Recognition Of Binding Sites Research Articles

Molecular dynamics simulations reveal how vinculin refolds partially unfolded talin rod helices to stabilize them against mechanical force.

Vinculin binds to specific sites of mechanically unfolded talin rod domains to reinforce the coupling of the cell's exterior to its force generation machinery. Force-dependent vinculin-talin complexation and dissociation was previously observed as contraction or extension of the unfolded talin domains respectively using magnetic tweezers. However, the structural mechanism underlying vinculin recognition of unfolded vinculin binding sites (VBSs) in talin remains unknown. Using molecular dynamics simulations, we demonstrate that a VBS dynamically refolds under force, and that vinculin can recognize and bind to partially unfolded VBS states. Vinculin binding enables refolding of the mechanically strained VBS and stabilizes its folded α-helical conformation, providing resistance against mechanical stress. Together, these results provide an understanding of a recognition mechanism of proteins unfolded by force and insight into the initial moments of how vinculin binds unfolded talin rod domains during the assembly of this mechanosensing meshwork.

Structures of CTCF-DNA complexes including all 11 zinc fingers.

The CCCTC-binding factor (CTCF) binds tens of thousands of enhancers and promoters on mammalian chromosomes by means of its 11 tandem zinc finger (ZF) DNA-binding domain. In addition to the 12-15-bp CORE sequence, some of the CTCF binding sites contain 5' upstream and/or 3' downstream motifs. Here, we describe two structures for overlapping portions of human CTCF, respectively, including ZF1-ZF7 and ZF3-ZF11 in complex with DNA that incorporatesthe CORE sequence together with either 3' downstream or 5' upstream motifs. Like conventional tandem ZF array proteins, ZF1-ZF7 followthe right-handed twist of the DNA, with each finger occupying and recognizing one triplet of three basepairs in the DNA major groove. ZF8 plays a unique role, acting as a spacer across the DNA minor groove and positioning ZF9-ZF11 to make cross-strand contacts with DNA. We ascribe the difference between the two subgroups of ZF1-ZF7 and ZF8-ZF11 to residues at the two positions -6 and -5 within each finger, with small residues for ZF1-ZF7 and bulkier and polar/charged residues for ZF8-ZF11. ZF8 is also uniquely rich in basic amino acids, which allows salt bridges to DNA phosphates in the minor groove. Highly specific arginine-guanine and glutamine-adenine interactions, used to recognize G:C or A:T base pairs at conventional base-interacting positions of ZFs, also apply to the cross-strand interactions adopted by ZF9-ZF11. The differences between ZF1-ZF7 and ZF8-ZF11 can be rationalized structurally and may contribute to recognition of high-affinity CTCF binding sites.

In silico prioritisation of microRNA-associated common variants in multiple sclerosis

BackgroundGenome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls.MethodsWe identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3′UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3′UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE.ResultsWe have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3′UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites.ConclusionsWe have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3′UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3′UTR target-binding site variation in multiple sclerosis using GWAS summary statistics.

Affinity binding of COVID-19 drug candidates (chloroquine/hydroxychloroquine) and serum albumin: Based on photochemistry and molecular docking.

Chloroquine (CQ) and hydroxychloroquine (HCQ) show good efficacy in the treatment of SARS-CoV-2 in the early stage, while they are no longer recommended due to their side effects. As an important drug delivery carrier, serum albumin (SA) is closely related to the efficacy of drugs. Here, the affinity behaviour of chloroquine and hydroxychloroquine with two SA were investigated through the multispectral method of biochemistry and computer simulation. The results showed that the intrinsic emission of both SA was quenched by CQ and HCQ in a spontaneous exothermic entropy reduction static process, which relied mainly on hydrogen bonding and van der Waals forces. The lower binding constants suggested weak binding between the two drugs and SA, which might lead to differences in efficacy and possibly even to varying side effects. Binding site recognition demonstrated that CQ preferred to bind to the two sites of both SA, while HCQ tended to bind to site I of SA. The results of conformational studies demonstrated that CQ and HCQ could affect the structure of both SA by slightly increasing the α-helix content of SA. Finally, we combine the results from experimental start with molecular simulations to suggest drug modifications to guide the design of drugs. This work has important implications for guiding drug design improvements to select CQ derivatives with fewer side effects for the treatment of COVID-19.

Deep learning for MYC binding site recognition.

