Comparative Computational Analysis of Spike Protein Structural Stability in SARS-CoV-2 Omicron Subvariants.

The continuous emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with multiple spike (S) protein mutations pose serious threats to current coronavirus disease 2019 (COVID-19) therapies. A comprehensive understanding of the structural stability of SARS-CoV-2 variants is vital for the development of effective therapeutic strategies as it can offer valuable insights into their potential impact on viral infectivity. S protein mediates a virus' attachment to host cells by binding to angiotensin-converting enzyme 2 (ACE2) through its receptor-binding domain (RBD), and mutations in this protein can affect its stability and binding affinity. We analyzed S protein structural stability in various Omicron subvariants computationally. Notably, the S protein sequences analyzed in this work were obtained directly from our own sample collection. We evaluated the binding free energy between S protein and ACE2 in several complex forms. Additionally, we measured distances between the RBD of each chain in S protein to analyze conformational changes. Unlike most of the prior studies, we analyzed full-length S protein-ACE2 complexes instead of only RBD-ACE2 complexes. Omicron subvariants including BA.1, BA.2, BA.2.12.1, BA.4/BA.5, BA.2.75, BA.2.75_K147E, BA.4.6 and BA.4.6_N658S showed enhanced stability compared to wild type, potentially due to distinct S protein mutations. Among them, BA.2.75 and BA.4.6_N658S exhibited the highest and lowest level of stability, respectively.

TWN-RENCOD: A novel method for protein binding site comparison.

Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).

Molecular Dynamics Studies on the Structural Stability Prediction of SARS-CoV-2 Variants Including Multiple Mutants.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease (COVID-19) pandemic worldwide. The spike protein in SARS-CoV-2 fuses with and invades cells in the host respiratory system by binding to angiotensin-converting enzyme 2 (ACE2). The spike protein, however, undergoes continuous mutation from a D614G single mutant to an omicron variant, including multiple mutants. In this study, variants, including multiple mutants (double, triple mutants, B.1.620, delta, alpha, delta_E484Q, mu, and omicron) were investigated in patients. The 3D structure of the full-length spike protein was used in conformational analysis depending on the SARS-CoV-2 variants. The structural stability of the variant types was analyzed based on the distance between the receptor-binding domain (RBD) of each chain in the spike protein and the binding free energy between the spike protein and bound ACE2 in the one-, two-, and three-open-complex forms using molecular dynamics (MD) simulation. Omicron variants, the most prevalent in the recent history of the global pandemic, which consist of 32 mutations, showed higher stability in all open-complex forms compared with that of the wild type and other variants. We suggest that the conformational stability of the spike protein is the one of the important determinants for the differences in viral infectivity among variants, including multiple mutants.

Molecular Dynamics Studies on the Structural Characteristics for the Stability Prediction of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the COVID-19 pandemic in the world. The spike protein of the various proteins encoded in SARS-CoV-2 binds to human ACE2, fuses, and enters human cells in the respiratory system. Spike protein, however, is highly variable, and many variants were identified continuously. In this study, Korean mutants for spike protein (D614G and D614A-C terminal domain, L455F and F456L-RBD, and Q787H-S2 domain) were investigated in patients. Because RBD in spike protein is related to direct interaction with ACE2, almost all researches were focused on the RBD region or ACE2-free whole domain region. The 3D structure for spike protein complexed with ACE2 was recently released. The stability analysis through RBD distance among each spike protein chain and the binding free energy calculation between spike protein and ACE2 were performed using MD simulation depending on mutant types in 1-, 2-, and 3-open-complex forms. D614G mutant of CT2 domain, showing to be the most prevalent in the global pandemic, showed higher stability in all open-complex forms than the wild type and other mutants. We hope this study will provide an insight into the importance of conformational fluctuation in the whole domain, although RBD is involved in the direct interaction with ACE2.

Small Molecule Inhibitors of DYRK1A Identified by Computational and Experimental Approaches.

