Dynamics Of Protein Complexes Research Articles

Global profiling of protein complex dynamics with an experimental library of protein interaction markers.

Methods to systematically monitor protein complex dynamics are needed. We introduce serial ultrafiltration combined with limited proteolysis-coupled mass spectrometry (FLiP-MS), a structural proteomics workflow that generates a library of peptide markers specific to changes in PPIs by probing differences in protease susceptibility between complex-bound and monomeric forms of proteins. The library includes markers mapping to protein-binding interfaces and markers reporting on structural changes that accompany PPI changes. Integrating the marker library with LiP-MS data allows for global profiling of protein-protein interactions (PPIs) from unfractionated lysates. We apply FLiP-MS to Saccharomyces cerevisiae and probe changes in protein complex dynamics after DNA replication stress, identifying links between Spt-Ada-Gcn5 acetyltransferase activity and the assembly state of several complexes. FLiP-MS enables protein complex dynamics to be probed on any perturbation, proteome-wide, at high throughput, with peptide-level structural resolution and informing on occupancy of binding interfaces, thus providing both global and molecular views of a system under study.

High resolution analysis of proteolytic substrate processing

Members of the widely conserved high temperature requirement A (HtrA) family of serine proteases are involved in multiple aspects of protein quality control. In this context, they have been shown to efficiently degrade misfolded proteins or protein fragments. However, recent reports suggest that folded proteins can also be native substrates. To gain a deeper understanding of how folded proteins are initially processed and subsequently degraded into short peptides by human HTRA1, we established an integrated and quantitative approach using time-resolved mass spectrometry, CD spectroscopy, and bioinformatics. The resulting data provide high-resolution information on up to 178 individual proteolytic sites within folded ANXA1 (consisting of 346 amino acids), the relative frequency of cuts at each proteolytic site, the preferences of the protease for the amino acid sequence surrounding the scissile bond, as well as the degrees of sequential structural relaxation and unfolding of the substrate that occur during progressive degradation. Our workflow provides precise molecular insights into protease-substrate interactions, which could be readily adapted to address other posttranslational modifications such as phosphorylation in dynamic protein complexes.

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

The Unusual Functional Role of Protein Flexibility in Photosynthetic Light Harvesting: Protein Dynamics Studied Using Neutron Scattering

In addition to investigations of the three-dimensional protein structure, information on the dynamical properties of proteins is indispensable for an understanding of protein function in general. Correlations between protein dynamics and function are typically anticipated when both molecular mobility and function are concurrently affected under specific temperatures or hydration conditions. In contrast, excitation energy transfer within the major photosynthetic light-harvesting complex II (LHC II) presents an atypical case, as it remains fully operational even at cryogenic temperatures, primarily depending on the interactions between electronic states and involving harmonic protein vibrations only. This review summarizes recent work on vibrational and conformational protein dynamics of LHC II and directly relates these findings to its light-harvesting function. In addition, we give a comprehensive introduction into the use of neutron spectroscopy and molecular dynamics simulations to investigate the protein dynamics of photosynthetic protein complexes in solution, which is information complementary to that obtained by protein crystallography.

Trioxane-based MS-cleavable Cross-linking Mass Spectrometry for Profiling Multimeric Interactions of Cellular Networks.

Cross-linking mass spectrometry (XL-MS) is a powerful technology for mapping protein-protein interactions (PPIs) at the systems-level. By covalently connecting pairs of proximal residues, cross-linking reagents provide distance restraints to infer protein conformations and interaction interfaces. While binary cross-links have been remarkably informative, multimeric cross-links can offer enhanced spatial resolution to facilitate the characterization of dynamic and heterogeneous protein complexes. However, the identification of multimeric cross-links remains extremely challenging due to fragmentation complexity and the vast expansion of database search space. Here, we present a novel trioxane-based MS-cleavable homotrifunctional cross-linker TSTO, which can target three proximal lysine residues simultaneously. Owing to its unique structure and MS-cleavability, TSTO enables fast and unambiguous identification of cross-linked peptides using LC-MS n analysis. Importantly, we have demonstrated that the TSTO-based XL-MS platform is effective for mapping PPIs of protein complexes and cellular networks. The trimeric interactions captured by TSTO have uncovered new structural details that cannot be easily revealed by existing reagents, allowing in-depth description of PPIs to facilitate structural modeling. This development not only advances XL-MS technologies for global PPI profiling from living cells, but also offers a new direction for creating multifunctional MS-cleavable cross-linkers to further push structural systems biology forward in the future.