Motivation: The definition of the genome distribution of the Myc transcription factor is extremely important since it may help predict its transcriptional activity particularly in the context of cancer. Myc is among the most powerful oncogenes involved in the occurrence and development of more than 80% of different types of pediatric and adult cancers. Myc regulates thousands of genes which can be in part different, depending on the type of tissues and tumours. Myc distribution along the genome has been determined experimentally through chromatin immunoprecipitation This approach, although powerful, is very time consuming and cannot be routinely applied to tumours of individual patients. Thus, it becomes of paramount importance to develop in silico tools that can effectively and rapidly predict its distribution on a given cell genome. New advanced computational tools (DeeperBind) can then be successfully employed to determine the function of Myc in a specific tumour, and may help to devise new directions and approaches to experiments first and personalized and more effective therapeutic treatments for a single patient later on. Results: The use of DeeperBind with DeepRAM on Colab platform (Google) can effectively predict the binding sites for the MYC factor with an accuracy above 0.96 AUC, when trained with multiple cell lines. The analysis of the filters in DeeperBind trained models shows, besides the consensus sequence CACGTG classically associated to the MYC factor, also the other consensus sequences G/C box or TGGGA, respectively bound by the SP1 and MIZ-1 transcription factors, which are known to mediate the MYC repressive response. Overall, our findings suggest a stronger synergy between the machine learning tools as DeeperBind and biological experiments, which may reduce the time consuming experiments by providing a direction to guide them.

Domain function and predicted structure of three heterodimeric endonuclease subunits of RNA editing catalytic complexes in Trypanosoma brucei.

Each of the three similar RNA Editing Catalytic Complexes (RECCs) that perform gRNA-directed uridine insertion and deletion during Trypanosoma brucei mitochondrial (mt) mRNA editing has a distinct endonuclease activity that requires two related RNase III proteins, with only one competent for catalysis. We identified multiple loss-of-function mutations in the RNase III and other motifs of the non-catalytic KREPB6, KREPB7, and KREPB8 components by random mutagenesis and screening. These mutations had various effects on growth, editing, and both the abundances and RECC associations of these RNase III protein pairs in bloodstream form (BF) and procyclic form (PF) cells. Protein structure modelling predicted that the Zinc Finger (ZnF) of each paired RNase III protein contacts RNA positioned at the heterodimeric active site which is flanked by helices of a novel RNase III-Associated Motif (RAM). The results indicate that the protein domains of the non-catalytic subunits function together in RECC integrity, substrate binding, and editing site recognition during the multistep RNA editing process. Additionally, several mutants display distinct functional consequences in different life cycle stages. These results highlight the complementary roles of protein pairs and three RECCs within the complicated T. brucei mRNA editing machinery that matures mt mRNAs differentially between developmental stages.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins.

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

SHREC 2022: Protein–ligand binding site recognition

This paper presents the methods that have participated in the SHREC 2022 contest on protein–ligand binding site recognition. The prediction of protein- ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem).

Cyclic Tetra-Adenylate (cA4) Recognition by Csa3; Implications for an Integrated Class 1 CRISPR-Cas Immune Response in Saccharolobus solfataricus.

Csa3 family transcription factors are ancillary CRISPR-associated proteins composed of N-terminal CARF domains and C-terminal winged helix-turn-helix domains. The activity of Csa3 transcription factors is thought to be controlled by cyclic oligoadenyate (cOA) second messengers produced by type III CRISPR-Cas surveillance complexes. Here we show that Saccharolobus solfataricus Csa3a recognizes cyclic tetra-adenylate (cA4) and that Csa3a lacks self-regulating “ring nuclease” activity present in some other CARF domain proteins. The crystal structure of the Csa3a/cA4 complex was also determined and the structural and thermodynamic basis for cA4 recognition are described, as are conformational changes in Csa3a associated with cA4 binding. We also characterized the effect of cA4 on recognition of putative DNA binding sites. Csa3a binds to putative promoter sequences in a nonspecific, cooperative and cA4-independent manner, suggesting a more complex mode of transcriptional regulation. We conclude the Csa3a/cA4 interaction represents a nexus between the type I and type III CRISPR-Cas systems present in S. solfataricus, and discuss the role of the Csa3/cA4 interaction in coordinating different arms of this integrated class 1 immune system to mount a synergistic, highly orchestrated immune response.

Distinct In Vitro Binding Profile of the Somatostatin Receptor Subtype 2 Antagonist [177Lu]Lu-OPS201 Compared to the Agonist [177Lu]Lu-DOTA-TATE.

