Protein Data Bank Entries Research Articles

Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning.

Protein aggregation (PA) is a critical phenomenon associated with Alzheimer's and Parkinson's disease. Recent studies have suggested that factors like aggregation-prone regions (APRs) and β-strand interactions are crucial in understanding such behavior. While experimental methods have provided valuable insights, there has been a shift towards computational strategies, particularly machine learning, for their efficacy and speed. The challenge, however, lies in effectively incorporating structural information into these models. This study constructs a Graph Convolutional Network (GCN) to predict PA scores with the expanded and refined Protein Data Bank (PDB) and AlphaFold2.0 dataset. We employed AGGRESCAN3D 2.0 to calculate PA propensity and to enhance the dataset, we systematically separated multi polypeptide chains within PDB data into single polypeptide chains, removing redundancy. This effort resulted in a dataset comprising 302 032 unique PDB entries. Subsequently, we compared sequence similarity and obtained 22 774 Homo sapiens data from AlphaFold2.0. Using this expanded and refined dataset, the trained GCN model for PA prediction achieves a remarkable coefficient of determination (R 2) score of 0.9849 and a low mean absolute error (MAE) of 0.0381. Furthermore, the efficacy of the active learning process was demonstrated through its rapid identification of proteins with high PA propensity. Consequently, the active learning approach achieved an MAE of 0.0291 in expected improvement, surpassing other methods. It identified 99% of the target proteins by exploring merely 29% of the entire search space. This improved GCN model demonstrates promise in selecting proteins susceptible to PA, advancing protein science. This work contributes to the development of efficient computational tools for PA prediction, with potential applications in disease diagnosis and therapy.

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin's Regulation on Actin Filaments.

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experiments suggest that cofilin forms functionally distinct oligomers via thiol post-translational modifications (PTMs) that promote actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Utilizing a biasing scheme in simulations, we focus on analyzing dimer conformations conducive to disulfide bond formation. Additionally, we explore potential PTMs arising from the examined conformational ensemble. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. We also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol PTM of cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

Universal parameters of bulk-solvent masks.

The bulk solvent is a major component of biomacromolecular crystals that contributes significantly to the observed diffraction intensities. Accurate modelling of the bulk solvent has been recognized as important for many crystallographic calculations. Owing to its simplicity and modelling power, the flat (mask-based) bulk-solvent model is used by most modern crystallographic software packages to account for disordered solvent. In this model, the bulk-solvent contribution is defined by a binary mask and a scale (scattering) function. The mask is calculated on a regular grid using the atomic model coordinates and their chemical types. The grid step and two radii, solvent and shrinkage, are the three parameters that govern the mask calculation. They are highly correlated and their choice is a compromise between the computer time needed to calculate the mask and the accuracy of the mask. It is demonstrated here that this choice can be optimized using a unique value of 0.6 Å for the grid step irrespective of the data resolution, and the radii values adjusted correspondingly. The improved values were tested on a large sample of Protein Data Bank entries derived from X-ray diffraction data and are now used in the computational crystallography toolbox (CCTBX) and in Phenix as the default choice.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold’s high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Computer-Assisted Design of Benzoisoxazol derivatives Inhibitors of Bromodomain-containing Protein 4 (BRD4) with Favorable Pharmacokinetic Profile