Measuring G protein activation by spectrally resolved imaging fluorescence fluctuation spectroscopy

The activation of heterotrimeric G proteins through G-protein-coupled receptors (GPCRs) is a ubiquitous signaling mechanism in eukaryotic biology. The three principal molecular components of this cascade are the GPCR, Gα subunit, and Gβγ subunit. Measurement of interactions between these components and their downstream effectors in live cells is paramount to understanding how cells fine-tune their physiology in response to many external stimuli. Multicolor fluorescence fluctuation spectroscopy (FFS) approaches allow the sensitive detection of heteromeric interactions by using spectrally distinct fluorophores to label biomolecules of interest. We considered three imaging FFS approaches to measuring molecular interactions from the signals produced by a spectrally resolved confocal microscopy: raster spectral image correlation spectroscopy (RSICS), spectral spatial cumulant analysis, and native resolution spatial cumulant analysis. We characterized these approaches using simulation and experiments on heteromers with known stoichiometries. We found that RSICS had the best sensitivity for measuring heteromeric interactions and employed it to measure G protein complexes. As measured by RSICS, interactions between the G protein subunits Gαi1 and Gβ1γ2 were sensitive to the stimulation of two GPCRs, the D2 dopamine receptor and the α-2A adrenergic receptor. Interactions between GPCRs and G proteins were not detectable above background, supporting a collisional model of GPCR/G protein interactions in contrast to a preassembly model where strong interactions would be present. These data are uniquely available by this FFS framework, which is appropriate for not only multiplexed measurements of G protein biology but any dynamic protein complexes in the cell.

Water Plays Key Roles in Stabilities of Wild Type and Mutant Transthyretin Complexes.

Transthyretin (TTR), a 56 kDa homotetramer that is involved in the transport of thyroxine and retinol, has been linked to amyloidosis through disassembly of tetramers to form monomers, dimers, and trimers that then reassemble into higher order oligomers and/or fibrils. Hybrid TTR (hTTR) tetramers are found in heterozygous individuals that express both wild type TTR (wt-TTR) and mutant TTR (mTTR) forms of the protein, and these states display increased rates of amyloidosis. Here we monitor subunit exchange (SUE) reactions involving homomeric and mixed tetramers using high resolution native mass spectrometry (nMS). Our results show evidence that differences in TTR primary structure alter tetramer stabilities, and hTTR products can form spontaneously by SUE reactions. In addition, we find that solution temperature has strong effects on TTR tetramer stabilities and formation of SUE products. Lower temperatures promote formation of hTTR tetramers containing L55P and V30M subunits, whereas small effects on the formation of hTTR tetramers containing F87A and T119M subunits are observed. We hypothesize that the observed temperature dependent stabilities and subsequent SUE behavior are a result of perturbations to the network of "two kinds of water": hydrating and structure stabilizing water molecules (Spyrakis et al. J. Med. Chem. 2017, 60 (16), 6781-6827; Xu et al. Soft Matter 2012, 8, 324-336) that stabilize wt-TTR and mTTR tetramers. The results presented in this work illustrate the utility of high resolution nMS for studies of the structures, stabilities, and dynamics of protein complexes that directly influence SUE reactions.

PROTAC-induced Protein Functional Dynamics in Targeted Protein Degradation.

