Biology subjects

Lindert, S.

Publications and source records attributed to Lindert, S..

11 recordsLinked to original sources

bioRxiv2026

Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network improves protein structure prediction

Surface-Induced Dissociation native Mass Spectrometry (SID-nMS) is a tandem MS activation method that yields information on the connectivity and stoichiometry of protein complexes. While insufficient for direct structure elucidation, the data derived from SID-nMS has considerable potential to inform multimeric protein structure prediction. We hypothesized that incorporating this data into a machine-learning framework could improve multimer prediction accuracy beyond that of existing deep-learning methods. To this end, we developed SIDFold, a novel AlphaFold-based deep-learning network. SIDFold is the first AlphaFold-like network to leverage experimental data during protein complex prediction, and the first deep-learning network to utilize nMS data for structure prediction. We benchmarked SIDFold on the BETA protein set, and observed an improvement in RMSD in 138 of 227 cases including 27 targets in which the predicted structure attained near-native accuracy. We then evaluated the network on 20 proteins with experimental SID-nMS data, yielding an improved RMSD in 18 cases, with five of these cases improving to a high-accuracy complex. Finally, we tested SIDFold against a previously published SID-guided Rosetta docking method, where we saw improvement in 13 of 16 proteins. SIDFold is freely available on GitHub, with example files and commands available in the Supplementary Information.

biochemistry↗

bioRxiv2026

Dynamic-Structure Redesign of Calmodulin Reveals Mechanistic Constraints on Ryr2 Regulation

2.Calmodulin (CaM) is a highly conserved Ca2+ sensor that regulates hundreds of cellular targets through Ca2+ -dependent conformational dynamics. Despite its central role in Ca2+ signaling and disease, its evolutionary conservation and structural flexibility have suggested that CaM is resistant to rational redesign. Here, using the cardiac Ca2+ release channel Ryanodine receptor 2 (RyR2) as a model system, we tested whether incorporating conformational dynamics into computational protein design enables functional reengineering of CaM. We first applied a static structure-based redesign to increase CaM-RyR2 affinity. Although the resulting variant bound more tightly to both the RyR2 peptide and the intact channel in vitro, it distorted peptide geometry and worsened Ca2+ leak in cardiomyocytes ex vivo. Guided by molecular dynamics simulations, we then developed a dynamic-structure redesign strategy that preserves conformational integrity while strengthening binding. The resulting CaM variant exhibited increased RyR2 affinity and reduced pathological Ca2+ leak in a disease-relevant model. These findings show that improved binding affinity alone is insufficient to enhance physiological regulation and that successful CaM redesign requires preservation of conformational dynamics. More broadly, they demonstrate that integrating conformational dynamics into protein redesign can enable functionally predictive engineering of flexible regulatory protein-protein interactions.

biophysics↗

bioRxiv2026

Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction

Ion mobility mass spectrometry (IM-MS) provides valuable structural information about protein shape and size through collision cross section (CCS). However, it lacks atomic level structural detail. While AlphaFold has been successful in predicting monomeric protein structure, it can struggle with modeling protein complexes. To address these limitations, we developed a method that integrates IM-MS data with AlphaFold and Rosetta to improve complex structure prediction. Our approach uses experimental CCS data to guide the assembly of AlphaFold predicted subunits using a Rosetta docking pipeline and evaluating the resulting complexes with a newly developed score. Using this strategy, we were able to improve root mean square deviation (RMSD) values for 26 of 38 (68%) complexes compared to AlphaFold-Multimer. Furthermore, 16 of these systems improved significantly from greater than 4 [A] RMSD to less than 4 [A]. This method demonstrates a robust approach to overcome limitations in complex assembly modeling. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/706193v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@cf71c3org.highwire.dtl.DTLVardef@135e09aorg.highwire.dtl.DTLVardef@2cc2fcorg.highwire.dtl.DTLVardef@b53feb_HPS_FORMAT_FIGEXP M_FIG In this integrative modeling work, protein complex structures were modeled by combining AlphaFold predicted subunits with Rosetta docking. Collision cross section data from ion-mobility mass spectrometry were used as evaluation constraints and docked models were scored using the IM-complex score. The best scoring models generally represent accurate protein complex structures. C_FIG

