α/β Hydrolase-like enzymes form a large and functionally diverse superfamily of proteins. Despite retaining a conserved structural core consisting of an eight-stranded, central β-sheet flanked with six α-helices, they display a modular architecture allowing them to perform a variety of functions, like esterases, lipases, peptidases, epoxidases, lyases, and others. At the same time, many α/β hydrolase-like families, even enzymatically distinct, share a high degree of sequence similarity. This imposes several problems for their annotation and classification, because available definitions of particular α/β hydrolase-like families overlap significantly, so the unambiguous functional assignment of these superfamily members remains a challenging task. For instance, two large and important peptidase families, namely S9 and S33, blend with lipases, epoxidases, esterases, and other enzymes unrelated to proteolysis, which hinders automatic annotations in high-throughput projects. With the use of thorough sequence and structure analyses, we newly annotate three protein families as α/β hydrolase-like and revise current classifications of the realm of α/β hydrolase-like superfamily. Based on manually curated structural superimpositions and multiple sequence and structure alignments, we comprehensively demonstrate structural conservation and diversity across the whole superfamily. Eventually, after detailed pairwise sequence similarity assessments, we develop a new clustering of the α/β hydrolases and provide a set of family profiles allowing for detailed, reliable, and automatic functional annotations of the superfamily members.

The RAF1-RAP1A interaction activates the MAPK/ERK pathway which is very crucial in the carcinogenesis process. This protein complex influences tumor formation, proliferation, and metastasis. Understanding aberrant interactions driven by clinical mutations is vital for targeted therapies. Hence, the current study focuses on the screening of clinically reported substitutions in the RAF1 and RAP1A genes using predictive algorithms integrated with all-atoms simulation, essential dynamics, and binding free energy methods. Survival analysis results revealed a strong association between RAF1 and RAP1A expression levels and diminished survival rates in cancer patients across different cancer types. Integrated machine learning algorithms showed that among the 134 mutations reported for these 2 proteins, only 13 and 35 were classified as deleterious mutations in RAF1 and RAP1P, respectively. Moreover, one mutation in RAF1 reported elevated levels of binding between RAF1 and RAP1P while in RAP1A, 7 mutations were reported to increase the binding affinity. The high-binding mutations, P34Q and V60F, were subjected to protein-protein coupling which confirmed the increase in the binding affinity. Wild-type and mutant RAF1-RAP1P bound complexes were subjected to molecular simulation investigation, revealing enhanced structural stability, increased compactness, and stabilized residue fluctuations of the mutant systems in contrast to the wild-type. In addition, hydrogen bonding analysis revealed a variation in the binding paradigm which further underscores the impact of these substitutions on the coupling of RAF1 and RAP1A. Principal component analysis (PCA) and free energy landscape (FEL) evaluation further determined dynamical variations in the wild-type and mutant complexes. Finally, the Gibbs free energy for each complex was estimated and found to be -71.94 ± 0.38 kcal/mol for the wild-type, -95.57 ± 0.37 kcal/mol for the V60F, and -85.76 ± 0.72 kcal/mol for P34Q complex. These findings confirm the effect of these variants on increasing the binding affinity of RAF1 to RAP1P. These mutations can therefore be targeted for cancer therapy to modulate the activity of the MAPK/ERK signaling pathway.

Exploring protein-protein interaction sites (PPIS) is of significance to elucidating the intrinsic mechanisms of diverse biological processes. On this basis, recent studies have applied deep learning-based technologies to overcome the high cost of wet experiments for PPIS determination. However, the existing methods still suffer from two limitations that remain to be solved. Firstly, the process of feature aggregation in most methods only took into account node features, but ignored the complex edge features of the target residue to its neighbor residues, resulting in insufficient local feature extraction. Secondly, such feature aggregation was limited to aggregating spatially adjacent residues, and could not capture the "remote" residues that played a critical role in determining PPIS, which can be summed up as the lack of global feature at the residue level. To break the above limitations, a local-global structural information aggregation framework, LGS-PPIS, was proposed in this study, including two modules of edge-aware graph convolutional network (EA-GCN) and self-attention integrated with initial residual and identity mapping (SA-RIM), which achieved the aggregation of local and global information for PPIS prediction. Evaluation results of LGS-PPIS showed that the proposed method outperformed state-of-the-art deep learning methods on three widely used PPIS prediction benchmarks. Besides, the results of ablation experiments demonstrated that the local features from spatially adjacent residues and global features from "remote" residues separately captured by EA-GCN and SA-RIM could benefit the model performance. Among them, the former was shown to have a more significant role in the PPIS prediction.

