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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Patient mutations in human ATP:cob(I)alamin adenosyltransferase differentially affect its catalytic versus chaperone functions

Human ATP:cob(I)alamin adenosyltransferase (ATR) is a mitochondrial enzyme that catalyzes an adenosyl transfer to cob(I)alamin, synthesizing 5'-deoxyadenosylcobalamin (AdoCbl) or coenzyme B 12 . ATR is also a chaperone that escorts AdoCbl, transferring it to methylmalonyl-CoA mutase, which is important in propionate metabolism. Mutations in ATR lead to methylmalonic aciduria type B, an inborn error of B12 metabolism. Our previous studies have furnished insights into how ATR protein dynamics influence redox-linked cobalt coordination chemistry, controlling its catalytic versus chaperone functions. In this study, we have characterized three patient mutations at two conserved active site residues in human ATR, R190C/H, and E193K and obtained crystal structures of R190C and E193K variants, which display only subtle structural changes. All three mutations were found to weaken affinities for the cob(II)alamin substrate and the AdoCbl product and increase K M(ATP) . 31 P NMR studies show that binding of the triphosphate product, formed during the adenosylation reaction, is also weakened. However, although the k cat of this reaction is significantly diminished for the R190C/H mutants, it is comparable with the WT enzyme for the E193K variant, revealing the catalytic importance of Arg-190. Furthermore, although the E193K mutation selectively impairs the chaperone function by promoting product release into solution, its catalytic function might be unaffected at physiological ATP concentrations. In contrast, the R190C/H mutations affect both the catalytic and chaperoning activities of ATR. Because the E193K mutation spares the catalytic activity of ATR, our data suggest that the patients carrying this mutation are more likely to be responsive to cobalamin therapy.

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

Effects of SARS-CoV-2 Main Protease Mutations at Positions L50, E166, and L167 Rendering Resistance to Covalent and Noncovalent Inhibitors

SARS-CoV-2 propagation under nirmatrelvir and ensitrelvir pressure selects for main protease (MPro) drug-resistant mutations E166V (DRM2), L50F/E166V (DRM3), E166A/L167F (DRM4), and L50F/E166A/L167F (DRM5). DRM2-DRM5 undergoes N-terminal autoprocessing to produce mature MPro with dimer dissociation constants (K dimer ) 2–3 times larger than that of the wildtype. Co-selection of L50F restores catalytic activity of DRM2 and DRM4 from ~10 to 30%, relative to that of the wild-type enzyme, without altering K dimer . Binding affinities and thermodynamic profiles that parallel the drug selection pressure, exhibiting significant decreases in affinity through entropy/enthalpy compensation, were compared with GC373. Reorganization of the active sites due to mutations observed in the inhibitor-free DRM3 and DRM4 structures as compared to MPro WT may account for the reduced binding affinities, although DRM2 and DRM3 complexes with ensitrelvir are almost identical to MPro WT -ensitrelvir. In conclusion, chemical reactivity changes of the mutant active sites due to differences in electrostatic and protein dynamics effects likely contribute to losses in binding affinities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Legionella effector MavC targets the Ube2N~Ub conjugate for noncanonical ubiquitination

The bacterial effector MavC modulates the host immune response by blocking Ube2N activity employing an E1-independent ubiquitin ligation, catalyzing formation of a γ-glutamyl-ε-Lys (Gln40 Ub -Lys92 Ube2N ) isopeptide crosslink using a transglutaminase mechanism. Here we provide biochemical evidence in support of MavC targeting the activated, thioester-linked Ube2N~ubiquitin conjugate, catalyzing an intramolecular transglutamination reaction, covalently crosslinking the Ube2N and Ub subunits effectively inactivating the E2~Ub conjugate. Ubiquitin exhibits weak binding to MavC alone, but shows an increase in affinity when tethered to Ube2N in a disulfide-linked substrate that mimics the charged E2~Ub conjugate. Crystal structures of MavC in complex with the substrate mimic and crosslinked product provide insights into the reaction mechanism and underlying protein dynamics that favor transamidation over deamidation, while revealing a crucial role for the structurally unique insertion domain in substrate recognition. This work provides a structural basis of ubiquitination by transglutamination and identifies this enzyme’s true physiological substrate.

