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General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1–3. Here, inspired by natural and re-engineered protein–peptide systems4–11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

Protein aggregation is initiated by structural changes from native polypeptides to cytotoxic oligomers, which form cross-β structured amyloid. Identification and characterization of oligomeric intermediates are critically important for understanding not only the molecular mechanism of aggregation but also the cytotoxic nature of amyloid oligomers. Preparation of misfolded oligomers for structural characterization is, however, challenging because of their transient, heterogeneous nature. Here, we report two distinct misfolded transthyretin (TTR) oligomers formed through different oligomerization pathways. A pathogenic TTR variant with a strong aggregation propensity (L55P) was used to prepare misfolded oligomers at physiological pH. Our mechanistic studies showed that the full-length TTR initially forms small oligomers, which self-assemble into short protofibrils at later stages. Enzymatic cleavage of the CD loop was also used to induce the formation of N-terminally truncated oligomers, which was detected in ex vivo cardiac TTR aggregates extracted from the tissues of patients. Structural characterization of the oligomers using solid-state nuclear magnetic resonance and circular dichroism revealed that the two TTR misfolded oligomers have distinct molecular conformations. In addition, the proteolytically cleaved TTR oligomers exhibit a higher surface hydrophobicity, suggesting the presence of distinct oligomerization pathways for TTR oligomer formation. Cytotoxicity assays also revealed that the cytotoxicity of cleaved oligomers is stronger than that of the full-length TTR oligomers, indicating that hydrophobicity might be an important property of toxic oligomers. These comparative biophysical analyses suggest that the toxic cleaved TTR oligomers formed through a different misfoling pathway may adopt distinct structural features that produce higher surface hydrophobicity, leading to the stronger cytotoxic activities.

For intrinsically disordered proteins (IDPs), a pressing question is how sequence codes for function. Dynamics serves as a crucial link, reminiscent of the role of structure in sequence-function relations of structured proteins. To define general rules governing sequence-dependent backbone dynamics, we carried out long molecular dynamics simulations of eight IDPs. Blocks of residues exhibiting large amplitudes in slow dynamics are rigidified by local inter-residue interactions or secondary structures. A long region or an entire IDP can be slowed down by long-range contacts or secondary- structure packing. On the other hand, glycines promote fast dynamics and either demarcate rigid blocks or facilitate multiple modes of local and long-range inter-residue interactions. The sequence-dependent backbone dynamics endows IDPs with versatile response to binding partners, with some blocks recalcitrant while others readily adapting to intermolecular interactions.

Structural biology of proteins emphasises that proteins ought to have an ordered structure to perform their biological role optimally. The over-reliance on the ordered structure of proteins is now slowly shifting towards a more comprehensive discussion platform. Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) are gaining momentum in protein structural biology as we update ourselves with evolutionary traits and functional importance in various organisms. The evolution and functional significance of this diverse class of protein conformations are based on sequence exhibition, structural attainment, and interactions with their immediate surroundings. In this review, we emphasise the evolutionary status of disordered proteins and correlate their functional importance in the physiology of specific organisms. We aim to close this review by establishing a positive correlation between IDPs and their importance in human health and future medicine. Establishing firm roles of IDPs and IDPRs with extensive research will help expand the field of structural biology, helping us understand the fundamentals of protein folding and misfolding, associated diseases and drug design.

To assess the potential benefits of molecular-dynamics (MD) simulations for macromolecular crystallography (MX), we performed room-temperature X-ray diffraction studies of the catalytic subunit of mouse protein kinase A (PKA-C). We then performed crystalline MD simulations of PKA-C, computed simulated electron densities from the water, protein, and ion components of the MD simulations, and carefully compared them to the initial crystal structure. The results led to the development of an MD-MX analysis procedure and several associated methods: 1) density comparison to evaluate consistency between the MD and the initial crystal structure model; 2) water building to generate alternative solvent models; and 3) protein remodeling to improve the crystal structure where interpretation of density is unclear. This procedure produced a revised structure of PKA with a new ordered water model and a modified protein structure. The revisions yield new insights into PKA mechanisms, including: a sensitivity of the His294 conformation to protonation state, with potential consequences for regulation of substrate binding; a remodeling of the Lys217 side chain along with a bound phosphate; an alternative conformation for Lys213 associated with binding to the regulatory subunit; and an alternative conformation for catalytic base Asp166 and nearby waters, suggesting a mechanism of progression of the phosphotransfer reaction via changes in Mg2+ coordination. Based on the benefits seen applying these methods to PKA, we recommend incorporating our MD-MX procedure into MX studies, to decide among ambiguous interpretations of electron density that occur, inevitably, as part of standard model refinement.

