Quercetin as a promising intervention for rat osteoarthritis by decreasing M1-polarized macrophages via blocking the TRPV1-mediated P2X7/NLRP3 signaling pathway.

Osteoarthritis (OA) is characterized by an imbalance between M1 and M2 polarized synovial macrophages. Quercetin has shown protective effects against OA by altering M1/M2-polarized macrophages, but the underlying mechanisms remain unclear. In this study, rat chondrocytes were treated with 10 ng/mL of IL-1ß. To create M1-polarized macrophages in vitro, rat bone marrow-derived macrophages (rBMDMs) were treated with 100 ng/mL LPS. To mimic OA conditions observed in vivo, a co-culture system of chondrocytes and macrophages was established. ATP release assays, immunofluorescence assays, Fluo-4 AM staining, Transwell assays, ELISA assays, and flow cytometry were performed. Male adult Sprague-Dawley (SD) rats were used to create an OA model. Histological analyses, including H&E, and safranin O-fast green staining were performed. Our data showed a quercetin-mediated suppression of calcium ion influx and ATP release, with concurrent downregulation of TRPV1 and P2X7 in the chondrocytes treated with IL-1ß. Activation of TRPV1 abolished the quercetin-mediated effects on calcium ion influx and ATP release in chondrocytes treated with IL-1ß. In the co-culture system, overexpression of P2X7 in macrophages attenuated the quercetin-mediated effects on M1 polarization, migration, and inflammation. Either P2X7 or NLRP3 knockdown attenuated IL-1ß-induced M1/M2 polarization, migration, and inflammation. Moreover, overexpression of TRPV1 reduced the quercetin-mediated suppressive effects on OA by promoting M1/M2-polarized macrophages in vivo. Collectively, our data showed that quercetin-induced suppression of TRPV1 leads to a delay in OA progression by shifting the macrophage polarization from M1 to M2 subtypes via modulation of the P2X7/NLRP3 pathway.

A wild boar cathelicidin peptide derivative inhibits severe acute respiratory syndrome coronavirus-2 and its drifted variants.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a clear threat to humanity. It has infected over 200 million and killed 4 million people worldwide, and infections continue with no end in sight. To control the pandemic, multiple effective vaccines have been developed, and global vaccinations are in progress. However, the virus continues to mutate. Even when full vaccine coverage is achieved, vaccine-resistant mutants will likely emerge, thus requiring new annual vaccines against drifted variants analogous to influenza. A complimentary solution to this problem could be developing antiviral drugs that inhibit SARS CoV-2 and its drifted variants. Host defense peptides represent a potential source for such an antiviral as they possess broad antimicrobial activity and significant diversity across species. We screened the cathelicidin family of peptides from 16 different species for antiviral activity and identified a wild boar peptide derivative that inhibits SARS CoV-2. This peptide, which we named Yongshi and means warrior in Mandarin, acts as a viral entry inhibitor. Following the binding of SARS-CoV-2 to its receptor, the spike protein is cleaved, and heptad repeats 1 and 2 multimerize to form the fusion complex that enables the virion to enter the cell. A deep learning-based protein sequence comparison algorithm and molecular modeling suggest that Yongshi acts as a mimetic to the heptad repeats of the virus, thereby disrupting the fusion process. Experimental data confirm the binding of Yongshi to the heptad repeat 1 with a fourfold higher affinity than heptad repeat 2 of SARS-CoV-2. Yongshi also binds to the heptad repeat 1 of SARS-CoV-1 and MERS-CoV. Interestingly, it inhibits all drifted variants of SARS CoV-2 that we tested, including the alpha, beta, gamma, delta, kappa and omicron variants.

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation.

MOTIVATION: Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. RESULTS: We present the predicted structural proteome (25 134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation. AVAILABILITY AND IMPLEMENTATION: All data and code are available in public repositories, detailed at https://github.com/BSDExabio/SAFA. The structural models of the S. divinum proteome have been deposited in the ModelArchive repository at https://modelarchive.org/doi/10.5452/ma-ornl-sphdiv.

Investigation on the Microstructure and Mechanical Properties of the Ti-Ta Alloy with Unmelted Ta Particles by Laser Powder Bed Fusion.