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

An on-demand, drop-on-drop method for studying enzyme catalysis by serial crystallography

Serial femtosecond crystallography has opened up many new opportunities in structural biology. In recent years, several approaches employing light-inducible systems have emerged to enable time-resolved experiments that reveal protein dynamics at high atomic and temporal resolutions. However, very few enzymes are light-dependent, whereas macromolecules requiring ligand diffusion into an active site are ubiquitous. In this work we present a drop-on-drop sample delivery system that enables the study of enzyme-catalyzed reactions in microcrystal slurries. The system delivers ligand solutions in bursts of multiple picoliter-sized drops on top of a larger crystal-containing drop inducing turbulent mixing and transports the mixture to the X-ray interaction region with temporal resolution. We demonstrate mixing using fluorescent dyes, numerical simulations and time-resolved serial femtosecond crystallography, which show rapid ligand diffusion through microdroplets. The drop-on-drop method has the potential to be widely applicable to serial crystallography studies, particularly of enzyme reactions with small molecule substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Conformational spread drives the evolution of the calcium–calmodulin protein kinase II

The calcium calmodulin (Ca 2+ /CaM) dependent protein kinase II (CaMKII) decodes Ca 2+ frequency oscillations. The CaMKIIα isoform is predominantly expressed in the brain and has a central role in learning. I matched residue and organismal evolution with collective motions deduced from the atomic structure of the human CaMKIIα holoenzyme to learn how its ring architecture abets function. Protein dynamic simulations showed its peripheral kinase domains (KDs) are conformationally coupled via lateral spread along the central hub. The underlying β-sheet motions in the hub or association domain (AD) were deconvolved into dynamic couplings based on mutual information. They mapped onto a coevolved residue network to partition the AD into two distinct sectors. A second, energetically stressed sector was added to ancient bacterial enzyme dimers for assembly of the ringed hub. The continued evolution of the holoenzyme after AD–KD fusion targeted the sector’s ring contacts coupled to the KD. Among isoforms, the α isoform emerged last and, it alone, mutated rapidly after the poikilotherm–homeotherm jump to match the evolution of memory. The correlation between dynamics and evolution of the CaMKII AD argues single residue substitutions fine-tune hub conformational spread. The fine-tuning could increase CaMKIIα Ca 2+ frequency response range for complex learning functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Nanobodies against the metal binding domains of ATP7B as tools to study copper transport in the cell

Abstract Nanobodies are genetically engineered single domain antibodies derived from the unusual heavy-chain only antibodies found in llamas and camels. The small size of the nanobodies and flexible selection schemes make them uniquely versatile tools for protein biochemistry and cell biology. We have developed a panel of nanobodies against the metal binding domains of the human copper transporter ATP7B, a multidomain membrane protein with a complex regulation of enzymatic activity and intracellular localization. To enable the use of the nanobodies as tools to investigate copper transport in the cell, we characterized their binding sites and affinity by isothermal titration calorimetry and NMR. We have identified nanobodies against each of the first four metal binding domains of ATP7B, with a wide affinity range, as evidenced by dissociation constants from below 10−9 to 10−6 M. We found both the inhibitory and activating nanobodies among those tested. The diverse properties of the nanobodies make the panel useful for the structural studies of ATP7B, immunoaffinity purification of the protein, modulation of its activity in the cell, protein dynamics studies, and as mimics of copper chaperone ATOX1, the natural interaction partner of ATP7B.