Treatment of neuroendocrine tumours with the radiolabelled somatostatin receptor subtype 2 (SST2) peptide agonist [177Lu]Lu-DOTA-TATE is effective and well-established. Recent studies suggest improved therapeutic efficacy using the SST2 peptide antagonist [177Lu]Lu-OPS201. However, little is known about the cellular mechanisms that lead to the observed differences. In the present in vitro study, we compared kinetic binding, saturation binding, competition binding, cellular uptake and release of [177Lu]Lu-OPS201 versus [177Lu]Lu-DOTA-TATE using HEK cells stably transfected with the human SST2. While [177Lu]Lu-OPS201 and [177Lu]Lu-DOTA-TATE exhibited comparable affinity (KD, 0.15 ± 0.003 and 0.08 ± 0.02 nM, respectively), [177Lu]Lu-OPS201 recognized four times more binding sites than [177Lu]Lu-DOTA-TATE. Competition assays demonstrated that a high concentration of the agonist displaced only 30% of [177Lu]Lu-OPS201 bound to HEK-SST2 cell membranes; an indication that the antagonist binds to additional sites that are not recognized by the agonist. [177Lu]Lu-OPS201 showed faster association and slower dissociation than [177Lu]Lu-DOTA-TATE. Whereas most of [177Lu]Lu-OPS201 remained at the cell surface, [177Lu]Lu-DOTA-TATE was almost completely internalised inside the cell. The present data identified distinct differences between [177Lu]Lu-OPS201 and [177Lu]Lu-DOTA-TATE regarding the recognition of receptor binding sites (higher for [177Lu]Lu-OPS201) and their kinetics (faster association and slower dissociation of [177Lu]Lu-OPS201) that explain, to a great extent, the improved therapeutic efficacy of [177Lu]Lu-OPS201 compared to [177Lu]Lu-DOTA-TATE.

Structure-based identification of galectin-1 selective modulators in dietary food polyphenols: a pharmacoinformatics approach

In this study, a set of dietary polyphenols was comprehensively studied for the selective identification of the potential inhibitors/modulators for galectin-1. Galectin-1 is a potent prognostic indicator of tumor progression and a highly regarded therapeutic target for various pathological conditions. This indicator is composed of a highly conserved carbohydrate recognition domain (CRD) that accounts for the binding affinity of β-galactosides. Although some small molecules have been identified as galectin-1 inhibitors/modulators, there are limited studies on the identification of novel compounds against this attractive therapeutic target. The extensive computational techniques include potential drug binding site recognition on galectin-1, binding affinity predictions of ~ 500 polyphenols, molecular docking, and dynamic simulations of galectin-1 with selective dietary polyphenol modulators, followed by the estimation of binding free energy for the identification of dietary polyphenol-based galectin-1 modulators. Initially, a deep neural network-based algorithm was utilized for the prediction of the druggable binding site and binding affinity. Thereafter, the intermolecular interactions of the polyphenol compounds with galectin-1 were critically explored through the extra-precision docking technique. Further, the stability of the interaction was evaluated through the conventional atomistic 100 ns dynamic simulation study. The docking analyses indicated the high interaction affinity of different amino acids at the CRD region of galectin-1 with the proposed five polyphenols. Strong and consistent interaction stability was suggested from the simulation trajectories of the selected dietary polyphenol under the dynamic conditions. Also, the conserved residue (His44, Asn46, Arg48, Val59, Asn61, Trp68, Glu71, and Arg73) associations suggest high affinity and selectivity of polyphenols toward galectin-1 protein.Graphic

Structural basis for glucocorticoid receptor recognition of both unmodified and methylated binding sites, precursors of a modern recognition element

The most common form of DNA methylation involves the addition of a methyl group to a cytosine base in the context of a cytosine–phosphate–guanine (CpG) dinucleotide. Genomes from more primitive organisms are more abundant in CpG sites that, through the process of methylation, deamination and subsequent mutation to thymine–phosphate–guanine (TpG) sites, can produce new transcription factor binding sites. Here, we examined the evolutionary history of the over 36 000 glucocorticoid receptor (GR) consensus binding motifs in the human genome and identified a subset of them in regulatory regions that arose via a deamination and subsequent mutation event. GR can bind to both unmodified and methylated pre-GR binding sequences (GBSs) that contain a CpG site. Our structural analyses show that CpG methylation in a pre-GBS generates a favorable interaction with Arg447 mimicking that made with a TpG in a GBS. This methyl-specific recognition arose 420 million years ago and was conserved during the evolution of GR and likely helps fix the methylation on the relevant cytosines. Our study provides the first genetic, biochemical and structural evidence of high-affinity binding for the likely evolutionary precursor of extant TpG-containing GBS.

Comparative Study of Drug Analysis via Spike Protein Binding Site Recognition and Vaccine Development of Novel Corona Virus

Comparative Study of Drug Analysis via Spike Protein Binding Site Recognition and Vaccine Development of Novel Corona Virus

Discovery of New Protein Targets of BPA Analogs and Derivatives Associated with Noncommunicable Diseases: A Virtual High-Throughput Screening.

