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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Sas20 is a highly flexible starch-binding protein in the Ruminococcus bromii cell-surface amylosome

Ruminococcus bromii is a keystone species in the human gut that has the rare ability to degrade dietary resistant starch (RS). This bacterium secretes a suite of starch-active proteins that work together within larger complexes called amylosomes that allow R. bromii to bind and degrade RS. Starch adherence system protein 20 (Sas20) is one of the more abundant proteins assembled within amylosomes, but little could be predicted about its molecular features based on amino acid sequence. Here, we performed a structure–function analysis of Sas20 and determined that it features two discrete starch-binding domains separated by a flexible linker. We show that Sas20 domain 1 contains an N-terminal β-sandwich followed by a cluster of α-helices, and the nonreducing end of maltooligosaccharides can be captured between these structural features. Furthermore, the crystal structure of a close homolog of Sas20 domain 2 revealed a unique bilobed starch-binding groove that targets the helical α1,4-linked glycan chains found in amorphous regions of amylopectin and crystalline regions of amylose. Affinity PAGE and isothermal titration calorimetry demonstrated that both domains bind maltoheptaose and soluble starch with relatively high affinity (K d ≤ 20 μM) but exhibit limited or no binding to cyclodextrins. Finally, small-angle X-ray scattering analysis of the individual and combined domains support that these structures are highly flexible, which may allow the protein to adopt conformations that enhance its starch-targeting efficiency. Taken together, we conclude that Sas20 binds distinct features within the starch granule, facilitating the ability of R. bromii to hydrolyze dietary RS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Structural, Functional, and Immunogenic Insights on Cu,Zn Superoxide Dismutase Pathogenic Virulence Factors from Neisseria meningitidis and Brucella abortus

ABSTRACT Bacterial pathogens Neisseria meningitidis and Brucella abortus pose threats to human and animal health worldwide, causing meningococcal disease and brucellosis, respectively. Mortality from acute N. meningitidis infections remains high despite antibiotics, and brucellosis presents alimentary and health consequences. Superoxide dismutases are master regulators of reactive oxygen and general pathogenicity factors and are therefore therapeutic targets. Cu,Zn superoxide dismutases (SODs) localized to the periplasm promote survival by detoxifying superoxide radicals generated by major host antimicrobial immune responses. We discovered that passive immunization with an antibody directed at N. meningitidis SOD (NmSOD) was protective in a mouse infection model. To define the relevant atomic details and solution assembly states of this important virulence factor, we report high-resolution and X-ray scattering analyses of NmSOD and of SOD from B. abortus (BaSOD). The NmSOD structures revealed an auxiliary tetrahedral Cu-binding site bridging the dimer interface; mutational analyses suggested that this metal site contributes to protein stability, with implications for bacterial defense mechanisms. Biochemical and structural analyses informed us about electrostatic substrate guidance, dimer assembly, and an exposed C-terminal epitope in the NmSOD dimer. In contrast, the monomeric BaSOD structure provided insights for extending immunogenic peptide epitopes derived from the protein. These collective results reveal unique contributions of SOD to pathogenic virulence, refine predictive motifs for distinguishing SOD classes, and suggest general targets for antibacterial immune responses. The identified functional contributions, motifs, and targets distinguishing bacterial and eukaryotic SOD assemblies presented here provide a foundation for efforts to develop SOD-specific inhibitors of or vaccines against these harmful pathogens. IMPORTANCE By protecting microbes against reactive oxygen insults, SODs aid survival of many bacteria within their hosts. Despite the ubiquity and conservation of these key enzymes, notable species-specific differences relevant to pathogenesis remain undefined. To probe mechanisms that govern the functioning of Neisseria meningitidis and Brucella abortus SODs, we used X-ray structures, enzymology, modeling, and murine infection experiments. We identified virulence determinants common to the two homologs, assembly differences, and a unique metal reservoir within meningococcal SOD that stabilizes the enzyme and may provide a safeguard against copper toxicity. The insights reported here provide a rationale and a basis for SOD-specific drug design and an extension of immunogen design to target two important pathogens that continue to pose global health threats.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Persistent Protein Motions in a Rugged Energy Landscape Revealed by Normal Mode Ensemble Analysis

