Structure, dynamics, assembly, and evolution of protein complexes

Residues in the Fructose Binding Pocket Are Required for Ketohexokinase-A Activity

Excessive fructose consumption is a primary contributor to the global surges in obesity, cancer, and metabolic syndrome. Fructolysis is not robustly regulated and is initiated by ketohexokinase (KHK). In this study, we determined the crystal structure of KHK-A, one of two human isozymes of KHK, in the apo-state at 1.85 Å resolution, and we investigated the roles of residues in the fructose-binding pocket by mutational analysis. Introducing alanine at D15, N42, or N45 inactivated KHK-A, whereas mutating R141 or K174 reduced activity and thermodynamic stability. Kinetic studies revealed that the R141A and K174A mutations reduced fructose affinity by 2- to 4-fold compared to WT KHK-A, without affecting ATP affinity. Molecular dynamics simulations provided mechanistic insights into the potential roles of the mutated residues in ligand coordination and the maintenance of an open state in one monomer and a closed state in the other. Protein-protein interactome analysis indicated distinct expression patterns and downregulation of partner proteins in different tumor tissues, warranting a re-evaluation of KHK's role in cancer development and progression. The connections between different cancer genes and the KHK signaling pathway suggest that KHK is a potential target for preventing cancer metastasis. This study enhances our understanding of KHK-A's structure and function and offers valuable insights into potential targets for developing treatments for obesity, cancer, and metabolic syndrome.

The oligomeric states of dye-decolorizing peroxidases from Streptomyces lividans and their implications for mechanism of substrate oxidation

A common evolutionary mechanism in biology to drive function is protein oligomerization. In prokaryotes, the symmetrical assembly of repeating protein units to form homomers is widespread, yet consideration in vitro of whether such assemblies have functional or mechanistic consequences is often overlooked. Dye-decolorizing peroxidases (DyPs) are one such example, where their dimeric α + β barrel units can form various oligomeric states, but the oligomer influence, if any, on mechanism and function has received little attention. In this work, we have explored the oligomeric state of three DyPs found in Streptomyces lividans, each with very different mechanistic behaviors in their reactions with hydrogen peroxide and organic substrates. Using analytical ultracentrifugation, we reveal that except for one of the A-type DyPs where only a single sedimenting species is detected, oligomer states ranging from homodimers to dodecamers are prevalent in solution. Using cryo-EM on preparations of the B-type DyP, we determined a 3.02 Å resolution structure of a hexamer assembly that corresponds to the dominant oligomeric state in solution as determined by analytical ultracentrifugation. Furthermore, cryo-EM data detected sub-populations of higher-order oligomers, with one of these formed by an arrangement of two B-type DyP hexamers to give a dodecamer assembly. Our solution and structural insights of these oligomer states provide a new framework to consider previous mechanistic studies of these DyP members and are discussed in terms of long-range electron transfer for substrate oxidation and in the "storage" of oxidizable equivalents on the heme until a two-electron donor is available.

Translation-independent association of mRNAs encoding protomers of the 5-HT 2A -mGlu2 receptor complex in living cells

The serotonin 2A receptor (5-HT 2A R) and the metabotropic glutamate 2 receptor (mGluR2) form heteromeric G protein-coupled receptor (GPCR) complexes through a direct physical interaction. Co-translational association of mRNAs encoding subunits of heteromeric ion channels has been reported, but whether complex assembly of GPCRs occurs during translation remains unknown. Our in vitro data reveal evidence of co-translational modulation in 5-HT 2A R and mGluR2 mRNAs following siRNA-mediated knockdown. Interestingly, immunoprecipitation of either 5-HT 2A R or mGluR2, using an antibody targeting epitope tags at their N-terminus, results in detection of both transcripts associated with ribonucleoprotein complexes containing RPS24. Additionally, we demonstrate that the mRNA transcripts of 5-HT 2A R and mGluR2 associate autonomously of their respective encoded proteins. Validation of this translation-independent association is extended ex vivo using mouse frontal cortex samples. Together, these findings provide mechanistic insights into the co-translational assembly of GPCR heteromeric complexes, unraveling regulatory processes governing protein-protein interactions and complex formation.

A new era of antibody discovery: an in-depth review of AI-driven approaches

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation

Macromolecular complexes are often composed of diverse subunits. The self-assembly of these subunits is inherently nonequilibrium and must avoid kinetic traps to achieve high yield over feasible timescales. We show how the kinetics of self-assembly benefits from diversity in subunits because it generates an expansive parameter space that naturally improves the "expressivity" of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched the parameter spaces of mass-action kinetic models to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate-constants or external and active control of subunits to efficiently assemble. Internal design of a hierarchy of subunit binding rates generates self-assembly that can robustly avoid kinetic traps for all concentrations and energetics, but it places strict constraints on selection of relative rates. External control via subunit titration is more versatile, avoiding kinetic traps for any system without requiring molecular engineering of binding rates, albeit less efficiently and robustly. We derive theoretical expressions for the timescales of kinetic traps, and we demonstrate our optimization method applies not just for design but inference, extracting intersubunit binding rates from observations of yield-vs.-time for a heterotetramer. Overall, we identify optimal kinetic protocols for self-assembly as a powerful mechanism to achieve efficient and high-yield assembly in synthetic systems whether robustness or ease of "designability" is preferred.