Effectively presenting epitopes on immunogens, in order to raise conformationally selective antibodies through active immunization, is a central problem in treating protein misfolding diseases, particularly neurodegenerative diseases such as Alzheimer’s disease or Parkinson’s disease. We seek to selectively target conformations enriched in toxic, oligomeric propagating species while sparing the healthy forms of the protein that are often more abundant. To this end, we computationally modelled scaffolded epitopes in cyclic peptides by inserting/deleting a variable number of flanking glycines (“glycindels”), to best mimic a misfolding-specific conformation of an epitope of α-synuclein enriched in the oligomer ensemble, as characterized by a region most readily disordered and solvent-exposed in a stressed, partially denatured protofibril. We screen and rank the cyclic peptide scaffolds of α-synuclein in silico based on their ensemble overlap properties with the fibril, oligomer-model, and isolated monomer ensembles. We present experimental data of seeded aggregation that supports nucleation rates consistent with computationally predicted cyclic peptide conformational similarity. We also introduce a method for screening against structured off-pathway targets in the human proteome, by selecting scaffolds with minimal conformational similarity between their epitope and the same solvent-exposed primary sequence in structured human proteins. Different cyclic peptide scaffolds with variable numbers of glycines are predicted computationally to have markedly different conformational ensembles. Ensemble comparison and overlap was quantified by the Jensen-Shannon Divergence, and a new measure introduced here—the embedding depth, which determines the extent to which a given ensemble is subsumed by another ensemble, and which may be a more useful measure in developing immunogens that confer conformational-selectivity to an antibody.Graphical TOC Entry

Human, rat, and mouse C-reactive protein (CRP) possess distinct expression patterns, but have similar conformations and conserved in vivo functions. We have previously demonstrated that this level-function mismatch is delicately tuned by the hidden activities of unfolded CRP. The cholesterol-binding sequence (CBS; a.a. 35-47) is a major functional motif exposed on monomeric CRP, which is the unfolded and activated conformation of CRP. We replaced the CBS of rat CRP with that of either mouse or human CRP, yielding two grafting mutants with unaffected pentameric assembly. However, these mutants exhibited altered cellular foldability and conformational activation efficiency that matched those of the CRP that provided the grafted CBS. These results indicate that CBS is a critical regulatory motif, whose variation maintains the pentameric assembly of CRP but derives distinct cellular foldabilities and conformational activation efficiencies, therefore helping to ensure that CRPs with various expression patterns exhibit overall conserved functions.

Dynamic protein complexes function in all cellular processes, from signaling to transcription, using distinct conformations that regulate their activity. Conformational switching of proteins can turn on or off their activity through protein-protein interactions, catalytic function, cellular localization, or membrane interaction. Recent advances in structural, computational, and chemical methodologies have enabled the discovery of small-molecule activators and inhibitors of conformationally dynamic proteins by using a more rational design than a serendipitous screening approach. Here, we discuss such recent examples, focusing on the mechanism of protein conformational switching and its regulation by small molecules. We emphasize the rational approaches to control protein oligomerization with small molecules that offer exciting opportunities for investigation of novel biological mechanisms and drug discovery.

Phosphorylation is the most common post-translational modification (PTM) in eukaryotes, occurring particularly frequently in intrinsically disordered proteins (IDPs). These proteins are highly flexible and dynamic by nature. Thus, it is intriguing that the addition of a single phosphoryl group to a disordered chain can impact its function so dramatically. Furthermore, as many IDPs carry multiple phosphorylation sites, the number of possible states increases, enabling larger complexities and novel mechanisms. Although a chemically simple and well-understood process, the impact of phosphorylation on the conformational ensemble and molecular function of IDPs, not to mention biological output, is highly complex and diverse. Since the discovery of the first phosphorylation site in proteins 75 years ago, we have come to a much better understanding of how this PTM works, but with the diversity of IDPs and their capacity for carrying multiple phosphoryl groups, the complexity grows. In this Essay, we highlight some of the basic effects of IDP phosphorylation, allowing it to serve as starting point when embarking on studies into this topic. We further describe how recent complex cases of multisite phosphorylation of IDPs have been instrumental in widening our view on the effect of protein phosphorylation. Finally, we put forward perspectives on the phosphorylation of IDPs, both in relation to disease and in context of other PTMs; areas where deep insight remains to be uncovered. Phosphorylation is the most common post-translational modification (PTM) in eukaryotes, occurring particularly frequently in intrinsically disordered proteins (IDPs). These proteins are highly flexible and dynamic by nature. Thus, it is intriguing that the addition of a single phosphoryl group to a disordered chain can impact its function so dramatically. Furthermore, as many IDPs carry multiple phosphorylation sites, the number of possible states increases, enabling larger complexities and novel mechanisms. Although a chemically simple and well-understood process, the impact of phosphorylation on the conformational ensemble and molecular function of IDPs, not to mention biological output, is highly complex and diverse. Since the discovery of the first phosphorylation site in proteins 75 years ago, we have come to a much better understanding of how this PTM works, but with the diversity of IDPs and their capacity for carrying multiple phosphoryl groups, the complexity grows. In this Essay, we highlight some of the basic effects of IDP phosphorylation, allowing it to serve as starting point when embarking on studies into this topic. We further describe how recent complex cases of multisite phosphorylation of IDPs have been instrumental in widening our view on the effect of protein phosphorylation. Finally, we put forward perspectives on the phosphorylation of IDPs, both in relation to disease and in context of other PTMs; areas where deep insight remains to be uncovered.

The type III-E CRISPR–Cas7-11 effector binds a CRISPR RNA (crRNA) and the putative protease Csx29 and catalyzes crRNA-guided RNA cleavage. We report cryo–electron microscopy structures of the Cas7-11–crRNA–Csx29 complex with and without target RNA (tgRNA), and demonstrate that tgRNA binding induces conformational changes in Csx29. Biochemical experiments revealed tgRNA-dependent cleavage of the accessory protein Csx30 by Csx29. Reconstitution of the system in bacteria showed that Csx30 cleavage yields toxic protein fragments that cause growth arrest, which is regulated by Csx31. Csx30 binds Csx31 and the associated sigma factor RpoE (RNA polymerase, extracytoplasmic E), suggesting that Csx30-mediated RpoE inhibition modulates the cellular response to infection. We engineered the Cas7-11–Csx29–Csx30 system for programmable RNA sensing in mammalian cells. Overall, the Cas7-11–Csx29 effector is an RNA-dependent nuclease-protease.

