Pin1-Catalyzed Conformation Changes Regulate Protein Ubiquitination and Degradation.

The unique prolyl isomerase Pin1 binds to and catalyzes cis-trans conformational changes of specific Ser/Thr-Pro motifs after phosphorylation, thereby playing a pivotal role in regulating the structure and function of its protein substrates. In particular, Pin1 activity regulates the affinity of a substrate for E3 ubiquitin ligases, thereby modulating the turnover of a subset of proteins and coordinating their activities after phosphorylation in both physiological and disease states. In this review, we highlight recent advancements in Pin1-regulated ubiquitination in the context of cancer and neurodegenerative disease. Specifically, Pin1 promotes cancer progression by increasing the stabilities of numerous oncoproteins and decreasing the stabilities of many tumor suppressors. Meanwhile, Pin1 plays a critical role in different neurodegenerative disorders via the regulation of protein turnover. Finally, we propose a novel therapeutic approach wherein the ubiquitin-proteasome system can be leveraged for therapy by targeting pathogenic intracellular targets for TRIM21-dependent degradation using stereospecific antibodies.

Correlations of dynamic changes in lipid and protein of salted large yellow croaker during storage.

Protein and lipid are two major components that undergo significant changes during processing of aquatic products. This study focused on the protein oxidation, protein conformational states, lipid oxidation and lipid molecule profiling of salted large yellow croaker during storage, and their correlations were investigated. The degree of oxidation of protein and lipid was time-dependent, leading to an increase in carbonyl content and surface hydrophobicity, a decrease in sulfhydryl groups, and an increase in conjugated diene, peroxide value and thiobarbituric acid reactive substances value. Oxidation caused protein structure denaturation and aggregation during storage. Lipid composition and content changed dynamically, with polyunsaturated phosphatidylcholine (PC) was preferentially oxidized compared to polyunsaturated triacylglycerol. Correlation analysis showed that the degradation of polyunsaturated key differential lipids (PC 18:2_20:5, PC 16:0_22:6, PC 16:0_20:5, etc.) was closely related to the oxidation of protein and lipid. The changes in protein conformation and the peroxidation of polyunsaturated lipids mutually promote each other's oxidation process.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics.

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins.

A protein sequence encodes its energy landscape-all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs-questioning the differences between them and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the consensus protein derived from our full Ribonuclease H sequence alignment is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted set of sequences is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order sequence correlations using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

Allosteric coupling asymmetry mediates paradoxical activation of BRAF by type II inhibitors.

The type II class of RAF inhibitors currently in clinical trials paradoxically activate BRAF at subsaturating concentrations. Activation is mediated by induction of BRAF dimers, but why activation rather than inhibition occurs remains unclear. Using biophysical methods tracking BRAF dimerization and conformation, we built an allosteric model of inhibitor-induced dimerization that resolves the allosteric contributions of inhibitor binding to the two active sites of the dimer, revealing key differences between type I and type II RAF inhibitors. For type II inhibitors the allosteric coupling between inhibitor binding and BRAF dimerization is distributed asymmetrically across the two dimer binding sites, with binding to the first site dominating the allostery. This asymmetry results in efficient and selective induction of dimers with one inhibited and one catalytically active subunit. Our allosteric models quantitatively account for paradoxical activation data measured for 11 RAF inhibitors. Unlike type II inhibitors, type I inhibitors lack allosteric asymmetry and do not activate BRAF homodimers. Finally, NMR data reveal that BRAF homodimers are dynamically asymmetric with only one of the subunits locked in the active αC-in state. This provides a structural mechanism for how binding of only a single αC-in inhibitor molecule can induce potent BRAF dimerization and activation.

Structural and mechanistic insights into Quinolone Synthase to address its functional promiscuity.