Biased agonism in G protein-coupled receptors is a phenomenon resulting in the selective activation of distinct intracellular signaling pathways by different agonists, which may exhibit bias toward either Gs, Gi, or arrestin-mediated pathways. This study investigates the structural basis of ligand-induced biased agonism within the context of the β-adrenergic receptor (β-AR). Atomistic molecular dynamics simulations were conducted for β-AR complexes with two stereoisomers of methoxynaphtyl fenoterol (MNFen), that is, compounds eliciting qualitatively different cellular responses. The simulations reveal distinct interaction patterns within the binding cavity, dependent on the stereoisomer. These changes propagate to the intracellular parts of the receptor, triggering various structural responses: the dynamic structure of the intracellular regions of the (R,R)-MNFen complex more closely resembles the "G-compatible" and "β-arrestin-compatible" conformation of β-AR, while both stereoisomers maintain structural responses equidistant from the inactive conformation. These findings are confirmed by independent coarse-grained simulations. In the context of deciphered molecular mechanisms, Trp313 plays a pivotal role, altering its orientation upon interactions with (R,R)-MNFen, along with the Lys305-Asp192 ionic bridge. This effect, accompanied by ligand interactions with residues on TM2, increases the strength of interactions within the extracellular region and the binding cavity, resulting in a slightly more open conformation and a minor (by ca. 0.2 nm) increase in the distance between the TM5-TM7, TM1-TM6, TM6-TM7, and TM1-TM5 pairs. On the other hand, an even slighter decrease in the distance between the TM1-TM4 and TM2-TM4 pairs is observed.

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

Of the five human antibody isotypes, the function of IgD is the least well-understood, although various studies point to a role for IgD in mucosal immunity. IgD is also the least well structurally characterized isotype. Until recently, when crystal structures were reported for the IgD Fab, the only structural information available was a model for intact IgD based on solution scattering data. We now report the crystal structure of human IgD-Fc solved at 3.0 Å resolution. Although similar in overall architecture to other human isotypes, IgD-Fc displays markedly different orientations of the Cδ3 domains in the Cδ3 domain dimer and the lowest interface area of all the human isotypes. The nature of the residues that form the dimer interface also differs from those conserved in the other isotypes. By contrast, the interface between the Cδ2 and Cδ3 domains in each chain is the largest among the human isotypes. This interface is characterized by two binding pockets, not seen in other isotypes, and points to a potential role for the Cδ2/Cδ3 interface in stabilizing the IgD-Fc homodimer. We investigated the thermal stability of IgD-Fc, alone and in the context of an intact IgD antibody, and found that IgD-Fc unfolds in a single transition. Human IgD-Fc clearly has unique structural features not seen in the other human isotypes, and comparison with other mammalian IgD sequences suggests that these unique features might be widely conserved.