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a protein kinase with diverse functions in cell regulation. Abnormal expression and activity of DYRK1A contribute to numerous human malignancies, Down syndrome, and Alzheimer's disease. Notably, DYRK1A has been proposed as a potential therapeutic target for the treatment of diabetes because of its key role in pancreatic ß-cell proliferation. Consequently, DYRK1A is an attractive drug target for a variety of diseases. Here, we report the identification of several DYRK1A inhibitors using our in-house topological water network-based approach. All inhibitors were further verified by in vitro assay.

The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.

Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure-activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.

Aggregation Mechanism of Alzheimer's Amyloid ß-Peptide Mediated by α-Strand/α-Sheet Structure.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and a widespread form of dementia. Aggregated forms of the amyloid ß-peptide (Aß) are identified as a toxic species responsible for neuronal damage in AD. Extensive research has been conducted to reveal the aggregation mechanism of Aß. However, the structure of pathological aggregates and the mechanism of aggregation are not well understood. Recently, experimental studies have confirmed that the α-sheet structure in Aß drives aggregation and toxicity in AD. However, how the α-sheet structure is formed in Aß and how it contributes to Aß aggregation remains elusive. In the present study, molecular dynamics simulations suggest that Aß adopts the α-strand conformation by peptide-plane flipping. Multiple α-strands interact through hydrogen bonding to form α-sheets. This structure acts as a nucleus that initiates and promotes aggregation and fibrillation of Aß. Our findings are supported by previous experimental as well as theoretical studies. This study provides valuable structural insights for the design of anti-AD drugs exploiting the α-strand/α-sheet structure.

Topological Water Network Analysis Around Amino Acids.

Water molecules play a key role in protein stability, folding, function and ligand binding. Protein hydration has been studied using free energy perturbation algorithms. However, the study of protein hydration without free energy calculation is also an active field of research. Accordingly, topological water network (TWN) analysis has been carried out instead of free energy calculation in the present work to investigate hydration of proteins. Water networks around 20 amino acids in the aqueous solution were explored through molecular dynamics (MD) simulations. These simulation results were compared with experimental observations. Water molecules from the protein data bank structures showed TWN patterns similar to MD simulations. This work revealed that TWNs are effected by the surrounding environment. TWNs could provide valuable clues about the environment around amino acid residues in the proteins. The findings from this study could be exploited for TWN-based drug discovery and development.

Computational insights into the role of α-strand/sheet in aggregation of α-synuclein.

The α-synuclein is a major component of amyloid fibrils found in Lewy bodies, the characteristic intracellular proteinaceous deposits which are pathological hallmarks of neurodegenerative diseases such as Parkinson's disease (PD) and dementia. It is an intrinsically disordered protein that may undergo dramatic structural changes to form amyloid fibrils. Aggregation process from α-synuclein monomers to amyloid fibrils through oligomeric intermediates is considered as the disease-causative toxic mechanism. However, mechanism underlying aggregation is not well-known despite several attempts. To characterize the mechanism, we have explored the effects of pH and temperature on the structural properties of wild-type and mutant α-synuclein using molecular dynamics (MD) simulation technique. MD studies suggested that amyloid fibrils can grow by monomer. Conformational transformation of the natively unfolded protein into partially folded intermediate could be accountable for aggregation and fibrillation. An intermediate α-strand was observed in the hydrophobic non-amyloid-ß component (NAC) region of α-synuclein that could proceed to α-sheet and initiate early assembly events. Water network around the intermediate was analyzed to determine its influence on the α-strand structure. Findings of this study provide novel insights into possible mechanism of α-synuclein aggregation and promising neuroprotective strategy that could aid alleviate PD and its symptoms.

Structural ensemble-based docking simulation and biophysical studies discovered new inhibitors of Hsp90 N-terminal domain.