PROteolysis TArgeting Chimeras (PROTACs) are small molecules that induce target protein degradation via the ubiquitin proteasome system. PROTACs recruit the target protein and E3 ligase; a critical first step is forming a ternary complex. However, while the formation a ternary complex is crucial, it may not always guarantee successful protein degradation. The dynamics of the PROTAC induced degradation complex play a key role in ubiquitination and subsequent degradation. In this study, we computationally modelled protein complex structures and dynamics associated with a series of PROTACs featuring different linkers to investigate why these PROTACs, all of which formed ternary complexes with Cereblon (CRBN) E3 ligase and the target protein bromodomain containing protein 4 (BRD4BD1), exhibited varying degrees of degradation potency. We constructed the degradation machinery complexes with Culling Ring Ligase 4A (CRL4A) E3 ligase scaffolds. Through atomistic molecular dynamics simulations, we illustrated how PROTAC dependent protein dynamics facilitate the arrangement of surface lysine residues of BRD4BD1 into the catalytic pocket of E2/ubiquitin for ubiquitination. Despite featuring identical warheads in this PROTAC series, the linkers were found to affect the residue interaction networks, and thus governing the essential motions of the entire degradation machine for ubiquitination. These findings offer a dynamic perspective on ligand induced protein degradation, providing insights to guide future PROTAC design endeavors.

Revealing biomolecular structure and motion with neural ab initio cryo-EM reconstruction.

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) can access the intrinsic heterogeneity of these complexes and is therefore a key tool for understanding mechanism and function. However, 3D reconstruction of the resulting imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, we introduce a method, DRGN-AI, for ab initio heterogeneous cryo-EM reconstruction. With a two-step hybrid approach combining search and gradient-based optimization, DRGN-AI can reconstruct dynamic protein complexes from scratch without input poses or initial models. Using DRGN-AI, we reconstruct the compositional and conformational variability contained in a variety of benchmark datasets, process an unfiltered dataset of the DSL1/SNARE complex fully ab initio, and reveal a new "supercomplex" state of the human erythrocyte ankyrin-1 complex. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM as a high-throughput tool for structural biology and discovery.

Mitigating the Blurring Effect of CryoEM Averaging on a Flexible and Highly Symmetric Protein Complex through Sub-Particle Reconstruction.

Many macromolecules are inherently flexible as a feature of their structure and function. During single-particle CryoEM processing, flexible protein regions can be detrimental to high-resolution reconstruction as signals from thousands of particles are averaged together. This "blurring" effect can be difficult to overcome and is possibly more pronounced when averaging highly symmetric complexes. Approaches to mitigating flexibility during CryoEM processing are becoming increasingly critical as the technique advances and is applied to more dynamic proteins and complexes. Here, we detail the use of sub-particle averaging and signal subtraction techniques to precisely target and resolve flexible DARPin protein attachments on a designed tetrahedrally symmetric protein scaffold called DARP14. Particles are first aligned as full complexes, and then the symmetry is reduced by alignment and focused refinement of the constituent subunits. The final reconstructions we obtained were vastly improved over the fully symmetric reconstructions, with observable secondary structure and side-chain placement. Additionally, we were also able to reconstruct the core region of the scaffold to 2.7 Å. The data processing protocol outlined here is applicable to other dynamic and symmetric protein complexes, and our improved maps could allow for new structure-guided variant designs of DARP14.

Visualization of the Association of Dimeric Protein Complexes on Specific Enhancers in the Salivary Gland Nuclei of Drosophila Larva.

Transcription factors (TFs) regulate gene expression by recognizing specific target enhancers in the genome. The DNA-binding and regulatory activity of TFs depend on the presence of additional protein partners, leading to the formation of versatile and dynamic multimeric protein complexes. Visualizing these protein-protein interactions (PPIs) in the nucleus is key for decrypting the molecular cues underlying TF specificity in vivo. Over the last few years, Bimolecular Fluorescence Complementation (BiFC) has been developed in several model systems and applied in the analysis of different types of PPIs. In particular, BiFC has been applied when analyzing PPIs with hundreds of TFs in the nucleus of live Drosophila embryos. However, the visualization of PPIs at the level of specific target enhancers or genomic regions of interest awaits the advent of DNA-labelling methods that can be coupled with BiFC. Here, we present a novel experimental strategy that we have called BiFOR and that is based on the coupling of BiFC with the bacterial ANCHOR DNA-labelling system. We demonstrate that BiFOR enables the precise quantification of the enrichment of specific dimeric protein complexes on target enhancers in Drosophila salivary gland nuclei. Given its versatility and sensitivity, BiFOR could be applied more widely to other tissues during Drosophila development. Our work sets up the experimental basis for future applications of this strategy.