Titanium-tantalum (Ti-Ta) alloy has excellent biomechanical properties with high strength and low Young's modulus, showing great application potential in the biomedical industry. In this study, Ti-Ta alloy samples were prepared by laser powder bed fusion (LPBF) technology with mixed pure 75 wt.% Ti and 25 wt.% Ta powders as the feedstock. The maximum relative density of Ti-Ta samples prepared by LPBF reached 99.9%. It is well-accepted that four nonequilibrium phases, namely, α', α″ and metastable ß phase exist in Ti-Ta alloys. The structure of α', α″ and ß are hexagonal close-packed (HCP), base-centered orthorhombic (BCO) and body-centered cubic (BCC), respectively. X-ray Diffraction (XRD) analysis showed that the α' phase transformed to the α″ phase with the increase of energy density. The lamellar α'/α″ phases and the α″ twins were generated in the prior ß phase. The microstructure and mechanical properties of the Ti-Ta alloy were optimized with different LPBF processing parameters. The samples prepared by LPBF energy density of 381 J/mm3 had a favorable ultimate strength (UTS) of 1076 ± 2 MPa and yield strength of 795 ± 16 MPa. The samples prepared by LPBF energy density of 76 had excellent ductility, with an elongation of 31% at fracture.

Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.

To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the Escherichia coli cell envelope and apply it to several proteins from an OMP biogenesis pathway. Among the top confident hits obtained from screening ~1500 envelope proteins, we find not only expected interactions but also unexpected ones with profound implications. Subsequently, we predict atomic structures for these protein complexes. These structures, typically of high confidence, explain experimental observations and lead to mechanistic hypotheses for how a chaperone assists a nascent, precursor OMP emerging from a translocon, how another chaperone prevents it from aggregating and docks to a ß-barrel assembly port, and how a protease performs quality control. This work presents a general strategy for investigating biological pathways by using structural insights gained from deep learning-based predictions.

All living cells are contained within a fatty cell membrane that allows water and only certain other molecules to pass through with ease. Bacteria only consist of a single cell, making their membrane the only interface with the surrounding environment. Gram-negative bacteria ­ which include Escherichia coli, a bacterium found in the gut of all humans ­ have an extra layer of protection, the 'outer membrane'. Proteins in this membrane are called 'outer membrane proteins' or OMPs and allow nutrients to enter the cell. But OMPs, which are made inside the cell, need to be transported to the outer membrane and folded correctly before they can perform their role. This multistep process, which involves interactions between many different proteins, is not fully understood. The journey of an OMP from the center of the cell where it is made to the outer membrane is complicated. First, the OMP needs to pass through the cell's inner membrane. To do this, it must interact with 'channel proteins' in the inner membrane that feed the OMP into the space between the two membranes, known as the bacterial envelope. This step requires the OMP to be unfolded. Once in the bacterial envelope the OMP interacts with proteins that help it fold correctly and integrate into the outer membrane. The interactions between proteins in the bacterial envelope are short-lived, making them difficult to study using lab-based experiments. An alternative approach is predicting a protein's structure from its amino acid sequence which is a difficult computational problem to solve. However, in 2020 developers behind the AlphaFold2, a deep learning program, were able to use a set of equations organized in a 'neural network' that can 'learn' from a library of known protein structures to predict unknown structures with high accuracy. Gao et al. used AF2Complex, a tool based AlphaFold2, tailored to predicting interactions between proteins, to investigate what interactions OMPs could be involved with on their way to the outer membrane. With the help of a supercomputer at the Oakridge National Laboratory, Gao et al. screened nearly 1,500 E. coli proteins within the bacterial envelope to see how they might interact with OMPs. The screen identified previously unknown interactions between proteins that suggest that the formation of the bacterial outer membrane and the integration of proteins into it involve protein complexes and molecular mechanisms that have not yet been characterized. Additionally, the screen also identified interactions that had been previously described, confirming that the deep learning approach can correctly capture real interactions. Overall, Gao et al.'s work inspires new hypotheses about the mechanisms through which OMPs are transported to the outer membrane, although further work will be needed to confirm the roles of protein interactions predicted by the computational model experimentally. Furthermore, the ability to design experiments based on computational predictions is exciting. If confirmed, the new protein interactions could help scientists better understand OMP transport, which is essential for bacterial biology. In the future, this could lead to the discovery of new targets for antibiotic drugs.

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning.

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

Antitarget Selectivity and Tolerability of Novel Pyrrolo[2,3-d]pyrimidine RET Inhibitors.

The selective inhibition of RET kinase as a treatment for relevant cancer types including lung adenocarcinoma has garnered considerable interest in recent years and prompted a variety of efforts toward the discovery of small-molecule therapeutics. Hits uncovered via the analysis of archival kinase data ultimately led to the identification of a promising pyrrolo[2,3-d]pyrimidine scaffold. The optimization of this pyrrolo[2,3-d]pyrimidine core resulted in compound 1, which demonstrated potent in vitro RET kinase inhibition and robust in vivo efficacy in RET-driven tumor xenografts upon multiday dosing in mice. The administration of 1 was well-tolerated at established efficacious doses (10 and 30 mg/kg, po, qd), and plasma exposure levels indicated a minimal risk of KDR or hERG inhibition in vivo, as evaluated by Miles assay and free plasma concentrations, respectively.

AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.

AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.

On the emergence of homochirality and life itself.

Many of life's molecules including proteins are built from chiral building blocks. What drove homochiral building block selection? Simulations on demi-chiral proteins containing equal numbers of d- and l-amino acids show that they possess many modern homochiral protein properties. They have the same global folds and could do the same biochemistry, with ancient, essential functions being most prevalent. They could synthesize chiral RNA and lipids which formed vesicles. RNA eventually combined with proteins creating ribosomes for more efficient protein synthesis, and thus, life began. Increased native state stability from homochiral secondary structure hydrogen bonding helped drive proteins towards homochirality.

A General Framework to Learn Tertiary Structure for Protein Sequence Characterization.

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

Structure-Based Design of Selective LONP1 Inhibitors for Probing In Vitro Biology.

LONP1 is an AAA+ protease that maintains mitochondrial homeostasis by removing damaged or misfolded proteins. Elevated activity and expression of LONP1 promotes cancer cell proliferation and resistance to apoptosis-inducing reagents. Despite the importance of LONP1 in human biology and disease, very few LONP1 inhibitors have been described in the literature. Herein, we report the development of selective boronic acid-based LONP1 inhibitors using structure-based drug design as well as the first structures of human LONP1 bound to various inhibitors. Our efforts led to several nanomolar LONP1 inhibitors with little to no activity against the 20S proteasome that serve as tool compounds to investigate LONP1 biology.

A novel sequence alignment algorithm based on deep learning of the protein folding code.

MOTIVATION: From evolutionary interference, function annotation to structural prediction, protein sequence comparison has provided crucial biological insights. While many sequence alignment algorithms have been developed, existing approaches often cannot detect hidden structural relationships in the 'twilight zone' of low sequence identity. To address this critical problem, we introduce a computational algorithm that performs protein Sequence Alignments from deep-Learning of Structural Alignments (SAdLSA, silent 'd'). The key idea is to implicitly learn the protein folding code from many thousands of structural alignments using experimentally determined protein structures. RESULTS: To demonstrate that the folding code was learned, we first show that SAdLSA trained on pure α-helical proteins successfully recognizes pairs of structurally related pure ß-sheet protein domains. Subsequent training and benchmarking on larger, highly challenging datasets show significant improvement over established approaches. For challenging cases, SAdLSA is ∼150% better than HHsearch for generating pairwise alignments and ∼50% better for identifying the proteins with the best alignments in a sequence library. The time complexity of SAdLSA is O(N) thanks to GPU acceleration. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes of SAdLSA are available free of charge for academic users at http://sites.gatech.edu/cssb/sadlsa/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

The role of local versus nonlocal physicochemical restraints in determining protein native structure.

The tertiary structure of a native protein is dictated by the interplay of local secondary structure propensities, hydrogen bonding, and tertiary interactions. It is argued that the space of known protein topologies covers all single domain folds and results from the compactness of the native structure and excluded volume. Protein compactness combined with the chirality of the protein's side chains also yields native-like Ramachandran plots. It is the many-body, tertiary interactions among residues that collectively select for the global structure that a particular protein sequence adopts. This explains why the recent advances in deep-learning approaches that predict protein side-chain contacts, the distance matrix between residues, and sequence alignments are successful. They succeed because they implicitly learned the many-body interactions among protein residues.

Discovery and Characterization of Clinical Candidate LXE408 as a Kinetoplastid-Selective Proteasome Inhibitor for the Treatment of Leishmaniases.

Visceral leishmaniasis is responsible for up to 30,000 deaths every year. Current treatments have shortcomings that include toxicity and variable efficacy across endemic regions. Previously, we reported the discovery of GNF6702, a selective inhibitor of the kinetoplastid proteasome, which cleared parasites in murine models of leishmaniasis, Chagas disease, and human African trypanosomiasis. Here, we describe the discovery and characterization of LXE408, a structurally related kinetoplastid-selective proteasome inhibitor currently in Phase 1 human clinical trials. Furthermore, we present high-resolution cryo-EM structures of the Leishmania tarentolae proteasome in complex with LXE408, which provides a compelling explanation for the noncompetitive mode of binding of this novel class of inhibitors of the kinetoplastid proteasome.

Efficacy and Tolerability of Pyrazolo[1,5-a]pyrimidine RET Kinase Inhibitors for the Treatment of Lung Adenocarcinoma.