Proteins are allosteric machines that couple motions at distinct, often distant, sites to control biological function. Low-frequency structural vibrations are a mechanism of this long-distance connection and are often used computationally to predict correlations, but experimentally identifying the vibrations associated with specific motions has proved challenging. Spectroscopy is an ideal tool to explore these excitations, but measurements have been largely unable to identify important frequency bands. The result is at odds with some previous calculations and raises the question what methods could successfully characterize protein structural vibrations. Here we show the lack of spectral structure arises in part from the variations in protein structure as the protein samples the energy landscape. However, by averaging over the energy landscape as sampled using an aggregate 18.5 μs of all-atom molecular dynamics simulation of hen egg white lysozyme and normal-mode analyses, we find vibrations with large overlap with functional displacements are surprisingly concentrated in narrow frequency bands. These bands are not apparent in either the ensemble averaged vibrational density of states or isotropic absorption. However, in the case of the ensemble averaged anisotropic absorption, there is persistent spectral structure and overlap between this structure and the functional displacement frequency bands. We systematically lay out heuristics for calculating the spectra robustly, including the need for statistical sampling of the protein and inclusion of adequate water in the spectral calculation. The results show the congested spectrum of these complex molecules obscures important frequency bands associated with function and reveal a method to overcome this congestion by combining structurally sensitive spectroscopy with robust normal mode ensemble analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗
DOE OSTI2015

Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods

Soybean (Glycine max) is a major source of vegetable oil and protein for both animal and human consumption. The completion of soybean genome sequence led to a number of transcriptomic studies (RNA-seq), which provide a resource for gene discovery and functional analysis. Several data-driven (e.g., based on gene expression data) and knowledge-based (e.g., predictions of molecular interactions) methods have been proposed and implemented. In order to better understand gene relationships and protein interactions, we applied probabilistic graphical methods, based on Bayesian network and knowledgebase constraints using gene expression data to reconstruct soybean metabolic pathways. The results show that this method can predict new relationships between genes, improving on traditional reference pathway maps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)

Abstract The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Complete genome sequence of the phenanthrene-degrading soil bacterium Delftia acidovorans Cs1-4

Polycyclic aromatic hydrocarbons (PAH) are ubiquitous environmental pollutants and microbial biodegradation is an important means of remediation of PAH-contaminated soil. Delftia acidovorans Cs1-4 (formerly Delftia sp. Cs1-4) was isolated by using phenanthrene as the sole carbon source from PAH contaminated soil in Wisconsin. Its full genome sequence was determined to gain insights into a mechanisms underlying biodegradation of PAH. Three genomic libraries were constructed and sequenced: an Illumina GAii shotgun library (916,416,493 reads), a 454 Titanium standard library (770,171 reads) and one paired-end 454 library (average insert size of 8 kb, 508,092 reads). The initial assembly contained 40 contigs in two scaffolds. The 454 Titanium standard data and the 454 paired end data were assembled together and the consensus sequences were computationally shredded into 2 kb overlapping shreds. Illumina sequencing data was assembled, and the consensus sequence was computationally shredded into 1.5 kb overlapping shreds. Gaps between contigs were closed by editing in Consed, by PCR and by Bubble PCR primer walks. A total of 182 additional reactions were needed to close gaps and to raise the quality of the finished sequence. The final assembly is based on 253.3 Mb of 454 draft data (averaging 38.4 X coverage) and 590.2 Mb of Illumina draft data (averaging 89.4 X coverage). The genome of strain Cs1-4 consists of a single circular chromosome of 6,685,842 bp (66.7 %G+C) containing 6,028 predicted genes; 5,931 of these genes were protein-encoding and 4,425 gene products were assigned to a putative function. Genes encoding phenanthrene degradation were localized to a 232 kb genomic island (termed the phn island), which contained near its 3’ end a bacteriophage P4-like integrase, an enzyme often associated with chromosomal integration of mobile genetic elements. Other biodegradation pathways reconstructed from the genome sequence included: benzoate (by the acetyl-CoA pathway), styrene, nicotinic acid (by the maleamate pathway) and the pesticides Dicamba and Fenitrothion. Lastly, determination of the complete genome sequence of D. acidovorans Cs1-4 has provided new insights the microbial mechanisms of PAH biodegradation that may shape the process in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Who Is Metabolizing What? Discovering Novel Biomolecules in the Microbiome and the Organisms Who Make Them

Even as the field of microbiome research has made huge strides in mapping microbial community composition in a variety of environments and organisms, explaining the phenotypic influences on the host by microbial taxa – both known and unknown – and their specific functions still remain major challenges. A pressing need is the ability to assign specific functions in terms of enzymes and small molecules to specific taxa or groups of taxa in the community. This knowledge will be crucial for advancing personalized therapies based on the targeted modulation of microbes or metabolites that have predictable outcomes to benefit the human host. This perspective article advocates for the combined use of standards-free metabolomics and activity-based protein profiling strategies to address this gap in functional knowledge in microbiome research via the identification of novel biomolecules and the attribution of their production to specific microbial taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Possible Dynamically Gated Conductance along Heme Wires in Bacterial Multiheme Cytochromes