Native Mass Spectrometry Dissects the Structural Dynamics of an Allosteric Heterodimer of SARS-CoV-2 Nonstructural Proteins.

Structure-based drug design, which relies on precise understanding of the target protein and its interaction with the drug candidate, is dramatically expedited by advances in computational methods for candidate prediction. Yet, the accuracy needs to be improved with more structural data from high throughput experiments, which are challenging to generate, especially for dynamic and weak associations. Herein, we applied native mass spectrometry (native MS) to rapidly characterize ligand binding of an allosteric heterodimeric complex of SARS-CoV-2 nonstructural proteins (nsp) nsp10 and nsp16 (nsp10/16), a complex essential for virus survival in the host and thus a desirable drug target. Native MS showed that the dimer is in equilibrium with monomeric states in solution. Consistent with the literature, well characterized small cosubstrate, RNA substrate, and product bind with high specificity and affinity to the dimer but not the free monomers. Unsuccessfully designed ligands bind indiscriminately to all forms. Using neutral gas collision, the nsp16 monomer with bound cosubstrate can be released from the holo dimer complex, confirming the binding to nsp16 as revealed by the crystal structure. However, we observed an unusual migration of the endogenous zinc ions bound to nsp10 to nsp16 after collisional dissociation. The metal migration can be suppressed by using surface collision with reduced precursor charge states, which presumably resulted in minimal gas-phase structural rearrangement and highlighted the importance of complementary techniques. With minimal sample input (∼μg), native MS can rapidly detect ligand binding affinities and locations in dynamic multisubunit protein complexes, demonstrating the potential of an "all-in-one" native MS assay for rapid structural profiling of protein-to-AI-based compound systems to expedite drug discovery.

Abstract C076: New insights into the role of FOXA1- HNF4 axis in pancreatic cancer

Abstract Despite recent advances in the treatment of pancreatic adenocarcinoma (PDAC), the median survival remains <12 months. Patients typically present with late-stage disease and have limited treatment options. Therefore, there is an immediate need for the generation of new and innovative therapeutic targets. Little is known about the role of pioneer factors such as FOXA1 and their interaction partners in a PDAC context. Inspired by literature reports implicating FOXA1 and GATA5/GATA6 in regulating pancreatic cancer resistance and metastasis, our study hypothesised the possibility of an undiscovered nuclear receptor that works with FOXA1 (similar to Estrogen receptor in breast cancer). Using innovative ‘omic’ based approaches (RIME) developed in our laboratory 1 we discovered a nuclear receptor (NR) complex involving HNF4A and HNF4G in the classical sub-type of pancreatic cancer. Subsequently the interaction was independently validated in Whipple surgical biopsies from PDAC patients using ChIP-sequencing studies. Across multiple patient tumors (n=7) a binding overlap specifically between FOXA1 and HNF4G (3461 sites) was established. To investigate the therapeutic potential of HNF4G in the classical subtype of PDAC, we generated CRISPR deletions (KO) of HNF4G in the HPAF-II cells. HNF4G-KO cells were orthotopically implanted into the pancreas of NSG mice. A significant survival advantage of 12 days (<0.001) and reduced growth (<0.02) was observed in these mice compared to the controls. To better understand the impact of HNF4G-KO on gene expression, the orthotopic tumors were subjected to RNA-seq analyses. Gene set enrichment and pathway analysis revealed significantly down-regulated pathways to include EMT transition. TCGA data highlighted HNF4G amplification in 9% of PDAC patients with concomitant decreased progression free survival. These data point towards HNF4G being a therapeutically viable target for further exploration. Protein Arginine Methyl Transferase 1 (PRMT1) is a common interactor of both FOXA1 and HNF4G. Our study reveals a unique dependency of PRMT1 on the HNF4G-FOXA1 complex in PDAC biopsies. HNF4G-KO drastically reduces the chromatin binding of PRMT1, implicating them as functionally dependent. Treatment of HNF4G-KO cells with GSK3368715 (PRMT1 inhibitor) further sensitizes these cells and results in a significant survival advantage (10 days <0.02). We propose a model of HNF4G inhibition in combination with PRMT1 as a novel therapeutic opportunity to treat the classical sub-type of PDAC. Further, our study reveals a unique reliance of primary classical tumors on HNF4G. Although HNF4G appears to dominate FOXA1 functionality in the primary disease, FOXA1 remains instrumental in priming metastasis and enhancer reprograming. 1. Papachristou EK, Kishore K, Holding AN, Harvey K, Roumeliotis TI, Chilamakuri CSR, et al. A quantitative mass spectrometry-based approach to monitor the dynamics of endogenous chromatin-associated protein complexes. Nature Communications. 2018;9(1):2311. Citation Format: Shalini V. Rao, Lisa Young, Danya Cheeseman, Stephanie Mack, Jill Temple, Chandra Shekar Reddy Chilamakuri, Evangelia Papachristou, Catherina Pelicano, Amy Smith, Dominique-Laurent Couturier, Michael Gill, Duncan Jodrell Jodrell, Alasdair Russell, Igor Chernukhin, Jason Carroll. New insights into the role of FOXA1- HNF4 axis in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C076.

