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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifies up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Protein changes associated with reprotonation of the Schiff base in the photocycle of Asp96-->Asn bacteriorhodopsin. The MN intermediate with unprotonated Schiff base but N-like protein structure

The difference Fourier transform infrared spectrum for the N intermediate in the photoreaction of the light-adapted form of bacteriorhodopsin can be recorded at pH 10 at 274 K (Pfefferle, J.-M., Maeda, A., Sasaki, J., and Yoshizawa, T. (1991) Biochemistry 30, 6548-6556). Under these conditions, Asp96-->Asn bacteriorhodopsin gives a photoproduct which shows changes in protein structure similar to those observed in N of wild-type bacteriorhodopsin. However, decreased intensity of the chromophore bands and the single absorbance maximum at about 400 nm indicate that the Schiff base is unprotonated, as in the M intermediate. This photoproduct was named MN. At pH 7, where the supply of proton is not as restricted as at pH 10, Asp96-->Asn bacteriorhodopsin yields N with a protonated Schiff base. The Asn96 residue, which cannot deprotonate as Asp96 in wild-type bacteriorhodopsin, is perturbed upon formation of both MN at pH 10 and N at pH 7. We suggest that the reprotonation of the Schiff base is preceded by a large change in the protein structure including perturbation of the residue at position 96.

Non-NASA Center↗
DOE OSTI2018

Solving protein structure from sparse serial microcrystal diffraction data at a storage-ring synchrotron source

In recent years, the success of serial femtosecond crystallography and the paucity of beamtime at X-ray free-electron lasers have motivated the development of serial microcrystallography experiments at storage-ring synchrotron sources. However, especially at storage-ring sources, if a crystal is too small it will have suffered significant radiation damage before diffracting a sufficient number of X-rays into Bragg peaks for peak-indexing software to determine the crystal orientation. As a consequence, the data frames of small crystals often cannot be indexed and are discarded. Introduced here is a method based on the expand–maximize–compress (EMC) algorithm to solve protein structures, specifically from data frames for which indexing methods fail because too few X-rays are diffracted into Bragg peaks. The method is demonstrated on a real serial microcrystallography data set whose signals are too weak to be indexed by conventional methods. In spite of the daunting background scatter from the sample-delivery medium, it was still possible to solve the protein structure at 2.1 Å resolution. The ability of the EMC algorithm to analyze weak data frames will help to reduce sample consumption. Furthermore, it will also allow serial microcrystallography to be performed with crystals that are otherwise too small to be feasibly analyzed at storage-ring sources.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2013

ModBase, a database of annotated comparative protein structure models and associated resources

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/ modweb). ModBase models are also available through the Protein Model Portal (http://www.prote inmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/ allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein–protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein–ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

IMAGINE: first neutron protein structure and new capabilities for neutron macromolecular crystallography

The first high-resolution neutron protein structure of perdeuterated rubredoxin from Pyrococcus furiosus ( Pf Rd) determined using the new IMAGINE macromolecular neutron crystallography instrument at the Oak Ridge National Laboratory is reported. Neutron diffraction data extending to 1.65 Å resolution were collected from a relatively small 0.7 mm 3 Pf Rd crystal using 2.5 d (60 h) of beam time. The refined structure contains 371 out of 391, or 95%, of the D atoms of the protein and 58 solvent molecules. The IMAGINE instrument is designed to provide neutron data at or near atomic resolution (1.5 Å) from crystals with volume <1.0 mm 3 and with unit-cell edges <100 Å. Beamline features include novel elliptical focusing mirrors that deliver neutrons into a 2.0 × 3.2 mm focal spot at the sample position with full-width vertical and horizontal divergences of 0.5 and 0.6°, respectively. Variable short- and long-wavelength cutoff optics provide automated exchange between multiple-wavelength configurations (λ min = 2.0, 2.8, 3.3 Å to λ max = 3.0, 4.0, 4.5, ~20 Å). These optics produce a more than 20-fold increase in the flux density at the sample and should help to enable more routine collection of high-resolution data from submillimetre-cubed crystals. Notably, the crystal used to collect these Pf Rd data was 5–10 times smaller than those previously reported.

