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Results for “protein function prediction”

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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Structure of the GH1 domain of guanylate kinase-associated protein from Rattus norvegicus

Graphical abstract: - Highlights: • The crystal structure of GKAP homology domain 1 (GH1) was determined. • GKAP GH1 is a three-helix bundle connected by short flexible loops. • The predicted helix α4 associates weakly with the helix α3, suggesting dynamic nature of the GH1 domain. - Abstract: Guanylate-kinase-associated protein (GKAP) is a scaffolding protein that links NMDA receptor-PSD-95 to Shank–Homer complexes by protein–protein interactions at the synaptic junction. GKAP family proteins are characterized by the presence of a C-terminal conserved GKAP homology domain 1 (GH1) of unknown structure and function. In this study, crystal structure of the GH1 domain of GKAP from Rattus norvegicus was determined in fusion with an N-terminal maltose-binding protein at 2.0 Å resolution. The structure of GKAP GH1 displays a three-helix bundle connected by short flexible loops. The predicted helix α4 which was not visible in the crystal structure associates weakly with the helix α3 suggesting dynamic nature of the GH1 domain. The strict conservation of GH1 domain across GKAP family members and the lack of a catalytic active site required for enzyme activity imply that the GH1 domain might serve as a protein–protein interaction module for the synaptic protein clustering.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Genome-wide identification and functional prediction of silicon (Si) transporters in poplar (Populus trichocarpa)

Abstract Silicon (Si) enhances plant tolerance to various biotic and abiotic stressors such as salinity, drought, and heat. In addition, Si can be biomineralized within plants to form organic carbon-containing phytoliths that can have ecosystem-level consequences by contributing to long-term carbon sequestration. Si is taken up and transported in plants via different transporter proteins such as influx transporters (e.g., Lsi1, Lsi6) and efflux transporters (e.g., Lsi2). Additionally, the imported Si can be deposited in plant leaves via silicification process using the Siliplant 1 (e.g., Slp1) protein. Functional homologs of these proteins have been reported in different food crops. Here, we performed a genome-wide analysis to identify different Si transporters and Slp1 homologs in the bioenergy crop poplar ( Populus trichocarpa Torr. and A. Gray ex W. Hook). We identified one channel-type Si influx transporter (PtLsi1; Potri.017G083300), one Si efflux transporter (PtLsi2; Potri.012G144000) and two proteins like Slp1 (PtSlp1a; Potri.004G168600 and PtSlp1b; Potri.009G129900 ) in the P. trichocarpa genome. We found a unique sequence (KPKPPVFKPPPVPI) in PtSlp1a which is repeated six times. Repeated presence of this sequence in PtSlp1a indicates that this protein might be important for silicification processes in P. trichocarpa. The mutation profiles of different Si transporters in a P. trichocarpa genome-wide association study population identified significant and impactful mutations in Potri.004G168600 and Potri.009G129900 . Using a publically accessible database ( http://bar.utoronto.ca/eplant_poplar/ ), digital expression analysis of the putative Si transporters in P. trichocarpa found low to moderate expression in the anticipated tissues, such as roots and leaves. Subcellular localization analysis found that PtLsi1/PtLsi2 are localized in the plasma membrane, whereas PtSlp1a/PtSlp1b are found in the extracellular spaces. Protein–Protein interaction analysis of PtLsi1/PtLsi2 identified Delta-1-pyrroline-5-carboxylate synthase (P5CS) as one of the main interacting partners of PtLsi2, which plays a key role in proline biosynthesis. Proline is a well-known participant in biotic and abiotic stress tolerance in plants. These findings will reinforce future efforts to modify Si accumulation for enhancing plant stress tolerance and carbon sequestration in poplar.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Toxoplasma gondii: Biochemical and biophysical characterization of recombinant soluble dense granule proteins GRA2 and GRA6

