Biology subjects

Montelione, G. T.

Publications and source records attributed to Montelione, G. T..

At least 19 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

Loop Plasticity Drives Paralog-Specific Recognition in BET ET Domains

The bromodomain and extraterminal domain (BET) family of proteins recognizes diverse peptide motifs via their conserved ET domains, yet exhibits paralog-specific binding preferences with unclear structural origins. Here, we use extensive molecular dynamics simulations and ensemble-based analyses to investigate how two known pep-tide epitope binders - one from the host regulatory protein NSD3 and one from the murine leukemia virus integrase - interact with the ET domains of BRD3 and BRD4. Our results reveal that differences in peptide recognition are not driven by large conformational changes, but by subtle variations in loop dynamics and local secondary structure. Two divergent residues (positions 35 and 36) in the flexible loop connecting helices 2 and 3 shape the conformational ensemble of each paralog by modulating the formation of flanking helices (1 and 2), which in turn control the opening of the peptide-binding cavity. These intrinsic dynamical differences influence the number and stability of accessible binding modes, with BRD3 showing greater plasticity and weaker binding, particularly for NSD3. Our findings support a model in which local sequence variation tunes conformational selection and induced fit mechanisms, offering a structural rationale for paralog-specific targeting of BET proteins. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/687848v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1215f22org.highwire.dtl.DTLVardef@1ecd6b0org.highwire.dtl.DTLVardef@2d45eorg.highwire.dtl.DTLVardef@1c473a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

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

Predicting pose distribution of protein domains connected by flexible linkers is an unsolved problem

In CASP16, we assessed the ability of computational methods to predict the distribution of relative orientations of two domains tethered by a flexible linker. The range of interdomain distances and orientations (poses) of such domain-linker-domain (D-L-D) proteins can play an important role in protein function, allostery, aggregation, and the thermodynamics of binding. The CASP16 Conformational Ensembles Experiment included two challenges to predict the interdomain pose distribution of a Staphylococcal protein A (SpA) D-L-D construct, called ZLBT-C, in which two of SpAs five nearly identical domains are connected by either (1) a six-residue wild-type (WT) linker (kadnkf), or (2) an all-glycine (Gly6) linker. The wild-type linker has a highly conserved sequence and is thought to contribute to the energetic barrier for binding with host antibodies. Ground truth was provided by nuclear magnetic resonance (NMR) residual dipolar coupling (RDC) data on WT protein and small angle X-ray scattering (SAXS) data on both proteins in solution. Twenty-five predictor groups submitted 35 sets of predicted conformational distributions, in the form of population-weighted finite ensembles of discrete structures. Unlike traditional CASP assessments that compare predicted atomic models to experimental atomic models, the accuracy of these predictions was assessed by back-calculating NMR RDCs and SAXS curves from each ensemble of atomic models and comparing these results to respective experimental data. Accuracy was also assessed by using kernelization to compare ensembles to the continuous orientational distributions optimally fit to experimental data. In our assessment, predictions spanned a wide range of accuracy, but none were close fits to the combined NMR and SAXS data. In addition, none were able to recapitulate the observed difference between WT and Gly6 proteins, as observed in the SAXS data. These results, and our analysis, highlighted strengths and weaknesses, plus complementarity of NMR RDC and SAXS analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/650885v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@148f520org.highwire.dtl.DTLVardef@d5450eorg.highwire.dtl.DTLVardef@1a8c6aborg.highwire.dtl.DTLVardef@18b2b5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

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

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

bioinformatics↗

bioRxiv2024

Modeling Alternative Conformational States of Pseudo-Symmetric Solute Carrier Transporters using Methods from Machine Learning

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/603529v3_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@15d91e0org.highwire.dtl.DTLVardef@142fc7org.highwire.dtl.DTLVardef@14ce940org.highwire.dtl.DTLVardef@6579d4_HPS_FORMAT_FIGEXP M_FIG C_FIG Conformational memorization impacts the performance of AlphaFold in modeling the dynamic structures of proteins. The Solute Carrier (SLC) superfamily of integral membrane proteins transport a wide array of small molecules across plasma and organelle membranes, and function as important drug transporters and as viral receptors. They populate different conformational states during the solute transport process, including outward-open, intermediate (occluded), and inward-open conformational states. For some SLC proteins this structural "flipping" corresponds to swapping between conformations of their N-terminal and C-terminal symmetry-related sub-structures. Conventional AlphaFold2, AlphaFold3, or Evolutionary Scale Modeling methods typically generate models for only one of these multiple conformational states of SLC proteins. While several AI-based protocols for modeling multiple conformational states of proteins have been described recently, these methods are often impacted by "memorization" of one of the alternative conformational states, and do not always provide both the inward- and outward-open conformations of SLC proteins. Here we assess the impact of memorization in modeling SLC proteins with AlphaFold, and describe a combined ESM - template-based-modeling process, based on a previously described template-based modeling method that relies on the internal pseudo-symmetry of many SLC proteins, to consistently model the alternate conformational states of SLC proteins. We also demonstrate how the resulting multi-state models can be validated experimentally by comparison with sequence-based evolutionary co-variance data (ECs) that encode information about contacts present in the various conformational states adopted by the protein. This simple, rapid, and robust approach for modeling conformational landscapes of pseudo-symmetric SLC proteins is demonstrated for several integral membrane protein transporters, including SLC35F2 the receptor of a feline leukemia virus envelope protein required for viral entry into eukaryotic cells.