We performed a relation computed-aided design based on the structure of benzo[d]isoxazol derivatives inhibitors (BDIO) derivatives, new potent inhibitors of the BRD4 protein. By using in-situ modifications of the three dimensional (3D) models of BRD4-BDIOx complex (Protein Data Bank (PDB) entry code: 5Y8Z) were prepared for the training and validation sets compounds of 29 BDIOx with observed inhibitory potencies (). We first built a quantitative structure activity relationship (QSAR) model in the gas phase, linearly correlating the calculated enthalpies of the BRD4-BDIOx complex formation with (; = 0,80) first and then a superior QSAR model was brought forth, correlating computed relative Gibbs’ free energies of complexation and ( = -0.1205 + 6.9374 ; = 0.96) which was then validated by a 3D-QSAR pharmacophore generation model (PH4) ( = 0.996 + 0.0554 ; = 0.95). The structural information of the active conformation of the training set BDIOs from the models guided us in the design of a virtual combinatorial library (VCL) of 99 225 analogs. We then filtered the VCL by applying Lipinski’s rule-of-five, in order to identify new BDIOs drug likely analogs. The pharmacophore (PH4)-based screening retained 106 new and potent BDIOs with predicted inhibitory potencies up to 158 times more active than the most active traing set BDIO1 (). Finally, the predicted pharmacokinetic profiles of the best potent of these new analogs () were compared to current orally administered anticancer drugs. This computational approach, which combines molecular mechanics and the Poisson–Boltzmann (PB) implicit solvation theory, the pharmacophore model, the analysis of BRD4-BDIOs interaction energies, the in-silico screening of VCL compounds, and the inference of ADME properties resulted in a set of new suggested BRD4 inhibitors for the fight against CRPC.

Potential antiviral peptides targeting the SARS-CoV-2 spike protein

BackgroundThe coronavirus disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection became an international pandemic and created a public health crisis. The binding of the viral Spike glycoprotein to the human cell receptor angiotensin-converting enzyme 2 (ACE2) initiates viral infection. The development of efficient treatments to combat coronavirus disease is considered essential.MethodsAn in silico approach was employed to design amino acid peptide inhibitor against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. The designed inhibitor (SARS-CoV-2 PEP 49) consists of amino acids with the α1 helix and the β4 - β5 sheets of ACE2. The PEP-FOLD3 web tool was used to create the 3D structures of the peptide amino acids. Analyzing the interaction between ACE2 and the RBD of the Spike protein for three protein data bank entries (6M0J, 7C8D, and 7A95) indicated that the interacting amino acids were contained inside two regions of ACE2: the α1 helical protease domain (PD) and the β4 - β5 sheets.ResultsMolecular docking analysis of the designed inhibitor demonstrated that SARS-CoV-2 PEP 49 attaches directly to the ACE2 binding site of the Spike protein with a binding affinity greater than the ACE2, implying that the SARS-CoV-2 PEP 49 model may be useful as a potential RBD binding blocker.

The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes.

Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

α-synuclein enfolds tyrosine hydroxylase and dopamine ß-hydroxylase, potentially reducing dopamine and norepinephrine synthesis.

Parkinson's disease (PD) results from degeneration of dopamine and norepinephrine neurons due to α-synuclein aggregates that likely have their origin in the gut. Tyrosine hydroxylase (TH) catalyses the formation of L-DOPA, the rate-limiting step in the biosynthesis of dopamine. A second enzyme, DOPA decarboxylase (DDC), catalyzes the conversion of L-DOPA to dopamine. A third enzyme, dopamine ß-hydroxylase (DBH), catalyzes the conversion of dopamine to norepinephrine. To analyze possible interactions of α-synuclein with TH, DDC and DBH, we performed in silico protein-protein docking. Protein data bank (pdb) entries were searched on the RCSB Protein Data Bank. We identified four structures that allowed us to examine the relationship of α-synuclein with TH, DDC, and DBH: (1) Human micelle-bound alpha-synuclein, (2) solution structure of the regulatory domain of tyrosine hydroxylase (Rattus norvegicus), (3) crystal structure of human aromatic L-amino acid decarboxylase (DOPA decarboxylase) in the apo form and (4) crystal structure of human dopamine ß-hydroxylase at 2.9 angstrom resolution. We used the ClusPro server (https://cluspro.org) for protein-protein docking. The protein structures were visualized with PyMOL v 2.3.4. α-synuclein partially enfolds tyrosine hydroxylase and dopamine ß-hydroxylase, potentially reducing dopamine and norepinephrine synthesis. α-synuclein may dock too far away from DOPA decarboxylase to affect its function directly. Our in silico finding of α-synuclein partly enfolding tyrosine hydroxylase and dopamine ß-hydroxylase suggests that α-synuclein docking inhibition could increase dopamine and norepinephrine biosynthesis, ameliorating PD symptoms. Small molecules that bind to α-synuclein have already been identified. Further studies may lead to new small molecule drugs that block α-synuclein enfolding of tyrosine hydroxylase and dopamine ß-hydroxylase.