Understanding ligand binding kinetics and thermodynamics, which involves investigating the free, transient, and final complex conformations, is important in fundamental studies and applications for chemical and biomedical systems. Examining the important but transient ligand-protein-bound conformations, in addition to experimentally determined structures, also provides a more accurate estimation for drug efficacy and selectivity. Moreover, obtaining the entire picture of the free energy landscape during ligand binding/unbinding processes is critical in understanding binding mechanisms. Here, we present a Binding Kinetics Toolkit (BKiT) that includes several utilities to analyze trajectories and compute a free energy and kinetics profile. BKiT uses principal component space to generate approximated unbinding or conformational transition coordinates for accurately describing and easily visualizing the molecular motions. We implemented a new partitioning approach to assign indexes along the approximated coordinates that can be used as milestones or microstates. The program can generate input files to run many short classical molecular dynamics simulations and uses milestoning theory to construct the free energy profile and estimate binding residence time. We first validated the method with a host-guest system, aspirin unbinding from β-cyclodextrin, and then applied the protocol to pyrazolourea compounds and cyclin-dependent kinase 8 and cyclin C complexes, a kinase system of pharmacological interest. Overall, our approaches yielded good agreement with published results and suggest ligand design strategies. The computed unbinding free energy landscape also provides a more complete picture of ligand-receptor binding barriers and stable local minima for deepening our understanding of molecular recognition. BKiT is easy to use and has extensible features for future expansion of utilities for postanalysis and calculations.

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.

An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.

Amphipathic cell-penetrating peptides based on the pep-1 sequence were synthesized by replacing the three hydrophilic glutamic acid residues with disubstituted, non-proteinogenic, hydrophobic amino acids. These substitutions facilitated maintenance of the peptides' secondary structure in a helical conformation, even in aqueous solution. Stability against enzymatic degradation was improved through the use of disubstituted amino acids. The resultant peptides exhibited high membrane permeability that remained relatively stable during prolonged incubation times. The results of this study indicate that the use of non-proteinogenic amino acids may be an effective approach to improve the cell membrane permeability for existing amphiphilic peptides.

Differential scanning calorimetry (DSC) is a standard tool for probing the resilience of monoclonal antibodies (mAbs) and other protein therapeutics against thermal degradation. Unfortunately, DSC usually only provides insights into global unfolding, although sequential steps are sometimes discernible for multidomain proteins. Temperature-dependent hydrogen/deuterium exchange (HDX) mass spectrometry (MS) has the potential to probe heat-induced events at a much greater level of detail. We recently proposed a strategy to deconvolute temperature-dependent HDX data into contributions from local dynamics, global unfolding/refolding, as well as chemical labeling. However, that strategy was validated only for a small protein (Tajoddin, N. N.; Konermann, L.

It has been recently suggested that diametral (so-called quality) similarity thresholds are superior to radial ones for the clustering of molecular three-dimensional structures. 1 The argument has been made for two clustering algorithms available in various software packages for the analysis of molecular structures from ensembles generated by computer simulations, attributed to Daura et al. 2,3 (radial threshold) and Heyer et al. 4 (diametral threshold). Here, we compare these two algorithms using the root-mean-squared-difference between the Cartesian coordinates of selected atoms as pairwise similarity metric. We discuss formally the relation between these two methods and illustrate their behavior with two examples, a set of points in two dimensions and the coordinates of the tau polypeptide along a trajectory extracted from a replica-exchange molecular-dynamics simulation. 1,5 We show that the two methods produce equally-sized clusters as long as adequate choices are made for the respective thresholds. The real issue is not whether the threshold is radial or diametral, but how to choose in either case a threshold value that is physically meaningful. We will argue that, when clustering molecular structures with the RMSD as metric, the simplest best guess for a threshold is actually radial in nature.

The combination of deep learning and sequence data has transformed protein structure prediction and modeling, evidenced in the success of AlphaFold (AF). For this reason, many methods have been developed to take advantage of this success in areas where inaccurate structural modeling may limit computational predictiveness. For example, many methods have been developed to predict protein intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach. Intrinsically disordered regions in proteins are parts of the sequence that do not form ordered, folded structures under typical physiological conditions. In the original implementation of RRD, Rosetta

Polyproline I helical structures are often considered as the hidden face of their most famous geminal sibling, Polyproline II, as PPI is generally spotted only within a conformational equilibrium. We designed and synthesized a stable Polyproline I structure exploiting the striking tendency of (

Aggregation of intrinsically disordered proteins (IDPs) can lead to neurodegenerative diseases. Although there is experimental evidence that acidic pH promotes IDP monomer compaction leading to aggregation, the general mechanism is unclear. We studied the pH effect on the conformational ensemble of prothymosin-α (proTα), which is involved in multiple essential functions, and probed its role in aggregation using computer simulations. We show that compaction in the proTα dimension at low pH is due to the protein's collapse in the intermediate region (E41-D80) rich in glutamic acid residues, enhancing its β-sheet content. We observed by performing dimer simulations that the conformations with high β-sheet content could act as aggregation-prone (N*) states and nucleate the aggregation process. The simulations initiated using N* states form dimers within a microsecond time scale, whereas the non-N* states do not form dimers within this time scale. This study contributes to understanding the general principles of pH-induced IDP aggregation.