Polymorphic Self-Assembly with Procedural Flexibility for Monodisperse Quaternary Protein Structures of DegQ Enzymes

As large molecular tertiary structures, some proteins can act as small robots that find, bind, and chaperone target protein clients, showing the potential to serve as smart building blocks in self-assembly fields. Instead of using such intrinsic functions, most self-assembly methodologies for proteins aim for de novo-designed structures with accurate geometric assemblies, which can limit procedural flexibility. Here, a strategy enabling polymorphic clustering of quaternary proteins, exhibiting simplicity and flexibility of self-assembling paths for proteins in forming monodisperse quaternary cage particles is presented. It is proposed that the enzyme protomer DegQ, previously solved at low resolution, may potentially be usable as a threefold symmetric building block, which can form polyhedral cages incorporated by the chaperone action of DegQ in the presence of protein clients. To obtain highly monodisperse cage particles, soft, and hence, less resistive client proteins, which can program the inherent chaperone activity of DegQ to efficient formations of polymorphic cages, depending on the size of clients are utilized. By reconstructing the atomic resolution cryogenic electron microscopy DegQ structures using obtained 12- and 24-meric clusters, the polymorphic clustering of DegQ enzymes is validated in terms of soft and rigid domains, which will provide effective routes for protein self-assemblies with procedural flexibility.

Oligomerization of drug transporters: Forms, functions, and mechanisms

Drug transporters are essential players in the transmembrane transport of a wide variety of clinical drugs. The broad substrate spectra and versatile distribution pattern of these membrane proteins infer their pharmacological and clinical significance. With our accumulating knowledge on the three-dimensional structure of drug transporters, their oligomerization status has become a topic of intense study due to the possible functional roles carried out by such kind of post-translational modification (PTM). In-depth studies of oligomeric complexes formed among drug transporters as well as their interactions with other regulatory proteins can help us better understand the regulatory mechanisms of these membrane proteins, provide clues for the development of novel drugs, and improve the therapeutic efficacy. In this review, we describe different oligomerization forms as well as their structural basis of major drug transporters in the ATP-binding cassette and solute carrier superfamilies, summarize our current knowledge on the influence of oligomerization for protein expression level and transport function of these membrane proteins, and discuss the regulatory mechanisms of oligomerization. Finally, we highlight the challenges associated with the current oligomerization studies and propose some thoughts on the pharmaceutical application of this important drug transporter PTM.

Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations

Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.

Design and Thermodynamics Principles to Program the Cooperativity of Molecular Assemblies

Most functional nanosystems in living organisms are constructed using multimeric assemblies that provide multiple advantages over their monomeric counterparts such as cooperative or anti-cooperative responses, integration of multiple signals and self-regulation. Inspired by these natural nanosystems, chemists have been synthesizing self-assembled supramolecular systems over the last 50 years with increasing complexity with applications ranging from biosensing, drug delivery, synthetic biology, and system chemistry. Although many advances have been made concerning the design principles of novel molecular architectures and chemistries, little is still known, however, about how to program their dynamic of assembly so that they can assemble at the required concentration and with the right sensitivity. Here, we used synthetic DNA assemblies and double-mutant cycle analysis to explore the thermodynamic basis to program the cooperativity of molecular assemblies. The results presented here exemplify how programmable molecular assemblies can be efficiently built by fusing interacting domains and optimizing their compaction. They may also provide the rational basis for understanding the thermodynamic and mechanistic principles driving the evolution of multimeric biological complexes.

Gut dysbiosis and neurological modalities: An engineering approach via proteomic analysis of gut-brain axis

The human gut microbiota is a complex and dynamic community of microorganisms, that influence metabolic, neurodevelopmental, and immune pathways. Microbial dysbiosis, characterized by changes in microbial diversity and relative abundances, is implicated in the development of various chronic neurological and neurodegenerative disorders. These disorders are marked by the accumulation of pathological protein aggregates, leading to the progressive loss of neurons and behavioural functions. Dysregulations in protein-protein interaction networks and signalling complexes, critical for normal brain function, are common in neurological disorders but challenging to unravel, particularly at the neuron and synapse-specific levels. To advance therapeutic strategies, a deeper understanding of neuropathogenesis, especially during the progressive disease phase, is needed. Biomarkers play a crucial role in identifying disease pathophysiology and monitoring disease progression. Proteomics, a powerful technology, shows promise in accelerating biomarker discovery and aiding in the development of novel treatments. In this chapter, we provide an in-depth overview of how proteomic techniques, utilizing various biofluid samples from patients with neurological conditions and diverse animal models, have contributed valuable insights into the pathogenesis of numerous neurological disorders. We also discuss the current state of research, potential challenges, and future directions in proteomic approaches to unravel neuro-pathological conditions.

The mineralocorticoid receptor forms higher order oligomers upon DNA binding

The prevailing model of steroid hormone nuclear receptor function assumes ligand-induced homodimer formation followed by binding to DNA hormone response elements (HREs). This model has been challenged by evidence showing that the glucocorticoid receptor (GR) forms tetramers upon ligand and DNA binding, which then drive receptor-mediated gene transactivation and transrepression. GR and the closely-related mineralocorticoid receptors (MR) interact to transduce corticosteroid hormone signaling, but whether they share the same quaternary arrangement is unknown. Here, we used a fluorescence imaging technique, Number & Brightness, to study oligomerization in a cell system allowing real-time analysis of receptor-DNA interactions. Agonist-bound MR forms tetramers in the nucleoplasm and higher order oligomers upon binding to HREs. Antagonists form intermediate-size quaternary arrangements, suggesting that large oligomers are essential for function. Divergence between MR and GR quaternary structure is driven by different functionality of known and new multimerization interfaces, which does not preclude formation of heteromers. Thus, influencing oligomerization may be important to selectively modulate corticosteroid signaling.