G-protein-coupled receptors (GPCRs) constitute one of the most prominent families of integral membrane receptor proteins that mediate most transmembrane signaling processes. Malfunction of these signal transduction processes is one of the underlying causes of many human pathologies (Parkinson's, Huntington's, heart diseases, etc), provoking that GPCRs are the largest family of druggable proteins. However, these receptors have been targeted traditionally by orthosteric ligands, which usually causes side effects due to the simultaneous targeting of homologous receptor subtypes. Allosteric modulation offers a promising alternative approach to circumvent this problematic and, thus, comprehending its details is a most important task. Here we use the Cannabinoid type-1 receptor (CB1R) in trying to shed light on this issue, focusing on positive allosteric modulation. This is done by using the protein-dipole Langevin-dipole (PDLD) within the linear response approximation (LRA) framework (PDLD/S-2000) along with our coarse-grained (CG) model of membrane proteins to evaluate the dissociation constants (Ks) and cooperativity factors (αs) for a diverse series of CB1R positive allosteric modulators belonging to the 2-phenylindole structural class, considering CP55940 as an agonist. The agreement with the experimental data evinces that significantly populated allosteric modulator:CB1R and allosteric modulator:CP55940:CB1R complexes have been identified and characterized successfully. Analyzing them, it has been determined that CB1R positive allosteric modulation lies in an outwards displacement of transmembrane α helix (TM) 4 extracellular end and in the regulation of the range of motion of a compound TM7 movement for binary and ternary complexes, respectively. In this respect, we achieved a better comprehension of the molecular architecture of CB1R positive allosteric site, identifying Lys192 and Gly194 as key residues regarding electrostatic interactions inside this cavity, and to rationalize (at both structural and molecular level) the exhibited stereoselectivity in relation to positive allosteric modulation activity by considered CB1R allosteric modulators. Additionally, putative/postulated allosteric binding sites have been screened successfully, identifying the real CB1R positive allosteric site, and most structure-activity relationship (SAR) studies of CB1R 2-phenylindole allosteric modulators have been rationalized. All these findings point out towards the predictive value of the methodology used in the current work, which can be applied to other biophysical systems of interest. The results presented in this study contribute significantly to understand GPCRs allosteric modulation and, hopefully, will encourage a more thorough exploration of the topic.

The last few years have seen the rapid proliferation of machine learning methods to design binding proteins. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when they are experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. Short molecular dynamics simulations of 20 antibody-protein complexes were conducted to identify features of interactions that should occur in binding interfaces. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was found that only the hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and meaningfully contribute to binding between the proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist. Still, interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies than other interactions. Two hundred and twenty real antibody-protein complexes and 8194 decoy complexes were used to train and test a random forest classifier using the features of expected persistent interactions identified in this study and the macromolecular features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. It was compared to a classifier trained only on the expected persistent interaction features and another trained only on the macromolecular features. Inclusion of the expected persistent interaction features reduced the false positive rate of the classifier by two- to five-fold across a range of true positive classification rates.

Variable lymphocyte receptors (VLRs) are antigen receptors derived from the adaptive immune system of jawless vertebrates such as lamprey (Petromyzon marinus). First discovered in 2004, VLRs have been the subject of numerous biochemical and structural investigations. Due to their unique antigen binding properties, VLRs have been leveraged as possible drug delivery agents. One such VLR, previously identified and referred to as P1C10, was shown to bind to the brain extracellular matrix. Here, we present the high-resolution X-ray crystal structure of this VLR determined to 1.3 Å resolution. The fold is dominated by a six-stranded mixed β-sheet which provides a concave surface for possible antigen binding. Electron density corresponding to a 4-(2-hydroxyethyl)piperazine-1-propanesulfonic acid buffer molecule (HEPPS) was found in this region. By comparing the P1C10 molecular architecture and its buffer binding residues with those of other VLRs previously reported, it was possible to illustrate how this unique class of proteins can accommodate diverse binding partners. Additionally, we provide an analysis of the experimentally determined structure compared to the models generated by the commonly used AlphaFold and iTASSER structure prediction software packages.

Intrinsically disordered regions (IDRs) of proteins remain understudied with enigmatic sequence features relevant to their functions. Members of the myotubularin-related protein (MTMR) family contain uncharacterized IDRs. After decades of research on their phosphatase activity, recent work on the C-terminal IDRs of MTMR7 revealed new interactions and important new functions beyond the phosphatase function. Here we take a broader look at the C-terminal domains (CTDs) of 14 human MTMRs and use bioinformatic tools and biophysical methods to ask which other functions may be probable in this protein family. The predictions show that the CTDs are disordered and carry short linear motifs (SLiMs) important for targeting of MTMRs to defined subcellular compartments and implicating them in signaling, phase separation, interaction with diverse proteins, including transcription factors and are of relevance for cancer research and neuroscience. We also present experimental methods to study the CTDs and use them to characterize the coiled coil (CC) domains of MTMR7 and MTMR9. We show homo- and hetero-oligomerization with preference for MTMR7-CC to form dimers, while MTMR9-CC forms trimers. We relate the results to sequence features and make predictions for the structural landscape of other MTMRs. Our work gives a broad insight into the so far unrecognized features and SLiMs in MTMR-CTDs, and provides the basis for more in-depth experimental research on this diverse protein family and understudied IDRs in proteins in general.