The most prominent structural feature of the parasitophorous vacuole (PV) in which the intracellular parasite Toxoplasma gondii proliferates is a membranous nanotubular network (MNN), which interconnects the parasites and the PV membrane. The MNN function remains unclear. The GRA2 and GRA6 proteins secreted from the parasite dense granules into the PV have been implicated in the MNN biogenesis. Amphipathic alpha-helices (AAHs) predicted in GRA2 and an alpha-helical hydrophobic domain predicted in GRA6 have been proposed to be responsible for their membrane association, thereby potentially molding the MMN in its structure. Here we report an analysis of the recombinant proteins (expressed in detergent-free conditions) by circular dichroism, which showed that full length GRA2 displays an alpha-helical secondary structure while recombinant GRA6 and GRA2 truncated of its AAHs are mainly random coiled. Dynamic light scattering and transmission electron microscopy showed that recombinant GRA6 and truncated GRA2 constitute a homogenous population of small particles (6–8 nm in diameter) while recombinant GRA2 corresponds to 2 populations of particles (∼8–15 nm and up to 40 nm in diameter, respectively). The unusual properties of GRA2 due to its AAHs are discussed. - Highlights: • Toxoplasma gondii: soluble GRA2 forms 2 populations of particles. • T. gondii: the dense granule protein GRA2 folds intrinsically as an alpha-helix. • T. gondii: monomeric soluble GRA6 forms particles of 6–8 nm in diameter. • T. gondii: monomeric soluble GRA6 is random coiled. • Unusual biophysical properties of the dense granule protein GRA2 from T. gondii.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

GBP3 promotes glioma cell proliferation via SQSTM1/p62-ERK1/2 axis

Highlights: • GBP3, an interferon-inducible large GTPase, is highly elevated in human glioma tumors. • GBP3 promotes glioma cell growth in vitro and in vivo through the SQSTM1/p62-ERK1/2 pathway. • GBP3 expression predicts poor outcome of glioma patients. Guanylate binding proteins (GBPs) are interferon-inducible large GTPases and play a crucial role in cell-autonomous immunity. However, the biology function of GBPs in cancer remains elusive. GBP3 is specifically expressed in adult brain. Here we show that GBP3 is highly elevated in human glioma tumors and glioma cell lines. Overexpression of GBP3 dramatically increased glioma cell proliferation whereas silencing GBP3 by RNA interference produced opposite effects. We further showed that GBP3 expression was able to induce sequestosome-1(SQSTM1, also named p62) expression and activate extracellular signal-regulated kinase (ERK1/2). The SQSTM1-ERK1/2 signaling cascade was essential for GBP3-promoted cell growth because depletion of SQSTM1 markedly reduced the phosphorylated ERK1/2 levels and GBP3-mediated cell growth, and inhibition of mitogen-activated protein kinase/ERK kinase abolished GBP3-induced glioma cell proliferation. Consistently, GBP3 overexpression significantly promoted glioma tumor growth in vivo and its expression was inversely correlated with the survival rate of glioma patients. Taken together, these results for the first time suggest that GBP3 contributes to the proliferation of glioma cells via regulating SQSTM1-ERK1/2 pathway, and GBP3 might represent as a new potential therapeutic target against glioma.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Bioinformatics Investigations of Universal Stress Proteins from Mercury-Methylating Desulfovibrionaceae