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents C

The adaptability of proteins to their work environments is fundamental for cellular life. Here, we describe how the hemopexin-like domain of the multifunctional blood glycoprotein vitronectin binds Ca

Intrinsically disordered proteins (IDPs) do not fold into a unique three-dimensional structure but sample different configurations of different probabilities that further change with the surrounding of the IDPs. The structural heterogeneity and dynamics of IDPs pose a challenge for the characterization of their structures by experimental techniques only. Molecular dynamics (MD) simulations provide a powerful complement to experimental approaches for that purpose. However, MD simulations on the micro- to millisecond timescale generate a lot of data of protein motions, necessitating advanced post-processing techniques to extract the relevant information. Here, we demonstrate how transition networks created from MD trajectories allow revealing the configurational ensemble and structural interconversions of IDPs, using the amyloid-β peptide as example. The construction of transition networks relies on molecular descriptors as input, and we show how the choice of descriptors influences the resulting transition network. The transition networks are generated with the open-source Python script ATRANET, and we explain the usage of ATRANET by providing a detailed workflow and exemplary analysis for amyloid-β, which can be easily generalized to other IDPs and even protein aggregation.

The introduction of the exact nuclear Overhauser enhancement (eNOE) methodology to solution-state nuclear magnetic resonance (NMR) spectroscopy results in tighter distance restraints from NOEs than in convention analysis. These improved restraints allow for higher resolution in structure calculation and even the disentanglement of different conformations of macromolecules. While initial work primarily focused on technical development of the eNOE, structural studies aimed at the elucidation of spatial sampling in proteins and nucleic acids were published in parallel prior to 2018. The period of 2018-2022 saw a continued series of technical innovation, but also major applications addressing biological questions. Here, we review both aspects, covering topics from the implementation of non-uniform sampling of NOESY buildups, novel pulse sequences, adaption of the eNOE to solid-state NMR, advances in eNOE data analysis, and innovations in structural ensemble calculation, to applications to protein, RNA, and DNA structure elucidation.

The ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.

8 pags., 2 figs., 2 tabs., We propose and validate a novel method to efficiently explore local protein loop conformations based on a new formalism for constrained normal mode analysis (NMA) in internal coordinates. The manifold of possible loop configurations imposed by the position and orientation of the fixed loop ends is reduced to an orthogonal set of motions (or modes) encoding concerted rotations of all the backbone dihedral angles. We validate the sampling power on a set of protein loops with highly variable experimental structures and demonstrate that our approach can efficiently explore the conformational space of closed loops. We also show an acceptable resemblance of the ensembles around equilibrium conformations generated by long molecular simulations and constrained NMA on a set of exposed and diverse loops. In comparison with other methods, the main advantage is the lack of restrictions on the number of dihedrals that can be altered simultaneously. Furthermore, the method is computationally efficient since it only requires the diagonalization of a tiny matrix, and the modes of motions are energetically contextualized by the elastic network model, which includes both the loop and the neighboring residues., The authors acknowledge grants PID2019-109041GB-C21/AEI/10.13039/501100011033 and PID2019-109041GB-C22/ 10.13039/501100011033 of the Spanish Agency of Research (AEI).

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

The identification of the structure of protein complexes in the subcellular niche of cells is necessary to understand their diverse functions. In this study, we developed a suborganelle proteome labeling assisted in vivo cross-linking (SubPiXL) strategy to identify regional protein conformations and interactions in living cells. Due to the mitochondria's functional importance and well-defined compartmental partitions, the specific conformations and interactome of protein complexes located in the mitochondrial matrix were identified. Compared to the commonly used approach of organelle isolation followed by intact mitochondria cross-linking, our method achieved a more refined spatial characterization for the subcompartment of the cellular organelle. Additionally, this approach avoided cross-contamination and cell microenvironment disruption during organelle isolation. As such, we achieved 73% selectivity for mitochondria and 98% specificity of known suborganelle annotation for the mitochondrial matrix and accessible inner membrane. Meanwhile, more protein-protein interactions (PPIs) with high dynamics were captured, resulting in a 1.67-fold increase in the number of PPI identifications in 1/11th of the time. On the basis of these structural cross-links and the specific characterization of the interactome and conformation, the structural dynamics targeted in the mitochondrial matrix were delineated. Mitochondrial matrix-restricted information for proteins with multisubcellular localizations was then clarified. In summary, SubPiXL is a promising technique for the investigation of suborganelle-resolved protein conformation and interaction analysis and contributes to a better understanding of structure-derived functions.

