Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Abrus precatorius Leaf Extract Stimulates Insulin-mediated Muscle Glucose Uptake: In vitro Studies and Phytochemical Analysis.

Diabetes mellitus, linked with insulin resistance and hyperglycaemia, is a leading cause of mortality. Glucose uptake through glucose transporter type 4, especially in skeletal muscle, is crucial for maintaining euglycaemia and is a key pathway targeted by antidiabetic medication. Abrus precatorius is a medicinal plant with demonstrated antihyperglycaemic activity in animal models, but its mechanisms are unclear.This study evaluated the effect of a 50% ethanolic (v/v) A. precatorius leaf extract on (1) insulin-stimulated glucose uptake and (2) related gene expression in differentiated C2C12 myotubes using rosiglitazone as a positive control, and (3) generated a comprehensive phytochemical profile of A. precatorius leaf extract using liquid chromatography-high resolution mass spectrometry to elucidate its antidiabetic compounds. A. precatorius leaf extract significantly increased insulin-stimulated glucose uptake, and insulin receptor substrate 1 and Akt substrate of 160 kDa gene expression; however, it had no effect on glucose transporter type 4 gene expression. At 250 µg/mL A. precatorius leaf extract, the increase in glucose uptake was significantly higher than 1 µM rosiglitazone. Fifty-five phytochemicals (primarily polyphenols, triterpenoids, saponins, and alkaloids) were putatively identified, including 24 that have not previously been reported from A. precatorius leaves. Abrusin, precatorin I, glycyrrhizin, hemiphloin, isohemiphloin, hispidulin 4'-O-ß-D-glucopyranoside, homoplantaginin, and cirsimaritin were putatively identified as known major compounds previously reported from A. precatorius leaf extract. A. precatorius leaves contain antidiabetic phytochemicals and enhance insulin-stimulated glucose uptake in myotubes via the protein kinase B/phosphoinositide 3-kinase pathway by regulating insulin receptor substrate 1 and Akt substrate of 160 kDa gene expression. Therefore, A. precatorius leaves may improve skeletal muscle insulin sensitivity and hyperglycaemia. Additionally, it is a valuable source of bioactive phytochemicals with potential therapeutic use for diabetes.

In silico design and validation of high-affinity RNA aptamers targeting epithelial cellular adhesion molecule dimers.

Nucleic acid aptamers hold great promise for therapeutic applications due to their favorable intrinsic properties, as well as high-throughput experimental selection techniques. Despite the utility of the systematic evolution of ligands by the exponential enrichment (SELEX) method for aptamer determination, complementary in silico aptamer design is highly sought after to facilitate virtual screening and increased understanding of important nucleic acid-protein interactions. Here, with a combined experimental and theoretical approach, we have developed two optimal epithelial cellular adhesion molecule (EpCAM) aptamers. Our structure-based in silico method first predicts their binding modes and then optimizes them for EpCAM with molecular dynamics simulations, docking, and free energy calculations. Our isothermal titration calorimetry experiments further confirm that the EpCAM aptamers indeed exhibit enhanced affinity over a previously patented nanomolar aptamer, EP23. Moreover, our study suggests that EP23 and the de novo designed aptamers primarily bind to EpCAM dimers (and not monomers, as hypothesized in previous published works), suggesting a paradigm for developing EpCAM-targeted therapies.

Mapping the human gut mycobiome in middle-aged and elderly adults: multiomics insights and implications for host metabolic health.

OBJECTIVE: The human gut fungal community, known as the mycobiome, plays a fundamental role in the gut ecosystem and health. Here we aimed to investigate the determinants and long-term stability of gut mycobiome among middle-aged and elderly adults. We further explored the interplay between gut fungi and bacteria on metabolic health. DESIGN: The present study included 1244 participants from the Guangzhou Nutrition and Health Study. We characterised the long-term stability and determinants of the human gut mycobiome, especially long-term habitual dietary consumption. The comprehensive multiomics analyses were performed to investigate the ecological links between gut bacteria, fungi and faecal metabolome. Finally, we examined whether the interaction between gut bacteria and fungi could modulate the metabolic risk. RESULTS: The gut fungal composition was temporally stable and mainly determined by age, long-term habitual diet and host physiological states. Specifically, compared with middle-aged individuals, Blastobotrys and Agaricomycetes spp were depleted, while Malassezia was enriched in the elderly. Dairy consumption was positively associated with Saccharomyces but inversely associated with Candida. Notably, Saccharomycetales spp interacted with gut bacterial diversity to influence insulin resistance. Bidirectional mediation analyses indicated that bacterial function or faecal histidine might causally mediate an impact of Pichia on blood cholesterol. CONCLUSION: We depict the sociodemographic and dietary determinants of human gut mycobiome in middle-aged and elderly individuals, and further reveal that the gut mycobiome may be closely associated with the host metabolic health through regulating gut bacterial functions and metabolites.