Creative approaches using proximity labeling to gain new biological insights

At its most fundamental level, life is a collection of synchronized cellular processes driven by interactions among biomolecules. Proximity labeling has emerged as a powerful technique to capture these interactions in native settings, revealing previously unexplored elements of biology. This review highlights recent developments in proximity labeling, focusing on methods that push the fundamental technologies beyond the classic bait-prey paradigm, such as RNA-protein interactions, ligand/small-molecule-protein interactions, cell surface protein interactions, and subcellular protein trafficking. The advancement of proximity labeling methods to address different biological problems will accelerate our understanding of the complex biological systems that make up life.

Chiral Induced Spin Selectivity

Since the initial landmark study on the chiral induced spin selectivity (CISS) effect in 1999, considerable experimental and theoretical efforts have been made to understand the physical underpinnings and mechanistic features of this interesting phenomenon. As first formulated, the CISS effect refers to the innate ability of chiral materials to act as spin filters for electron transport; however, more recent experiments demonstrate that displacement currents arising from charge polarization of chiral molecules lead to spin polarization without the need for net charge flow. With its identification of a fundamental connection between chiral symmetry and electron spin in molecules and materials, CISS promises profound and ubiquitous implications for existing technologies and new approaches to answering age old questions, such as the homochiral nature of life. This review begins with a discussion of the different methods for measuring CISS and then provides a comprehensive overview of molecules and materials known to exhibit CISS-based phenomena before proceeding to identify structure-property relations and to delineate the leading theoretical models for the CISS effect. Next, it identifies some implications of CISS in physics, chemistry, and biology. The discussion ends with a critical assessment of the CISS field and some comments on its future outlook.

Euglena's atypical respiratory chain adapts to the discoidal cristae and flexible metabolism

Euglena gracilis, a model organism of the eukaryotic supergroup Discoba harbouring also clinically important parasitic species, possesses diverse metabolic strategies and an atypical electron transport chain. While structures of the electron transport chain complexes and supercomplexes of most other eukaryotic clades have been reported, no similar structure is currently available for Discoba, limiting the understandings of its core metabolism and leaving a gap in the evolutionary tree of eukaryotic bioenergetics. Here, we report high-resolution cryo-EM structures of Euglena's respirasome I + III2 + IV and supercomplex III2 + IV2. A previously unreported fatty acid synthesis domain locates on the tip of complex I's peripheral arm, providing a clear picture of its atypical subunit composition identified previously. Individual complexes are re-arranged in the respirasome to adapt to the non-uniform membrane curvature of the discoidal cristae. Furthermore, Euglena's conformationally rigid complex I is deactivated by restricting ubiquinone's access to its substrate tunnel. Our findings provide structural insights for therapeutic developments against euglenozoan parasite infections.

Simple visualization of submicroscopic protein clusters with a phase-separation-based fluorescent reporter

Protein clustering plays numerous roles in cell physiology and disease. However, protein oligomers can be difficult to detect because they are often too small to appear as puncta in conventional fluorescence microscopy. Here, we describe a fluorescent reporter strategy that detects protein clusters with high sensitivity called CluMPS (clusters magnified by phase separation). A CluMPS reporter detects and visually amplifies even small clusters of a binding partner, generating large, quantifiable fluorescence condensates. We use computational modeling and optogenetic clustering to demonstrate that CluMPS can detect small oligomers and behaves rationally according to key system parameters. CluMPS detected small aggregates of pathological proteins where the corresponding GFP fusions appeared diffuse. CluMPS also detected and tracked clusters of unmodified and tagged endogenous proteins, and orthogonal CluMPS probes could be multiplexed in cells. CluMPS provides a powerful yet straightforward approach to observe higher-order protein assembly in its native cellular context. A record of this paper's transparent peer review process is included in the supplemental information.

Comment on "Asymmetric Bistricyclic Aromatic Enes"

This Correspondence argues against the use of the adjectives "symmetrical" and "asymmetric" in the recent publication entitled "Fluorenylidene-Cyclopentadithiophene Based Asymmetric Bistricyclic Aromatic Ene Compounds: Synthesis and Substituents Effects", by Beibei Xiao, Yongrui Yang, Shengnan Chen, Ye Zou, Xing Chen, Kanglei Liu, Nan Wang, Yali Qiao, and Xiaodong Yin (Chem. Eur. J., 2023, 29, e202301055).[1].

Drugs targeting the particle for arrangement of quaternary structure (PAQosome) and protein complex assembly

INTRODUCTION

The PAQosome is a 12-subunit complex that acts as a co-factor of the molecular chaperones HSP90 and HSP70. This co-chaperone has been shown to participate in assembly and maturation of several protein complexes, including nuclear RNA polymerases, RNA processing factors, the ribosome, PIKKs, and others. Subunits of the PAQosome, adaptors, and clients have been reported to be involved in various diseases, making them interesting targets for drug discovery.