The presence of methylmercury in aquatic environments and marine food sources is of global concern. The chemical reaction for the addition of a methyl group to inorganic mercury occurs in diverse bacterial taxonomic groups including the Gram-negative, sulfate-reducing Desulfovibrionaceae family that inhabit extreme aquatic environments. The availability of whole-genome sequence datasets for members of the Desulfovibrionaceae presents opportunities to understand the microbial mechanisms that contribute to methylmercury production in extreme aquatic environments. We have applied bioinformatics resources and developed visual analytics resources to categorize a collection of 719 putative universal stress protein (USP) sequences predicted from 93 genomes of Desulfovibrionaceae. We have focused our bioinformatics investigations on protein sequence analytics by developing interactive visualizations to categorize Desulfovibrionaceae universal stress proteins by protein domain composition and functionally important amino acids. We identified 651 Desulfovibrionaceae universal stress protein sequences, of which 488 sequences had only one USP domain and 163 had two USP domains. The 488 single USP domain sequences were further categorized into 340 sequences with ATP-binding motif and 148 sequences without ATP-binding motif. The 163 double USP domain sequences were categorized into (1) both USP domains with ATP-binding motif (3 sequences); (2) both USP domains without ATP-binding motif (138 sequences); and (3) one USP domain with ATP-binding motif (21 sequences). We developed visual analytics resources to facilitate the investigation of these categories of datasets in the presence or absence of the mercury-methylating gene pair (hgcAB). Future research could utilize these functional categories to investigate the participation of universal stress proteins in the bacterial cellular uptake of inorganic mercury and methylmercury production, especially in anaerobic aquatic environments.

Isokpehi, Raphael D. (ORCID:0000000268770840)↗
DOE OSTI2018

Intracellular hepatitis C virus modeling predicts infection dynamics and viral protein mechanisms

Hepatitis C virus infection is a global health problem, with nearly 2 million new infections occurring every year and up to 85% of these becoming chronic infections that pose serious long-term health risks. To effectively reduce the prevalence of HCV infection and associated diseases, it is important to understand the intracellular dynamics of the viral lifecycle. Here, we present a detailed mathematical model that represents the full hepatitis C lifecycle. It is the first full HCV model to be fit to acute intracellular infection data and the first to explore the functions of distinct viral proteins, probing multiple hypotheses of cis - and trans -acting mechanisms to provide insights for drug targeting. Model parameters were derived from the literature, experiments, and fitting to experimental intracellular viral RNA, extracellular viral titer, and HCV core and NS3 protein kinetic data from viral inoculation to steady-state. Our model predicts faster rates for protein translation and polyprotein cleavage than previous replicon models and demonstrates that the processes of translation and synthesis of viral RNA have the most influence on the levels of the species we tracked in experiments. Altogether, our experimental data and the resulting mathematical infection model reveal information about the regulation of core protein during infection, produce specific insights into the roles of the viral core, NS5A, and NS5B proteins, and demonstrate the sensitivities of viral proteins and RNA to distinct reactions within the lifecycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Solitons and protein folding: An In Silico experiment

Protein folding [1] is the process of formation of a functional 3D structure from a random coil — the shape in which amino-acid chains leave the ribosome. Anfinsen’s dogma states that the native 3D shape of a protein is completely determined by protein’s amino acid sequence. Despite the progress in understanding the process rate and the success in folding prediction for some small proteins, with presently available physics-based methods it is not yet possible to reliably deduce the shape of a biologically active protein from its amino acid sequence. The protein-folding problem endures as one of the most important unresolved problems in science; it addresses the origin of life itself. Furthermore, a wrong fold is a common cause for a protein to lose its function or even endanger the living organism. Soliton solutions of a generalized discrete non-linear Schrödinger equation (GDNLSE) obtained from the energy function in terms of bond and torsion angles κ and τ provide a constructive theoretical framework for describing protein folds and folding patterns [2]. Here we study the dynamics of this process by means of molecular-dynamics simulations. The soliton manifestation is the pattern helix–loop–helix in the secondary structure of the protein, which explains the importance of understanding loop formation in helical proteins. We performed in silico experiments for unfolding one subunit of the core structure of gp41 from the HIV envelope glycoprotein (PDB ID: 1AIK [3]) by molecular-dynamics simulations with the MD package GROMACS. We analyzed 80 ns trajectories, obtained with one united-atom and two different all-atom force fields, to justify the side-chain orientation quantification scheme adopted in the studies and to eliminate force-field based artifacts. Our results are compatible with the soliton model of protein folding and provide first insight into soliton-formation dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Genome sequence, phylogenetic analysis, and structure-based annotation reveal metabolic potential of Chlorella sp. SLA-04