Quinolone synthase from Aegle marmelos (AmQNS) is a type III polyketide synthase that yields therapeutically effective quinolone and acridone compounds. Addressing the structural and molecular underpinnings of AmQNS and its substrate interaction in terms of its high selectivity and specificity can aid in the development of numerous novel compounds. This paper presents a high-resolution AmQNS crystal structure and explains its mechanistic role in synthetic selectivity. Additionally, we provide a model framework to comprehend structural constraints on ketide insertion and postulate that AmQNS's steric and electrostatic selectivity plays a role in its ability to bind to various core substrates, resulting in its synthetic diversity. AmQNS prefers quinolone synthesis and can accommodate large substrates because of its wide active site entrance. However, our research suggests that acridone is exclusively synthesized in the presence of high malonyl-CoA concentrations. Potential implications of functionally relevant residue mutations were also investigated, which will assist in harnessing the benefits of mutations for targeted polyketide production. The pharmaceutical industry stands to gain from these findings as they expand the pool of potential drug candidates, and these methodologies can also be applied to additional promising enzymes.

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein.

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

LoCoHD: a metric for comparing local environments of proteins.

Protein folds and the local environments they create can be compared using a variety of differently designed measures, such as the root mean squared deviation, the global distance test, the template modeling score or the local distance difference test. Although these measures have proven to be useful for a variety of tasks, each fails to fully incorporate the valuable chemical information inherent to atoms and residues, and considers these only partially and indirectly. Here, we develop the highly flexible local composition Hellinger distance (LoCoHD) metric, which is based on the chemical composition of local residue environments. Using LoCoHD, we analyze the chemical heterogeneity of amino acid environments and identify valines having the most conserved-, and arginines having the most variable chemical environments. We use LoCoHD to investigate structural ensembles, to evaluate critical assessment of structure prediction (CASP) competitors, to compare the results with the local distance difference test (lDDT) scoring system, and to evaluate a molecular dynamics simulation. We show that LoCoHD measurements provide unique information about protein structures that is distinct from, for example, those derived using the alignment-based RMSD metric, or the similarly distance matrix-based but alignment-free lDDT metric.

Structural and dynamic insights into the activation of the µ-opioid receptor by an allosteric modulator.

G-protein-coupled receptors (GPCRs) play pivotal roles in various physiological processes. These receptors are activated to different extents by diverse orthosteric ligands and allosteric modulators. However, the mechanisms underlying these variations in signaling activity by allosteric modulators remain largely elusive. Here, we determine the three-dimensional structure of the µ-opioid receptor (MOR), a class A GPCR, in complex with the Gi protein and an allosteric modulator, BMS-986122, using cryogenic electron microscopy. Our results reveal that BMS-986122 binding induces changes in the map densities corresponding to R1673.50 and Y2545.58, key residues in the structural motifs conserved among class A GPCRs. Nuclear magnetic resonance analyses of MOR in the absence of the Gi protein reveal that BMS-986122 binding enhances the formation of the interaction between R1673.50 and Y2545.58, thus stabilizing the fully-activated conformation, where the intracellular half of TM6 is outward-shifted to allow for interaction with the Gi protein. These findings illuminate that allosteric modulators like BMS-986122 can potentiate receptor activation through alterations in the conformational dynamics in the core region of GPCRs. Together, our results demonstrate the regulatory mechanisms of GPCRs, providing insights into the rational development of therapeutics targeting GPCRs.

Interplay of structural preorganization and conformational sampling in UDP-glucuronic acid 4-epimerase catalysis.

Understanding enzyme catalysis as connected to protein motions is a major challenge. Here, based on temperature kinetic studies combined with isotope effect measurements, we obtain energetic description of C-H activation in NAD-dependent UDP-glucuronic acid C4 epimerase. Approach from the ensemble-averaged ground state (GS) to the transition state-like reactive conformation (TSRC) involves, alongside uptake of heat ( Δ H ‡ = 54 kJ mol-1), significant loss in entropy ( - T Δ S ‡ = 20 kJ mol-1; 298 K) and negative activation heat capacity ( Δ C p ‡ = -0.64 kJ mol-1 K-1). Thermodynamic changes suggest the requirement for restricting configurational freedom at the GS to populate the TSRC. Enzyme variants affecting the electrostatic GS preorganization reveal active-site interactions important for precise TSRC sampling and H-transfer. Collectively, our study captures thermodynamic effects associated with TSRC sampling and establishes rigid positioning for C-H activation in an enzyme active site that requires conformational flexibility in fulfillment of its natural epimerase function.