A Comprehensive Analysis of Human CYP3A4 Crystal Structures as a Potential Tool for Molecular Docking-Based Site of Metabolism and Enzyme Inhibition Studies

The notable ability of human liver cytochrome P450 3A4 (CYP3A4) to metabolize diverse xenobiotics encourages researchers to explore in-depth the mechanism of enzyme action. Numerous CYP3A4 protein crystal structures have been deposited in protein data bank (PDB) and are majorly used in molecular docking analysis. The quality of the molecular docking results depends on the three-dimensional CYP3A4 protein crystal structures from the PDB. Present review endeavors to provide a brief outline of some technical parameters of CYP3A4 PDB entries as valuable information for molecular docking research. PDB entries between 22 April 2004 and 2 June 2021 were compiled and the active sites were thoroughly observed. The present review identified 76 deposited PDB entries and described basic information that includes CYP3A4 from human genetic, Escherichia coli (E. coli) use for protein expression, crystal structure obtained from X-ray diffraction method, taxonomy ID 9606, Uniprot ID P08684, ligand–protein structure description, co-crystal ligand, protein site deposit and resolution ranges between 1.7[Formula: see text]Å and 2.95[Formula: see text]Å. The observation of protein–ligand interactions showed the various residues on the active site depending on the ligand. The residues Ala305, Ser119, Ala370, Phe304, Phe108, Phe213 and Phe215 have been found to frequently interact with ligands from CYP3A4 PDB. Literature surveys of 17 co-crystal ligands reveal multiple mechanisms that include competitive inhibition, noncompetitive inhibition, mixed-mode inhibition, mechanism-based inhibition, substrate with metabolite, inducer, or combination modes of action. This overview may help researchers choose a trustworthy CYP3A4 protein structure from the PDB database to apply the protein in molecular docking analysis for drug discovery.

Allosteric Regulation of 3CL Protease of SARS-CoV-2 and SARS-CoV Observed in the Crystal Structure Ensemble

The 3C-like protease (3CLpro) of SARS-CoV-2 is a potential therapeutic target for COVID-19. Importantly, it has an abundance of structural information solved as a complex with various drug candidate compounds. Collecting these crystal structures (83 Protein Data Bank (PDB) entries) together with those of the highly homologous 3CLpro of SARS-CoV (101 PDB entries), we constructed the crystal structure ensemble of 3CLpro to analyze the dynamic regulation of its catalytic function. The structural dynamics of the 3CLpro dimer observed in the ensemble were characterized by the motions of four separate loops (the C-loop, E-loop, H-loop, and Linker) and the C-terminal domain III on the rigid core of the chymotrypsin fold. Among the four moving loops, the C-loop (also known as the oxyanion binding loop) causes the order (active)–disorder (collapsed) transition, which is regulated cooperatively by five hydrogen bonds made with the surrounding residues. The C-loop, E-loop, and Linker constitute the major ligand binding sites, which consist of a limited variety of binding residues including the substrate binding subsites. Ligand binding causes a ligand size dependent conformational change to the E-loop and Linker, which further stabilize the C-loop via the hydrogen bond between the C-loop and E-loop. The T285A mutation from SARS-CoV 3CLpro to SARS-CoV-2 3CLpro significantly closes the interface of the domain III dimer and allosterically stabilizes the active conformation of the C-loop via hydrogen bonds with Ser1 and Gly2; thus, SARS-CoV-2 3CLpro seems to have increased activity relative to that of SARS-CoV 3CLpro.