bioinformatics↗

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

Hidden Structural States of Proteins Revealed byConformer Selection with AlphaFold-NMR

We introduce AlphaFold-NMR, a novel approach to NMR structure determination that reveals previously undetected protein conformational states. Unlike conventional NMR methods that rely on NOE-derived spatial restraints, AlphaFold-NMR combines AI-driven conformational sampling with Bayesian scoring of realistic protein models against NOESY and chemical shift data. This method uncovers alternative conformational states of the enzyme Gaussia luciferase, involving large-scale changes in the lid, binding pockets, and other surface cavities. It also identifies similar yet distinct conformational states of the human tumor suppressor Cyclin-Dependent Kinase 2-Associated Protein 1. These studies demonstrate the potential of AI-based modeling with enhanced sampling to generate diverse structural models followed by conformer selection and validation with experimental data as an alternative to traditional restraint-satisfaction protocols for protein NMR structure determination. The AlphaFold-NMR framework enables discovery of conformational heterogeneity and cryptic pockets that conventional NMR analysis methods do not distinguish, providing new insights into protein structure-function relationships. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/600902v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1d52566org.highwire.dtl.DTLVardef@8a8c14org.highwire.dtl.DTLVardef@1f28ffeorg.highwire.dtl.DTLVardef@1cba82a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

An isothermal calorimetry assay for determining steady state kinetic and enzyme inhibition parameters for SARS-CoV-2 3CL-protease

This manuscript describes the application of Isothermal Titration Calorimetry (ITC) to characterize the kinetics of 3CLpro from the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) and its inhibition by Ensitrelvir, a known non-covalent inhibitor. 3CLpro is the main protease that plays a crucial role of producing the whole array of proteins necessary for the viral infection that caused the spread of COVID-19, responsible for millions of deaths worldwide as well as global economic and healthcare crises in recent years. The proposed calorimetric method proved to have several advantages over the two types of enzymatic assays so far applied to this system, namely Forster Resonance Energy Transfer (FRET) and Liquid Chromatography-Mass Spectrometry (LC-MS). The developed ITC-based assay provided a rapid response to 3CLpro activity, which was used to directly derive the kinetic enzymatic constants KM and kcat reliably and reproducibly, as well as their temperature dependence, from which the activation energy of the reaction was obtained for the first time. The assay further revealed the existence of two modes of inhibition of 3CLpro by Ensitrelvir, namely a competitive mode as previously inferred by crystallography as well as an unprecedented uncompetitive mode, further yielding the respective inhibition constants with high precision. The calorimetric method described in this paper is thus proposed to be generally and widely used in the discovery and development of drugs targeting 3CLpro.

biochemistry↗

bioRxiv2024

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein- Protein Binding Candidates in High-Throughput Protein Libraries

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/576374v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@b90071org.highwire.dtl.DTLVardef@1cc2f66org.highwire.dtl.DTLVardef@3c1109org.highwire.dtl.DTLVardef@1827353_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

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

The NS1 protein of influenza B virus binds 5'-triphosphorylated dsRNA to suppress RIG-I activation and the antiviral innate immune response

Influenza A and B viruses overcome the host antiviral response to cause a contagious and often severe human respiratory disease. Here, integrative structural biology and biochemistry studies on non- structural protein 1 of influenza B virus (NS1B) reveal a previously unrecognized viral mechanism for innate immune evasion. Conserved basic groups of its C-terminal domain (NS1B-CTD) bind 5- triphosphorylated double-stranded RNA (5ppp-dsRNA), the primary pathogen-associated feature that activates the host retinoic acid-inducible gene I protein (RIG-I) to initiate interferon synthesis and the cellular antiviral response. Like RIG-I, NS1B-CTD preferentially binds blunt-end 5ppp-dsRNA. NS1B-CTD also competes with RIG-I for binding 5ppp-dsRNA, and thus suppresses activation of RIG-Is ATPase activity. Although the NS1B N-terminal domain also binds dsRNA, it utilizes a different binding mode and lacks 5ppp-dsRNA end preferences. In cells infected with wild-type influenza B virus, RIG-I activation is inhibited. In contrast, RIG-I activation and the resulting phosphorylation of transcription factor IRF-3 are not inhibited in cells infected with a mutant virus encoding NS1B with a R208A substitution it its CTD that eliminates its 5ppp-dsRNA binding activity. These results reveal a novel mechanism in which NS1B binds 5ppp-dsRNA to inhibit the RIG-I antiviral response during influenza B virus infection, and open the door to new avenues for antiviral drug discovery.

biochemistry↗

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

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracy rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

biochemistry↗

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↗

bioRxiv2022

Structure determination of protein-peptide complexes from NMR chemical shift data using MELD.

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding upon binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine structures of peptide-protein complexes. Here, we demonstrate this approach for polypeptide complexes formed with the extraterminal (ET) domain of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can form different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding poses, using only backbone chemical shift data, in excellent agreement with experimentally-determined structures. The approach also predicts relative binding affinities of different peptides. This hybrid MELD-NMR approach provides a powerful new tool for structural analysis of protein-polypeptide complexes in the low NMR information content regime, which can be used successfully for flexible systems where one polypeptide binding partner folds upon complex formation.

biophysics↗