The staggered cross decaheme configuration of electron transfer co-factors in the outer-membrane cytochrome MtrF may serve as a prototype for conformationally-gated multi-heme electron transport. Derived from the bacterium Shewanella oneidensis, the staggered cross configuration reveals intersecting c-type octaheme and tetraheme “wires” containing thermodynamic “hills” and “valleys”, suggesting that the protein structure may include a dynamical mechanism for conductance and pathway switching depending on enzymatic functional need. Recent molecular simulations have established the pair-wise electronic couplings, redox potentials, and reorganization energies to predict the maximum conductance along the various heme wire pathways by sequential hopping of a single electron (PNAS (2014) 11,611-616). Here, we expand this information with classical molecular and statistical mechanics calculations of large-amplitude protein dynamics in MtrF, to address its potential to modulate pathway conductance, including assessment of the effect of the total charge state. Explicit solvent molecular dynamics simulations of fully oxidized and fully reduced MtrF employing ten independent 50-ns simulations at 300 K and 1 atm showed that reduced MtrF is more expanded and explores more conformational space than oxidized MtrF, and that heme reduction leads to increased heme solvent exposure. The slowest mode of collective decaheme motion is 90% similar between the oxidized and reduced states, and consists primarily of inter-heme separation with minor rotational contributions. The frequency of this motion is 1.7×107 s 1 for fully-oxidized and fully-reduced MtrF, respectively, slower than the downhill electron transfer rates between stacked heme pairs at the octaheme termini and faster than the electron transfer rates between parallel hemes in the tetraheme chain. This implies that MtrF uses slow conformational fluctuations to modulate electron flow along the octaheme pathway, apparently for the purpose of increasing the residence time of electrons on lowest potential hemes 4 and 9. This apparent gating mechanism should increase the success rate of electron transfer from MtrF to low potential environmental acceptors via these two solvent-exposed hemes.

c-type cytochromes, MtrF, Shewanella oneidensis, h↗
DOE OSTI2025

Enzyme property prediction using artificial intelligence

Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.

Yuan, Le [University of Illinois at Urbana-Champai↗
DOE OSTI2014

Supersize me: Cronobacter sakazakii phage GAP32

Cronobacter sakazakii is a Gram-negative pathogen found in milk-based formulae that causes infant meningitis. Bacteriophages have been proposed to control bacterial pathogens; however, comprehensive knowledge about a phage is required to ensure its safety before clinical application. We have characterized C. sakazakii phage vB{sub C}saM{sub G}AP32 (GAP32), which possesses the second largest sequenced phage genome (358,663 bp). A total of 571 genes including 545 protein coding sequences and 26 tRNAs were identified, thus more genes than in the smallest bacterium, Mycoplasma genitalium G37. BLASTP and HHpred searches, together with proteomic analyses reveal that only 23.9% of the putative proteins have defined functions. Some of the unique features of this phage include: a chromosome condensation protein, two copies of the large subunit terminase, a predicted signal-arrest-release lysin; and an RpoD-like protein, which is possibly involved in the switch from immediate early to delayed early transcription. Its closest relatives are all extremely large myoviruses, namely coliphage PBECO4 and Klebsiella phage vB{sub K}leM-RaK2, with whom it shares approximately 44% homologous proteins. Since the homologs are not evenly distributed, we propose that these three phages belong to a new subfamily. - Highlights: • Cronobacter sakazakii phage vB{sub C}saM{sub G}AP32 has a genome of 358,663 bp. • It encodes 545 proteins which is more than Mycoplasma genitalium G37. • It is a member of the Myoviridae. • It is peripherally related to coliphage PBECO4 and Klebsiella phage vB{sub K}leM-RaK2. • GAP32 encodes a chromosome condensation protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dual Targeting Factors Are Required for LXG Toxin Export by the Bacterial Type VIIb Secretion System