Size exclusion chromatography based proteomic and degradomic profiling of inflammasome-activated, murine bone marrow-derived dendritic cells highlights complex retention and release of cleavage products.

Coupling size exclusion chromatography (SEC) with mass spectrometry-based proteomics enables investigating protein complexes, with degradomic profiling providing deeper insights into complex-associated proteolytic processing and retaining of cleavage products. This study aims to map protein complex formation upon inflammasome activation in bone marrow-derived dendritic cells (BMDCs) from gasdermin D-deficient mice, focusing on proteolytic enzymes and truncated proteins in higher molecular weight complexes. Cultured BMDCs were primed with LPS and subsequently treated with nigericin or Val-boroPro (VbP). SEC-fractionated proteins were TMT-labelled and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified 6862 proteins and 70 802 peptides, including 14 714 semi-tryptic peptides indicating elevated endogenous proteolytic processing. The sequence motif of numerous cleavage sites maps to caspase-like activity. Inflammasome activation was corroborated by elevated levels of apoptosis-associated speck-like protein containing a CARD (ASC) in higher molecular weight (MW) fractions and increased IL-1β levels in low MW fractions upon nigericin or VbP treatment. The majority of truncated cleavage products remained within their corresponding, higher MW protein complexes while caspase-specific cleavage products of Rho-associated protein kinase 1, gelsolin, and AP-2 complex subunit alpha-2 dissociated to lower MW fractions. SEC profiles identified 174 proteases, with cell surface proteases forming high MW complexes, including ADAMs and DPP4 but not MMP14. VbP treatment led to the accumulation of ISG15 in low MW fractions while RNA polymerase II coactivator p15 shifted to higher MW fractions. This study demonstrates that SEC-coupled proteomics and degradomic profiling offer unique insights into protein complex dynamics and proteolytic processes upon inflammasome activation.

Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations.

In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamics (MD) simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and MD simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and MD simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.

Probing the hierarchical dynamics of DNA-sperm nuclear transition protein complexes through fuzzy interaction and mesoscale condensation.

Nuclear transition protein TNP1 is a crucial player mediating histone-protamine exchange in condensing spermatids. A unique combination of intrinsic disorder and multivalent properties turns TNP1 into an ideal agent for orchestrating the formation of versatile TNP-DNA assemblies. Despite its significance, the physicochemical property and the molecular mechanism followed by TNP1 for histone replacement and DNA condensation are still poorly understood. This study reports the first-time in vitro expression and purification of human TNP1 and investigates the hierarchical dynamics of TNP1-DNA interaction using a combination of computational simulations, biochemical assays, fluorescence imaging, and atomic force microscopy. We explored three crucial facets of TNP1-DNA interactions. Initially, we delve into the molecular binding process that entails fuzzy interactions between TNP1 and DNA at the atomistic scale. Subsequently, we analyze how TNP1 binding affects the electrostatic and mechanical characteristics of DNA and influences its morphology. Finally, we study the biomolecular condensation of TNP1-DNA when subjected to high concentrations. The findings of our study set the foundation for comprehending the potential involvement of TNP1 in histone replacement and DNA condensation in spermatogenesis.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/.

