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At least 235 records · Page 13
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 OSTI2021

Hidden Markov Model: a shortest unique representative approach to detect the protein toxins, virulence factors and antibiotic resistance genes

Objective: Currently, next generation sequencing (NGS) is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient identification of novel or diverged pathogenomic compositions remains a big challenge. It is especially true for short DNA sequence fragments from NGS, since sequence similarity searching is vulnerable to false negatives or false positives, as is mismatching or matching with unrelated proteins. Therefore, this study aimed to establish a bioinformatics approach that can generate unique motif sequences for profiling searching, resulting in high specificity and sensitivity. Results: In this study, we introduced a Shortest Unique Representative Hidden Markov Model (HMM) approach to identify bacterial toxin, virulence factor (VF), and antimicrobial resistance (AR) in short sequence reads. We first construct unique representative domain sequences of toxin genes, VFs, and ARs to avoid potential false positives, and then to use HMM models to accurately identify potential toxin, VF, and AR fragments. The benchmark shows this approach can achieve relatively high specificity and sensitivity if the appropriate cutoff value is applied. Our approach can be used to recognize the protein sequences of known toxins and pathogens, identifies their common characteristics and then searches for similar sequences in other organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Substitution Models of Protein Evolution with Selection on Enzymatic Activity

Abstract Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme–substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

Ferreiro, David↗
DOE OSTI2021

RNA nanotechnology to build a dodecahedral genome of single-stranded RNA virus

The quest for artificial RNA viral complexes with authentic structure while being non-replicative is on its way for the development of viral vaccines. RNA viruses contain capsid proteins that interact with the genome during morphogenesis. The sequence and properties of the protein and genome determine the structure of the virus. For example, the Pariacoto virus ssRNA genome assembles into a dodecahedron. Virus-inspired nanotechnology has progressed remarkably due to the unique structural and functional properties of viruses, which can inspire the design of novel nanomaterials. RNA is a programmable biopolymer able to self-assemble sophisticated 3D structures with rich functionalities. RNA dodecahedrons mimicking the Pariacoto virus quasi-icosahedral genome structures were constructed from both native and 2'-F modified RNA oligos. The RNA dodecahedron easily self-assembled using the stable pRNA three-way junction of bacteriophage phi29 as building blocks. The RNA dodecahedron cage was further characterized by cryo-electron microscopy and atomic force microscopy, confirming the spontaneous and homogenous formation of the RNA cage. The reported RNA dodecahedron cage will likely provide further studies on the mechanisms of interaction of the capsid protein with the viral genome while providing a template for further construction of the viral RNA scaffold to add capsid proteins for the assembly of the viral nucleocapsid as a model. Understanding the self-assembly and RNA folding of this RNA cage may offer new insights into the 3D organization of viral RNA genomes. Finally, the reported RNA cage also has the potential to be explored as a novel virus-inspired nanocarrier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

De novo design of protein structure and function with RFdiffusion

Abstract There has been considerable recent progress in designing new proteins using deep-learning methods 1–9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Science & Technology - Other Topics↗
DOE OSTI2020

Amino acid interacting network in the receptor-binding domain of SARS-CoV-2 spike protein

The relation between amino acid (AA) sequence and biologically active conformation controls the process of polypeptide chains folding into three-dimensional (3d) protein structures. The recent achievements in the resolution achieved in cryo-electron microscopy coupled with improvements in computational methodologies have accelerated the analysis of structures and properties of proteins. However, the detailed interaction between AAs has not been fully elucidated. Herein, we present a de novo method to evaluate inter-amino acid interactions based on the concept of accurately evaluating the amino acid bond pairs (AABP). The results obtained enabled the identification of complex 3d long-range interconnected AA interacting network in proteins. The method is applied to the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. We show that although nearest-neighbor AAs in the primary sequence have large AABP, other nonlocal AAs make substantial contribution to AABP with significant participation of both covalent and hydrogen bonding. Detailed analysis of AABP in RBD reveals the pivotal role they play in sequence conservation with profound implications on residue mutations and for therapeutic drug design. This approach could be easily applied to many other proteins of biomedical interest in life sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae

Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.

09 BIOMASS FUELS↗
DOE OSTI2024

Functional diversification within the heme-binding split-barrel family

Due to neofunctionalization, a single fold can be identified in multiple proteins that have distinct molecular functions. Depending on the time that has passed since gene duplication and the number of mutations, the sequence similarity between functionally divergent proteins can be relatively high, eroding the value of sequence similarity as the sole tool for accurately annotating the function of uncharacterized homologs. Here, we combine bioinformatic approaches with targeted experimentation to reveal a large multifunctional family of putative enzymatic and nonenzymatic proteins involved in heme metabolism. This family (homolog of HugZ (HOZ)) is embedded in the “FMN-binding split barrel” superfamily and contains separate groups of proteins from prokaryotes, plants, and algae, which bind heme and either catalyze its degradation or function as nonenzymatic heme sensors. In prokaryotes these proteins are often involved in iron assimilation, whereas several plant and algal homologs are predicted to degrade heme in the plastid or regulate heme biosynthesis. In the plant Arabidopsis thaliana, which contains two HOZ subfamilies that can degrade heme in vitro (HOZ1 and HOZ2), disruption of AtHOZ1 (AT3G03890) or AtHOZ2A (AT1G51560) causes developmental delays, pointing to important biological roles in the plastid. In the tree Populus trichocarpa, a recent duplication event of a HOZ1 ancestor has resulted in localization of a paralog to the cytosol. Structural characterization of this cytosolic paralog and comparison to published homologous structures suggests conservation of heme-binding sites. This study unifies our understanding of the sequence-structure-function relationships within this multilineage family of heme-binding proteins and presents new molecular players in plant and bacterial heme metabolism.

