Understanding the molecular mechanisms of odorant binding and activation of the human OR52 family.

Structural and mechanistic studies on human odorant receptors (ORs), key in olfactory signaling, are challenging because of their low surface expression in heterologous cells. The recent structure of OR51E2 bound to propionate provided molecular insight into odorant recognition, but the lack of an inactive OR structure limited understanding of the activation mechanism of ORs upon odorant binding. Here, we determined the cryo-electron microscopy structures of consensus OR52 (OR52cs), a representative of the OR52 family, in the ligand-free (apo) and octanoate-bound states. The apo structure of OR52cs reveals a large opening between transmembrane helices (TMs) 5 and 6. A comparison between the apo and active structures of OR52cs demonstrates the inward and outward movements of the extracellular and intracellular segments of TM6, respectively. These results, combined with molecular dynamics simulations and signaling assays, shed light on the molecular mechanisms of odorant binding and activation of the OR52 family.

ECPAS/Ecm29-mediated 26S proteasome disassembly is an adaptive response to glucose starvation.

The 26S proteasome comprises 20S catalytic and 19S regulatory complexes. Approximately half of the proteasomes in cells exist as free 20S complexes; however, our mechanistic understanding of what determines the ratio of 26S to 20S species remains incomplete. Here, we show that glucose starvation uncouples 26S holoenzymes into 20S and 19S subcomplexes. Subcomplex affinity purification and quantitative mass spectrometry reveal that Ecm29 proteasome adaptor and scaffold (ECPAS) mediates this structural remodeling. The loss of ECPAS abrogates 26S dissociation, reducing degradation of 20S proteasome substrates, including puromycylated polypeptides. In silico modeling suggests that ECPAS conformational changes commence the disassembly process. ECPAS is also essential for endoplasmic reticulum stress response and cell survival during glucose starvation. In vivo xenograft model analysis reveals elevated 20S proteasome levels in glucose-deprived tumors. Our results demonstrate that the 20S-19S disassembly is a mechanism adapting global proteolysis to physiological needs and countering proteotoxic stress.

Ebastine impairs metastatic spread in triple-negative breast cancer by targeting focal adhesion kinase.

We sought to investigate the utility of ebastine (EBA), a second-generation antihistamine with potent anti-metastatic properties, in the context of breast cancer stem cell (BCSC)-suppression in triple-negative breast cancer (TNBC). EBA binds to the tyrosine kinase domain of focal adhesion kinase (FAK), blocking phosphorylation at the Y397 and Y576/577 residues. FAK-mediated JAK2/STAT3 and MEK/ERK signaling was attenuated after EBA challenge in vitro and in vivo. EBA treatment induced apoptosis and a sharp decline in the expression of the BCSC markers ALDH1, CD44 and CD49f, suggesting that EBA targets BCSC-like cell populations while reducing tumor bulk. EBA administration significantly impeded BCSC-enriched tumor burden, angiogenesis and distant metastasis while reducing MMP-2/-9 levels in circulating blood in vivo. Our findings suggest that EBA may represent an effective therapeutic for the simultaneous targeting of JAK2/STAT3 and MEK/ERK for the treatment of molecularly heterogeneous TNBC with divergent profiles. Further investigation of EBA as an anti-metastatic agent for the treatment of TNBC is warranted.

Benchmarking applicability of medium-resolution cryo-EM protein structures for structure-based drug design.

Cryo-electron microscopy (cryo-EM) is gaining large attention for high-resolution protein structure determination in solutions. However, a very high percentage of cryo-EM structures correspond to resolutions of 3-5 Å, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo-EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross-docking scenarios using medium resolution (3-5 Å) cryo-EM structures and a popular docking tool Autodock-Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross-docking but using high-resolution (<2 Å) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution-dependent and independent factors. The heterogeneity in the protein side-chain and backbone conformations is identified as the major resolution-dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution-independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo-EM structures for in silico drug design.

GalaxyDock2-HEME: Protein-ligand docking for heme proteins.

Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2-HEME, a new protein-ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation-dependent scoring term to describe heme iron-ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein-ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein-ligand complexes in which ligands do not bind iron show that GalaxyDock2-HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins.

Structural basis for Y2 receptor-mediated neuropeptide Y and peptide YY signaling.