Harnessing the 3D-Beacons Network: A Comprehensive Guide to Accessing and Displaying Protein Structure Data.

Recent advancements in protein structure determination and especially in protein structure prediction techniques have led to the availability of vast amounts of macromolecular structures. However, the accessibility and integration of these structures into scientific workflows are hindered by the lack of standardization among publicly available data resources. To address this issue, we introduced the 3D-Beacons Network, a unified platform that aims to establish a standardized framework for accessing and displaying protein structure data. In this article, we highlight the importance of standardized approaches for accessing protein structure data and showcase the capabilities of 3D-Beacons. We describe four protocols for finding and accessing macromolecular structures from various specialist data resources via 3D-Beacons. First, we describe three scenarios for programmatically accessing and retrieving data using the 3D-Beacons API. Next, we show how to perform sequence-based searches to find structures from model providers. Then, we demonstrate how to search for structures and fetch them directly into a workflow using JalView. Finally, we outline the process of facilitating access to data from providers interested in contributing their structures to the 3D-Beacons Network. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Programmatic access to the 3D-Beacons API Basic Protocol 2: Sequence-based search using the 3D-Beacons API Basic Protocol 3: Accessing macromolecules from 3D-Beacons with JalView Basic Protocol 4: Enhancing data accessibility through 3D-Beacons.

A finely balanced order-disorder equilibrium sculpts the folding-binding landscape of an antibiotic sequestering protein.

TipA, a MerR family transcription factor from Streptomyces lividans, promotes antibiotic resistance by sequestering broad-spectrum thiopeptide-based antibiotics, thus counteracting their inhibitory effect on ribosomes. TipAS, a minimal binding motif which is expressed as an isoform of TipA, harbors a partially disordered N-terminal subdomain that folds upon binding multiple antibiotics. The extent and nature of the underlying molecular heterogeneity in TipAS that shapes its promiscuous folding-function landscape is an open question and is critical for understanding antibiotic-sequestration mechanisms. Here, combining equilibrium and time-resolved experiments, statistical modeling, and simulations, we show that the TipAS native ensemble exhibits a pre-equilibrium between binding-incompetent and binding-competent substates, with the fully folded state appearing only as an excited state under physiological conditions. The binding-competent state characterized by a partially structured N-terminal subdomain loses structure progressively in the physiological range of temperatures, swells on temperature increase, and displays slow conformational exchange across multiple conformations. Binding to the bactericidal antibiotic thiostrepton follows a combination of induced-fit and conformational-selection-like mechanisms, via partial binding and concomitant stabilization of the binding-competent substate. These ensemble features are evolutionarily conserved across orthologs from select bacteria that infect humans, underscoring the functional role of partial disorder in the native ensemble of antibiotic-sequestering proteins belonging to the MerR family.

Conformations of a highly expressed Z19 α-zein studied with AlphaFold2 and MD simulations.