Background:Bisphenol A analogs and derivatives (BPs) have emerged as new contaminants with little or no information about their toxicity. These have been found in numerous everyday products, from thermal paper receipts to plastic containers, and measured in human samples.Objectives:The objectives of this research were to identify in silico new protein targets of BPs associated with seven noncommunicable diseases (NCDs), and to study their protein–ligand interactions using computer-aided tools.Methods:Fifty BPs were identified by a literature search and submitted to a virtual high-throughput screening (vHTS) with 328 proteins associated with NCDs. Protein–protein interactions between predicted targets were examined using STRING, and the protocol was validated in terms of binding site recognition and correlation between in silico affinities and in vitro data.Results:According to the vHTS, several BPs may target proteins associated with NCDs, some of them with stronger affinities than bisphenol A (BPA). The best affinity score (the highest in silico affinity absolute value) was obtained after docking 4,4′-bis(N-carbamoyl-4-methylbenzensulfonamide)diphenylmethane (BTUM) on estradiol 17-beta-dehydrogenase 1 (). However, other molecules, such as bisphenol A bis(diphenyl phosphate) (BDP), bisphenol PH (BPPH), and Pergafast 201 also exhibited great affinities (top 10 affinity scores for each disease) with proteins related to NCDs.Discussion:Molecules such as BTUM, BDP, BPPH, and Pergafast 201 could be targeting key signaling pathways related to NCDs. These BPs should be prioritized for in vitro and in vivo toxicity testing and to further assess their possible role in the development of these diseases. https://doi.org/10.1289/EHP7466

Recognition of small molecule-RNA binding sites using RNA sequence and structure.

MotivationRNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule–RNA binding sites.ResultsWe developed a novel method RNAsite to predict small molecule–RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure’s flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.Availability and implementation http://yanglab.nankai.edu.cn/RNAsite.Supplementary information Supplementary data are available at Bioinformatics online.

Characterization of Recombinant Endonuclease IV from Mycobacterium tuberculosis

Mycobacterium tuberculosis cells contain two apurinic/apyrimidinic (AP) endonucleases, endonuclease IV (MtbEnd) and exonuclease III (MtbXthA), the former playing a dominant role in protecting mycobacterial DNA from oxidative stress. Mycobacterial endonuclease IV substantially differs from its homologs found in Escherichia coli and other proteobacteria in a number of conserved positions important for DNA binding and AP site recognition. The M. tuberculosis end gene was cloned, and recombinant MtbEnd purified and characterized. The protein efficiently hydrolyzed DNA at the natural AP site and its 1'-deoxy analog in the presence of divalent cations, of which Ca^(2+), Mn^(2+), and Co^(2+) supported the highest activity. Exonuclease activity was not detected in MtbEnt preparations. The pH optimum was estimated at 7.0-8.0; the ionic strength optimum, at ~50 mM NaCl. Enzymatic activity of MtbEnd was suppressed in the presence of methoxyamine, a chemotherapeutic agent that modifies AP sites. Based on the results, MtbEnd was assumed to provide a possible target for new anti-tuberculosis drugs.

Cellular reprogramming with multigene activation by the delivery of CRISPR/dCas9 ribonucleoproteins via magnetic peptide-imprinted chitosan nanoparticles.

Induced pluripotent stem cells are usually derived by reprogramming transcription factors (OSKM), such as octamer-binding transcription factor 4 (OCT4), (sex determining region Y)-box 2 (SOX2), Krüppel-like factor 4 (KLF4), and cellular proto-oncogene (c-Myc). However, the genomic integration of transcription factors risks the insertion of mutations into the genome of the target cells. Recently, the clustered regularly interspaced short palindromic repeat-associated protein 9 (CRISPR/Cas9) system has been used to edit genomes. In this work, dCas9-VPR (dCas9 with a gene activator, VP64-p65-Rta (VPR), fused to its c-terminus) and guide RNA (gRNA) combined to form ribonucleoproteins, which were immobilized on magnetic peptide-imprinted chitosan nanoparticles. These were then used to activate OSKM genes in human embryonic kidney (HEK) 293T cells. Four pairs of gRNAs were used for the binding site recognition to activate the OSKM genes. Transfected HEK293T cells were then prescreened for the high expression of OSKM proteins by immunohistochemistry images. The optimal gRNAs for OSKM expression were identified using quantitative real-time polymerase chain reaction and the staining of OSKM proteins. Finally, we found that the activated expression of one of the OSKM genes is up to three-fold higher than that of the other genes, enabling precise control of the cell differentiation.

