Biology subjects

Ramelot, T. A.

Publications and source records attributed to Ramelot, T. A..

9 recordsLinked to original sources

bioRxiv2026

Allosteric Protein Chemical Shift Perturbations are Ubiquitous

While allosteric protein function has been appreciated for decades, the ubiquity of conformational shifts, particularly those distant from the interaction interface, has not been broadly characterized. For example, ligand binding frequently triggers allosteric effects far from the interaction interface, yet the prevalence of these conformational shifts underpinning protein function remain poorly documented. We systematically assessed the generality of allosteric effects as monitored by NMR Chemical Shift Perturbations (CSPs) distant from the interaction interface. In a set of 139 protein-protein complexes, a striking 74% of all significant CSPs are non-local to the binding site. Notably, more than 35% of significant CSPs outside the binding site occur in residues for which the shortest receptor-ligand interatomic distance is more than 10 [A]. Every protein analyzed exhibits a significant fraction (> 8%) of CSPs distant from the binding site. This analysis across a large number of protein structures demonstrates and documents that structural plasticity is a ubiquitous and fundamental property of proteins. Significance StatementStudies of protein dynamics have had a profound impact on biology. Ruth Nussinov famously postulated that multiple protein conformations preexist in dynamic equilibrium, with interconversions that mediate function. While conformational flexibility has been characterized in many specific case studies, the extent to which structural plasticity can be considered a fundamental and ubiquitous property of proteins remains poorly documented. We address a central question: how common is protein structural plasticity? To do so, we compiled a database of protein-protein and protein-peptide complexes with NMR chemical shift data for both bound (holo) and unbound (apo) states. These data reveal the widespread prevalence of long-range structural perturbations induced by ligand binding, demonstrating that structural plasticity is a pervasive and fundamental property of proteins.

bioinformatics↗

bioRxiv2026

The NMR Exchange Format (NEF): Specification and Applications

The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEFs growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.

biochemistry↗

bioRxiv2025

Modeling Alternative Conformational States in CASP16

The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score >0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality based selection. While the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score <0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains a critical frontier for structural biology.

biochemistry↗

bioRxiv2025

From Plasmid to Pure Protein: Production and Characterization of SARS-CoV-2 PLpro

Papain-like protease (PLpro) from SARS-CoV-2 is a high-priority target for COVID-19 antiviral drug development. We present protocols for PLpro production in Escherichia coli. PLpro expressed as a fusion with the Saccharomyces cerevisiae Smt3 protein (SUMO), is purified and obtained in its native form upon hydrolysis, with yields as high as 38 mg L-1. The protocol also provides isotope-enriched samples suitable for NMR studies. Protocols are also presented for PLpro characterization by mass spectrometry, 1D 19F-NMR and 2D heteronuclear NMR, and a fluorescence-based enzyme assay. HighlightsO_LIProduction, purification, and biochemical analysis of native N- and C-termini PLpro C_LIO_LIHigh yields in E. coli, up to 38 mg L-1 using lysogeny broth. C_LIO_LISupports labeled samples for inhibitor interaction studies. C_LIO_LI19F NMR and fluorescence assays for inhibitor screening and IC50 determination. C_LI eTOC BlurbSARS-CoV-2 PLpro is a key cysteine protease involved in viral replication and immune evasion, making it an important target for antiviral drug development. This study presents a detailed protocol for PLpro production, purification, and biochemical analysis, achieving high yields in E. coli. The workflow includes fusion expression with a His-SUMO tag, isotope labeling for inhibitor studies, and assays for screening and quantifying inhibitors. This comprehensive guide facilitates large-scale production of active PLpro for drug discovery and structural studies.