The Protein Data Bank (PDB) file format developed at Brookhaven National Laboratory is the most popular file format to store biological data and is widely supported by many software programs for the editing and visualization of macromolecular structures. Unfortunately, many of these structures have variety of issues ranging from missing side chains and/or atoms to alternate locations (rotamers). To fix these flaws, one has to learn a new program, compile modules, and install libraries. To overcome these challenges, we present Protein Repair and Analysis Server (PRAS), an easy-to-use web server to repair protein structures and add missing atoms. The PRAS program flow has two main sections. In the first section, several subroutines are called to deal with sequence microheterogeneity, rotamers, missing side chains, heavy atoms, or hydrogen atoms. A log file is generated detailing irregularities and their fixes as carried out by the program. In the later section, the program uses the error-corrected protein structure to assign secondary structure elements based on amide-amide interactions of the backbone. Plots of four Ramachandran types (general, glycine, proline, and pre-proline) and percentage of secondary structure elements; a log file and the error-corrected PDB file can be downloaded by clicking on the download link generated by the server. The server is freely available and is accessed through its web address at www.protein-science.com. Alternatively, users can download the source code from the server or install the program on their local machines following the instructions at https://pypi.org/project/Pras-Server/ or https://github.com/osita-sunday-nnyigide/Pras_Server. While the present work focuses on the repair of structural data in the PDB format, the server is capable of fixing similar errors in the extensible and newly adopted mmCIF format.

The gas phase is an idealized laboratory for the study of protein structure, from which it is possible to examine stable and transient forms of mass-selected ions in the absence of bulk solvent. With ion mobility-mass spectrometry (IM-MS) apparatus built to operate at both cryogenic and elevated temperatures, we have examined conformational transitions that occur to the monomeric proteins: ubiquitin, lysozyme, and α-synuclein as a function of temperature and in source activation. We rationalize the experimental observations with a temperature-dependent framework model and comparison to known conformers. Data from ubiquitin show unfolding transitions that proceed through diverse and highly elongated intermediate states, which converge to more compact structures. These findings contrast with data obtained from lysozyme─a protein where (un)-folding plasticity is restricted by four disulfide linkages, although this is alleviated in its reduced form. For structured proteins, collision activation of the protein ions in-source enables subsequent "freezing" or thermal annealing of unfolding intermediates, whereas disordered proteins restructure substantially at 250 K even without activation, indicating that cold denaturation can occur without solvent. These data are presented in the context of a toy model framework that describes the relative occupancy of the available conformational space.

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Protein flexibility is important for ligand binding but often ignored in drug design. Considering proteins as ensembles rather than static snapshots creates opportunities to target dynamic proteins that lack FDA-approved drugs, such as the human chaperone, heat shock protein 90 (Hsp90). Hsp90α accommodates ligands with a dynamic lid domain, yet no comprehensive analysis relating lid conformations to ligand properties is available. To date, ∼300 ligand-bound Hsp90α crystal structures are deposited in the Protein Data Bank, which enables us to consider ligand binding as a perturbation of the protein conformational landscape. By estimating binding site volumes, we classified structures into distinct major and minor lid conformations. Supported by retrospective docking, each conformation creates unique hotspots that bind chemically distinguishable ligands. Clustering revealed insightful exceptions and the impact of crystal packing. Overall, Hsp90α's plasticity provides a cautionary tale of overinterpreting individual crystal structures and motivates an ensemble-based view of drug design.

The connection between protein sequences and tertiary structures has intrigued investigators for decades. A plausible hypothesis for the coding scheme postulates that atomic burial information obtainable from the sequence could be sufficient for structural determination when combined to sequence-independent constraints. Accordingly, folding simulations using native burial information expressed by atomic central distances, discretized into a small number

Salts modulate the behavior of intrinsically disordered proteins (IDPs) and influence the formation of membraneless organelles through liquid-liquid phase separation (LLPS). In low ionic strength solutions, IDP conformations are perturbed by the screening of electrostatic interactions, independent of the salt identity. In this regime, insight into the IDP behavior can be obtained using the theory for salt-induced transitions in charged polymers. However, salt-specific interactions with the charged and uncharged residues, known as the Hofmeister effect, influence IDP behavior in high ionic strength solutions. There is a lack of reliable theoretical models in high salt concentration regimes to predict the salt effect on IDPs. We propose a simulation methodology using a coarse-grained IDP model and experimentally measured water to salt solution transfer free energies of various chemical groups that allowed us to study the salt-specific transitions induced in the IDPs conformational ensemble. We probed the effect of three different monovalent salts on five IDPs belonging to various polymer classes based on charged residue content. We demonstrate that all of the IDPs of different polymer classes behave as self-avoiding walks (SAWs) at physiological salt concentration. In high salt concentrations, the transitions observed in the IDP conformational ensembles are dependent on the salt used and the IDP sequence and composition. Changing the anion with the cation fixed can result in the IDP transition from a SAW-like behavior to a collapsed globule. An important implication of these results is that a suitable salt can be identified to induce condensation of an IDP through LLPS.

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone.

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Aggregation of profilin-1 (PFN1) causes a fatal neurodegenerative disease, familial amyotrophic lateral sclerosis (fALS). Histidine (His) tautomerism has been linked to the formation of fibril aggregation causing neurodegenerative disease. Characterization of intermediate species that form during aggregation is crucial, however, this has proven very challenging for experimentalists due to their transient nature. Hence, molecular dynamics (MD) simulations have been performed on the His tautomeric isomers εε, εδ, δε, and δδ of PFN1 to explain the structural changes and to correlate them with its aggregation propensity. MD simulations show that His133 presumably plays a major role in the aggregation of PFN1 upon His tautomerism compared to His119. Further, the formation of a new 3