Acquired resistance to first-line treatments in various cancers both promotes cancer recurrence as well as limits effective treatment. This is true for epidermal growth factor receptor (EGFR) mutations, for which secondary EGFR mutations are one of the principal mechanisms conferring resistance to the covalent inhibitor osimertinib. Thus, it is very important to develop a deeper understanding of the secondary mutational resistance mechanisms associated with EGFR mutations arising in tumors treated with osimertinib to expedite the development of innovative therapeutic drugs to overcome acquired resistance. This work uses all-atom molecular dynamics (MD) simulations to investigate the conformational variation of two reported EGFR mutants (L858R/L718Q and L858R/L792H) that resist osimertinib. The wild-type EGFR kinase domain and the L858R mutant are used as the reference. Our MD simulation results revealed that both the L718Q and L792H secondary mutations induce additional hydrogen bonds between the residues in the active pocket and the residues with the water molecules. These additional hydrogen bonds reduce the exposure area of C797, the covalent binding target of osimertinib. The additional hydrogen bonds also influence the binding affinity of the EGFR kinase domain by altering the secondary structure and flexibility of the amino acid residues in the domain. Our work highlights how the two reported mutations may alter both residue-residue and residue-solvent hydrogen bonds, affecting protein binding properties, which could be helpful for future drug discovery.

Sequence conservation analyses offer us a powerful glimpse of natural selection at work. Standard tools for measuring sequence conservation report conservation as a function of a specific location in a multiple sequence alignment and have proven indispensable in identifying highly constrained features such as active site residues. The advent of large-scale genomic sequencing efforts allows researchers to expand this paradigm and investigate more nuanced relationships between sequence and function. Here, we present a simple tool (SWiLoDD: Sliding Window Localized Differentiation Detection) that allows researchers to analyze local, rather than site-specific, conservation using a sliding window approach. Our tool accepts multiple sequence alignments partitioned based on a biological differentiator and returns alignment position-based, localized differential enrichment metrics for amino acids of choice. We present two case studies of this analysis in action: local-but-diffuse glycine enrichments in the ATPase subunits of thermophilic and psychrophilic bacterial gyrase homologs, and ligand- and interface-specific amino acid enrichments in halophilic bacterial crotonyl-CoA carboxylases/reductases. Though we have described examples of extremophilic bacterial proteins in this study, our tool may be used to investigate any set of homologous sequences from which sub-groups can be meaningfully partitioned. Our results suggest that investigating differential localized conservation in partitioned MSAs will expand our understanding of how sequence conservation and protein function are connected.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most serious infectious immunosuppressive diseases in the world. The nonstructural protein Nsp4 can be used as an ideal target for anti-PRRSV replication inhibitors. However, little is known about potential inhibitors that target Nsp4 to affect PRRSV replication. The purpose of this study was to screen potential natural inhibitors that affect PRRSV replication by inhibiting Nsp4. Five compounds with strong binding affinity to Nsp4 were selected by structure-based molecular docking method. The complexes of naringin dihydrochalcone (NDC), agathisflavone (AGT), and amentoflavone (AMF) with Nsp4 were stable throughout the molecular dynamics simulation. According to MM/PBSA analysis, the free energies of binding of NDC, AGT, and AMF to Nsp4 were less than-30 Kcal/mol. In conclusion, these three compounds are worthy of further investigation as novel inhibitors of PRRSV. This study provides a theoretical basis for the development of anti-PRRSV natural drugs.

Protein cis-regulatory elements (CREs) are regions that modulate the activity of a protein through intramolecular interactions. Kinases, pivotal enzymes in numerous biological processes, often undergo regulatory control via inhibitory interactions in cis. This study delves into the mechanisms of cis regulation in kinases mediated by CREs, employing a combined structural and sequence analysis. To accomplish this, we curated an extensive dataset of kinases featuring annotated CREs, organized into homolog families through multiple sequence alignments. Key molecular attributes, including disorder and secondary structure content, active and ATP-binding sites, post-translational modifications, and disease-associated mutations, were systematically mapped onto all sequences. Additionally, we explored the potential for conformational changes between active and inactive states. Finally, we explored the presence of these kinases within membraneless organelles and elucidated their functional roles therein. CREs display a continuum of structures, ranging from short disordered stretches to fully folded domains. The adaptability demonstrated by CREs in achieving the common goal of kinase inhibition spans from direct autoinhibitory interaction with the active site within the kinase domain, to CREs binding to an alternative site, inducing allosteric regulation revealing distinct types of inhibitory mechanisms, which we exemplify by archetypical representative systems. While this study provides a systematic approach to comprehend kinase CREs, further experimental investigations are imperative to unravel the complexity within distinct kinase families. The insights gleaned from this research lay the foundation for future studies aiming to decipher the molecular basis of kinase dysregulation, and explore potential therapeutic interventions.