Algae are a broad class of photosynthetic eukaryotes that are phylogenetically and physiologically diverse. Most of the phylogenetic diversity has been inferred from 18S rDNA sequencing since there are only a few complete genomes available in public databases. Here we use ultra-long-read Nanopore sequencing to determine a gapless, telomere-to-telomere complete genome sequence of Chlorella sp. SLA-04, previously described as Chlorella sorokiniana SLA-04. Chlorella sp. SLA-04 is a green alga that grows to high cell density in a wide variety of environments - high and neutral pH, high and low alkalinity, and high and low salinity. SLA-04's ability to grow in high pH and high alkalinity media without external CO 2 supply is favorable for large-scale algal biomass production. Phylogenetic analysis performed using ribosomal DNA and conserved protein sequences consistently reveal that Chlorella sp. SLA-04 forms a distinct lineage from other strains of Chlorella sorokiniana. We complement traditional genome annotation methods with high throughput structural predictions and demonstrate that this approach expands functional prediction of the SLA-04 proteome. Genomic analysis of the SLA-04 genome identifies the genes capable of utilizing TCA cycle intermediates to replenish cytosolic acetyl-CoA pools for lipid production. We also identify a complete metabolic pathway for sphingolipid anabolism that may allow SLA-04 to readily adapt to changing environmental conditions and facilitate robust cultivation in mass production systems. Altogether, this work clarifies the phylogeny of Chlorella sp. SLA-04 within Trebouxiophyceae and demonstrates how structural predictions can be used to improve annotation beyond sequencebased methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

PreDSLpmo: A neural network-based prediction tool for functional annotation of lytic polysaccharide monooxygenases

Lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent oxidative enzymes, boost the degradation of polysaccharides such as cellulose, chitin, and others. While experimental methods are used to validate LPMO function, a computational method that can aid experimental methods and provide fast and accurate classification of sequences into LPMOs and its families would be an important step towards understanding the breadth of contributions these enzymes make in deconstruction of recalcitrant polysaccharides. In this study, we developed a machine learning-based tool called PreDSLpmo that employs two different approaches to functionally classify protein sequences into the major LPMO families (AA9 and AA10). The first approach uses a traditional neural network or multilayer percerptron-based approach, while the second employs bi-directional long short-term memory for sequence classification. Finally, our method shows improvement in predictive power when compared with dbCAN2, an existing HMM-profile-based CAZyme predicting tool, on both validation and independent benchmark set.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

MjCyc: Rediscovering the pathway-genome landscape of the first sequenced archaeon, Methanocaldococcus (Methanococcus) jannaschii

The genome of Methanocaldococcus (Methanococcus) jannaschii DSM 2661 was the first Archaeal genome to be sequenced in 1996. Subsequent sequence-based annotation cycles led to its first metabolic reconstruction in 2005. Leveraging new experimental results and function assignments, we have now re-annotated M. jannaschii, creating an updated resource with novel information and testable predictions in a pathway-genome database available at BioCyc.org. This reannotation effort has resulted in 652 function assignments with enzyme roles, accounting for a third of the total protein-coding entries for this genome. The updated resource includes 883 reactions, 540 enzymes, and 142 individual pathways. Despite notable progress in computational genomics, more than a third of the genome remains functionally uncharacterized. The publicly available MjCyc pathway-genome database holds great potential for the wider community to conduct research on the biology of methanogenic Archaea.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nudge-nudge, WNK-WNK (kinases), say no more?