AREA COVERED

In this review, the authors cover the detailed mechanisms of PAQosome and chaperone function. Specifically, the authors summarize the status of the PAQosome and some related chaperones and co-chaperones as candidate targets for drug discovery. Indeed, a number of compounds are currently being tested for the development of treatments against diseases, such as cancers and neurodegenerative conditions.

EXPERT OPINION

Searching for new drugs targeting the PAQosome requires a better understanding of PAQosome subunit interactions and the discovery of new interaction partners. Thus, PAQosome subunit crystallization is an important experiment to initiate virtual screening against new target and the development of in silico tools such as AlphaFold-multimer could accelerate the search for new interaction partner and determine more rapidly the interaction pocket needed for virtual drug screening.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

Structural insights into the oligomeric effects on catalytic activity of a decameric feruloyl esterase and its application in ferulic acid production

Oligomeric feruloyl esterase (FAE) has great application prospect in industry due to its potentially high stability and fine-tuned activity. However, the relationship between catalytic capability and oligomeric structure remains undetermined. Here we identified and characterized a novel, cold-adapted FAE (BtFae) derived from Bacteroides thetaiotaomicron. Structural studies unraveled that BtFae adopts a barrel-like decameric architecture unique in esterase families. By disrupting the interface, the monomeric variant exhibited significantly reduced catalytic activity and stability toward methyl ferulate, potentially due to its impact on the flexibility of the catalytic triad. Additionally, our results also showed that the monomerization of BtFae severely decreased the ferulic acid release from de-starched wheat bran and insoluble wheat arabinoxylan by 75 % and 80 %, respectively. Collectively, this study revealed novel connections between oligomerization and FAE catalytic function, which will benefit for further protein engineering of FAEs at the quaternary structure level for improved industrial applications.

Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic - tetrahedral, octahedral and icosahedral - symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.

Interplay between self-assembly and phase separation in a polymer-complex model

We present a theoretical model for predicting the phase behavior of polymer solutions in which phase separation competes with oligomerization. Specifically, we consider scenarios in which the assembly of polymer chains into stoichiometric complexes prevents the chains from phase-separating via attractive polymer-polymer interactions. Combining statistical associating fluid theory with a two-state description of self-assembly, we find that this model exhibits rich phase behavior, including reentrance, and we show how system-specific phase diagrams can be derived graphically. Importantly, we discuss why these phase diagrams can resemble-and yet are qualitatively distinct from-phase diagrams of polymer solutions with lower critical solution temperatures.

Diacylglycerol-dependent hexamers of the SNARE-assembling chaperone Munc13-1 cooperatively bind vesicles

Munc13-1 is essential for vesicle docking and fusion at the active zone of synapses. Here, we report that Munc13-1 self-assembles into molecular clusters within diacylglycerol-rich microdomains present in phospholipid bilayers. Although the copy number of Munc13-1 molecules in these clusters has a broad distribution, a systematic Poisson analysis shows that this is most likely the result of two molecular species: monomers and mainly hexameric oligomers. Each oligomer is able to capture one vesicle independently. Hexamers have also been observed in crystals of Munc13-1 that form between opposed phospholipid bilayers [K. Grushin, R. V. Kalyana Sundaram, C. V. Sindelar, J. E. Rothman, Proc. Natl. Acad. Sci. U.S.A. 119, e2121259119 (2022)]. Mutations targeting the contacts stabilizing the crystallographic hexagons also disrupt the isolated hexamers, suggesting they are identical. Additionally, these mutations also convert vesicle binding from a cooperative to progressive mode. Our study provides an independent approach showing that Munc13-1 can form mainly hexamers on lipid bilayers each capable of vesicle capture.

Elucidating Structures of Protein Complexes by Collision-Induced Dissociation at Elevated Gas Pressures

Ion activation methods carried out at gas pressures compatible with ion mobility separations are not yet widely established. This limits the analytical utility of emerging tandem-ion mobility spectrometers that conduct multiple ion mobility separations in series. The present work investigates the applicability of collision-induced dissociation (CID) at 1 to 3 mbar in a tandem-trapped ion mobility spectrometer (tandem-TIMS) to study the architecture of protein complexes. We show that CID of the homotetrameric protein complexes streptavidin (53 kDa), neutravidin (60 kDa), and concanavalin A (110 kDa) provides access to all subunits of the investigated protein complexes, including structurally informative dimers. We report on an "atypical" dissociation pathway, which for concanavalin A proceeds via symmetric partitioning of the precursor charges and produces dimers with the same charge states that were previously reported from surface induced dissociation. Our data suggest a correlation between the formation of subunits by CID in tandem-TIMS/MS, their binding strengths in the native tetramer structures, and the applied activation voltage. Ion mobility spectra of in situ-generated subunits reveal a marked structural heterogeneity inconsistent with annealing into their most stable gas phase structures. Structural transitions are observed for in situ-generated subunits that resemble the transitions reported from collision-induced unfolding of natively folded proteins. These observations indicate that some aspects of the native precursor structure is preserved in the subunits generated from disassembly of the precursor complex. We rationalize our observations by an approximately 100-fold shorter activation time scale in comparison to traditional CID in a collision cell. Finally, the approach discussed here to conduct CID at elevated pressures appears generally applicable also for other types of tandem-ion mobility spectrometers.