Principal component analysis of alpha-helix deformations in transmembrane proteins.

α-helices are deformable secondary structural components regularly observed in protein folds. The overall flexibility of an α-helix can be resolved into constituent physical deformations such as bending in two orthogonal planes and twisting along the principal axis. We used Principal Component Analysis to identify and quantify the contribution of each of these dominant deformation modes in transmembrane α-helices, extramembrane α-helices, and α-helices in soluble proteins. Using three α-helical samples from Protein Data Bank entries spanning these three cellular contexts, we determined that the relative contributions of these modes towards total deformation are independent of the α-helix's surroundings. This conclusion is supported by the observation that the identities of the top three deformation modes, the scaling behaviours of mode eigenvalues as a function of α-helix length, and the percentage contribution of individual modes on total variance were comparable across all three α-helical samples. These findings highlight that α-helical deformations are independent of cellular location and will prove to be valuable in furthering the development of flexible templates in de novo protein design.

Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein–ligand complexes containing “non-macrocyclic” (“linear”) ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.

PDBrenum: A webserver and program providing Protein Data Bank files renumbered according to their UniProt sequences.

The Protein Data Bank (PDB) was established at Brookhaven National Laboratories in 1971 as an archive for biological macromolecular crystal structures. In mid 2021, the database has almost 180,000 structures solved by X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and other methods. Many proteins have been studied under different conditions, including binding partners such as ligands, nucleic acids, or other proteins; mutations, and post-translational modifications, thus enabling extensive comparative structure-function studies. However, these studies are made more difficult because authors are allowed by the PDB to number the amino acids in each protein sequence in any manner they wish. This results in the same protein being numbered differently in the available PDB entries. For instance, some authors may include N-terminal signal peptides or the N-terminal methionine in the sequence numbering and others may not. In addition to the coordinates, there are many fields that contain structural and functional information regarding specific residues numbered according to the author. Here we provide a webserver and Python3 application that fixes the PDB sequence numbering problem by replacing the author numbering with numbering derived from the corresponding UniProt sequences. We obtain this correspondence from the SIFTS database from PDBe. The server and program can take a list of PDB entries or a list of UniProt identifiers (e.g., "P04637" or "P53_HUMAN") and provide renumbered files in mmCIF format and the legacy PDB format for both asymmetric unit files and biological assembly files provided by PDBe.

2.85 and 2.99 Å resolution structures of 110 kDa nitrite reductase determined by 200 kV cryogenic electron microscopy

Cu-containing nitrite reductases (NiRs) are 110 kDa enzymes that play central roles in denitrification. Although the NiRs have been well studied, with over 100 Protein Data Bank entries, such issues as crystal packing, photoreduction, and lack of high pH cases have impeded structural analysis of their catalytic mechanisms. Here we show the cryogenic electron microscopy (cryo-EM) structures of Achromobacter cycloclastes NiR (AcNiR) at pH 6.2 and 8.1. The optimization of 3D-reconstruction parameters achieved 2.99 and 2.85 Å resolution. Comprehensive comparisons with cryo-EM and 56 AcNiR crystal structures suggested crystallographic artifacts in residues 185–215 and His255′ due to packing and photoreduction, respectively. We used a newly developed map comparison method to detect structural change around the type 2 Cu site. While the theoretical estimation of coordinate errors of cryo-EM structures remains difficult, combined analysis using X-ray and cryo-EM structures will allow deeper insight into the local structural changes of proteins.

Atomic-Resolution 1.3 Å Crystal Structure, Inhibition by Sulfate, and Molecular Dynamics of the Bacterial Enzyme DapE.