Heat shock protein 90 (Hsp90) is one of the most abundant cellular proteins and plays a substantial role in the folding of client proteins. The inhibition of Hsp90 has been regarded as an attractive therapeutic strategy for treating cancer because many oncogenic kinases are Hsp90 client proteins. In this study, we report new inhibitors that directly bind to N-terminal ATP-binding pocket of Hsp90. Optimized structure-based virtual screening predicted candidate molecules, which was followed by confirmation using biophysical and cell-based assays. Among the reported crystal structures, we chose the two structures that show the most favourable early enrichments of true-positives in the receiver operating characteristic curve. Four molecules showed significant changes in the signals of 2D [1H, 15N] correlation NMR spectroscopy. Differential scanning calorimetry analysis supported the results indicating direct binding. Quantified dissociation constant values of the molecules, determined by a series of 2D NMR experiments, lie in the range of 0.1-33 µM. Growth inhibition assay with breast and lung cancer cells confirmed the cellular activities of the molecules. Cheminformatics revealed that the molecules share limited chemical similarities with known inhibitors. Molecular dynamics simulations detailed the putative binding modes of the inhibitors.

Ensemble-Based Virtual Screening Led to the Discovery of New Classes of Potent Tyrosinase Inhibitors.

In this study, we report new classes of potent tyrosinase inhibitors identified by enhanced structure-based virtual screening prediction; the enzyme and melanin content assays were also confirmed. Tyrosinase, a type-3 copper protein, participates in two distinct reactions, hydroxylation of tyrosine to DOPA and conversion of DOPA to dopaquinone, in melanin biosynthesis. Although numerous inhibitors of this reaction have been reported, there is a lag in the discovery of the new functional moieties. In order to improve the performance of virtual screening, we first produced an ensemble of 10,000 structures using molecular dynamics simulation. Quantum mechanical calculation was used to determine the partial charges of catalytic copper ions based on the met and deoxy states. Second, we selected a structure showing an optimal receiver operating characteristic (ROC) curve with known direct binders and their physicochemically matched decoys. The structure revealed more than 10-fold higher enrichment at 1% of the ROC curve than those observed in X-ray structures. Third, high-throughput virtual screening with DOCK 3.6 was performed using a library consisting of approximately 400,000 small molecules derived from the ZINC database. Fourth, we obtained the top 60 molecules and tested their inhibition of mushroom tyrosinase. The extended assays included 21 analogs of the 21 initial hits to test their inhibition properties. Here, the moieties of tetrazole and triazole were identified as new binding cores interacting with the dicopper catalytic center. All 42 inhibitors showed inhibitory constant, Ki, values ranging from 11.1 nM and 33.4 µM, with a tetrazole compound exhibiting the strongest activity. Among the 42 molecules, five displayed more than 30% reduction in melanin production when treated in B16F10 melanoma cells; cell viability was >90% at 20 µM. Particularly, a thiosemicarbazone-containing compound reduced melanin content by 55%.

Comparison of selective uterine artery double ligation at the isthmic level of uterus and bipolar uterine artery coagulation in total laparoscopic hysterectomy.

In this article, we would like to compare the clinical characteristics and effectiveness of selective uterine artery double ligation and bipolar uterine artery coagulation in total laparoscopic hysterectomy (TLH) retrospectively. TLH was performed on 72 patients with selective uterine artery double ligation and on 312 patients with uterine artery bipolar coagulation in tertiary university hospital from January 2004 through January 2006. Both groups were similar with respect to age, body mass index, parities, rate of menopause and uterine weight. The mean transfusion, postoperative hospital stay and drain insertion in the selective uterine artery double ligation group were significantly lower than those in the bipolar uterine artery coagulation group in TLH, respectively (p < .05). There were no differences in the hemoglobin, hematocrite change, additional operations, operation time, rate of complication between the two kinds of operation (p > .05). In conclusion, selective uterine artery double ligation in TLH showed lower transfusion rate, less hospitalization and less discomfort due to drain than bipolar uterine artery coagulation. Also, it did not take a longer time for operation and thus provides a feasible and secure method to manage uterine vessels at the level of uterine isthmus inside of the broad ligament.