Oligomeric Changes Regulate Flavin Transfer in Two-Component FMN Reductases Involved in Sulfur Metabolism

The FMN reductases (SsuE and MsuE of the alkanesulfonate monooxygenase systems) supply reduced flavin to their partner monooxygenases for the desulfonation of alkanesulfonates. Flavin reductases that comprise two-component systems must be able to regulate both flavin reduction and transfer. One mechanism to control these distinct processes is through changes in the oligomeric state of the enzymes. Despite their similar overall structures, SsuE and MsuE showed clear differences in their oligomeric states in the presence of substrates. The oligomeric state of SsuE was converted from a tetramer to a dimer/tetramer equilibrium in the presence of FMN or NADPH in analytical ultracentrifugation studies. Conversely, MsuE shifted from a dimer to a single tetrameric state with FMN, and the NADPH substrate did not induce a similar oligomeric shift. There was a fast tetramer to dimer equilibrium shift occurring at the dimer/dimer interface in H/D-X investigations with apo SsuE. Formation of the SsuE/FMN complex slowed the tetramer/dimer conversion, leading to a slower exchange along the dimer/dimer interface. The oligomeric shift of the MsuE/FMN complex from a dimer to a distinct tetramer showed a decrease in H/D-X in the region around the π-helices at the dimer/dimer interface. Both SsuE and MsuE showed a comparable and significant increase in the melting temperature with the addition of FMN, indicating the conformers formed by each FMN-bound enzyme had increased stability. A mechanism that supports the different structural shifts is rationalized by the different roles these enzymes play in providing reduced flavin to single or multiple monooxygenase enzymes.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation

During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subunit binding kinetics or external design of subunit titration in time. Our third protocol acts to recycle intermediates, mimicking energy-consuming enzymes. Preventative solutions via interface design are the most efficient and scale better with more subunits, but external control via titration or recycling are effective even for poorly evolved binding kinetics. Whilst all protocols can produce good solutions, diverse subunits always helps; these complexes access more efficient solutions when following external control protocols, and are simpler to design for internal control, as molecular interfaces do not need modification during assembly given sufficient variation in dimerization rates. Our results identify universal scaling in the cost of kinetic trapping, and provide multiple strategies for eliminating trapping and maximizing assembly yield across large parameter spaces.

ProSight Native: Defining Protein Complex Composition from Native Top-Down Mass Spectrometry Data

Native mass spectrometry has recently moved alongside traditional structural biology techniques in its ability to provide clear insights into the composition of protein complexes. However, to date, limited software tools are available for the comprehensive analysis of native mass spectrometry data on protein complexes, particularly for experiments aimed at elucidating the composition of an intact protein complex. Here, we introduce ProSight Native as a start-to-finish informatics platform for analyzing native protein and protein complex data. Combining mass determination via spectral deconvolution with a top-down database search and stoichiometry calculations, ProSight Native can determine the complete composition of protein complexes. To demonstrate its features, we used ProSight Native to successfully determine the composition of the homotetrameric membrane complex Aquaporin Z. We also revisited previously published spectra and were able to decipher the composition of a heterodimer complex bound with two noncovalently associated ligands. In addition to determining complex composition, we developed new tools in the software for validating native mass spectrometry fragment ions and mapping top-down fragmentation data onto three-dimensional protein structures. Taken together, ProSight Native will reduce the informatics burden on the growing field of native mass spectrometry, enabling the technology to further its reach.

The mineralocorticoid receptor forms higher order oligomers upon DNA binding

The prevailing model of steroid hormone nuclear receptor function assumes ligand-induced homodimer formation followed by binding to DNA hormone response elements (HREs). This model has been challenged by evidence showing that the glucocorticoid receptor (GR) forms tetramers upon ligand and DNA binding, which then drive receptor-mediated gene transactivation and transrepression. GR and the closely-related mineralocorticoid receptors (MR) interact to transduce corticosteroid hormone signaling, but whether they share the same quaternary arrangement is unknown. Here, we used a fluorescence imaging technique, Number & Brightness, to study oligomerization in a cell system allowing real-time analysis of receptor-DNA interactions. Agonist-bound MR forms tetramers in the nucleoplasm and higher order oligomers upon binding to HREs. Antagonists form intermediate quaternary arrangements, suggesting that large oligomers are essential for function. Divergence between MR and GR quaternary structure is driven by different functionality of known and new multimerization interfaces, which does not preclude formation of heteromers. Thus, influencing oligomerization may be important to selectively modulate corticosteroid signaling.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with β-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

GDockScore: a graph-based protein-protein docking scoring function

Summary

Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.

Availability and implementation

The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

A Flexible, Modular Platform for Multidimensional Ion Mobility of Native-like Ions

Native ion mobility (IM) mass spectrometry (MS) is used to probe the size, shape, and assembly of biomolecular complexes. IM-IM-MS can increase the amount of information available in structural studies by isolating subpopulations of structures for further analysis. Previously, IM-IM-MS has been implemented using the Structures for Lossless Ion Manipulations (SLIM) architecture to probe the structural stability of gas-phase protein ions. Here, a new multidimensional IM instrument constructed from SLIM devices is characterized using multiple operational modes. In this new design, modular devices are used to perform all ion manipulations, including initial accumulation, injection, separation, selection, and trapping. Using single-dimension IM, collision cross section (Ω) values are determined for a set of native-like ions. These Ω values are within 3% of those reported previously based on measurements using RF-confining drift cells. Tandem IM experiments are performed on a sample of ubiquitin ions that contains both compact and partially unfolded structures, demonstrating that this platform can isolate subpopulations of structures. Finally, additional modes of analysis, including multiplexed IM and inverse IM, are demonstrated using this platform. The ability of this platform to quickly switch between different modes of IM analysis makes it a highly flexible tool for studying protein structures and dynamics.