We report the atomic-resolution (1.3 Å) X-ray crystal structure of an open conformation of the dapE-encoded N-succinyl-l,l-diaminopimelic acid desuccinylase (DapE, EC 3.5.1.18) from Neisseria meningitidis. This structure [Protein Data Bank (PDB) entry 5UEJ] contains two bound sulfate ions in the active site that mimic the binding of the terminal carboxylates of the N-succinyl-l,l-diaminopimelic acid (l,l-SDAP) substrate. We demonstrated inhibition of DapE by sulfate (IC50 = 13.8 ± 2.8 mM). Comparison with other DapE structures in the PDB demonstrates the flexibility of the interdomain connections of this protein. This high-resolution structure was then utilized as the starting point for targeted molecular dynamics experiments revealing the conformational change from the open form to the closed form that occurs when DapE binds l,l-SDAP and cleaves the amide bond. These simulations demonstrated closure from the open to the closed conformation, the change in RMS throughout the closure, and the independence in the movement of the two DapE subunits. This conformational change occurred in two phases with the catalytic domains moving toward the dimerization domains first, followed by a rotation of catalytic domains relative to the dimerization domains. Although there were no targeting forces, the substrate moved closer to the active site and bound more tightly during the closure event.

Functional and Structural Characterization of the UDP-Glucose Dehydrogenase Involved in Capsular Polysaccharide Biosynthesis from Campylobacter jejuni.

Campylobacter jejuni is a pathogenic organism that can cause campylobacteriosis in children and adults. Most commonly, campylobacter infection is brought on by consumption of raw or undercooked poultry, unsanitary drinking water, or pet feces. Surrounding the C. jejuni bacterium is a coat of sugar molecules known as the capsular polysaccharide (CPS). The capsular polysaccharide can be very diverse among the different strains of C. jejuni, and this diversity is considered important for evading the host immune system. Modifications to the CPS of C. jejuni NCTC 11168 include O-methylation, phosphoramidylation, and amidation of glucuronate with either serinol or ethanolamine. The enzymes responsible for amidation of glucuronate are currently unknown. In this study, Cj1441, an enzyme expressed from the CPS biosynthetic gene cluster in C. jejuni NCTC 11168, was shown to catalyze the oxidation of UDP-α-d-glucose into UDP-α-d-glucuronic acid with NAD+ as the cofactor. No amide products were found in an attempt to determine whether the putative thioester intermediate formed during the oxidation of UDP-glucose by Cj1441 could be captured in the presence of added amines. The three-dimensional crystal structure of Cj1441 was determined in the presence of NAD+ and UDP-glucose bound in the active site of the enzyme (Protein Data Bank entry 7KWS). A more thorough bioinformatic analysis of the CPS gene cluster suggests that the amidation activity is localized to the t-terminal half of Cj1438, a bifunctional enzyme that is currently annotated as a sugar transferase.

BanLec-eGFP Chimera as a Tool for Evaluation of Lectin Binding to High-Mannose Glycans on Microorganisms.

Fluorescently labeled lectins are useful tools for in vivo and in vitro studies of the structure and function of tissues and various pathogens such as viruses, bacteria, and fungi. For the evaluation of high-mannose glycans present on various glycoproteins, a three-dimensional (3D) model of the chimera was designed from the crystal structures of recombinant banana lectin (BanLec, Protein Data Bank entry (PDB): 5EXG) and an enhanced green fluorescent protein (eGFP, PDB 4EUL) by applying molecular modeling and molecular mechanics and expressed in Escherichia coli. BanLec-eGFP, produced as a soluble cytosolic protein of about 42 kDa, revealed β-sheets (41%) as the predominant secondary structures, with the emission peak maximum detected at 509 nm (excitation wavelength 488 nm). More than 65% of the primary structure was confirmed by mass spectrometry. Competitive BanLec-eGFP binding to high mannose glycans of the influenza vaccine (Vaxigrip®) was shown in a fluorescence-linked lectin sorbent assay (FLLSA) with monosaccharides (mannose and glucose) and wild type BanLec and H84T BanLec mutant. BanLec-eGFP exhibited binding to mannose residues on different strains of Salmonella in flow cytometry, with especially pronounced binding to a Salmonella Typhi clinical isolate. BanLec-eGFP can be a useful tool for screening high-mannose glycosylation sites on different microorganisms.