biophysics↗

bioRxiv2026

Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling

Proteins perform essential roles across nearly all cellular processes, and accurate three-dimensional structures remain critical for elucidating structure-function relationships and studies on drug discovery. Cryo-electron microscopy (cryo-EM), X-ray crystallography, and nuclear magnetic resonance can provide detailed structural information. However, for many proteins, structural information is available only as lower-resolution experimental data or sparse data. Such information is more difficult to translate into accurate atomic coordinates; a common example is low-resolution cryo-EM density maps. In parallel, mass spectrometry-based methods, including ion mobility (IM-MS), offer rapid, broadly applicable structural descriptors such as collisional cross section (CCS), a global measure of molecular shape and size, but CCS values also do not provide atomistic detail. Here we present CRIM (cryo-EM + IM-MS), an integrative Rosetta scoring function that combines low-resolution cryo-EM density information with IM-MS derived CCS as restraints to improve monomeric protein structure prediction. CRIM incorporates the Rosetta REF2015 (RS) energy with a CCS agreement penalty (computed via PARCS) and an electron-density agreement term (elec_dens_fast). We tested CRIM on an ideal dataset of 60 monomeric proteins using simulated CCS values and density maps. Across the ideal dataset, the CRIM score function improved or maintained prediction quality for many targets, reducing the mean RMSD from 3.65 [A] (RS) to 2.90 [A] and increasing the mean TM-score from 0.88 to 0.90. Furthermore, an experimental benchmark dataset of 54 proteins was curated to include either experimental cryo-EM maps or published CCS values. On the experimental dataset, CRIM similarly improved model selection, lowering the mean RMSD from 6.65 [A] to 4.38 [A] and raising the mean TM-score from 0.73 to 0.79. In comparison to AlphaFold3 predictions, CRIM frequently yielded competitive predictions and was able to substantially outperform AlphaFold3 for select difficult targets where sparse experimental restraints provide strong discriminatory power. The CRIM score function is freely available within the Rosetta software suite and provides a practical framework for leveraging complementary IM-MS and cryo-EM data to improve monomeric protein structure prediction.

biochemistry↗

bioRxiv2023

Mechanisms of Pathogenicity of Hypertrophic Cardiomyopathy-Associated Troponin T (TNNT2) Variant R278C+/- During Development

Hypertrophic cardiomyopathy (HCM) is one of the most common heritable cardiovascular diseases and variants of TNNT2 (cardiac troponin T) are linked to increased risk of sudden cardiac arrest despite causing limited hypertrophy. In this study, a TNNT2 variant, R278C+/-, was generated in both human cardiac recombinant/reconstituted thin filaments (hcRTF) and human-induced pluripotent stem cells (hiPSCs) to investigate the mechanisms by which the R278C+/- variant affects cardiomyocytes at the proteomic and functional levels. The results of proteomics analysis showed a significant upregulation of markers of cardiac hypertrophy and remodeling in R278C+/- vs. the isogenic control. Functional measurements showed that R278C+/- variant enhances the myofilament sensitivity to Ca2+, increases the kinetics of contraction, and causes arrhythmia at frequencies >75 bpm. This study uniquely shows the profound impact of the TNNT2 R278C+/- variant on the cardiomyocyte proteomic profile, cardiac electrical and contractile function in the early stages of cardiac development. Translational PerspectiveHypertrophic cardiomyopathy (HCM) is the leading known cause of sudden cardiac arrest in the young. Thin-variant associated HCM variants make up to 15% of familial HCM yet their molecular mechanisms remain less clear relative to thick filament variants. Here, we employ computational modeling, human cardiac recombinant/reconstituted thin filaments (hcRTF), and hiPSC-CMs to study the thin filament TNNT2 R278C+/- variant, revealing its extensive pathogenicity and potential mechanisms by which it can lead to HCM and sudden death. Mavacamten, the recently FDA-approved treatment, was effective in alleviating contractile dysfunction in TNNT2 R278C+/- hiPSC-CMs, positing it as a potential therapy for thin filament HCM. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=117 HEIGHT=200 SRC="FIGDIR/small/542948v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@102a8b0org.highwire.dtl.DTLVardef@190e356org.highwire.dtl.DTLVardef@13d3f9aorg.highwire.dtl.DTLVardef@1a9c33_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Targeting Troponin C with Small Molecules Containing Diphenyl Moieties: Calcium Sensitivity Effects on Striated Muscle and Structure Activity Relationship

Despite large investments from academia and industry, heart failure, which results from a disruption of the contractile apparatus, remains a leading cause of death. Cardiac muscle contraction is a calcium-dependent mechanism, which is regulated by the troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). There is an increasing need for the development of small molecules that increase calcium sensitivity without altering systolic calcium concentration, thereby strengthening cardiac function. Here, we examined the effect of our previously identified calcium sensitizing small molecule, ChemBridge compound 7930079, in the context of several homologous muscle systems. The effect of this molecule on force generation in isolated cardiac trabeculae and slow skeletal muscle fibers was measured. Furthermore, we explored the use of Gaussian accelerated molecular dynamics in sampling highly predictive receptor conformations based on NMR derived starting structures. Additionally, we took a rational computational approach for lead optimization based on lipophilic diphenyl moieties. This led to the identification of three novel low affinity binders, which had similar binding affinities to known positive inotrope trifluoperazine. The most potent identified calcium sensitizer was compound 16 with an apparent affinity of 117 {+/-} 17 M.

