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Results for “peptide design”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Cytosolic expression, solution structures, and molecular dynamics simulation of genetically encodable disulfide‐rich de novo designed peptides

Abstract Disulfide‐rich peptides represent an important protein family with broad pharmacological potential. Recent advances in computational methods have made it possible to design new peptides which adopt a stable conformation de novo. Here, we describe a system to produce disulfide‐rich de novo peptides using Escherichia coli as the expression host. The advantage of this system is that it enables production of uniformly 13 C‐ and 15 N‐labeled peptides for solution nuclear magnetic resonance (NMR) studies. This expression system was used to isotopically label two previously reported de novo designed peptides, and to determine their solution structures using NMR. The ensemble of NMR structures calculated for both peptides agreed well with the design models, further confirming the accuracy of the design protocol. Collection of NMR data on the peptides under reducing conditions revealed a dependency on disulfide bonds to maintain stability. Furthermore, we performed long‐time molecular dynamics (MD) simulations with tempering to assess the stability of two families of de novo designed peptides. Initial designs which exhibited a stable structure during simulations were more likely to adopt a stable structure in vitro , but attempts to utilize this method to redesign unstable peptides to fold into a stable state were unsuccessful. Further work is therefore needed to assess the utility of MD simulation techniques for de novo protein design.

Buchko, Garry W.↗
DOE OSTI2018

Nanotubes, Plates, and Needles: Pathway-Dependent Self-Assembly of Computationally Designed Peptides

Computationally designed peptides form desired antiparallel, tetrameric coiled-coil bundles that hierarchically assemble into a variety of well-controlled nanostructures depending on aqueous solution conditions. The bundles selectively self-assemble into different structures: nanotubes, platelets, or needle-like structures at solution pH values of 4.5, 7, and 10, respectively. The self-assembly produces hollow tubes or elongated needle-like structures at pH conditions associated with charged bundles (pH 4.5 or 10); at neutral pH, near the pI of the bundle, a plate-like self-assembled structure forms. Transmission electron microscopy and small-angle X-ray scattering show the nanotubes to be uniform with a tube diameter of ~13 nm and lengths of up to several μm, yielding aspect ratios >1000. Combining the measured nanostructure geometry with the apparent charged states of the constituent amino acids, a tilted-bundle packing model is proposed for the formation of the homogeneous nanotubes. Here, this work demonstrates the successful use of assembly pathway control for the construction of nanostructures with diverse, well-structured morphologies associated with the folding and self-association of a single type of molecule.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Peptide design by optimization on a data-parameterized protein interaction landscape

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x L , Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x L , Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l - and d -amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC 50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.

42 ENGINEERING↗
DOE OSTI2018

Escherichia coli tolerance of ultraviolet radiation by in vivo expression of a short peptide designed from late embryogenesis abundant protein

Highlights: • A peptide of late embryogenesis abundant (LEA) protein was expressed in E. coli. • LEA peptide expression was induced with IPTG in a concentration-dependent manner. • LEA peptide expression improved E. coli growth under ultraviolet radiation. • LEA peptide could be used to genetically modify organisms for ultraviolet resistance. Ultraviolet (UV) radiation causes damage in all living organisms, including DNA damage that leads to cell death. Herein, we provide a new technique for UV radiation protection through intracellular short peptide expression. The late embryogenesis abundant (LEA) peptide, which functions as a shield that protects macromolecules from various abiotic stress, was obtained from the Polypedilum vanderplanki group 3 LEA protein. Recombinant Escherichia coli BL21 (DE3) expressing functional LEA short peptide in vivo were exposed to UVA and UVC radiation for 4, 6, and 8 h. E. coli transformants expressing the LEA peptide showed higher cell viability under both UVA and UVC treatment at all time points as compared with that of the control. Furthermore, the cells expressing LEA peptide showed a higher number of colony-forming units per dilution under UVA and UVC treatment. These results suggested that expression of the short peptide could be useful for the development of genetically modified organisms and in applications that require resilience of organisms to UV radiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Designing Peptide Fossils That Model the Evolution of the Bacterial Ferredoxin Fold

Electron transfer coupled to redox chemistry is at the heart of metabolism. The proteins responsible for moving electrons (protein electron carriers) must have emerged at the origin of life. The small iron–sulfur-binding bacterial ferredoxins were likely among these first proteins. Embedded within the ferredoxin sequence and structure is a symmetry that points to an ancient gene duplication event. Little is understood about the nature of ferredoxins prior to this duplication event or what environmental factors may have driven the selection for more complex forms. The deep-time molecular history of ferredoxins goes back billions of years and cannot be reconstructed by phylogenetic analyses based on amino acid sequences. Here, we use structure-guided protein design to model a fossil half-ferredoxin stage in the evolution of this fold, the semidoxins, and their symmetric full-length counterparts, the symdoxins. Semidoxin designs homodimerize, exhibiting structural, thermodynamic, and electrochemical behaviors in most cases identical to cognate symdoxins. However, the semi- and symdoxin fossil stages behave differently when incorporated into an in vivo electron transfer complementation assay. Both can support bacterial growth dependent on protein expression. Growth rates of bacteria expressing the semidoxins are much more sensitive to oxygen than those of bacteria expressing symdoxins. Motivated by the in vivo functionality of designed semidoxins, we identified putative naturally occurring semidoxins in extant anaerobic microorganisms. This is consistent with the observed in vivo oxygen sensitivity of the semidoxin designs. One natural semidoxin is shown to be folded and redox active. However, it exists as a mixture of monomers and dimers, suggesting a potential connection between semidoxins and even simpler single iron–sulfur cluster-binding peptides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

VHH antibody loop guides design of a synthetic macrocyclic peptide that potently blocks influenza virus membrane fusion

Abstract Miniaturizing biologically complex structural motifs to produce synthetic functional mimetics holds significant promise for development of new therapeutic modalities. Here, we demonstrate a unique approach using the key binding loop of the single variable domain of a heavy chain (V H H) llama antibody as a starting point for peptide design. V H H antibodies of camelids and sharks generally have longer, but more ligand-efficient complementarity determining region 3 (CDR3) loops and are relatively stable structures. We harnessed these attributes as templates for design of a series of synthetic macrocyclic peptides. The designed peptides exhibit nanomolar binding to influenza hemagglutinin (HA) and heterosubtypic in vitro neutralization breadth against influenza A viruses by inhibiting the low pH mediated HA conformational changes that lead to membrane fusion. X-ray structures of peptide-HA complexes reveal high structural mimicry with the parent V H H antibody. One such macrocycle peptide candidate is promising for further development of broad protection against influenza A group 1 viruses.

Kadam, Rameshwar U.↗
DOE OSTI2017

Comprehensive computational design of ordered peptide macrocycles

Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with l -amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of l - and d -amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods.

Hosseinzadeh, Parisa (ORCID:0000000231287433)↗
DOE OSTI2025

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. Here, we report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

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

Recombinant expression of computationally designed peptide-bundlemers in Escherichia coli

Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