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 OSTI2024

AlphaFold -assisted structure determination of a bacterial protein of unknown function using X-ray and electron crystallography

Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

molecular replacement↗
DOE OSTI2023

Structure-guided design of a broadly cross-reactive multivalent group a streptococcal vaccine

The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal region of the M protein is variable in sequence, defines the M/emm type, and contains epitopes that elicit opsonic antibodies that protect animals from challenge infections. Although there are >200 M types of Strep A, there is now evidence that structurally related M proteins can be grouped into clusters and that immunity may be cluster-specific in addition to M type-specific. This observation has led to recent studies of structure-based design of multivalent M peptide vaccines to select peptides predicted to cross-react with heterologous M types to improve vaccine coverage. In the current study, we have applied a refined series of peptide structural algorithms to predict immunological cross-reactivity among 117N-terminal M peptides representing the most prevalent M types of Strep A. Based on the results of the structural analyses, in combination with global M type prevalence data, we constructed a 32-valent vaccine containing 19 cross-reactive vaccine candidates predicted to cross-react with 37 heterologous M peptides to which were added 13 type-specific M peptides. Further, the 4-protein recombinant vaccine was immunogenic in rabbits and elicited significant levels of antibodies against 31/32 (97%) vaccine peptides and 28/37 (76%) peptides predicted to cross-react. The vaccine antisera also promoted opsonophagocytic killing of vaccine and cross-reactive M types of Strep A. Based on a recent analysis of M type prevalence of Strep A, the potential global coverage of the 32-valent vaccine is ~90%, ranging from 68% in Africa to 95% in North America. Our results indicate the utility of structure-based design that may be applied to future studies of broadly protective M peptide vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Structural coordination between active sites of a CRISPR reverse transcriptase-integrase complex

CRISPR-Cas systems provide adaptive immunity in bacteria and archaea, beginning with integration of foreign sequences into the host CRISPR genomic locus and followed by transcription and maturation of CRISPR RNAs (crRNAs). In some CRISPR systems, a reverse transcriptase (RT) fusion to the Cas1 integrase and Cas6 maturase creates a single protein that enables concerted sequence integration and crRNA production. To elucidate how the RT-integrase organizes distinct enzymatic activities, we present the cryo-EM structure of a Cas6-RT-Cas1—Cas2 CRISPR integrase complex. The structure reveals a heterohexamer in which the RT directly contacts the integrase and maturase domains, suggesting functional coordination between all three active sites. Together with biochemical experiments, our data support a model of sequential enzymatic activities that enable CRISPR sequence acquisition from RNA and DNA substrates. These findings highlight an expanded capacity of some CRISPR systems to acquire diverse sequences that direct CRISPR-mediated interference.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Draft genome sequences of strains CBS6241 and CBS6242 of the basidiomycetous yeast Filobasidium floriforme

The Tremellomycetes are a species-rich group within the basidiomycete fungi; however, most analyses of this group to date have focused on pathogenic Cryptococcus species within the order Tremellales. Recent genome-assisted studies of other Tremellomycetes have identified interesting features with respect to biotechnological applications as well as the evolution of genes involved in mating and sexual development. Here, we report genome sequences of two strains of Filobasidium floriforme, a species from the order Filobasidiales, which branches basally to the Tremellales, Trichosporonales, and Holtermanniales. The assembled genomes of strains CBS6241 and CBS6242 are 27.4 Mb and 26.4 Mb in size, respectively, with 8314 and 7695 predicted protein-coding genes. Overall sequence identity at nucleic acid level between the strains is 97%. Among the predicted genes are pheromone precursor and pheromone receptor genes as well as two genes encoding homedomain (HD) transcription factors, which are predicted to be part of the mating type (MAT) locus. Sequence analysis indicates that CBS6241 and CBS6242 carry different alleles for both the pheromone/receptor genes as well as the HD transcription factors. Orthology inference identified 1482 orthogroups exclusively found in F. floriforme, some of which were involved in carbohydrate transport and metabolism. Subsequent CAZyme repertoire characterization identified 267 and 247 enzymes for CBS6241 and CBS6242, respectively, the second highest number of CAZymes among the analyzed Tremellomycete species. In addition, F. floriforme contains five CAZymes absent in other species and several plant-cell-wall degrading CAZymes with the highest copy number in Tremellomycota, indicating the biotechnological potential of this species.

59 BASIC BIOLOGICAL SCIENCES↗