Crystal-structures-guided design of fragment-based drugs for inhibiting the main protease of SARS-CoV-2.

Since the beginning of the COVID-19 pandemic, scientists across the globe are racing to find a cure for the highly contagious infectious disease caused by the SARS-CoV-2 virus. Despite many promising ongoing progress, there are currently no FDA approved drug to treat infected patients. Recently, the crowdsourcing of drug discovery for inhibiting the main protease (Mpro) of SARS-CoV-2 have yielded a plenty of drug fragments resolved inside the active site of Mpro via the crystallography method. Following the principle of fragment-based drug design (FBDD), we are motivated to design a potent drug candidate (named B19) by merging three fragments JFM, U0P, and HWH. Through extensive all-atom molecular dynamics simulation and molecular docking, we found that B19 among all designed ones is most stable inside the Mpro's active site and the binding free energy of B19 is comparable to or even a little better than that of a native protein ligand processed by Mpro. Our promising results suggest that B19 and its derivatives can potentially be efficacious drug candidates for COVID-19.

Apolipoprotein E4 Reduction with Antisense Oligonucleotides Decreases Neurodegeneration in a Tauopathy Model.

OBJECTIVE: Apolipoprotein E (ApoE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease, with the ε4 allele increasing risk in a dose-dependent fashion. In addition to ApoE4 playing a crucial role in amyloid-ß deposition, recent evidence suggests that it also plays an important role in tau pathology and tau-mediated neurodegeneration. It is not known, however, whether therapeutic reduction of ApoE4 would exert protective effects on tau-mediated neurodegeneration. METHODS: Herein, we used antisense oligonucleotides (ASOs) against human APOE to reduce ApoE4 levels in the P301S/ApoE4 mouse model of tauopathy. We treated P301S/ApoE4 mice with ApoE or control ASOs via intracerebroventricular injection at 6 and 7.5 months of age and performed brain pathological assessments at 9 months of age. RESULTS: Our results indicate that treatment with ApoE ASOs reduced ApoE4 protein levels by ~50%, significantly protected against tau pathology and associated neurodegeneration, decreased neuroinflammation, and preserved synaptic density. These data were also corroborated by a significant reduction in levels of neurofilament light chain (NfL) protein in plasma of ASO-treated mice. INTERPRETATION: We conclude that reducing ApoE4 levels should be explored further as a therapeutic approach for APOE4 carriers with tauopathy including Alzheimer's disease. ANN NEUROL 2021;89:952-966.

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model.

Recent advances in deep learning have enabled the development of large-scale multimodal models for virtual screening and de novo molecular design. The human kinome with its abundant sequence and inhibitor data presents an attractive opportunity to develop proteochemometric models that exploit the size and internal diversity of this family of targets. Here, we challenge a standard practice in sequence-based affinity prediction models: instead of leveraging the full primary structure of proteins, each target is represented by a sequence of 29 discontiguous residues defining the ATP binding site. In kinase-ligand binding affinity prediction, our results show that the reduced active site sequence representation is not only computationally more efficient but consistently yields significantly higher performance than the full primary structure. This trend persists across different models, data sets, and performance metrics and holds true when predicting pIC50 for both unseen ligands and kinases. Our interpretability analysis reveals a potential explanation for the superiority of the active site models: whereas only mild statistical effects about the extraction of three-dimensional (3D) interaction sites take place in the full sequence models, the active site models are equipped with an implicit but strong inductive bias about the 3D structure stemming from the discontiguity of the active sites. Moreover, in direct comparisons, our models perform similarly or better than previous state-of-the-art approaches in affinity prediction. We then investigate a de novo molecular design task and find that the active site provides benefits in the computational efficiency, but otherwise, both kinase representations yield similar optimized affinities (for both SMILES- and SELFIES-based molecular generators). Our work challenges the assumption that the full primary structure is indispensable for modeling human kinases.

On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction.

Recent work showed that active site rather than full-protein-sequence information improves predictive performance in kinase-ligand binding affinity prediction. To refine the notion of an "active site", we here propose and compare multiple definitions. We report significant evidence that our novel definition is superior to previous definitions and better models of ATP-noncompetitive inhibitors. Moreover, we leverage the discontiguity of the active site sequence to motivate novel protein-sequence augmentation strategies and find that combining them further improves performance.