In-silico prediction of TGF-β1 non-synonymous variants and their impact on binding affinity to Fresolimumab

TGF-β1 is a potent immunoregulatory cytokine that plays diverse roles in development, bone healing, fibrosis, and cancer. However, characterizing TGF-β1 gene variants is challenging because the structural and functional consequences of these variants are still undetermined. In this study, we aimed to perform an in-silico analysis of TGF-β1 non-synonymous variants and their pathogenic effects on the TGF-β1 protein. A total of 10,252 TGF-β1 SNPs were collected from the NCBI dbSNP database and in-silico tools (SIFT, PROVEAN, Mutation Taster, ClinVar, PolyPhen-2, CScape, MutPred, and ConSurf) were used. The in-silico predicted potential variants were further investigated for their binding to the TGF-β1 targeting drug “Fresolimumab”. Molecular docking was performed using HADDOCK and confirmed by PRODIGY and PDBsum. The in-silico analysis predicted four potential TGF-β1 nsSNPs: E47G in the LAP domain of the propeptide and I22T, L28F, and E35D in the mature TGF-β1 peptide. HADDOCK and molecular dynamics simulations revealed that the I22T and E35D variants have higher binding affinity for Fresolimumab as compared to the wild type and L28F variants. Molecular dynamics simulations (100 ns) and principal component analysis showed that TGF-β1 variants influenced the protein structure and caused variations in the internal dynamics of protein complexes with the antibody. Among them, the E35D variant significantly destabilized the TGF-β1 protein structure, resulting in rearrangement in the binding site and affecting the interactions with the Fresolimumab. This study identified four variants that can affect the TGF-β1 protein structure and result in functional consequences such as impaired response to Fresolimumab. Communicated by Ramaswamy H. Sarma

Next-generation proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions are pivotal in regulating establishment, progression, and outcome of an infection. While affinity-purification mass spectrometry has become instrumental in characterizing such interactions, it suffers from limitations in scalability and biological authenticity. Here we present the use of co-fractionation mass spectrometry for high throughput analysis of host-pathogen interactions from native viral infections of two jumbophages (ϕKZ and ϕPA3) in Pseudomonas aeruginosa. This approach enabled the detection of > 6000 unique host-pathogen interactions for each phage, encompassing > 50% of their respective proteomes. This deep coverage provided evidence for interactions between KZ-like phage proteins and the host ribosome, and revealed protein complexes for previously undescribed phage ORFs, including a ϕPA3 complex showing strong structural and sequence similarity to ϕKZ non-virion RNA polymerase. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction (https://phagemap.ucsf.edu/). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of host-pathogen interactomes and protein complex dynamics upon infection.

Let's make it clear: systematic exploration of mitochondrial DNA- and RNA-protein complexes by complexome profiling.

Complexome profiling (CP) is a powerful tool for systematic investigation of protein interactors that has been primarily applied to study the composition and dynamics of mitochondrial protein complexes. Here, we further optimized this method to extend its application to survey mitochondrial DNA- and RNA-interacting protein complexes. We established that high-resolution clear native gel electrophoresis (hrCNE) is a better alternative to preserve DNA- and RNA-protein interactions that are otherwise disrupted when samples are separated by the widely used blue native gel electrophoresis (BNE). In combination with enzymatic digestion of DNA, our CP approach improved the identification of a wide range of protein interactors of the mitochondrial gene expression system without compromising the detection of other multiprotein complexes. The utility of this approach was particularly demonstrated by analysing the complexome changes in human mitochondria with impaired gene expression after transient, chemically induced mitochondrial DNA depletion. Effects of RNase on mitochondrial protein complexes were also evaluated and discussed. Overall, our adaptations significantly improved the identification of mitochondrial DNA- and RNA-protein interactions by CP, thereby unlocking the comprehensive analysis of a near-complete mitochondrial complexome in a single experiment.