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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a ‘living data resource.’ Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Structural analysis of extracellular ATP-independent chaperones of streptococcal species and protein substrate interactions

ABSTRACT During infection, bacterial pathogens rely on secreted virulence factors to manipulate the host cell. However, in gram-positive bacteria, the molecular mechanisms underlying the folding and activity of these virulence factors after membrane translocation are not clear. Here, we solved the protein structures of two secreted parvulin and two secreted cyclophilin-like peptidyl-prolyl isomerase (PPIase) ATP-independent chaperones found in gram-positive streptococcal species. The extracellular parvulin-type PPIase, PrsA inStreptococcus pneumoniaeandStreptococcus mutansmaintain dimeric crystal structures reminiscent of folding catalysts that consist of two domains, a PPIase and foldase domain. Structural comparison of the two cyclophilin-like extracellular chaperones fromS. pneumoniaeandStreptococcus pyogeneswith other cyclophilins demonstrates that this group of cyclophilin-like chaperones has novel structural appendages formed by 9- and 24-residue insertions. Furthermore, we demonstrate that deletion ofprsAandslrAgenes impairs the secretion of the cholesterol-dependent pore-forming toxin, pneumolysin inS. pneumoniae. Using protein pull-down and biophysical assays, we demonstrate a direct interaction between PrsA and SlrA with Ply. Then, we developed chaperone-assisted folding assays that show that theS. pneumoniaePrsA and SlrA extracellular chaperones accelerate pneumolysin folding. In addition, we demonstrate that SlrA and, for the first time,S. pyogenes PpiA exhibit PPIase activity and can bind the immunosuppressive drug, cyclosporine A. Altogether, these findings suggest a mechanistic role for streptococcal PPIase chaperones in the activity and folding of secreted virulence factors such as pneumolysin. IMPORTANCE Streptococcal species are a leading cause of lower respiratory infections that annually affect millions of people worldwide. During infection, streptococcal species secrete a medley of virulence factors that allow the bacteria to colonize and translocate to deeper tissues. In many gram-positive bacteria, virulence factors are secreted from the cytosol across the bacterial membrane in an unfolded state. The bacterial membrane-cell wall interface is exposed to the potentially harsh extracellular environment, making it difficult for native virulence factors to fold before being released into the host. ATP-independent PPIase-type chaperones, PrsA and SlrA, are thought to facilitate folding and stabilization of several unfolded proteins to promote the colonization and spread of streptococci. Here, we present crystal structures of the molecular chaperones of PrsA and SlrA homologs from streptococcal species. We provide evidence that theStreptococcus pyogenesSlrA homolog, PpiA, has PPIase activity and binds to cyclosporine A. In addition, we show thatStreptococcus pneumoniaePrsA and SlrA directly interact and fold the cholesterol-dependent pore-forming toxin and critical virulence determinant, pneumolysin.

Microbiology↗
DOE OSTI2023

All polymer microfluidic chips—A fixed target sample delivery workhorse for serial crystallography

The development of x-ray free electron laser (XFEL) light sources and serial crystallography methodologies has led to a revolution in protein crystallography, enabling the determination of previously unobtainable protein structures and near-atomic resolution of otherwise poorly diffracting protein crystals. However, to utilize XFEL sources efficiently demands the continuous, rapid delivery of a large number of difficult-to-handle microcrystals to the x-ray beam. A recently developed fixed-target system, in which crystals of interest are enclosed within a sample holder, which is rastered through the x-ray beam, is discussed in detail in this Perspective. The fixed target is easy to use, maintains sample hydration, and can be readily modified to allow a broad range of sample types and different beamline requirements. Recent innovations demonstrate the potential of such microfluidic-based fixed targets to be an all-around “workhorse” for serial crystallography measurements. In conclusion, this Perspective will summarize recent advancements in microfluidic fixed targets for serial crystallography, examine needs for future development, and guide users in designing, choosing, and utilizing a fixed-target sample delivery device for their system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