Structure-Function Analysis of Resistance to Bamlanivimab by SARS-CoV-2 Variants Kappa, Delta, and Lambda.

The newly emerging Kappa, Delta, and Lambda SARS-CoV-2 variants are worrisome, characterized with the double mutations E484Q/L452R, T478K/L452R, and F490S/L452Q, respectively, in their receptor binding domains (RBDs) of the spike proteins. As revealed in crystal structures, most of these residues (e.g., 452 and 484 in RBDs) are not in direct contact with interfacial residues in the angiotensin-converting enzyme 2 (ACE2). This suggests that albeit there are some possibly nonlocal effects, these mutations might not significantly affect RBD's binding with ACE2, which is an important step for viral entry into host cells. Thus, without knowing the molecular mechanism, these successful mutations (from the point of view of SARS-CoV-2) may be hypothesized to evade human antibodies. Using all-atom molecular dynamics (MD) simulation, here, we show that the E484Q/L452R mutations significantly reduce the binding affinity between the RBD of the Kappa variant and the antibody LY-CoV555 (also named as Bamlanivimab), which was efficacious for neutralizing the wild-type SARS-CoV-2. To verify simulation results, we further carried out experiments with both pseudovirions- and live virus-based neutralization assays and demonstrated that LY-CoV555 completely lost neutralizing activity against the L452R/E484Q mutant. Similarly, we show that mutations in the Delta and Lambda variants can also destabilize the RBD's binding with LY-CoV555. With the revealed molecular mechanism on how these variants evade LY-CoV555, we expect that more specific therapeutic antibodies can be accordingly designed and/or a precise mixing of antibodies can be achieved as a cocktail treatment for patients infected with these variants.

Oral Commissure Lift: A Retrospective Analysis of Complication Rates and Overall Outcomes.

BACKGROUND: Downward-turning oral commissures and sagging mouth corners can present an unfavorable impression. We introduced a new oral commissure lift procedure and investigated its effectiveness and complication rates. METHODS: Patients who underwent oral commissure lift in the plastic surgery clinic between January 2010 and December 2017 were enrolled retrospectively. Pre-and postoperative photographs were evaluated to measure oral commissure angles and analyze surgical complications, including visible scarring, unnatural appearance, and asymmetry. Many patients underwent a oral commissure lift with a simultaneous facelift. To exclude potential bias, we compared angular changes between patients receiving both oral commissure lift and facelift, with those receiving only oral commissure lift. Moreover, oral commissure angles of patients only receiving facelift were also measured. Statistical significance was set at p < 0.05. RESULTS: Oral commissure lift was performed in 51 patients. The mean ages and follow-up periods were 46.7 ± 11.9 years, and 25.2 ± 22.9 months, respectively. The preoperative mean angles of the right and left oral commissures measured - 3.1 ± 4.0° and - 3.4 ± 3.7°, respectively, and postoperative mean angles measured 3.6 ± 3.2° and 3.3 ± 3.5°, respectively. Postoperative changes in oral commissure angles were statistically significant (p < 0.05). The low complication rate included undercorrection in one patient, asymmetry in one patient, and visible scarring in three patients. We found no statistically significant differences in the studies excluding bias. CONCLUSIONS: The new oral commissure lift procedure for correcting sagging oral commissures was simple, safe, and effective with a low complication rate. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Targeting Proteases for Treating COVID-19.

The unprecedented pandemic of coronavirus disease 2019 (COVID-19) demands effective treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The infection of SARS-CoV-2 critically depends on diverse viral or host proteases, which mediate viral entry, viral protein maturation, as well as the pathogenesis of the viral infection. Endogenous and exogenous agents targeting for proteases have been proved to be effective toward a variety of viral infections ranging from HIV to influenza virus, suggesting protease inhibitors as a promising antiviral treatment for COVID-19. In this Review, we discuss how host and viral proteases participated in the pathogenesis of COVID-19 as well as the prospects and ongoing clinical trials of protease inhibitors as treatments.

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach.

We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate subnetworks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that data set bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein-ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased data set is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking data sets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.

Structure-based lead optimization of herbal medicine rutin for inhibiting SARS-CoV-2's main protease.