RET (REarranged during Transfection) kinase gain-of-function aberrancies have been identified as potential oncogenic drivers in lung adenocarcinoma, along with several other cancer types, prompting the discovery and assessment of selective inhibitors. Internal mining and analysis of relevant kinase data informed the decision to investigate a pyrazolo[1,5-a]pyrimidine scaffold, where subsequent optimization led to the identification of compound WF-47-JS03 (1), a potent RET kinase inhibitor with >500-fold selectivity against KDR (Kinase insert Domain Receptor) in cellular assays. In subsequent mouse in vivo studies, compound 1 demonstrated effective brain penetration and was found to induce strong regression of RET-driven tumor xenografts at a well-tolerated dose (10 mg/kg, po, qd). Higher doses of 1, however, were poorly tolerated in mice, similar to other pyrazolo[1,5-a]pyrimidine compounds at or near the efficacious dose, and indicative of the narrow therapeutic windows seen with this scaffold.

Differential kinase activity of ACVR1 G328V and R206H mutations with implications to possible TßRI cross-talk in diffuse intrinsic pontine glioma.

Diffuse intrinsic pontine glioma (DIPG) is a lethal pediatric brain cancer whose median survival time is under one year. The possible roles of the two most common DIPG associated cytoplasmic ACVR1 receptor kinase domain mutants, G328V and R206H, are reexamined in the context of new biochemical results regarding their intrinsic relative ATPase activities. At 37 °C, the G328V mutant displays a 1.8-fold increase in intrinsic kinase activity over wild-type, whereas the R206H mutant shows similar activity. The higher G328V mutant intrinsic kinase activity is consistent with the statistically significant longer overall survival times of DIPG patients harboring ACVR1 G328V tumors. Based on the potential cross-talk between ACVR1 and TßRI pathways and known and predicted off-targets of ACVR1 inhibitors, we further validated the inhibition effects of several TßRI inhibitors on ACVR1 wild-type and G328V mutant patient tumor derived DIPG cell lines at 20-50 µM doses. SU-DIPG-IV cells harboring the histone H3.1K27M and activating ACVR1 G328V mutations appeared to be less susceptible to TßRI inhibition than SF8628 cells harboring the H3.3K27M mutation and wild-type ACVR1. Thus, inhibition of hidden oncogenic signaling pathways in DIPG such as TßRI that are not limited to ACVR1 itself may provide alternative entry points for DIPG therapeutics.

On the possible origin of protein homochirality, structure, and biochemical function.

Living systems have chiral molecules, e.g., native proteins that almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life's origin. The origin of homochirality and its implications are explored in computer simulations examining the stability and structural and functional properties of an artificial library of compact proteins containing 1:1 (termed demi-chiral), 3:1, and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures and fewer internal hydrogen bonds and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0-Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) 4-digit (3-digit) enzyme classification (E.C.) numbers, native active site amino acids were generated at random for 472 of 550 cases. This increases to 548 of 550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.

DESTINI: A deep-learning approach to contact-driven protein structure prediction.

The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DESTINI (deep structural inference for proteins), a novel computational approach that combines a deep-learning algorithm for protein residue/residue contact prediction with template-based structural modelling. For the first time, the significantly improved predictive ability is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain proteins. DESTINI successfully predicts the tertiary structure of four times the number of "hard" targets (those with poor quality templates) that were previously intractable, viz, a "glass-ceiling" for previous template-based approaches, and also improves model quality for "easy" targets (those with good quality templates). The significantly better performance by DESTINI is largely due to the incorporation of better contact prediction into template modelling. To understand why deep-learning accomplishes more accurate contact prediction, systematic clustering reveals that deep-learning predicts coherent, native-like contact patterns compared to co-evolutionary analysis. Taken together, this work presents a promising strategy towards solving the protein structure prediction problem.

The crystal structure of a tetrahydrofolate-bound dihydrofolate reductase reveals the origin of slow product release.

Dihydrofolate reductase (DHFR) catalyzes the stereospecific reduction of 7,8-dihydrofolate (FH2) to (6s)-5,6,7,8-tetrahydrofolate (FH4) via hydride transfer from NADPH. The consensus Escherichia coli DHFR mechanism involves conformational changes between closed and occluded states occurring during the rate-limiting product release step. Although the Protein Data Bank (PDB) contains over 250 DHFR structures, the FH4 complex structure responsible for rate-limiting product release is unknown. We report to our knowledge the first crystal structure of an E. coli. DHFR:FH4 complex at 1.03 Å resolution showing distinct stabilizing interactions absent in FH2 or related (6R)-5,10-dideaza-FH4 complexes. We discover the time course of decay of the co-purified endogenous FH4 during crystal growth, with conversion from FH4 to FH2 occurring in 2-3 days. We also determine another occluded complex structure of E. coli DHFR with a slow-onset nanomolar inhibitor that contrasts with the methotrexate complex, suggesting a plausible strategy for designing DHFR antibiotics by targeting FH4 product conformations.