Computational approaches for molecular characterization and structure-based functional elucidation of a hypothetical protein from Mycobacterium tuberculosis

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

Functional interdependence of N6-methyladenosine methyltransferase complex subunits in Arabidopsis

Addition of N6-methyladenosine (m6A), the most prevalent internal mRNA modification in eukaryotes, is catalyzed by an evolutionarily conserved m6A methyltransferase complex. In the model plant Arabidopsis thaliana, the m6A methyltransferase complex is composed of 2 core methyltransferases, mRNA adenosine methylase (MTA) and MTB, and several accessory subunits such as FK506-BINDING PROTEIN 12 KD INTERACTING PROTEIN 37KD (FIP37), VIRILIZER (VIR), and HAKAI. It is yet largely unknown whether these accessory subunits influence the functions of MTA and MTB. Herein, I reveal that FIP37 and VIR are indispensable for stabilizing the methyltransferases MTA and MTB, thus functioning as key subunits to maintain the functionality of the m6A methyltransferase complex. Furthermore, VIR affects FIP37 and HAKAI protein accumulation, while MTA and MTB mutually influence each other. In contrast, HAKAI has little effect on protein abundance or localization of MTA, MTB, and FIP37. These findings uncover unique functional interdependence at the post-translational level among individual components in the Arabidopsis m6A methyltransferase complex, suggesting that maintenance of protein homeostasis among various subunits of the m6A methyltransferase complex is essential for maintaining the protein stoichiometry required for the proper function of the m6A methyltransferase complex in m6A deposition in plants.

TTT (Tel2-Tti1-Tti2) Complex, the Co-Chaperone of PIKKs and a Potential Target for Cancer Chemotherapy

The heterotrimeric Tel2-Tti1-Tti2 or TTT complex is essential for cell viability and highly observed in eukaryotes. As the co-chaperone of ATR, ATM, DNA-PKcs, mTOR, SMG1, and TRRAP, the phosphatidylinositol 3-kinase-related kinases (PIKKs) and a group of large proteins of 300-500 kDa, the TTT plays crucial roles in genome stability, cell proliferation, telomere maintenance, and aging. Most of the protein kinases in the kinome are targeted by co-chaperone Cdc37 for proper folding and stability. Like Cdc37, accumulating evidence has established the mechanism by which the TTT interacts with chaperone Hsp90 via R2TP (Rvb1-Rvb2-Tah1-Pih1) complex or other proteins for co-translational maturation of the PIKKs. Recent structural studies have revealed the α-solenoid structure of the TTT and its interactions with the R2TP complex, which shed new light on the co-chaperone mechanism and provide new research opportunities. A series of mutations of the TTT have been identified that cause disease syndrome with neurodevelopmental defects, and misregulation of the TTT has been shown to contribute to myeloma, colorectal, and non-small-cell lung cancers. Surprisingly, Tel2 in the TTT complex has recently been found to be a target of ivermectin, an antiparasitic drug that has been used by millions of patients. This discovery provides mechanistic insight into the anti-cancer effect of ivermectin and thus promotes the repurposing of this Nobel-prize-winning medicine for cancer chemotherapy. Here, we briefly review the discovery of the TTT complex, discuss the recent studies, and describe the perspectives for future investigation.

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein-protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein-protein interactions and a unique perspective on possible novel protein complexes.

Functional advantages of building nanosystems using multiple molecular components

Over half of all the natural nanomachines in living organisms are multimeric and likely exploit the self-assembly of their components to provide functional benefits. However, the advantages and disadvantages of building nanosystems using multiple molecular components remain relatively unexplored at the thermodynamic, kinetic and functional levels. In this study we used theory and a simple DNA-based model that forms the same nanostructures with different numbers of components to advance our knowledge in this area. Despite its lower assembly rate, we found that a system built with three components may undergo a more cooperative assembly transition from less preorganized components, which facilitates the emergence of functionalities. Using simple variations of its components, we also found that trimeric nanosystems display a much higher level of programmability than their dimeric counterparts because they can assemble with various levels of cooperativity, self-inhibition and time-dependent properties. We show here how two simple strategies (for example, cutting and adding components) can be employed to efficiently programme the regulatory function of a more complex, artificially selected, RNA-cleaving catalytic nanosystem.