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic with very limited specific treatments. To fight COVID-19, various traditional antiviral medicines have been prescribed in China to infected patients with mild to moderate symptoms and received unexpected success in controlling the disease. However, the molecular mechanisms of how these herbal medicines interact with the SARS-CoV-2 virus that causes COVID-19 have remained elusive. It is well known that the main protease (Mpro) of SARS-CoV-2 plays an important role in maturation of many viral proteins such as the RNA-dependent RNA polymerase. Here, we explore the underlying molecular mechanisms of the computationally determined top candidate, namely, rutin which is a key component in many traditional antiviral medicines such as Lianhuaqinwen and Shuanghuanlian, for inhibiting the viral target-Mpro. Using in silico methods (docking and molecular dynamics simulations), we revealed the dynamics and energetics of rutin when interacting with the Mpro of SARS-CoV-2, suggesting that the highly hydrophilic rutin molecule can be bound inside the Mpro's pocket (active site) and possibly inhibit its biological functions. In addition, we optimized the structure of rutin and designed two more hydrophobic analogs, M1 and M2, which satisfy the rule of five for western medicines and demonstrated that they (M2 in particular) possess much stronger binding affinities to the SARS-COV-2s Mpro than rutin, due to the enhanced hydrophobic interaction as well as more hydrogen bonds. Therefore, our results provide invaluable insights into the mechanism of a ligand's binding inside the Mpro and shed light on future structure-based designs of high-potent inhibitors for SARS-CoV-2 Mpro.

Exploring the Sequence Diversity of Cyclotides from Vietnamese Viola Species.

Viola is the largest genus in the Violaceae plant family and is known for its ubiquitous natural production of cyclotides. Many Viola species are used as medicinal herbs across Asia and are often consumed by humans in teas for the treatment of diseases, including ulcers and asthma. Previous studies reported the isolation of cyclotides from Viola species in many countries in the hope of discovering novel compounds with anti-cancer activities; however, Viola species from Vietnam have not been investigated to date. Here, the discovery of cyclotides from three Viola species (V. arcuata, V. tonkinensis, and V. austrosinensis) collected in the northern mountainous region of Vietnam is reported. Ten cyclotides were isolated from these three Viola species: four are novel and six were previously reported to be expressed in other plants. The structures of three of the new bracelet cyclotides are similar to that of cycloviolacin O2. Because cycloviolacin O2 has previously been shown to have potent activity against a wide range of cancer cell lines including HeLa (human cervical cancer cells) and PC-3 (human prostate cancer cells), the cancer cytotoxicity of the cyclotides isolated from V. arcuata was assessed. All tested cyclotides were cytotoxic against cancer cells, albeit to varying degrees. The sequences discovered in this study significantly expand the understanding of cyclotide diversity, especially in comparison with other cyclotides found in plants from the Asian region.

Apolipoprotein E and Alzheimer's disease: the influence of apolipoprotein E on amyloid-ß and other amyloidogenic proteins.

Alzheimer's disease (AD) is one of the fastest-growing causes of death and disability in persons 65 years of age or older, affecting more than 5 million Americans alone. Clinical manifestations of AD include progressive decline in memory, executive function, language, and other cognitive domains. Research efforts within the last three decades have identified APOE as the most significant genetic risk factor for late-onset AD, which accounts for >99% of cases. The apoE protein is hypothesized to affect AD pathogenesis through a variety of mechanisms, from its effects on the blood-brain barrier, the innate immune system, and synaptic function to the accumulation of amyloid-ß (Aß). Here, we discuss the role of apoE on the biophysical properties and metabolism of the Aß peptide, the principal component of amyloid plaques and cerebral amyloid angiopathy (CAA). CAA is characterized by the deposition of amyloid proteins (including Aß) in the leptomeningeal medium and small arteries, which is found in most AD cases but sometimes occurs as an independent entity. Accumulation of these pathologies in the brain is one of the pathological hallmarks of AD. Beyond Aß, we will extend the discussion to the potential role of apoE on other amyloidogenic proteins found in AD, and also a number of diverse neurodegenerative diseases.

A Critical Role of Ser26 Hydrogen Bonding in Aß42 Assembly and Toxicity.

Amyloid ß-protein (Aß) assembly is a seminal process in Alzheimer's disease. Elucidating the mechanistic features of this process is thought to be vital for the design and targeting of therapeutic agents. Computational studies of the most pathologic form of Aß, the 42-residue Aß42 peptide, have suggested that hydrogen bonding involving Ser26 may be particularly important in organizing a monomer folding nucleus and in subsequent peptide assembly. To study this question, we experimentally determined structure-activity relationships among Aß42 peptides in which Ser26 was replaced with Gly, Ala, α-aminobutryic acid (Abu), or Cys. We observed that aliphatic substitutions (Ala and Abu) produced substantially increased rates of formation of ß-sheet, hydrophobic surface, and fibrils, and higher levels of cellular toxicity. Replacement of the Ser hydroxyl group with a sulfhydryl moiety (Cys) did not have these effects. Instead, this peptide behaved like native Aß42, even though the hydropathy of Cys was similar to that of Abu and very different from that of Ser. We conclude that H bonding of Ser26 is the factor most important in its contribution to Aß42 conformation, assembly, and subsequent toxicity.