For a variety of applications, protein structures are clustered by sequence similarity, and sequence-redundant structures are disregarded. Sequence-similar chains are likely to have similar structures, but significant structural variation, as measured with RMSD, has been documented for sequence-similar chains and found usually to have a functional explanation. Moving two neighboring stretches of backbone through each other may change the chain topology and alter possible folding paths. The size of this motion is compatible to a variation in a flexible loop. We search and find domains with alternate chain topology in CATH4.2 sequence families relatively independent of sequence identity and of structural similarity as measured by RMSD. Structural, topological, and functional representative sets should therefore keep sequence-similar domains not just with structural variation but also with topological variation. We present BCAlign that finds Alignment and superposition of protein Backbone Curves by optimizing a user chosen convex combination of structural derivation and derivation between the structure-based sequence alignment and an input sequence alignment. Steric and topological obstructions from deforming a curve into an aligned curve are then found by a previously developed algorithm. For highly sequence-similar domains, sequence-based structural alignment better represents the chains motion and generally reveals larger structural and topological variation than structure-based does. Fold-switching protein pairs have been reported to be most frequent between X-ray and NMR structures and estimated to be underrepresented in the PDB as the alternate configuration is harder to resolve. Here we similarly find chain topology most frequently altered between X-ray and NMR structures.

RfaH is a two-domain metamorphic protein involved in transcription regulation and translation initiation. To carry out its dual functions, RfaH relies on two coupled structural changes: Domain dissociation and fold switching. In the free state, the C-terminal domain (CTD) of RfaH adopts an all-α fold and is tightly associated with the N-terminal domain (NTD). Upon binding to RNA polymerase (RNAP), the domains dissociate and the CTD transforms into an all-β fold while the NTD remains largely, but not entirely, unchanged. We test the idea that a change in the conformation of an extended β-hairpin (β3-β4) located on the NTD, helps trigger domain dissociation. To this end, we use homology modeling to construct a structure, H, which is similar to free RfaH but with a remodeled β3-β4 hairpin. We then use an all-atom physics-based model enhanced with a dual basin structure-based potential to simulate domain separation driven by the thermal unfolding of the CTD with NTD in a fixed, folded conformation. We apply our model to both free RfaH and H. For H we find, in line with our hypothesis, that the CTD exhibits lower stability and the domains dissociate at a lower temperature T, as compared to free RfaH. We do not, however, observe complete refolding to the all-β state in these simulations, suggesting that a change in β3-β4 orientation aids in, but is not sufficient for, domain dissociation. In addition, we study the reverse fold switch in which RfaH returns from a domain-open all-β state to its domain-closed all-α state. We observe a T-dependent transition rate; fold switching is slow at low T, where the CTD tends to be kinetically trapped in its all-β state, and at high-T, where the all-α state becomes unstable. Consequently, our simulations suggest an optimal T at which fold switching is most rapid. At this T, the stabilities of both folds are reduced. Overall, our study suggests that both inter-domain interactions and conformational changes within NTD may be important for the proper functioning of RfaH.

Microneme protein 2 (MIC2) and its associated protein M2AP are pivotal for the gliding motility and host cell invasion by Toxoplasma gondii. In our prior work, we showed that M2AP binds specifically to the sixth TSR domain of MIC2, with this interaction mediated dominantly by the hotspot residue H620 situated at the center of TSR6. To delve deeper into the functional significance of H620 and explore the dynamic behavior of Y602, we conducted molecular dynamic (MD) simulations of the Toxoplasma TSR6-M2AP complex, encompassing both wild-type and mutant forms. Our findings underscore the critical role of H620 within TSR6, particularly its hydrogen bond interaction with K72 of M2AP. The H620A mutation disrupts the nearby hydrophobic network while minimally affecting other hydrophilic interactions. Furthermore, our data reveal a highly conserved binding pose between M2AP and TSR6 across different species, consistent with previous trans-genera studies, thereby offering insights for future strategies in infection control development.