Eismann, Stephan↗
DOE OSTI2025

Assessing the potential of deep learning for protein–ligand docking

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein–ligand docking have recently been introduced, so far no previous works have systematically studied the behaviour of the latest docking and structure prediction methods within the broadly applicable context of: (1) using predicted (apo) protein structures for docking (for example, for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (for example, for enzyme design); and (3) having no previous knowledge of binding pockets (for example, for generalization to unknown pockets). To enable a deeper understanding of the real-world utility of docking methods, we introduce PoseBench, a comprehensive benchmark for broadly applicable protein–ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein–ligand docking and protein–ligand structure prediction using both primary ligand and multiligand benchmark datasets, the latter of which we introduce to the DL community. Empirically, using PoseBench, we find that: (1) DL cofolding methods generally outperform comparable conventional and DL docking baseline algorithms, but popular methods such as AlphaFold 3 are still challenged by prediction targets with new protein–ligand binding poses; (2) certain DL cofolding methods are highly sensitive to their input multiple sequence alignments, whereas others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting new or multiligand protein targets.

Morehead, Alex [Lawrence Berkeley National Laborat↗
DOE OSTI2025

Structure and mechanism of human vesicular polyamine transporter

Polyamines play essential roles in gene expression and modulate neuronal transmission in mammals. Vesicular polyamine transporters (VPAT) from the SLC18 family exploit the transmembrane H + gradient to translocate polyamines into secretory vesicles, enabling the quantal release of polyamine neuromodulators and underpinning learning and memory formation. Here, we report the cryo-electron microscopy structures of human VPAT in complex with spermine, spermidine, H + , or tetrabenazine, elucidating discrete lumen-facing states of the antiporter and pivotal interactions between VPAT and its substrate or inhibitor. Leveraging structure-inspired mutagenesis studies and protein structure prediction, we deduce an unforeseen mechanism whereby the polyamine and H + compete for multiple acidic protein residues both directly and indirectly, and rationalize how the antidopaminergic therapeutic tetrabenazine impedes vesicular transport of polyamines. This study unravels the mechanism of an H + -coupled polyamine antiporter, reveals mechanistic diversity between VPAT and other SLC18 antiporters, and raises new prospects for combating human disorders of polyamine homeostasis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of a secretory heme‐binding protein from Nocardia seriolae involved in cell apoptosis

Abstract According to the whole‐genome bioinformatics analysis, a heme‐binding protein from Nocardia seriolae (HBP) was found. HBP was predicted to be a bacterial secretory protein, located at mitochondrial membrane in eukaryotic cells and have a similar protein structure with the heme‐binding protein of Mycobacterium tuberculosis , Rv0203. In this study, HBP was found to be a secretory protein and co‐localized with mitochondria in FHM cells. Quantitative analysis of mitochondrial membrane potential value, caspase‐3 activity, and transcription level of apoptosis‐related genes suggested that overexpression of HBP protein can induce cell apoptosis. In conclusion, HBP was a secretory protein which may target to mitochondria and involve in cell apoptosis in host cells. This research will promote the function study of HBP and deepen the comprehension of the virulence factors and pathogenic mechanisms of N. seriolae .

Wen, Yiming↗
DOE OSTI2025

Probing lanmodulin's mechanisms of rare-earth selectivity for protein-based bioseparations

Our BES Separation Science program project, DE-SC0021007, supported our efforts to begin to understand the mechanisms underlying selectivity of a novel class of lanthanide-binding proteins discovered by our laboratory, called lanmodulin (LanM), and to leverage these proteins for recovery and separations of trivalent rare earth elements (REEs) as well as of trivalent actinides. Overall, our work provides important insights into how higher-order (e.g., secondary, tertiary, and quaternary) protein structure modulates selectivity profiles of proteins that bind f-elements highly selectively. These results are important for advancing the concept of protein-based separations of REEs and, perhaps, of other critical minerals.