In silico exploration and molecular dynamics of deleterious SNPs on the human TERF1 protein triggering male infertility

By limiting chromosome erosion and end-to-end fusions, telomere integrity is critical for chromosome stability and cell survival. During mitotic cycles or due to environmental stresses, telomeres become progressively shorter and dysfunctional, thus triggering cellular senescence, genomic instability and cell death. To avoid such consequences, the telomerase action, as well as the Shelterin and CST complexes, assure the telomere's protection. Telomeric repeat binding factor 1 (TERF1), which is one of the primary components of the Shelterin complex, binds directly to the telomere and controls its length and function by regulating the telomerase activity. Several reports about TERF1 gene variations have been associated with different diseases, and some of them have linked these variations to male infertility. Hence, this paper can be advantageous to investigate the association between the missense variants of the TERF1 gene and the susceptibility to male infertility. The stepwise prediction of SNPs pathogenicity followed in this study was based on stability and conservation analysis, post-translational modification, secondary structure, functional interaction prediction, binding energy evaluation and finally molecular dynamic simulation. Prediction matching among the tools revealed that out of 18 SNPs, only four (rs1486407144, rs1259659354, rs1257022048 and rs1320180267) were predicted as the most damaging and highly deleterious SNPs affecting the TERF1 protein and its molecular dynamics when interacting with the TERB1 protein by influencing the function, structural stability, flexibility and compaction of the overall complex. Interestingly, these polymorphisms should be considered during genetic screening so they can be used effectively as genetic biomarkers for male infertility diagnosis.Communicated by Ramaswamy H. Sarma.

Conformational and oligomeric states of SPOP from small-angle X-ray scattering and molecular dynamics simulations

Speckle-type POZ protein (SPOP) is a substrate adaptor in the ubiquitin proteasome system, and plays important roles in cell-cycle control, development, and cancer pathogenesis. SPOP forms linear higher-order oligomers following an isodesmic self-association model. Oligomerization is essential for SPOP's multivalent interactions with substrates, which facilitate phase separation and localization to biomolecular condensates. Structural characterization of SPOP in its oligomeric state and in solution is, however, challenging due to the inherent conformational and compositional heterogeneity of the oligomeric species. Here, we develop an approach to simultaneously and self-consistently characterize the conformational ensemble and the distribution of oligomeric states of SPOP by combining small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations. We build initial conformational ensembles of SPOP oligomers using coarse-grained molecular dynamics simulations, and use a Bayesian/maximum entropy approach to refine the ensembles, along with the distribution of oligomeric states, against a concentration series of SAXS experiments. Our results suggest that SPOP oligomers behave as rigid, helical structures in solution, and that a flexible linker region allows SPOP's substrate-binding domains to extend away from the core of the oligomers. Additionally, our results are in good agreement with previous characterization of the isodesmic self-association of SPOP. In the future, the approach presented here can be extended to other systems to simultaneously characterize structural heterogeneity and self-assembly.

Structural evolution of an amphibian-specific globin: A computational evolutionary biochemistry approach

Studies on the globin family are continuously revealing insights into the mechanisms of gene and protein evolution. The rise of a new globin gene type in Pelobatoidea and Neobatrachia (Amphibia:Anura) from an α-globin precursor provides the opportunity to investigate the genetic and physical mechanisms underlying the origin of new protein structural and functional properties. This amphibian-specific globin (globin A/GbA) discovered in the heart of Rana catesbeiana is a monomer. As the ancestral oligomeric state of α-globins is a homodimer, we inferred that the ancestral state was lost somewhere in the GbA lineage. Here, we combined computational molecular evolution with structural bioinformatics to determine the extent to which the loss of the homodimeric state is pervasive in the GbA clade. We also characterized the loci of GbA genes in Bufo bufo. We found two GbA clades in Neobatrachia. One was deleted in Ranidae, but retained and expanded to yield a new globin cluster in Bufonidae species. Loss of the ancestral oligomeric state seems to be pervasive in the GbA clade. However, a taxonomic sampling that includes more Pelobatoidea, as well as early Neobatrachia, lineages would be necessary to determine the oligomeric state of the last common ancestor of all GbA. The evidence presented here points out a possible loss of oligomerization in Pelobatoidea GbA as a result of amino acid substitutions that weaken the homodimeric state. In contrast, the loss of oligomerization in both Neobatrachia GbA clades was linked to independent deletions that disrupted many packing contacts at the homodimer interface.

Grand canonical Brownian dynamics simulations of adsorption and self-assembly of SAS-6 rings on a surface

The Spindle Assembly Abnormal Protein 6 (SAS-6) forms dimers, which then self-assemble into rings that are critical for the nine-fold symmetry of the centriole organelle. It has recently been shown experimentally that the self-assembly of SAS-6 rings is strongly facilitated on a surface, shifting the reaction equilibrium by four orders of magnitude compared to the bulk. Moreover, a fraction of non-canonical symmetries (i.e., different from nine) was observed. In order to understand which aspects of the system are relevant to ensure efficient self-assembly and selection of the nine-fold symmetry, we have performed Brownian dynamics computer simulation with patchy particles and then compared our results with the experimental ones. Adsorption onto the surface was simulated by a grand canonical Monte Carlo procedure and random sequential adsorption kinetics. Furthermore, self-assembly was described by Langevin equations with hydrodynamic mobility matrices. We find that as long as the interaction energies are weak, the assembly kinetics can be described well by coagulation-fragmentation equations in the reaction-limited approximation. By contrast, larger interaction energies lead to kinetic trapping and diffusion-limited assembly. We find that the selection of nine-fold symmetry requires a small value for the angular interaction range. These predictions are confirmed by the experimentally observed reaction constant and angle fluctuations. Overall, our simulations suggest that the SAS-6 system works at the crossover between a relatively weak binding energy that avoids kinetic trapping and a small angular range that favors the nine-fold symmetry.

