting a segment of a metabolic pathway. The proteins of the shell are unique to BMCs. They also provide selective permeability; this selectivity is dictated by the requirements of their cargo enzymes. We have recently surveyed the wealth of different BMC types and their occurrence in all available genome sequence data by analyzing and categorizing their components found in chromosomal loci using HMM (Hidden Markov Model) protein profiles. To make this a “do-it yourself” analysis for the public we have devised a webserver, BMC Caller (https://bmc-caller.prl.msu.edu), that compares user input sequences to our HMM profiles, creates a BMC locus visualization, and defines the functional type of BMC, if known. Shell proteins in the input sequence data are also classified according to our function-agnostic naming system and there are links to similar proteins in our database as well as an external link to a structure prediction website to easily generate structural models of the shell proteins, which facilitates understanding permeability properties of the shell. Additionally, the BMC Caller website contains a wealth of information on previously analyzed BMC loci with links to detailed data for each BMC protein and phylogenetic information on the BMC shell proteins. Our tools greatly facilitate BMC type identification to provide the user information about the associated organism’s metabolism and enable discovery of new BMC types by providing a reference database of all currently known examples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The protein structurome of Orthornavirae and its dark matter

Metatranscriptomics is uncovering more and more diverse families of viruses with RNA genomes comprising the viral kingdom Orthornavirae in the realm Riboviria. Thorough protein annotation and comparison are essential to get insights into the functions of viral proteins and virus evolution. In addition to sequence- and hmm profile-based methods, protein structure comparison adds a powerful tool to uncover protein functions and relationships. We constructed an Orthornavirae “structurome” consisting of already annotated as well as unannotated (“dark matter”) proteins and domains encoded in viral genomes. We used protein structure modeling and similarity searches to illuminate the remaining dark matter in hundreds of thousands of orthornavirus genomes. The vast majority of the dark matter domains showed either “generic” folds, such as single α-helices, or no high confidence structure predictions. Nevertheless, a variety of lineage-specific globular domains that were new either to orthornaviruses in general or to particular virus families were identified within the proteomic dark matter of orthornaviruses, including several predicted nucleic acid-binding domains and nucleases. In addition, we identified a case of exaptation of a cellular nucleoside monophosphate kinase as an RNA-binding protein in several virus families. Notwithstanding the continuing discovery of numerous orthornaviruses, it appears that all the protein domains conserved in large groups of viruses have already been identified. The rest of the viral proteome seems to be dominated by poorly structured domains including intrinsically disordered ones that likely mediate specific virus-host interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

Abstract In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sequence-structure-function characterization of the emerging tetracycline destructase family of antibiotic resistance enzymes

Tetracycline destructases (TDases) are flavin monooxygenases which can confer resistance to all generations of tetracycline antibiotics. The recent increase in the number and diversity of reported TDase sequences enables a deep investigation of the TDase sequence-structure-function landscape. Here, we evaluate the sequence determinants of TDase function through two complementary approaches: (1) constructing profile hidden Markov models to predict new TDases, and (2) using multiple sequence alignments to identify conserved positions important to protein function. Using the HMM-based approach we screened 50 high-scoring candidate sequences in Escherichia coli, leading to the discovery of 13 new TDases. The X-ray crystal structures of two new enzymes from Legionella species were determined, and the ability of anhydrotetracycline to inhibit their tetracycline-inactivating activity was confirmed. Using the MSA-based approach we identified 31 amino acid positions 100% conserved across all known TDase sequences. The roles of these positions were analyzed by alanine-scanning mutagenesis in two TDases, to study the impact on cell and in vitro activity, structure, and stability. These results expand the diversity of TDase sequences and provide valuable insights into the roles of important residues in TDases, and flavin monooxygenases more broadly.

60 APPLIED LIFE SCIENCES↗