biochemistry↗

bioRxiv2022

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling

Structural mass spectrometry offers several techniques for the characterization of protein structures. Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. Degrees of modification obtained from CL experiments for specific labeled residues can be compared between the unbound and bound states of complexes. This analysis can yield insights into structural features of these protein assemblies, specifically the proximity of specific residues to the protein-protein interface. However, this data is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work we present a novel hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 [A] for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. The average improvement in RMSD observed upon inclusion of CL data was 5.2 [A]. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models. Significance StatementStructural mass spectrometry can be a powerful and versatile approach to characterize the structure of protein complexes. Data obtained from covalent labeling mass spectrometry can provide insights into higher order protein structure (particularly with respect to residue interactions and solvent accessibility) but needs to be supplemented by computational techniques to elucidate accurate, atomic-detail structural information. Here, we present a method to combine bioanalytical data obtained from covalent labeling with models generated using AlphaFold to accurately predict protein-protein complexes in Rosetta. Differential covalent labeling data can be used to determine the proximity of residues to the binding interface of complexes which we utilized to analyze computational models and improve structure prediction algorithms.

biophysics↗

bioRxiv2021

Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally-informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure leaving questions of unintentional bias and applicability to unknown structures unresolved. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 [A]. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

biophysics↗

bioRxiv2021

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations of proteins, ion mobility (IM) provides structural information about protein shape and size in the form of an orientationally averaged collision cross-section (CCS). While IM data have been predominantly employed for the structural assessment of protein complexes, CCS data from IM experiments have not yet been used to predict tertiary structure from sequence. Here, we are showing that IM data can significantly improve protein structure determination using the modeling suite Rosetta. The Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm was developed that allows for fast and accurate prediction of CCS from structure. Following successful rigorous testing for accuracy, speed, and convergence of PARCS, an integrative modelling approach was developed in Rosetta to use CCS data from IM experiments. Using this method, we predicted protein structures from sequence for a benchmark set of 23 proteins. When using IM data, the predicted structure improved or remained unchanged for all 23 proteins, compared to the predicted models in the absence of CCS data. For 15/23 proteins, the RMSD (root-mean-square deviation) of the predicted model was less than 5.50 [A], compared to only 10/23 without IM data. We also developed a confidence metric that successfully identified near-native models in the absence of a native structure. These results demonstrate the ability of IM data in de novo structure determination.

biophysics↗

bioRxiv2021

The effect of magnesium on calcium binding to cardiac troponin C related hypertrophic cardiomyopathy mutants

Cardiac troponin C (cTnC) is the calcium (Ca2+) sensing component of the troponin complex. Binding of Ca2+ to cTnC triggers a cascade of myofilament conformational changes that culminate in force production. Mutations in cTnC linked to hypertrophic myocardial myopathy (HCM) induce a a greater degree and duration of Ca2+ binding, which may underly the hypertrophic phenotype. Recent evidence from our laboratories demonstrated novel modifications of cTnC Ca2+ binding by cellular magnesium (Mg2+) that we hypothesize may be of significance in promoting HCM. Regulation of contraction has long been thought to occur exclusively through Ca2+ binding to site II of cTnC. However, abundant cellular Mg2+ is a potential competitor for binding to the same sites; work by several groups also suggests this is possible. We have used isothermal titration calorimetry (ITC) to explore the thermodynamic properties associated with the interaction between Ca2+/Mg2+ and site II of cTnC; these experiments demonstrated that physiological concentrations of Mg2+ may compete with Ca2+ to bind site II of cTnC. In experiments reported here, we studied a series of mutations in cTnC thought to be causal in HCM. Three mutants (A8V, L29Q, and A31S) slightly elevated the affinity for both Ca2+ and Mg2+, whereas other mutants (L48Q, Q50R, and C84Y), that are closer to the C-terminal domain and surrounding the EF hand binding motif of site II had a more significant effect on affinity and the thermodynamics of the binding interaction. To the best of our knowledge, this work is the first to explore the role of Mg2+ in modifying the Ca2+ affinity ofcTnC mutations linked to HCM. Our results indicate a physiologically significant role for cellular Mg2+ at baseline conditions and when elevated on the control of the dynamics of contraction by modifications in the Ca2+ binding properties of cTnC.

cell biology↗

bioRxiv2020

Binding of Calcium and Magnesium to Cardiac Troponin C

Cardiac troponin C (cTnC) is the Ca2+-sensing component of the thin filament. It contains structural sites (III/IV) which bind both Ca2+ and Mg2+, and a regulatory site (II) that has been thought to bind only Ca2+. The latter binding initiates a series of conformational changes that culminate in force production. We have quantified the interaction between site II and Ca2+/Mg2+ through Isothermal Titration Calorimetry and Thermodynamic Integration simulations. Direct and competitive binding titrations using wild type and a double mutant that significantly reduces binding to site II demonstrated that physiologically relevant concentrations of both Ca2+/Mg2+ interact with the same locus. Cytosolic free Mg2+ (~1 mM) could occupy a significant population of available site II, as this concentration of Mg2+ decreased the affinity for Ca2+ 1.4-fold. Interaction of Mg2+ with site II of cTnC likely has important functional consequences for the heart at baseline and in diseased states which decrease or increase availability of Mg2+ such as secondary hyperparathyroidism or ischemia, respectively.

cell biology↗