α-zeins are amphiphilic maize seed storage proteins with material properties suitable for a multitude of applications e.g., in renewable plastics, foods, therapeutics and additive manufacturing (3D-printing). To exploit their full potential, molecular-level insights are essential. The difficulties in experimental atomic-resolution characterization of α-zeins have resulted in a diversity of published molecular models. However, deep-learning α-zein models are largely unexplored. Therefore, this work studies an AlphaFold2 (AF2) model of a highly expressed α-zein using molecular dynamics (MD) simulations. The sequence of the α-zein cZ19C2 gave a loosely packed AF2 model with 7 α-helical segments connected by turns/loops. Compact tertiary structure was limited to a C-terminal bundle of three α-helices, each showing notable agreement with a published consensus sequence. Aiming to chart possible α-zein conformations in practically relevant solvents, rather than the native solid-state, the AF2 model was subjected to MD simulations in water/ethanol mixtures with varying ethanol concentrations. Despite giving structurally diverse endpoints, the simulations showed several patterns: In water and low ethanol concentrations, the model rapidly formed compact globular structures, largely preserving the C-terminal bundle. At ≥ 50 mol% ethanol, extended conformations prevailed, consistent with previous SAXS studies. Tertiary structure was partially stabilized in water and low ethanol concentrations, but was disrupted in ≥ 50 mol% ethanol. Aggregated results indicated minor increases in helicity with ethanol concentration. ß-sheet content was consistently low (∼1%) across all conditions. Beyond structural dynamics, the rapid formation of branched α-zein aggregates in aqueous environments was highlighted. Furthermore, aqueous simulations revealed favorable interactions between the protein and the crosslinking agent glycidyl methacrylate (GMA). The proximity of GMA epoxide carbons and side chain hydroxyl oxygens simultaneously suggested accessible reactive sites in compact α-zein conformations and pre-reaction geometries for methacrylation. The findings may assist in expanding the applications of these technologically significant proteins, e.g., by guiding chemical modifications.

Single-Molecule FRET Analyses of NMDA Receptors.

Single-molecule fluorescence resonance energy transfer (smFRET) enables the real-time observation of conformational changes in a single protein molecule of interest. These observations are achieved by attaching fluorophores to proteins of interest in a site-specific manner and investigating the FRET between the fluorophores. Here we describe the method wherein the FRET is studied by adhering the protein molecules to a slide using affinity-based interactions and measuring the fluorophores' fluorescence intensity from a single molecule over time. The resulting information can be used to derive distance values for a point-to-point measurement within a protein or to calculate kinetic transition rates between various conformational states of a protein. Comparing these parameters between different conditions such as the presence of protein binding partners, application of ligands, or changes in the primary sequence of the protein can provide insights into protein structural changes as well as kinetics of these changes (if in the millisecond to second timescale) that underlie functional effects. Here we describe the procedure for conducting analyses of NMDA receptor conformational changes using the above methodology and provide a discussion of various considerations that affect the design, execution, and interpretation of similar smFRET studies.

Modeling and Simulation of the NMDA Receptor at Coarse-Grained and Atomistic Levels.

N-Methyl-D-aspartate (NMDA) receptors are glutamate-gated excitatory channels that play essential roles in brain functions. While high-resolution structures were solved for an allosterically inhibited form of functional NMDA receptor, other key functional states (particularly the active open-channel state) have not yet been resolved at atomic resolutions. To decrypt the molecular mechanism of the NMDA receptor activation, structural modeling and simulation are instrumental in providing detailed information about the dynamics and energetics of the receptor in various functional states. In this chapter, we describe coarse-grained modeling of the NMDA receptor using an elastic network model and related modeling/analysis tools (e.g., normal mode analysis, flexibility and hotspot analysis, cryo-EM flexible fitting, and transition pathway modeling) based on available structures. Additionally, we show how to build an atomistic model of the active-state receptor with targeted molecular dynamics (MD) simulation and explore its energetics and dynamics with conventional MD simulation. Taken together, these modeling and simulation can offer rich structural and dynamic information which will guide experimental studies of the activation of this key receptor.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

The critical importance of conditions: Reconciling GPCR functionality and biophysical findings.

G-protein-coupled receptor (GPCR) activation relies on conformational sampling, a nuanced but functionally key behavior well suited to elucidation by nuclear magnetic resonance (NMR) spectroscopy. In this issue of Structure, Thakur et al.1 demonstrate that judicious choice of experimental conditions for 19F NMR studies of a GPCR enables rationalization of functional and pharmacological behavior, leading to testable hypotheses correlating structure, dynamics, and function.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.