BIN1 rs744373 SNP and COVID-19 mortality.

Apolipoprotein E (APOE) e4 genotype, which increases the risk of Alzheimer’s disease (AD), predicted severe COVID-19 infection in one UK Biobank (UKB) cohort. Single nucleotide polymorphisms (SNPs) in the bridging integrator 1 (BIN1) gene indicate the second highest odds-ratios for sporadic AD, exceeded only by APOE variants. The objective of the present study was to evaluate the effects of BIN1 and the SNP rs744373 on COVID-19-relaetd survival using UKB-derived data. In addition, the interaction and alignment of BIN1 and SARS-Cov-2 were evaluated. For this purpose, the major (non-Alzheimer’s) BIN1 allele was designated as BIN and the SNP rs744373 minor (Alzheimer’s) allele as RS7. To evaluate the interaction and alignment of BIN1 and SARS-Cov-2, Protein Data Bank (pdb) entries were searched on the RCSB Protein Data Bank. The results revealed that the BIN RS7 heterozygote was associated with the lowest mortality rate (11.7%), followed by the BIN BIN homozygote (17.2%). The RS7 RS7 homozygote was associated with the highest mortality rate (28.1%). Logistic regression analysis was also performed using survival or mortality as the dependent variable, and sex, age, genotype, AD and coronary heart disease (CHD) as independent variables. The effects of sex, age and genotype were significant at the 95% level. The male sex and older-aged subjects were more likely to succumb to test-confirmed COVID-19 than females and younger subjects. The effects of AD and CHD were insignificant. Protein molecule alignment analyses suggested that the BIN allele may interfere with the replication of the SARs-Cov2 virus. The findings of the present study demonstrate that the risks for COVID-19 mortality are not simply related to an advanced chronological age or the comorbidities commonly observed in aged subjects, such as CHD and AD, but also with AD genes.

Fluorescent Visualization of Nucleolar G-Quadruplex RNA and Dynamics of Cytoplasm and Intranuclear Viscosity

The nucleolus, the locus of ribosome biogenesis, was found to be the predominant intracellular target of a new fluorescent probe, V-P1. In solution, the probe demonstrated both a selectivity to RNA...

The Interaction between PsbT and the DE Loop of D1 in Photosystem II Stabilizes the Quinone-Iron Electron Acceptor Complex.

The X-ray-derived Photosystem II (PS II) structure from the thermophilic cyanobacterium Thermosynechococcus vulcanus (Protein Data Bank entry 4UB6) indicates Phe239 of the DE loop of the D1 protein forms a hydrophobic interaction with Pro27 and Ile29 at the C-terminus of the 5 kDa PsbT protein found at the monomer-monomer interface of the PS II dimer. To investigate the importance of this interaction, we created the F239A and F239L mutants in Synechocystis sp. PCC 6803 through targeted mutagenesis of the D1:Phe239 residue into either an alanine or a leucine. Under moderate-light conditions, the F239A strain displayed reduced rates of oxygen evolution and impaired rates of fluorescence decay following a single-turnover actinic flash, while the F239L strain behaved like the control; however, under high-light conditions, the F239A and F239L strains grew more slowly than the control. Our results indicate the quinone-iron acceptor complex becomes more accessible to exogenously added electron acceptors in the F239A mutant and a ΔPsbT strain when compared with the control and F239L strains. This led to the hypothesis that the interaction between D1:Phe239 and the PsbT subunit is required to restrict movement of the DE loop of the D1 subunit, and we suggest disruption of this interaction may perturb the binding of bicarbonate to the non-heme iron and contribute to the signal for PS II to undergo repair following photodamage.