WITH NO LYSINE (WNK) kinases are serine/threonine kinases uniquely characterized by an anomalous placement of a catalytic lysine, hence their moniker. In animals, WNK protein kinases play critical roles in protein trafficking of components that mediate renal ion transport processes and regulate osmoregulation of cell volume. In plants, the WNK kinase gene family is larger and more diverse. Recent studies revealed WNK kinase roles in orchestrating the trafficking of an ion channel, a lipid kinase complex in animals, and a heterotrimeric G protein signaling component in plants that is necessary for signal transduction. For this reason, new attention is geared toward investigating the mechanisms adopted by WNK kinases to nudge intracellular proteins to their destinations. In this review, the functions of WNK kinases in protein trafficking are derived from what we have learned from the model organism Arabidopsis thaliana. To place this new idea in context, we provide the predicted WNK kinase structures, their predicted expression patterns, a speculated evolutionary pathway, and the regulatory roles of plant WNKs in transport processes and other physiologies. We brazenly predict that the WNK kinases in both plants and animals will soon be recognized as a nexus for trafficking-based signal transduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Multiscale multiphysics and multidomain models—Flexibility and rigidity

The emerging complexity of large macromolecules has led to challenges in their full scale theoretical description and computer simulation. Multiscale multiphysics and multidomain models have been introduced to reduce the number of degrees of freedom while maintaining modeling accuracy and achieving computational efficiency. A total energy functional is constructed to put energies for polar and nonpolar solvation, chemical potential, fluid flow, molecular mechanics, and elastic dynamics on an equal footing. The variational principle is utilized to derive coupled governing equations for the above mentioned multiphysical descriptions. Among these governing equations is the Poisson-Boltzmann equation which describes continuum electrostatics with atomic charges. The present work introduces the theory of continuum elasticity with atomic rigidity (CEWAR). The essence of CEWAR is to formulate the shear modulus as a continuous function of atomic rigidity. As a result, the dynamics complexity of a macromolecular system is separated from its static complexity so that the more time-consuming dynamics is handled with continuum elasticity theory, while the less time-consuming static analysis is pursued with atomic approaches. We propose a simple method, flexibility-rigidity index (FRI), to analyze macromolecular flexibility and rigidity in atomic detail. The construction of FRI relies on the fundamental assumption that protein functions, such as flexibility, rigidity, and energy, are entirely determined by the structure of the protein and its environment, although the structure is in turn determined by all the interactions. As such, the FRI measures the topological connectivity of protein atoms or residues and characterizes the geometric compactness of the protein structure. As a consequence, the FRI does not resort to the interaction Hamiltonian and bypasses matrix diagonalization, which underpins most other flexibility analysis methods. FRI's computational complexity is of O(N{sup 2}) at most, where N is the number of atoms or residues, in contrast to O(N{sup 3}) for Hamiltonian based methods. We demonstrate that the proposed FRI gives rise to accurate prediction of protein B-Factor for a set of 263 proteins. We show that a parameter free FRI is able to achieve about 95% accuracy of the parameter optimized FRI. An interpolation algorithm is developed to construct continuous atomic flexibility functions for visualization and use with CEWAR.

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

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A phosphorylation-deficient ribosomal protein eS6 is largely functional in Arabidopsis thaliana , rescuing mutant defects from global translation and gene expression to photosynthesis and growth

The eukaryote-specific ribosomal protein of the small subunit eS6 is phosphorylated through the target of rapamycin (TOR) kinase pathway. Although this phosphorylation event responds dynamically to environmental conditions and has been studied for over 50 years, its biochemical and physiological significance remains controversial and poorly understood. Here, we report data from Arabidopsis thaliana, which indicate that plants expressing only a phospho-deficient isoform of eS6 grow essentially normally under laboratory conditions. The eS6z (RPS6A) paralog of eS6 functionally rescued a double mutant in both rps6a and rps6b genes when expressed at approximately twice the wild-type dosage. A mutant isoform of eS6z lacking the major six phosphorylatable serine and threonine residues in its carboxyl-terminal tail also rescued the lethality, rosette growth, and polyribosome loading of the double mutant. This isoform also complemented many mutant phenotypes of rps6 that were newly characterized here, including photosynthetic efficiency, and most of the gene expression defects that were measured by transcriptomics and proteomics. However, compared with plants rescued with a phospho-enabled version of eS6z, the phospho-deficient seedlings retained a mild pointed-leaf phenotype, root growth was reduced, and certain cell cycle-related mRNAs and ribosome biogenesis proteins were misexpressed. The residual defects of the phospho-deficient seedlings could be understood as an incomplete rescue of the rps6 mutant defects. There was little or no evidence for gain-of-function defects. As previously published, the phospho-deficient eS6z also rescued the rps6a and rps6b single mutants; however, phosphorylation of the eS6y (RPS6B) paralog remained lower than predicted, further underscoring that plants can tolerate phospho-deficiency of eS6 well. Our data also yield new insights into how plants cope with mutations in essential, duplicated ribosomal protein isoforms.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