Bacterial type VIIb secretion systems (T7SSb) are multisubunit integral membrane protein complexes found in Firmicutes that play a role in both bacterial competition and virulence by secreting toxic effector proteins. The majority of characterized T7SSb effectors adopt a polymorphic domain architecture consisting of a conserved N-terminal Leu-X-Gly (LXG) domain and a variable C-terminal toxin domain. Recent work has started to reveal the diversity of toxic activities exhibited by LXG effectors; however, little is known about how these proteins are recruited to the T7SSb apparatus. In this work, we sought to characterize genes encoding domains of unknown function (DUFs) 3130 and 3958, which frequently cooccur with LXG effector-encoding genes. Using coimmunoprecipitation-mass spectrometry analyses, in vitro copurification experiments, and T7SSb secretion assays, we found that representative members of these protein families form heteromeric complexes with their cognate LXG domain and in doing so, function as targeting factors that promote effector export. Additionally, an X-ray crystal structure of a representative DUF3958 protein, combined with predictive modeling of DUF3130 using AlphaFold2, revealed structural similarity between these protein families and the ubiquitous WXG100 family of T7SS effectors. Interestingly, we identified a conserved FxxxD motif within DUF3130 that is reminiscent of the YxxxD/E “export arm” found in mycobacterial T7SSa substrates and mutation of this motif abrogates LXG effector secretion. Overall, our data experimentally link previously uncharacterized bacterial DUFs to type VIIb secretion and reveal a molecular signature required for LXG effector export.

24 antibacterial toxins↗
DOE OSTI2015

High-quality permanent draft genome sequence of the extremely osmotolerant diphenol degrading bacterium Halotalea alkalilenta AW-7T, and emended description of the genus Halotalea

Members of the genus Halotalea (family Halomonadaceae) are of high significance since they can tolerate the greatest glucose and maltose concentrations ever reported for known bacteria and are involved in the degradation of industrial effluents. Here, the characteristics and the permanent-draft genome sequence and annotation of Halotalea alkalilenta AW-7T are described. The microorganism was sequenced as a part of the Genomic Encyclopedia of Type Strains, Phase I: the one thousand microbial genomes (KMG) project at the DOE Joint Genome Institute, and it is the only strain within the genus Halotalea having its genome sequenced. The genome is 4,467,826 bp long and consists of 40 scaffolds with 64.62 % average GC content. A total of 4,104 genes were predicted, comprising of 4,028 protein-coding and 76 RNA genes. Most protein-coding genes (87.79 %) were assigned to a putative function. Halotalea alkalilenta AW-7T encodes the catechol and protocatechuate degradation to β-ketoadipate via the β-ketoadipate and protocatechuate ortho-cleavage degradation pathway, and it possesses the genetic ability to detoxify fluoroacetate, cyanate and acrylonitrile. Lastly, an emended description of the genus Halotalea Ntougias et al. 2007 is also provided in order to describe the delayed fermentation ability of the type strain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A chromosome-level genome assembly of the Chinese cork oak (Quercus variabilis)

Quercus variabilis (Fagaceae) is an ecologically and economically important deciduous broadleaved tree species native to and widespread in East Asia. It is a valuable woody species and an indicator of local forest health, and occupies a dominant position in forest ecosystems in East Asia. However, genomic resources from Q. variabilis are still lacking. Here, we present a high-quality Q. variabilis genome generated by PacBio HiFi and Hi-C sequencing. The assembled genome size is 787 Mb, with a contig N50 of 26.04 Mb and scaffold N50 of 64.86 Mb, comprising 12 pseudo-chromosomes. The repetitive sequences constitute 67.6% of the genome, of which the majority are long terminal repeats, accounting for 46.62% of the genome. We used ab initio , RNA sequence-based and homology-based predictions to identify protein-coding genes. A total of 32,466 protein-coding genes were identified, of which 95.11% could be functionally annotated. Evolutionary analysis showed that Q. variabilis was more closely related to Q. suber than to Q. lobata or Q. robur. We found no evidence for species-specific whole genome duplications in Quercus after the species had diverged. This study provides the first genome assembly and the first gene annotation data for Q. variabilis. These resources will inform the design of further breeding strategies, and will be valuable in the study of genome editing and comparative genomics in oak species.