The aim of the current study is to investigate the role of the CAD domain in the activation mechanism of calcium dependent protein kinase-1 of Plasmodium falciparum (PfCDPK1) and explore the possibility of allosteric inhibition of this kinase. PfCDPK1 belongs to CDPK family of apicomplexan kinases which have a C-terminal CAD domain. Microsecond scale MD simulations were performed on modeled structures of complete PfCDPK1 and its kinase domain alone. The simulations revealed that in absence of CAD the salt bridge between Glu116 in αC-helix and Lys85 in β3-sheet of kinase breaks after 200 ns resulting in inactive conformation of the kinase, but the salt bridge stays intact in the complete protein stabilizing it in active conformation. These results highlight the novel CAD mediated allosteric stabilization of the crucial salt bridge which is a hallmark of active conformation of kinase domains. The mechanistic details of the allosteric activation revealed by our study, opens up the possibility for design of allosteric inhibitors of PfCDPK1 kinase by disrupting the kinase:CAD interactions. Using a combination of machine learning and structure-based in silico screening, we have identified novel PPI modulators for allosteric inactivation of PfCDPK1 kinase.

IGF1R plays an important role in regulating cellular metabolism and growth. As a single transmembrane protein, its structure is flexible. Although previous studies revealed some structures of IGF1R, the cryo-EM apo structures of the receptor have never been reported. Herein, we reported four distinct cryo-EM structures that reveal the apo states of IGF1R. These conformations were classified as "Resting states" and "Active states", according to the orientation of α-CT helices and structural symmetry. In addition, a "Ligand-pocket" was formed in the active conformations, which presented a new view of conformational changes of apo-IGF1R. These results suggest a new dynamic change model to show the details of why and how ligands can bind to IGF1R.

AlpA positively regulates a programmed cell death pathway linked to the virulence of Pseudomonas aeruginosa by recognizing an AlpA binding element within the promoter, then binding RNA polymerase directly and allowing it to bypass an intrinsic terminator positioned downstream. Here, we report the single-particle cryo-electron microscopy structures of both an AlpA-loading complex and an AlpA-loaded complex. These structures indicate that the C-terminal helix-turn-helix motif of AlpA binds to the AlpA binding element and that the N-terminal segment of AlpA forms a narrow ring inside the RNA exit channel. AlpA was also revealed to render RNAP resistant to termination signals by prohibiting RNA hairpin formation in the RNA exit channel. Structural analysis predicted that AlpA, 21Q, λQ and 82Q share the same mechanism of transcription antitermination.

Bromodomain and extraterminal domain (BET) proteins are important regulators of gene transcription and chromatin remodeling. BET family members BRD4 and BRDT are validated targets for cancer and male contraceptive drug development, respectively. Due to the high structural similarity of the acetyl-lysine binding sites, most reported inhibitors lack intra-BET selectivity. We surmised that protein-protein interactions induced by bivalent inhibitors may differ between BRD4 and BRDT, conferring an altered selectivity profile. Starting from nonselective monovalent inhibitors, we developed cell-active bivalent BET inhibitors with increased activity and selectivity for BRDT. X-ray crystallographic and solution studies revealed unique structural states of BRDT and BRD4 upon interaction with bivalent inhibitors. Varying spacer lengths and symmetric vs unsymmetric connections resulted in the same dimeric states, whereas different chemotypes induced different dimers. The findings indicate that the increased intra-BET selectivity of bivalent inhibitors is due to the differential plasticity of BET bromodomains upon inhibitor-induced dimerization.

Protein kinases intrinsically translate their conformations between active and inactive states, which is key to their enzymatic activities. The conformational flipping of the three-residue conservative motif, Asp-Phe-Gly (DFG), is crucial for many kinases' biological functions. Obtaining a detailed demonstration of the DFG flipping process and its corresponding dynamical and thermodynamical features could broaden our understanding of kinases' conformation-activity relationship. In this study, we employed metadynamics simulation, a widely used enhanced sampling technique, to analyze the conformational transition pathways of the DFG flipping for the c-Met kinase. The corresponding free energy landscape suggested two distinct transition pathways between the "DFG-in" and "DFG-out" states of the DFG-flip from c-Met. On the basis of the orientation direction of the F1223 residue, we correspondingly named the two pathways the "DFG-up" path, featuring forming a commonly discovered "DFG-up" transition state, and the "DFG-down" path, a unique transition pathway with F1223 rotating along the opposite direction away from the hydrophobic cavity. The free energies along the two pathways were then calculated using the Path Collective Variable (PCV) metadynamics simulation. The simulation results showed that, though having similar free energy barriers, the free energy cuve for the DFG-down path suggested a two-step conformational transition mechanism, while that for the DFG-up path showed the one-step transition feature. The c-Met DFG flipping mechanism and the new intermediate state discovered in this work could provide a deeper understanding of the conformation-activity relationship for c-Met and, possibly, reveal a new conformational state as the drug target for c-Met and other similar kinases.