Asymmetric horseshoe-like assembly of peroxisomal yeast oxalyl-CoA synthetase

Oxalyl-CoA synthetase from Saccharomyces cerevisiae is one of the most abundant peroxisomal proteins in yeast and hence has become a model to study peroxisomal translocation. It contains a C-terminal Peroxisome Targeting Signal 1, which however is partly dispensable, suggesting additional receptor bindings sites. To unravel any additional features that may contribute to its capacity to be recognized as peroxisomal target, we determined its assembly and overall architecture by an integrated structural biology approach, including X-ray crystallography, single particle cryo-electron microscopy and small angle X-ray scattering. Surprisingly, it assembles into mixture of concentration-dependent dimers, tetramers and hexamers by dimer self-association. Hexameric particles form an unprecedented asymmetric horseshoe-like arrangement, which considerably differs from symmetric hexameric assembly found in many other protein structures. A single mutation within the self-association interface is sufficient to abolish any higher-level oligomerization, resulting in a homogenous dimeric assembly. The small C-terminal domain of yeast Oxalyl-CoA synthetase is connected by a partly flexible hinge with the large N-terminal domain, which provides the sole basis for oligomeric assembly. Our data provide a basis to mechanistically study peroxisomal translocation of this target.

Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins

The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.

Dissecting the general mechanisms of protein cage self-assembly by coarse-grained simulations

The development of artificial protein cages has recently gained massive attention due to their promising application prospect as novel delivery vehicles for therapeutics. These nanoparticles are formed through a process called self-assembly, in which individual subunits spontaneously arrange into highly ordered patterns via non-covalent but specific interactions. Therefore, the first step toward the design of novel engineered protein cages is to understand the general mechanisms of their self-assembling dynamics. Here we have developed a new computational method to tackle this problem. Our method is based on a coarse-grained model and a diffusion-reaction simulation algorithm. Using a tetrahedral cage as test model, we showed that self-assembly of protein cage requires of a seeding process in which specific configurations of kinetic intermediate states are identified. We further found that there is a critical concentration to trigger self-assembly of protein cages. This critical concentration allows that cages can only be successfully assembled under a persistently high concentration. Additionally, phase diagram of self-assembly has been constructed by systematically testing the model across a wide range of binding parameters. Finally, our simulations demonstrated the importance of protein's structural flexibility in regulating the dynamics of cage assembly. In summary, this study throws lights on the general principles underlying self-assembly of large cage-like protein complexes and thus provides insights to design new nanomaterials.

Structural basis of the mycobacterial stress-response RNA polymerase auto-inhibition via oligomerization

Self-assembly of macromolecules into higher-order symmetric structures is fundamental for the regulation of biological processes. Higher-order symmetric structure self-assembly by the gene expression machinery, such as bacterial DNA-dependent RNA polymerase (RNAP), has never been reported before. Here, we show that the stress-response σ^B factor from the human pathogen, Mycobacterium tuberculosis, induces the RNAP holoenzyme oligomerization into a supramolecular complex composed of eight RNAP units. Cryo-electron microscopy revealed a pseudo-symmetric structure of the RNAP octamer in which RNAP protomers are captured in an auto-inhibited state and display an open-clamp conformation. The structure shows that σ^B is sequestered by the RNAP flap and clamp domains. The transcriptional activator RbpA prevented octamer formation by promoting the initiation-competent RNAP conformation. Our results reveal that a non-conserved region of σ is an allosteric controller of transcription initiation and demonstrate how basal transcription factors can regulate gene expression by modulating the RNAP holoenzyme assembly and hibernation.

Perspective on the potential of tandem-ion mobility /mass spectrometry methods for structural proteomics applications

Cellular processes are usually carried out collectively by the entirety of all proteins present in a biological cell, i.e. the proteome. Mass spectrometry-based methods have proven particularly successful in identifying and quantifying the constituent proteins of proteomes, including different molecular forms of a protein. Nevertheless, protein sequences alone do not reveal the function or dysfunction of the identified proteins. A straightforward way to assign function or dysfunction to proteins is characterization of their structures and dynamics. However, a method capable to characterize detailed structures of proteins and protein complexes in a large-scale, systematic manner within the context of cellular processes does not yet exist. Here, we discuss the potential of tandem-ion mobility / mass spectrometry (tandem-IM/MS) methods to provide such ability. We highlight the capability of these methods using two case studies on the protein systems ubiquitin and avidin using the tandem-TIMS/MS technology developed in our laboratory and discuss these results in the context of other developments in the broader field of tandem-IM/MS.

The Need for Biomarkers in the ALS-FTD Spectrum: A Clinical Point of View on the Role of Proteomics

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are severely debilitating and progressive neurodegenerative disorders. A distinctive pathological feature of several neurodegenerative diseases, including ALS and FTD, is the deposition of aberrant protein inclusions in neuronal cells, which leads to cellular dysfunction and neuronal damage and loss. Despite this, to date, the biological process behind developing these protein inclusions must be better clarified, making the development of disease-modifying treatment impossible until this is done. Proteomics is a powerful tool to characterize the expression, structure, functions, interactions, and modifications of proteins of tissue and biological fluid, including plasma, serum, and cerebrospinal fluid. This protein-profiling characterization aims to identify disease-specific protein alteration or specific pathology-based mechanisms which may be used as markers of these conditions. Our narrative review aims to highlight the need for biomarkers and the potential use of proteomics in clinical practice for ALS-FTD spectrum disorders, considering the emerging rationale in proteomics for new drug development. Certainly, new data will emerge in the near future in this regard and support clinicians in the development of personalized medicine.