biochemistry↗

bioRxiv2024

Sequence-specific targeting of intrinsically disordered protein regions

Intrinsically disordered proteins and peptides play key roles in biology, but the lack of defined structures and the high variability in sequence and conformational preferences has made targeting such systems challenging. We describe a general approach for designing proteins that bind intrinsically disordered protein regions in diverse extended conformations with side chains fitting into complementary binding pockets. We used the approach to design binders for 39 highly diverse unstructured targets and obtain designs with pM to 100 nM affinities in 34 cases, testing [~]22 designs per target (including polar targets). The designs function in cells and as detection reagents, and are specific for their intended targets in all-by-all binding experiments. Our approach is a major step towards a general solution to the intrinsically disordered protein and peptide recognition problem.

biochemistry↗

bioRxiv2024

Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/575520v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@bac5eforg.highwire.dtl.DTLVardef@190cdborg.highwire.dtl.DTLVardef@9b3d83org.highwire.dtl.DTLVardef@1e38ccc_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPDB Structure Validation Report expanded to include Restraint Analysis C_LIO_LINMR Exchange Format (NEF) and NMR-STAR for distance restraint representation C_LIO_LIStandard distance and dihedral restraint formats for model vs. restraint assessment C_LIO_LIStandardized restraint formats provide interoperability between modeling programs C_LI

bioinformatics↗

bioRxiv2023

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. HighlightsO_LIAF2 models assessed against NMR data for 9 monomeric proteins not used in training. C_LIO_LIAF2 models fit NMR data almost as well as the experimentally-determined structures. C_LIO_LIRPF-DP, PSVS, and PDBStat software provide structure quality and RDC assessment. C_LIO_LIRPF-DP analysis using AF2 models suggests multiple conformational states. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/525096v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@be0a41org.highwire.dtl.DTLVardef@9336e9org.highwire.dtl.DTLVardef@fa1d24org.highwire.dtl.DTLVardef@8f4efe_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

SpecDB: A Relational Database for Archiving Biomolecular NMR Spectra Data

NMR is a valuable experimental tool in the structural biologists toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Protein NMR research groups produce large amounts of data, and there is renewed interest in organizing this data to train new, sophisticated machine learning architectures to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectra Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/480212v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11a8109org.highwire.dtl.DTLVardef@10da6a6org.highwire.dtl.DTLVardef@137ffb5org.highwire.dtl.DTLVardef@174c5b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Hepatitis C Virus Drugs Simeprevir and Grazoprevir Synergize with Remdesivir to Suppress SARS-CoV-2 Replication in Cell Culture

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2 virus. Here we assess ten available HCV protease inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS- CoV-2 Mpro and HCV NS3/4A proteases, and virtual docking experiments show that all ten HCV drugs can potentially bind into the Mpro binding cleft. Seven of these HCV drugs inhibit SARS-CoV-2 Mpro protease activity, while four dock well into the PLpro substrate binding cleft and inhibit PLpro protease activity. These same seven HCV drugs inhibit SARS-CoV-2 virus replication in Vero and/or human cells, demonstrating that HCV drugs that inhibit Mpro, or both Mpro and PLpro, suppress virus replication. Two HCV drugs, simeprevir and grazoprevir synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, thereby increasing remdesivir inhibitory activity as much as 10-fold. HighlightsO_LISeveral HCV protease inhibitors are predicted to inhibit SARS-CoV-2 Mpro and PLpro. C_LIO_LISeven HCV drugs inhibit Mpro enzyme activity, four HCV drugs inhibit PLpro. C_LIO_LISeven HCV drugs inhibit SARS-CoV-2 replication in Vero and/or human cells. C_LIO_LIHCV drugs simeprevir and grazoprevir synergize with remdesivir to inhibit SARS- CoV-2. C_LI eTOC blurbBafna, White and colleagues report that several available hepatitis C virus drugs inhibit the SARS-CoV-2 Mpro and/or PLpro proteases and SARS-CoV-2 replication in cell culture. Two drugs, simeprevir and grazoprevir, synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir antiviral activity as much as 10-fold. O_FIG O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/422511v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1c12181org.highwire.dtl.DTLVardef@7ed993org.highwire.dtl.DTLVardef@1fe56aaorg.highwire.dtl.DTLVardef@ebc34e_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