Lanmodulin, rare earth elements, protein-based met↗
DOE OSTI2020

Zymogen and activated protein C have similar structural architecture

Activated protein C is a trypsin-like protease with anticoagulant and cytoprotective properties that is generated by thrombin from the zymogen precursor protein C in a reaction greatly accelerated by the cofactor thrombomodulin. The molecular details of this activation remain elusive due to the lack of structural information. We now fill this gap by providing information on the overall structural organization of these proteins using single molecule Förster resonance energy transfer and small angle X-ray scattering. Under physiological conditions, both zymogen and protease adopt a conformation with all domains vertically aligned along an axis 76 Å long and maximal particle size of 120 Å. This conformation is stabilized by binding of Ca 2+ to the Gla domain and is affected minimally by interaction with thrombin. Hence, the zymogen protein C likely interacts with the thrombin-thrombomodulin complex through a rigid body association that produces a protease with essentially the same structural architecture. This scenario stands in contrast to an analogous reaction in the coagulation cascade where conversion of the zymogen prothrombin to the protease meizothrombin by the prothrombinase complex is linked to a large conformational transition of the entire protein. The presence of rigid EGF domains in protein C as opposed to kringles in prothrombin likely accounts for the different conformational plasticity of the two zymogens. The new structural features reported here for protein C have general relevance to vitamin K-dependent clotting factors containing EGF domains, such as factors VII, IX, and X.

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

Exposing structural variations in SARS-CoV-2 evolution

The mutation of SARS-CoV-2 influences viral function as residue replacements affect both physiochemical properties and folding conformations. Although a large amount of data on SARS-CoV-2 is available, the investigation of how viral functions change in response to mutations is hampered by a lack of effective structural analysis. Here, we exploit the advances of protein structure fingerprint technology to study the folding conformational changes induced by mutations. With integration of both protein sequences and folding conformations, the structures are aligned for SARS-CoV to SARS-CoV-2, including Alpha variant (lineage B.1.1.7) and Delta variant (lineage B.1.617.2). The results showed that the virus evolution with change in mutational positions and physicochemical properties increased the affinity between spike protein and ACE2, which plays a critical role in coronavirus entry into human cells. Additionally, these structural variations impact vaccine effectiveness and drug function over the course of SARS-CoV-2 evolution. The analysis of structural variations revealed how the coronavirus has gradually evolved in both structure and function and how the SARS-CoV-2 variants have contributed to more severe acute disease worldwide.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Aggregation Methods for Quantifying PTM and Structural Changes in Bottom-Up Proteomics

Bottom-up proteomic workflows rely on sequential preprocessing steps, commonly including peptide-to-protein aggregation (“roll-up”), to enhance data reliability and interpretability. While roll-up is effective for protein-centered analyses, it may be suboptimal for applications focused on post-translational modifications (PTMs) or protein structural changes, such as limited proteolysis–mass spectrometry (LiP-MS). Here, we investigate how different roll-up strategies influence site-level quantification in PTM differential analysis. Moreover, we introduce a novel site-centric roll-up approach tailored for LiP-MS, which quantifies proteolytic fragments rather than solely tryptic peptides. We benchmark these methods through simulation studies, comparing their sensitivity and specificity in detecting structural and PTM-driven changes. We found that the median and mean roll-up methods outperform the sum method in both PTM and LiP proteomics, and site-level quantification in LiP outperforms peptide-level quantification. Our findings offer the first systematic, data-driven guidance for selecting roll-up techniques in site-level proteomic analyses, with implications for both PTM-focused and structural proteomics studies.

aggregation↗
DOE OSTI2021

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

Abstract Background Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å 2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å 2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SCOPe: improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning

Abstract The Structural Classification of Proteins—extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

59 BASIC BIOLOGICAL SCIENCES↗