Uhlemann, Eva-Maria E.↗
DOE OSTI2022

Disruption of electrostatic contacts in the HNH nuclease from a thermophilic Cas9 rewires allosteric motions and enhances high-temperature DNA cleavage

Allosteric signaling within multidomain proteins is a driver of communication between spatially distant functional sites. Understanding the mechanism of allosteric coupling in large multidomain proteins is the most promising route to achieving spatial and temporal control of the system. The recent explosion of CRISPR-Cas9 applications in molecular biology and medicine has created a need to understand how the atomic level protein dynamics of Cas9, which are the driving force of its allosteric crosstalk, influence its biophysical characteristics. In this study, we used a synergistic approach of nuclear magnetic resonance (NMR) and computation to pinpoint an allosteric hotspot in the HNH domain of the thermostable GeoCas9. We show that mutation of K597 to alanine disrupts a salt-bridge network, which in turn alters the structure, the timescale of allosteric motions, and the thermostability of the GeoHNH domain. This homologous lysine-to-alanine mutation in the extensively studied mesophilic S. pyogenes Cas9 similarly alters the dynamics of the SpHNH domain. We have previously demonstrated that the alteration of allostery via mutations is a source for the specificity enhancement of SpCas9 (eSpCas9). Hence, this may also be true in GeoCas9.

Chemistry↗
DOE OSTI2025

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗
DOE OSTI2024

Dynamic entity formed by protein and its hydration water

The interaction between protein and water plays a pivotal role in shaping the structure, dynamics, and function of biomacromolecules. A comprehensive understanding of this intricate interplay necessitates a systematic evaluation of interaction strength and its consequential impact on the dynamics of proteins and water across diverse protein systems. Despite numerous works on understanding the dynamics of water and proteins and the coupling between them, there are still unanswered questions. Here, we combine neutron scattering and isotope labeling to probe the dynamics of proteins and their hydration water in a variety of protein systems. We consider proteins of different structures and varying thermostability as well as proteins within living cells with distinct growth temperatures. Simultaneous characterization of protein and hydration water dynamics across diverse systems was achieved. Moreover, we performed water sorption isothermal measurements on three representative proteins to correlate the observed dynamics with the strength of the interaction energies governing each system. The experimental results underscore that proteins manifesting stronger attractive interactions with water display diffusionlike dynamics with higher flexibility upon hydration, concomitant with a reduced mobility in hydration water. Significantly, our findings suggest that, in fact, it is the interaction between protein and its hydration water that facilitates the transfer of mobility from water to protein, with stronger interactions correlating to greater protein flexibility and slower hydration water diffusion. Published by the American Physical Society 2024

Ye, Yongfeng (ORCID:0009000792374198)↗
DOE OSTI2020

Microsecond Molecular Dynamics Simulations of Proteins Using a Quasi-Equilibrium Solvation Shell Model

In this work, we describe the development and implementation of a quasi-equilibrium hydration shell model of biomolecular solvation with adaptive boundaries. Applying the model to microsecond-long molecular dynamics simulations of several protein systems of varying complexity, we find that the model simulation results are of comparable quality to those obtained from simulations of fully solvated systems, but at a reduced computational cost. We discuss the dominant sources of error in the model and outline directions for future improvements.

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

Allosteric prediction via convolutional neural networks and protein structural and dynamical features

Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.

Rajeshwar T., Rajitha [Oak Ridge National Laborato↗
DOE OSTI2023

Hindered segmental dynamics in associative protein hydrogels studied by neutron spin-echo spectroscopy