Evolutionary, structural and biochemical evidence for a new interaction site of the leptin obesity protein

The Leptin protein is central to the regulation of energy metabolism in mammals. By integrating evolutionary, structural, and biochemical information, a surface segment, outside of its known receptor contacts, is predicted as a second interaction site that may help to further define its roles in energy balance and its functional differences between humans and other mammals.

Evolution, Molecular↗
DOE OSTI2022

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Constructing a folding model for protein S6 guided by native fluctuations deduced from NMR structures

The diversity in a set of protein nuclear magnetic resonance (NMR) structures provides an estimate of native state fluctuations that can be used to refine and enrich structure-based protein models (SBMs). Dynamics are an essential part of a protein’s functional native state. The dynamics in the native state are controlled by the same funneled energy landscape that guides the entire folding process. SBMs apply the principle of minimal frustration, drawn from energy landscape theory, to construct a funneled folding landscape for a given protein using only information from the native structure. On an energy landscape smoothed by evolution towards minimal frustration, geometrical constraints, imposed by the native structure, control the folding mechanism and shape the native dynamics revealed by the model. Native-state fluctuations can alternatively be estimated directly from the diversity in the set of NMR structures for a protein. Based on this information, we identify a highly flexible loop in the ribosomal protein S6 and modify the contact map in a SBM to accommodate the inferred dynamics. By taking into account the probable native state dynamics, the experimental transition state is recovered in the model, and the correct order of folding events is restored. Our study highlights how the shared energy landscape connects folding and function by showing that a better description of the native basin improves the prediction of the folding mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cross-reactive immunogenicity of group A streptococcal vaccines designed using a recurrent neural network to identify conserved M protein linear epitopes

The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal sequence of the protein defines the more than 200 M types of Strep A and also contains epitopes that elicit opsonic antibodies, some of which cross-react with heterologous M types. Current efforts to develop broadly protective M protein-based vaccines are directed at identifying potential cross-protective epitopes located in the N-terminal regions of cluster-related M proteins for use as vaccine antigens. In this study, we have used a comprehensive approach using the recurrent neural network ABCpred and IEDB epitope conservancy analysis tools to predict 16 residue linear B-cell epitopes from 117 clinically relevant M types of Strep A (~88% of global Strep A infections). Furthermore, to examine the immunogenicity of these epitope-based vaccines, nine peptides that together shared ≥60% sequence identity with 37 heterologous M proteins were incorporated into two recombinant hybrid protein vaccines, in which the epitopes were repeated 2 or 3 times, respectively. The combined immune responses of immunized rabbits showed that the vaccines elicited significant levels of antibodies against all nine vaccine epitopes present in homologous N-terminal 1–50 amino acid synthetic M peptides, as well as cross-reactive antibodies against 16 of 37 heterologous M peptides predicted to contain similar epitopes. The epitope-specificity of the cross-reactive antibodies was confirmed by ELISA inhibition assays and functional opsonic activity was assayed in HL-60-based bactericidal assays. The results provide important information for the future design of broadly protective M protein-based Strep A vaccines.

60 APPLIED LIFE SCIENCES↗