Neuropeptide Y (NPY) and its receptors are expressed in various human tissues including the brain where they regulate appetite and emotion. Upon NPY stimulation, the neuropeptide Y1 and Y2 receptors (Y1R and Y2R, respectively) activate GI signaling, but their physiological responses to food intake are different. In addition, deletion of the two N-terminal amino acids of peptide YY (PYY(3-36)), the endogenous form found in circulation, can stimulate Y2R but not Y1R, suggesting that Y1R and Y2R may have distinct ligand-binding modes. Here, we report the cryo-electron microscopy structures of the PYY(3-36)‒Y2R‒Gi and NPY‒Y2R‒Gi complexes. Using cell-based assays, molecular dynamics simulations, and structural analysis, we revealed the molecular basis of the exclusive binding of PYY(3-36) to Y2R. Furthermore, we demonstrated that Y2R favors G protein signaling over ß-arrestin signaling upon activation, whereas Y1R does not show a preference between these two pathways.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking.

Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies.

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction.

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.

HProteome-BSite: predicted binding sites and ligands in human 3D proteome.

Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.

GalaxyDomDock: An Ab Initio Domain-domain Docking Web Server for Multi-domain Protein Structure Prediction.

A significant proportion of proteins comprise multiple domains. Domain-domain docking is a tool that predicts multi-domain protein structures when individual domain structures can be accurately predicted but when domain orientations cannot be predicted accurately. GalaxyDomDock predicts an ensemble of domain orientations from given domain structures by docking. Such information would also be beneficial in elucidating the functions of proteins that have multiple states with different domain orientations. GalaxyDomDock is an ab initio domain-domain docking method based on GalaxyTongDock, a previously developed protein-protein docking method. Infeasible domain orientations for the given linker are effectively screened out from the docked conformations by a geometric filter, using the Dijkstra algorithm. In addition, domain linker conformations are predicted by adopting a loop sampling method FALC. The proposed GalaxyDomDock outperformed existing ab initio domain-domain docking methods, such as AIDA and Rosetta, in performance tests on the Rosetta benchmark set of two-domain proteins. GalaxyDomDock also performed better than or comparable to AIDA on the AIDA benchmark set of two-domain proteins and two-domain proteins containing discontinuous domains, including the benchmark set in which each domain of the set was modeled by the recent version of AlphaFold. The GalaxyDomDock web server is freely available as a part of GalaxyWEB at http://galaxy.seoklab.org/domdock.

GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network.

Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein-protein interfaces, and protein-compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.

Structural basis of neuropeptide Y signaling through Y1 receptor.

Neuropeptide Y (NPY) is highly abundant in the brain and involved in various physiological processes related to food intake and anxiety, as well as human diseases such as obesity and cancer. However, the molecular details of the interactions between NPY and its receptors are poorly understood. Here, we report a cryo-electron microscopy structure of the NPY-bound neuropeptide Y1 receptor (Y1R) in complex with Gi1 protein. The NPY C-terminal segment forming the extended conformation binds deep into the Y1R transmembrane core, where the amidated C-terminal residue Y36 of NPY is located at the base of the ligand-binding pocket. Furthermore, the helical region and two N-terminal residues of NPY interact with Y1R extracellular loops, contributing to the high affinity of NPY for Y1R. The structural analysis of NPY-bound Y1R and mutagenesis studies provide molecular insights into the activation mechanism of Y1R upon NPY binding.

A pGpG-specific phosphodiesterase regulates cyclic di-GMP signaling in Vibrio cholerae.

The bacterial second messenger bis-(3'-5')-cyclic diguanylate monophosphate (c-di-GMP) controls various cellular processes, including motility, toxin production, and biofilm formation. c-di-GMP is enzymatically synthesized by GGDEF domain-containing diguanylate cyclases and degraded by HD-GYP domain-containing phosphodiesterases (PDEs) to 2 GMP or by EAL domain-containing PDE-As to 5'-phosphoguanylyl-(3',5')-guanosine (pGpG). Since excess pGpG feedback inhibits PDE-A activity and thereby can lead to the uncontrolled accumulation of c-di-GMP, a PDE that degrades pGpG to 2 GMP (PDE-B) has been presumed to exist. To date, the only enzyme known to hydrolyze pGpG is oligoribonuclease Orn, which degrades all kinds of oligoribonucleotides. Here, we identified a pGpG-specific PDE, which we named PggH, using biochemical approaches in the gram-negative bacteria Vibrio cholerae. Biochemical experiments revealed that PggH exhibited specific PDE activity only toward pGpG, thus differing from the previously reported Orn. Furthermore, the high-resolution structure of PggH revealed the basis for its PDE activity and narrow substrate specificity. Finally, we propose that PggH could modulate the activities of PDE-As and the intracellular concentration of c-di-GMP, resulting in phenotypic changes including in biofilm formation.