Transient binding between associating macromolecules can cause qualitative changes to chain dynamics, including modes of conformational relaxation and diffusion, through tethering effects imparted by long-range connectivity. Here, in this work, the role of binding on short-time segmental dynamics in associative polymer gels is investigated by neutron spin-echo (NSE) measurements on a class of model artificial coiled-coil proteins with a systematically varied architecture, probing timescales of 0.1–130 ns, and length scales close to the molecular radius of gyration. The results illustrate effects of transient cross-linking on chain dynamics on different timescales, manifested in changes in segmental relaxation behavior with variations in strand length, chain concentration, and sticker distribution (endblock- vs midblock-functionalized). In all gels, a short-time cooperative diffusion mode is seen over all wave vectors, analogous to a semidilute solution, with no transitions seen at any known structural length scale. However, the diffusion coefficients are found to decrease with increasing junction density across all gels, with the strand length and number of stickers per chain in each gel appearing to play a relatively minor role. The slowing of cooperative diffusion with junction density contrasts with classical predictions of a greater restoring force for fluctuation dissipation due to the increased elasticity, suggesting additional effects of the coiled-coil junctions such as an enhancement in local viscosity that slows dynamics. Notably, the relaxation rates for all gels can be rescaled by the interjunction spacing inferred from small-angle neutron scattering, where they collapse onto a master curve suggestive of self-similar dynamics even in networks with different strand lengths and chain architectures. On long timescales (but shorter than the junction exchange time), a slowing of network relaxation is observed, resulting in a nondecaying plateau in the spin-echo amplitude attributed to a freezing of chain dynamics due to tethering. A characteristic length scale corresponding to the extent of dynamic fluctuations is estimated for each gel, which appears to be smaller than the interjunction spacing but similar to the correlation blob size of the overlapping strands. The results indicate an important role of transient binding on molecular-scale dynamics in associative polymer gels, even on timescales shorter than the junction exchange time, in addition to its effects on long-range self-diffusion previously observed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reflectin: Protein Driver of Dynamically Tunable Biophotonics; New Paradigm for Tunably Reconfigurable Materials

We discovered that the reflectins, with architecture and sequences unlike those of any other proteins known, exhibit a novel interplay between signal-controlled nucleation and growth of dynamically arrested liquid-liquid phase-segregated assemblies that uniquely enables the continuous and precise fine-tuning of physical properties governing the color and brightness of light reflected from the membrane-enclosed nanostructures that contain them. Our research included high-resolution structural analyses conducted at the DOE-BES user facilities of the Stanford Synchrotron Light Source and LBL National Laboratory’s Molecular Foundry, leveraging their capabilities for time-resolved SAXS and cryo-TEM with our ongoing genetic engineering, biophysical and computational analyses to identify the structural determinants and mechanisms governing the dynamic behaviors of the reflectins. We augmented these methods with EPR analyses that revealed for the first time the existence of a specific pathway of reflectin assembly. Using dynamic light scattering and continuously monitored circular dichroism and Raman microscopy, we elucidated the progression of structural changes underlying the mechanisms of signal-induced condensation, folding, assembly, liquefaction, dynamic arrest, regulation and reversal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗
DOE OSTI2022

Applying Crystallography and 19 F NMR to investigate dynamics and partner protein interactions in a long chain Flavodoxin

Flavodoxin (Fld) is a small FMN containing protein that is involved in single electron transfer. The long‐chain flavodoxin in Rhodopseudomonas palustris bacteria replaces ferredoxin as a low‐potential electron carrier when iron is scarce. Thus it is proposed to interact with the bifurcating electron transfer flavoprotein (ETF) that yields low‐potential electrons. A surface loop on Fld interacts with another of Fld's partner proteins, so we hypothesize that it also mediates Fld's interaction with ETF. To monitor interactions with ETF directly and investigate dynamics in this loop, we are using 19 F NMR in solution. 19 F is hyperresponsive to changes in its chemical environment with a chemical shift range of >300 ppm. To provide a static reference point and assess structural heterogeneity, we are also exploiting X‐ray crystallography. In this study we selectively fluorinated the five tyrosine residues in Fld. We obtained resonance assignments from 19 F spectra of Fld variants in which individual tyrosine residues have been replaced. We obtain well resolved signals for each residue, but the resonances' linewidths indicate dynamics that affects some resonances more than others. Y90 residue displays two resonances demonstrating two different conformations that interconvert slowly on an NMR time scale. Meanwhile the crystal structure solved at 2.1Å resolution reveals two molecules per asymmetric unit providing two perspectives on the details of the structure. The crystal symmetry is monoclinic in contrast to most of the other Flds, which are orthorhombic. Interestingly, the long loop bearing Y121 and Y123 is not well resolved in chain B of the crystal structure, and the NMR line of Y123 is exceptionally broad, both indicating that the loop is dynamic and capable of altering its conformation to accomodate binding to a partner protein. Future directions include monitoring the changes in the 19 F NMR of the Fld when titrated with the partner protein, temperature dependence of the NMR spectrum and relaxation studies to evaluate time scales of motions.

Khan, Sharique↗