Comparative analysis of carotenoid content in Momordica cochinchinensis (Cucurbitaceae) collected from Australia, Thailand and Vietnam.

Momordica cochinchinensis (Cucurbitaceae) is the richest source of lycopene and ß-carotene of all known fruits but the influences of collection sites, variety and environment on carotenoid accumulation is unknown. This study analysed the carotenoid content of 44 M. cochinchinensis aril samples collected from Australia, Thailand and Vietnam using HPLC, UV-visible spectrophotometry and compared with the colorimetry method. The highest lycopene content was observed in samples collected from Ha Noi (7.76 mg/g) of Northern Vietnam and Lam Ha (6.45 mg/g) and Lam Dong (6.64 mg/g) provinces of Central Vietnam. The highest ß-carotene content was observed in a sample from Nam Dinh (9.60 mg/g) in Northern Vietnam while a variety from Hoa Binh province in Northern Vietnam had high contents of both lycopene (5.17 mg/g) and ß-carotene (5.66 mg/g). Lycopene content was higher in samples collected from low temperatures (<14 °C) and higher elevations whilst ß-carotene content was greatest at temperatures between 27 and 33 °C. Crop improvement for increased lycopene and ß-carotene requires rapid and accurate methods of quantification. All three analytical methods utilised were in agreement for lycopene quantification. The (a*/b*)2 transformed colour value resulted in more linear relationship for lycopene indicating that colorimetry method could potentially be developed to select lycopene rich fruits in the field.

Exploring the Membrane Potential of Simple Dual-Membrane Systems as Models for Gap-Junction Channels.

The conductance of ion channels can be modulated by a transmembrane potential difference, due to alterations on ion-mobility and also by changes in the pore structure. Despite the vast knowledge regarding the influence of voltage on transport properties of ion channels, little attention has been paid to describe, with atomic detail, the modulation of ionic transport in gap-junction channels (GJCs). Hence, molecular dynamics simulations were performed to explore the conductance of simple dual-membrane systems that account for the very basic features of GJCs. In doing so, we studied the influence of different charge distributions in the channel surface on these idealized systems under external electric fields, paying attention to the behavior of the electrostatic potential, ion density, ion currents, and equilibrium properties. Our results demonstrate that the incorporation of a charge distribution akin GJCs decreased anionic currents, favoring the transport of cationic species. Moreover, a thermodynamic characterization of ionic transport in these systems demonstrate the existence of a kinetic barrier that hinders anionic currents, reinforcing the role played by the internal arrangement of charges in GJCs. Overall, our results provide insights at the atomic scale on the effects of charge distributions over ionic transport, constituting a step forward into a better understanding of GJCs.

Salts drive controllable multilayered upright assembly of amyloid-like peptides at mica/water interface.

Surface-assisted self-assembly of amyloid-like peptides has received considerable interest in both amyloidosis research and nanotechnology in recent years. Despite extensive studies, some controlling factors, such as salts, are still not well understood, even though it is known that some salts can promote peptide self-assemblies through the so-called "salting-out" effect. However, they are usually noncontrollable, disordered, amorphous aggregates. Here, we show via a combined experimental and theoretical approach that a conserved consensus peptide NH2-VGGAVVAGV-CONH2 (GAV-9) (from representative amyloidogenic proteins) can self-assemble into highly ordered, multilayered nanofilaments, with surprising all-upright conformations, under high-salt concentrations. Our atomic force microscopy images also demonstrate that the vertical stacking of multiple layers is highly controllable by tuning the ionic strength, such as from 0 mM (monolayer) to 100 mM (mainly double layer), and to 250 mM MgCl2 (double, triple, quadruple, and quintuple layers). Our atomistic molecular dynamics simulations then reveal that these individual layers have very different internal nanostructures, with parallel ß-sheets in the first monolayer but antiparallel ß-sheets in the subsequent upper layers due to their different microenvironment. Further studies show that the growth of multilayered, all-upright nanostructures is a common phenomenon for GAV-9 at the mica/water interface, under a variety of salt types and a wide range of salt concentrations.