RNase P is a ribonucleoprotein (RNP) that catalyzes removal of the 5 ' leader from precursor tRNAs in all domains of life. A recent cryo-EM study of Methanocaldococcus jannaschii (Mja) RNase P produced a model at 4.6-angstrom resolution in a dimeric configuration, with each holoenzyme monomer containing one RNase P RNA (RPR) and one copy each of five RNase P proteins (RPPs; POP5, RPP30, RPP21, RPP29, L7Ae). Here, we used native mass spectrometry (MS), mass photometry (MP), and biochemical experiments that (i) validate the oligomeric state of the Mja RNase P holoenzyme in vitro, (ii) find a different stoichiometry for each holoenzyme monomer with up to two copies of L7Ae, and (iii) assess whether both L7Ae copies are necessary for optimal cleavage activity. By mutating all kink-turns in the RPR, we made the discovery that abolishing the canonical L7Ae-RPR interactions was not detrimental for RNase P assembly and function due to the redundancy provided by protein-protein interactions between L7Ae and other RPPs. Our results provide new insights into the architecture and evolution of RNase P, and highlight the utility of native MS and MP in integrated structural biology approaches that seek to augment the information obtained from low/medium-resolution cryo-EM models. NIH [GM-120582, P41 GM128577, S10OD023582]; NASA [NNX13AI05G]; Virginia Tech Agricultural Experiment Station Hatch Program [CRIS project] [VA160021]; OSU Comprehensive Cancer Center for a Pelotonia Pre-doctoral Fellowship; Virginia Tech Genetics, Bioinformatics, and Computational Biology Doctoral Program Fellowship Published version NIH [GM-120582 to M.P.F., V.H.W., V.G., P41 GM128577 to V.H.W., S10OD023582 to J.E.J.]; NASA [NNX13AI05G to B.M.]; Virginia Tech Agricultural Experiment Station Hatch Program [CRIS project VA160021 to B.M.]; OSU Comprehensive Cancer Center for a Pelotonia Pre-doctoral Fellowship (to H.D.P.); Virginia Tech Genetics, Bioinformatics, and Computational Biology Doctoral Program Fellowship (to B.K.). Funding for open access charge: University funds.

The application of sulfur single-wavelength anomalous dispersion (S-SAD) to determine the crystal structures of macromolecules can be challenging if the asymmetric unit is large, the crystals are small, the size of the anomalously scattering sulfur structure is large and the resolution at which the anomalous signals can be accurately measured is modest. Here, as a study of such a case, approaches to the SAD phasing of orthorhombic Ric-8A crystals are described. The structure of Ric-8A was published with only a brief description of the phasing process [Zeng et al. (2019), Structure, 27, 1137–1141]. Here, alternative approaches to determining the 40-atom sulfur substructure of the 103 kDa Ric-8A dimer that composes the asymmetric unit are explored. At the data-collection wavelength of 1.77 Å measured at the Frontier micro-focusing Macromolecular Crystallography (FMX) beamline at National Synchrotron Light Source II, the sulfur anomalous signal strength, |Δano|/σΔano (d′′/sig), approaches 1.4 at 3.4 Å resolution. The highly redundant, 11 000 000-reflection data set measured from 18 crystals was segmented into isomorphous clusters using BLEND in the CCP4 program suite. Data sets within clusters or sets of clusters were scaled and merged using AIMLESS from CCP4 or, alternatively, the phenix.scale_and_merge tool from the Phenix suite. The latter proved to be the more effective in extracting anomalous signals. The HySS tool in Phenix, SHELXC/D and PRASA as implemented in the CRANK2 program suite were each employed to determine the sulfur substructure. All of these approaches were effective, although HySS, as a component of the phenix.autosol tool, required data from all crystals to find the positions of the sulfur atoms. Critical contributors in this case study to successful phase determination by SAD included (i) the high-flux FMX beamline, featuring helical-mode data collection and a helium-filled beam path, (ii) as recognized by many authors, a very highly redundant, multiple-crystal data set and (iii) the inclusion within that data set of data from crystals that were scanned over large ω ranges, yielding highly isomorphous and highly redundant intensity measurements.

Antigen processing in the class II MHC pathway depends on conventional proteolytic enzymes, potentially acting on antigens in native-like conformational states. CD4+ epitope dominance arises from a competition between antigen folding, proteolysis, and MHCII binding. Protease-sensitive sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped in the plague vaccine candidate F1-V to evaluate the various contributions to CD4+ epitope dominance. Using X-ray crystal structures, antigen processing likelihood (APL) predicts CD4+ epitopes with significant accuracy without considering peptide-MHCII binding affinity. The profiles of conformational flexibility derived from the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV, were similar to the biochemical profiles of linear antibody epitope reactivity and protease-sensitivity, suggesting that the role of structure in proteolysis was captured by the analysis of the crystal structures. The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and BALB/c mice were compared to epitope predictions based on APL, peptide binding to MHCII proteins, or both. For a sample of 13 diverse antigens larger than 200 residues, accuracy of epitope prediction by the combination of APL and I-Ab-MHCII-peptide affinity approached 40%. When MHCII allele specificity is also diverse, such as in human immunity, prediction of dominant epitopes by APL alone approached 40%. Since dominant CD4+ epitopes tend to occur in conformationally stable antigen domains, crystal structures typically are available for analysis by APL; and thus, the requirement for a crystal structure is not a severe limitation.