SR/RS dipeptide repeats vary in both length and position, and are phosphorylated by SR protein kinases (SRPKs). PIM-1L, the long isoform of PIM-1 kinase, the splicing of which has been implicated in acute myeloid leukemia, contains a domain that consists largely of repeating SR/RS and SH/HS dipeptides (SR/SH-rich). In order to extend our knowledge on the specificity and cellular functions of SRPK1, here we investigate whether PIM-1L could act as substrate of SRPK1 by a combination of biochemical and computational approaches. Our biochemical data showed that the SR/SH-rich domain of PIM-1L was able to associate with SRPK1, yet it could not act as a substrate but, instead, inactivated the kinase. In line with our biochemical data, molecular modeling followed by a microsecond-scale all-atom molecular dynamics (MD) simulation suggests that the SR/SH-rich domain acts as a pseudo-docking peptide that binds to the same acidic docking-groove used in other SRPK1 interactions and induces inactive SRPK1 conformations. Comparative community network analysis of the MD trajectories, unraveled the dynamic architecture of apo SRPK1 and notable alterations of allosteric communications upon PIM-1L peptide binding. This analysis also allowed us to identify key SRPK1 residues, including unique ones, with a pivotal role in mediating allosteric signal propagation within the kinase core. Interestingly, most of the identified amino acids correspond to cancer-associated amino acid changes, validating our results. In total, this work provides insights not only on the details of SRPK1 inhibition by the PIM-1L SR/SH-domain, but also contributes to an in-depth understanding of SRPK1 regulation.

Grp94 is the endoplasmic reticulum paralog of the hsp90 family of chaperones, which have been targeted for therapeutic intervention via their highly conserved ATP binding sites. The design of paralog-selective inhibitors relies on understanding the protein structural elements that drive higher affinity in selective inhibitors. Here, we determined the structures of Grp94 and Hsp90 in complex with the Grp94-selective inhibitor PU-H36, and of Grp94 with the non-selective inhibitor PU-H71. In Grp94, PU-H36 derives its higher affinity by utilizing Site 2, a Grp94-specific side pocket adjoining the ATP binding cavity, but in Hsp90 PU-H36 occupies Site 1, a side pocket that is accessible in all paralogs with which it makes lower affinity interactions. The structure of Grp94 in complex with PU-H71 shows only Site 1 binding. While changes in the conformation of helices 4 and 5 in the N-terminal domain occur when ligands bind to Site 1 of both Hsp90 and Grp94, large conformational shifts that also involve helix 1 are associated with the engagement of the Site 2 pocket in Grp94 only. Site 2 in Hsp90 is blocked and its helix 1 conformation is insensitive to ligand binding. To understand the role of helix 1 in ligand selectivity, we tested the binding of PU-H36 and other Grp94-selective ligands to chimeric Grp94/Hsp90 constructs. These studies show that helix 1 is the major determinant of selectivity for Site 2 targeted ligands and also influences the rate of ATPase activity in Hsp90 paralogs.

Domains of unknown function (DUFs) continue to comprise a significant portion of bacterial proteomes, with more than 20% of bacterial proteins remaining annotated as DUFs. The characterization of their molecular structure can provide valuable insight that is not captured by the primary sequence analysis, thus providing a segue into the identification of the molecular function of DUF representatives. Here, we present the crystal structure of KPN_02352 from Klebsiella pneumoniae subsp. pneumoniae, a DUF1480 domain-containing protein, which was determined to be 1.75 Å resolution. Representatives of the DUF1480 family are found broadly across Enterobacterales and have been previously shown to contribute to the antibiotic response. Our structural analysis suggests that DUF1480 is comprised of a six-stranded split barrel fold featuring a small alpha helix that is positioned to cap one end of the split barrel. DUF1480 was found to be monomeric in solution, and harbors structural similarity to response regulators. The crystal structure of DUF1480 is the first experimental insight into the molecular structure of this conserved protein family, revealing several conserved features that may be functionally relevant.