Cooperative Activity of α4β1 and α4β7 Integrins in Mediating Human B-Cell Lymphoma Adhesion and Chemotaxis on Fibronectin Through Recognition of Multiple Synergizing Binding Sites Within the Central Cell-Binding Domain

AbstractWe have quantitated the relative contributions of the constitutively active α4β1 and α4β7 integrins and the domains embodying their cognate binding sites in mediating human B-cell lymphoma adhesion and chemotaxis on fibronectin. By cooperating, the central cell-binding and IIICS carboxy-terminal domains were entirely responsible for the adhesion activity displayed by fibronectin, and their relative contribution to this process was estimated to be 30% versus 70%. Assessment of the leukocyte-substrate binding strength (ie, dynes/cell) indicated a 10-fold higher avidity of the cell-IIICS domain interaction. The two integrins interchangeably recognized both domains, but differed quantitatively in their participation in the adhesive event, as well as in domain preference. The use of 3Fn (according to the nomenclature proposed by Bork and Koonin [Curr Opin Struct Biol 6:366, 1996] for the type III fibronectin modules) module-specific antibodies and recombinant polypeptides showed that α4 integrins recognized both the RGD sequence (3Fn10) and an apparently novel synergistic site located within the 3Fn8 module; even in this case, the integrins displayed a distinct binding site preference. Interleukin-1β (IL-1β)/IL-2–induced chemotaxis also involved cooperative function of the central cell-binding and IIICS domains, but the mechanisms regulating this phenomenon differed markedly from those controlling cell adhesion. First, the relative contribution of the individual domains was comparable, but neither of the individual domains promoted migration to the extent observed on intact fibronectin. Secondly, α4β1 and α4β7 integrins were both involved in the domain-binding necessary for initiation of migration, but the relative contribution of each receptor in the chemotactic process was less disparate than for initial cell adhesion. Thirdly, the mode by which chemotactic B-lymphoma movement was supported by the central cell-binding domain differed from that sustaining cell adhesion in that it involved independent recognition of either the 3Fn8 or the 3Fn9 module, which acted in synergy with the 3Fn10 module. Our data provide novel evidence concerning the relative importance of the constitutively active α4β1 and α4β7 integrins for the interaction of B-cell lymphoma cells with fibronectin, and they emphasize a multiple and diverse recognition of sites responsible for either anchorage or locomotion of tumor leukocytes on this matrix molecule.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection: Triggering a Lethal Fight to Keep Control of the Ten-Eleven Translocase (TET)-Associated DNA Demethylation?

The extended and diverse interference of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in multiple host functions and the diverse associated symptoms implicate its involvement in fundamental cellular regulatory processes. The activity of ten-eleven translocase 2 (TET2) responsible for selective DNA demethylation, has been recently identified as a regulator of endogenous virus inactivation and viral invasion, possibly by proteasomal deregulation of the TET2/TET3 activities. In a recent report, we presented a detailed list of factors that can be affected by TET activity, including recognition of zinc finger protein binding sites and bimodal promoters, by enhancing the flexibility of adjacent sequences. In this review, we summarize the TET-associated processes and factors that could account for SARS-CoV-2 diverse symptoms. Moreover, we provide a correlation for the observed virus-induced symptoms that have been previously associated with TET activities by in vitro and in vitro studies. These include early hypoxia, neuronal regulation, smell and taste development, liver, intestinal, and cardiomyocyte differentiation. Finally, we propose that the high mortality of SARS-CoV-2 among adult patients, the different clinical symptoms of adults compared to children, the higher risk of patients with metabolic deregulation, and the low mortality rates among women can all be accounted for by the complex balance of the three enzymes with TET activity, which is developmentally regulated. This activity is age-dependent, related to telomere homeostasis and integrity, and associated with X chromosome inactivation via (de)regulation of the responsible XIST gene expression.

Recognition of Ion Ligand Binding Sites Based on Amino Acid Features with the Fusion of Energy, Physicochemical and Structural Features.

In this work, we selected the binding residues of 4 acid radical ion ligands (NO2-, CO32-, SO42- and PO43-) and 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+) as research objects. Based on the protein sequence information, we extracted amino acid features, energy, physicochemical, and structure features. Then, we incorporated the above features and input them into the MultilayerPerceptron (MLP) and support vector machine (SVM) algorithms. In the independent test, the best accuracy was higher than 92.5%, which was better than the previous results on the same dataset. In addition, we found that energy information is an important factor affecting the prediction results. Finally, we set up a free web server for the prediction of protein-ion ligand binding sites (http://39.104.77.103:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular drug design.