Many enzymes that show a large specificity in binding the enzymatic transition state with a higher affinity than the substrate utilize substrate binding energy to drive protein conformational changes to form caged substrate complexes. These protein cages provide strong stabilization of enzymatic transition states. Using part of the substrate binding energy to drive the protein conformational change avoids a similar strong stabilization of the Michaelis complex and irreversible ligand binding. A seminal step in the development of modern enzyme catalysts was the evolution of enzymes that couple substrate binding to a conformational change. These include enzymes that function in glycolysis (triosephosphate isomerase), the biosynthesis of lipids (glycerol phosphate dehydrogenase), the hexose monophosphate shunt (6-phosphogluconate dehydrogenase), and the mevalonate pathway (isopentenyl diphosphate isomerase), catalyze the final step in the biosynthesis of pyrimidine nucleotides (orotidine monophosphate decarboxylase), and regulate the cellular levels of adenine nucleotides (adenylate kinase). The evolution of enzymes that undergo ligand-driven conformational changes to form active protein-substrate cages is proposed to proceed by selection of variants, in which the selected side chain substitutions destabilize a second protein conformer that shows compensating enhanced binding interactions with the substrate. The advantages inherent to enzymes that incorporate a conformational change into the catalytic cycle provide a strong driving force for the evolution of flexible protein folds such as the TIM barrel. The appearance of these folds represented a watershed event in enzyme evolution that enabled the rapid propagation of enzyme activities within enzyme superfamilies.

Fast photochemical oxidation of proteins (FPOP) footprinting is a structural mass spectrometry method that maps proteins by fast and irreversible chemical reactions. The position of oxidative modification reflects solvent accessibility and site reactivity and thus provides information about protein conformation, structural dynamics, and interactions. Bottom-up mass spectrometry is an established standard method to analyze FPOP samples. In the bottom-up approach, all forms of the protein are digested together by a protease of choice, which results in a mixture of peptides from various subpopulations of proteins with varying degrees of photochemical oxidation. Here, we investigate the possibility to analyze a specifically selected population of only singly oxidized proteins. This requires utilization of more specific top-down mass spectrometry approaches. The key element of any top-down experiment is the selection of a suitable method of ion isolation, excitation, and fragmentation. Here, we employ and compare collision-induced dissociation, electron-transfer dissociation, and electron-capture dissociation combined with multi-continuous accumulation of selected ions. A singly oxidized subpopulation of FPOP-labeled ubiquitin was used to optimize the method. The top-down approach in FPOP is limited to smaller proteins, but its usefulness was demonstrated by using it to visualize structural changes induced by co-factor removal from the holo/apo myoglobin system. The top-down data were compared with the literature and with the bottom-up data set obtained on the same samples. The top-down results were found to be in good agreement, which indicates that monitoring a singly oxidized FPOP ion population by the top-down approach is a functional workflow for oxidative protein footprinting.

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

The prevailing understanding of various aspects of biochemical processes, including folding, stability, intermolecular interactions, and the binding of metals, substrates, and inhibitors, is derived from studies carried out under dilute and homogeneous conditions devoid of a crowding-related environment. The effect of crowding-induced modulation on the structure and stability of native and magnesium-dependent Chemotaxis Y (CheY), a bacterial signaling protein, was probed in the presence and absence of poly(ethylene glycol) (PEG). A combined analysis from circular dichroism, intrinsic and extrinsic fluorescence, and tryptophan fluorescence lifetime changes indicates that PEG perturbs the structure but leaves the thermal stability largely unchanged. Intriguingly, while the stability of the protein is enhanced in the presence of magnesium under dilute buffer conditions, PEG-induced crowding leads to reduced thermal stability in the presence of magnesium. Nuclear magnetic resonance (NMR) chemical shift perturbations and resonance broadening for a subset of residues indicate that PEG interacts specifically with a subset of hydrophilic and hydrophobic residues found predominantly in α helices, β strands, and in the vicinity of the metal-binding region. Thus, PEG prompted conformational perturbation, presumably provides a different situation for magnesium interaction, thereby perturbing the magnesium-prompted stability. In summary, our results highlight the dominance of enthalpic contributions between PEG and CheY via both hydrophilic and hydrophobic interactions, which can subtly affect the conformation, modulating the metal-protein interaction and stability, implying that in the context of cellular situation, structure, stability, and magnesium binding thermodynamics of CheY may be different from those measured in dilute solution.

Abstract In this study, we conducted a comparative analysis of the structure of agonists and antagonists of transmembrane (TM) β-adrenoceptors (β-ARs) and their interactions with the β-ARs and proposed the mechanism of receptor activation. A characteristic feature of agonist and antagonist molecules is the presence of a hydrophobic head (most often, one or two aromatic rings) and a tail with a positively charged amino group. All β-adrenergic agonists have two carbon atoms between the aromatic ring of the head and the nitrogen atom of the amino group. In antagonist molecules, this fragment can be either reduced or increased to four atoms due to the additional carbon and oxygen atoms. The agonist head, as a rule, has two H-bond donors or acceptors in the para- and meta-positions of the aromatic rings, while in the antagonist heads, these donors/acceptors are absent or located in other positions. Analysis of known three-dimensional structures of β-AR complexes with agonists showed that the agonist head forms two H-bonds with the TM5 helix, and the tail forms an ionic bond with the D3.32 residue of the TM3 helix and one or two H-bonds with the TM7 helix. The tail of the antagonist can form similar bonds, but the interaction between the head and the TM5 helix is much weaker. As a result of these interactions, the agonist molecule acquires an extended “strained string” conformation, in contrast to the antagonist molecule, which has a longer, bended, and flexible tail. The “strained string” of the agonist interacts with the TM6 helix (primarily with the W6.48 residue) and turns it, which leads to the opening of the G protein-binding site on the intracellular side of the receptor, while flexible and larger antagonist molecules do not have the same effect on the receptor.

Precise control of molecular arrangement is essential for functional molecular assemblies. A linear (I-shaped) amphiphilic block copolypeptide, polysarcosine-b-(l-Leu-Aib)