Meilleur, Flora↗
DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A Better Understanding of Protein Structure and Function by the Synthesis and Incorporation of Selenium- and Tellurium Containing Tryptophan Analogs

Unnatural heavy metal-containing amino acid analogs have shown to be very important in the analysis of protein structure, using methods such as X-ray crystallography, mass spectroscopy, and NMR spectroscopy. Synthesis and incorporation of selenium-containing methionine analogs has already been shown in the literature however with some drawbacks due to toxicity to host organisms. Thus synthesis of heavy metal tryptophan analogs should prove to be more effective since the amino acid tryptophan is naturally less abundant in many proteins. For example, bioincorporation of β-seleno[3,2-b]pyrrolyl-L-alanine ([4,5]SeTrp) and β-selenolo[2,3-b]pyrrolyl-L-alanine ([6,7]SeTrp) has been shown in the following proteins without structural or catalytic perturbations: human annexin V, barstar, and dihydrofolate reductase. The reported synthesis of these Se-containing analogs is currently not efficient for commercial purposes. Thus a more efficient, concise, high-yield synthesis of selenotryptophan, as well as the corresponding, tellurotryptophan, will be necessary for wide spread use of these unnatural amino acid analogs. This research will highlight our progress towards a synthetic route of both [6,7]SeTrp and [6,7]TeTrp, which ultimately will be used to study the effect on the catalytic activity of Lignin Peroxidase (LiP).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

Abstract In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

De novo design of protein structure and function with RFdiffusion

Abstract There has been considerable recent progress in designing new proteins using deep-learning methods 1–9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Science & Technology - Other Topics↗
DOE OSTI2024

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF1% value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1982

Protein folding, protein structure and the origin of life: Theoretical methods and solutions of dynamical problems

Theoretical methods and solutions of the dynamics of protein folding, protein aggregation, protein structure, and the origin of life are discussed. The elements of a dynamic model representing the initial stages of protein folding are presented. The calculation and experimental determination of the model parameters are discussed. The use of computer simulation for modeling protein folding is considered.

Weaver, D. L.↗
DOE OSTI2012

Structural Basis for the Acyltransferase Activity of Lecithin: Retinol Acyltransferase-like Proteins

Lecithin:retinol acyltransferase-like proteins, also referred to as HRAS-like tumor suppressors, comprise a vertebrate subfamily of papain-like or NlpC/P60 thiol proteases that function as phospholipid-metabolizing enzymes. HRAS-like tumor suppressor 3, a representative member of this group, plays a key role in regulating triglyceride accumulation and energy expenditure in adipocytes and therefore constitutes a novel pharmacological target for treatment of metabolic disorders causing obesity. Here, we delineate a catalytic mechanism common to lecithin:retinol acyltransferase-like proteins and provide evidence for their alternative robust lipid-dependent acyltransferase enzymatic activity. We also determined high resolution crystal structures of HRAS-like tumor suppressor 2 and 3 to gain insight into their active site architecture. Based on this structural analysis, two conformational states of the catalytic Cys-113 were identified that differ in reactivity and thus could define the catalytic properties of these two proteins. Finally, these structures provide a model for the topology of these enzymes and allow identification of the protein-lipid bilayer interface. This study contributes to the enzymatic and structural understanding of HRAS-like tumor suppressor enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Protein structure determination using metagenome sequence data

Filling in the protein fold picture Fewer than a third of the 14,849 known protein families have at least one member with an experimentally determined structure. This leaves more than 5000 protein families with no structural information. Protein modeling using residue-residue contacts inferred from evolutionary data has been successful in modeling unknown structures, but it requires large numbers of aligned sequences. Ovchinnikov et al. augmented such sequence alignments with metagenome sequence data (see the Perspective by Söding). They determined the number of sequences required to allow modeling, developed criteria for model quality, and, where possible, improved modeling by matching predicted contacts to known structures. Their method predicted quality structural models for 614 protein families, of which about 140 represent newly discovered protein folds. Science , this issue p. 294 ; see also p. 248

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Gao, Mu↗