bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.06.17.156638

REDCRAFT: A Computational Platform Using Residual Dipolar Coupling NMR Data for Determining Structures of Perdeuterated Proteins Without NOEs

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination by NOEs imposes increasing quantity of data for analysis, increased cost of structure determination and is less available in the study of perdeuterated proteins. In the recent decade, Residual Dipolar Couplings (RDCs) have been investigated as an alternative source of data for structural elucidation of proteins by NMR. Several methods have been reported that utilize RDCs in addition to NOEs, and a few utilize RDC data alone. While these methods have individually demonstrated some successes, none of these methods have exposed the full potential of protein structure determination from RDCs. To date, structure determination of proteins from RDCs is limited to small proteins (less than 8.5 kDa) using RDC data from many alignment media (>3) that cannot be collected from larger proteins. Here we present the latest version of the REDCRAFT software package designed for structure determination of proteins from RDC data alone. We have demonstrated the success of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data and large proteins (145 to 573 residues) using simulated RDC data that can be collected from perdeuterated proteins. Finally, we demonstrate the accuracy of structure determination of REDCRAFT from RDCs alone in application to the structurally novel PF.2048 protein. The RDC-based structure of PF.2048 exhibited 1.0 [A] of BB-RMSD with respect to the NOE-based structure by only using a small amount of backbone RDCs ([~]3 restraints per residue) compared to what is required by other approaches. Author SummaryResidual Dipolar Couplings have the potential to reduce the cost and the time needed to characterize protein structures. In addition, RDC data have been demonstrated to concurrently elucidate structure of proteins, perform assignment of resonances, and be used in characterization of the internal dynamics of proteins. Given all the advantages associated with the study of proteins from RDC data, based on the statistics provided by the Protein Databank (PDB), surprisingly the only 124 proteins (out of nearly 150,000 proteins) have utilized RDCs as part of their structure determination. Even a smaller subset of these proteins (approximately 7) have utilized RDCs as the primary source of data for structure determination. The impeding factor in the use of RDCs is the challenging computational and analytical aspects of this source of data. In this report, we demonstrate the success of the REDCRAFT software package in structure determination of proteins using RDC data that can be collected from small and large proteins in a routine fashion. REDCRAFT accomplishes the challenging task of structure determination from RDCs by introducing a unique search and optimization technique that is both robust and computationally tractable. Structure determination from routinely collectable RDC data using REDCRAFT can lead to faster and cheaper study of larger and more complex proteins by NMR spectroscopy in solution state.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cole, C. A., Daigham, N. S., Liu, G., Montelione, G. T., Valafar, H.. 2020-06-17. REDCRAFT: A Computational Platform Using Residual Dipolar Coupling NMR Data for Determining Structures of Perdeuterated Proteins Without NOEs. https://doi.org/10.1101/2020.06.17.156638

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.

bioinformatics↗

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