Han, Biao↗
DOE OSTI2025

Identification and Classification of Fungal GPCR Gene Families

G protein-coupled receptors (GPCRs) are transmembrane proteins crucial for signal transduction in eukaryotes, responding to diverse extracellular signals. Researchers have found and systematically summarized 14 distinct types of GPCRs in fungi but their distribution among numerous fungal species remained largely unexamined. Additionally, three families of mammalian homologs (Rhodopsin, Glutamate, and Frizzled) have been found in previous studies, but they are not included in the systematic classification of fungal GPCRs. Our study establishes a unified classification of 17 GPCR classes in fungi, combining 14 fungal and 3 mammalian previously recognized groups, and classifies 28,294 GPCRs across 1357 fungal species, significantly expanding the scale of GPCRs in fungi and demonstrating their broader distribution. We found that mammalian homologs are notably more prevalent in Early Diverging Fungi (EDF), whereas the previous 14 classes are predominantly found in Ascomycota and Basidiomycota. The most abundant class detected in fungi was Pth11-like GPCRs, exclusively found in Pezizomycotina and involved in fungal pathogenicity. Our analysis suggested that Pezizomycotina ancestor possessed an extensive array of Pth11-like GPCRs, but over time, some species underwent considerable reductions in these GPCRs in conjunction with genome contractions. Utilizing a custom-built convolutional neural network (CNN) for the identification of fungal GPCRs, we identified several putative novel fungal GPCRs. Predicted interactions between these prospective new GPCRs and G-alpha proteins, as simulated by AlphaFold Multimer, provided additional support for their functional relevance. In conclusion, our work defines the first large-scale, unified classification of fungal GPCRs, reveals lineage-specific expansions and contractions, and uncovers previously unrecognized GPCR candidates with potential functional roles in fungal signaling.

G protein-coupled receptors↗
DOE OSTI2013

Detailed analysis of putative genes encoding small proteins in legume genomes

Diverse plant genome sequencing projects coupled with powerful bioinformatics tools have facilitated massive data analysis to construct specialized databases classified according to cellular function. However, there are still a considerable number of genes encoding proteins whose function has not yet been characterized. Included in this category are small proteins (SPs, 30–150 amino acids) encoded by short open reading frames (sORFs). SPs play important roles in plant physiology, growth, and development. Unfortunately, protocols focused on the genome-wide identification and characterization of sORFs are scarce or remain poorly implemented. As a result, these genes are underrepresented in many genome annotations. In this work, we exploited publicly available genome sequences of Phaseolus vulgaris, Medicago truncatula, Glycine max, and Lotus japonicus to analyze the abundance of annotated SPs in plant legumes. Our strategy to uncover bona fide sORFs at the genome level was centered in bioinformatics analysis of characteristics such as evidence of expression (transcription), presence of known protein regions or domains, and identification of orthologous genes in the genomes explored. We collected 6170, 10,461, 30,521, and 23,599 putative sORFs from P. vulgaris, G. max, M. truncatula, and L. japonicus genomes, respectively. Expressed sequence tags (ESTs) available in the DFCI Gene Index database provided evidence that ~one-third of the predicted legume sORFs are expressed. Most potential SPs have a counterpart in a different plant species and counterpart regions or domains in larger proteins. Potential functional sORFs were also classified according to a reduced set of GO categories, and the expression of 13 of them during P. vulgaris nodule ontogeny was confirmed by qPCR. This analysis provides a collection of sORFs that potentially encode for meaningful SPs, and offers the possibility of their further functional evaluation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Regulation of channel function due to physical energetic coupling with a lipid bilayer

Highlights: • Lipid membrane regulation of membrane protein functions has been addressed. • Energetics behind ion channel-membrane coupling phenomena has been investigated. • Charge based interactions stabilize peptide–lipid complex. • Screened Coulomb interaction model explains the energetics. • Van der Waals and electrostatic forces drive peptides and lipids to close proximity. - Abstract: Regulation of membrane protein functions due to hydrophobic coupling with a lipid bilayer has been investigated. An energy formula describing interactions between lipid bilayer and integral ion channels with different structures, which is based on the screened Coulomb interaction approximation, has been developed. Here the interaction energy is represented as being due to charge-based interactions between channel and lipid bilayer. The hydrophobic bilayer thickness channel length mismatch is found to induce channel destabilization exponentially while negative lipid curvature linearly. Experimental parameters related to channel dynamics are consistent with theoretical predictions. To measure comparable energy parameters directly in the system and to elucidate the mechanism at an atomistic level we performed molecular dynamics (MD) simulations of the ion channel forming peptide–lipid complexes. MD simulations indicate that peptides and lipids experience electrostatic and van der Waals interactions for short period of time when found within each other’s proximity. The energies from these two interactions are found to be similar to the energies derived theoretically using the screened Coulomb and the van der Waals interactions between peptides (in ion channel) and lipids (in lipid bilayer) due to mainly their charge properties. The results of in silico MD studies taken together with experimental observable parameters and theoretical energetic predictions suggest that the peptides induce ion channels inside lipid membranes due to peptide–lipid physical interactions. This study provides a new insight helping better understand of the underlying mechanisms of membrane protein functions in cell membrane leading to important biological implications.

60 APPLIED LIFE SCIENCES↗