MOLGENGO: Finding Novel Molecules with Desired Electronic Properties by Capitalizing on Their Global Optimization.

The discovery of novel and favorable fluorophores is critical for understanding many chemical and biological studies. High-resolution biological imaging necessitates fluorophores with diverse colors and high quantum yields. The maximum oscillator strength and its corresponding absorption wavelength of a molecule are closely related to the quantum yields and the emission spectrum of fluorophores, respectively. Thus, the core step to design favorable fluorophore molecules is to optimize the desired electronic transition properties of molecules. Here, we present MOLGENGO, a new molecular property optimization algorithm, to discover novel and favorable fluorophores with machine learning and global optimization. This study reports novel molecules from MOLGENGO with high oscillator strength and absorption wavelength close to 200, 400, and 600 nm. The results of MOLGENGO simulations have the potential to be candidates for new fluorophore frameworks.

Discovery of a transdermally deliverable pentapeptide for activating AdipoR1 to promote hair growth.

Alopecia induced by aging or side effects of medications affects millions of people worldwide and impairs the quality of life; however, there is a limit to the current medications. Here, we identify a small transdermally deliverable 5-mer peptide (GLYYF; P5) that activates adiponectin receptor 1 (AdipoR1) and promotes hair growth. P5 sufficiently reproduces the biological effect of adiponectin protein via AMPK signaling pathway, increasing the expression of hair growth factors in the dermal papilla cells of human hair follicle. P5 accelerates hair growth ex vivo and induces anagen hair cycle in mice in vivo. Furthermore, we elucidate a key spot for the binding between AdipoR1 and adiponectin protein using docking simulation and mutagenesis studies. This study suggests that P5 could be used as a topical peptide drug for alleviating pathological conditions, which can be improved by adiponectin protein, such as alopecia.

Dynamic Interactions of Fully Glycosylated SARS-CoV-2 Spike Protein with Various Antibodies.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a public health crisis, and the vaccines that can induce highly potent neutralizing antibodies are essential for ending the pandemic. The spike (S) protein on the viral envelope mediates human angiotensin-converting enzyme 2 binding and thus is the target of a variety of neutralizing antibodies. In this work, we built various S trimer-antibody complex structures on the basis of the fully glycosylated S protein models described in our previous work and performed all-atom molecular dynamics simulations to gain insight into the structural dynamics and interactions between S protein and antibodies. Investigation of the residues critical for S-antibody binding allows us to predict the potential influence of mutations in SARS-CoV-2 variants. Comparison of the glycan conformations between S-only and S-antibody systems reveals the roles of glycans in S-antibody binding. In addition, we explored the antibody binding modes and the influences of antibody on the motion of S protein receptor binding domains. Overall, our analyses provide a better understanding of S-antibody interactions, and the simulation-based S-antibody interaction maps could be used to predict the influences of S mutation on S-antibody interactions, which will be useful for the development of vaccine and antibody-based therapy.

Protein oligomer structure prediction using GALAXY in CASP14.

Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo- or hetero-oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins. To this end, computational prediction of oligomer structures may be a useful tool in the absence of experimentally resolved structures. Here, we describe our server and human-expert methods used to predict oligomer structures in the CASP14 experiment. Examples are provided for cases in which manual domain-splitting led to improved oligomeric domain structures by ab initio docking, automated oligomer structure refinement led to improved subunit orientation and terminal structure, and manual oligomer modeling utilizing literature information generated a reasonable oligomer model. We also discussed the results of post-prediction docking calculations with AlphaFold2 monomers as input in comparison to our blind prediction results. Overall, ab initio docking of AlphaFold2 models did not lead to better oligomer structure prediction, which may be attributed to the interfacial structural difference between the AlphaFold2 monomer structures and the crystal oligomer structures. This result poses a next-stage challenge in oligomer structure prediction after the success of AlphaFold2. For successful protein assembly structure prediction, a different approach that exploits further evolutionary information on the interface and/or flexible docking taking the interfacial conformational flexibilities of subunit structures into account is needed.