Pre-eclampsia: Re-visiting pathophysiology, role of immune cells, biomarker identification and recent advances in its management.

Pre-eclampsia (PE) is a hypertension condition that occurs exclusively during pregnancy and has the potential to impact nearly all organ systems. It is estimated to complicate approximately 2-8% of pregnancies worldwide. PE is a prominent medical disorder that poses a significant risk to pregnant mothers and their infants. This review commences by giving the most up-to- date concepts about the pathophysiology of PE. The condition involves atypical infiltration of trophoblast cells into the spiral arteries of the decidua and myometrium, resulting in an insufficient establishment of proper blood flow between the uterus and placenta. The aberrant activation of natural killer (NK) cells in both the peripheral blood and the decidua has been identified as one of the contributing factors to the development of PE. The strong evidence for the genetic etiology of PE is provided by the association between maternal killer cell immunoglobulin-like receptor (KIR) and Human Leukocyte Antigen (HLA-C) in trophoblast cells. Recent observations provide evidence that changes in the expression of anti-angiogenic factors in the placenta are the underlying cause of the clinical symptoms associated with the condition. This review also provides a comprehensive overview of the latest advancements in understanding the underlying causes of PE. It specifically highlights the emergence of new diagnostic biomarkers and their potential implications for therapeutic interventions in managing this medical condition.

A π-π Bonding-Assisted Molecular-Wiring of Folded-Cytochrome c and Naphthoquinone and Its Electron-Relay-Based Bioelectrocatalytic H2O2 Reduction Reaction Visualized by Redox-Competitive Scanning Electrochemical Microscopy.

The electron-transfer (ET) reaction of cytochrome c (Cytc) protein with biomolecules is a cutting-edge research area of interest in understanding the functionalities of natural systems. Several electrochemical biomimicking studies based on Cytc-protein-modified electrodes prepared via electrostatic interaction and covalent bonding approaches have been reported. Indeed, natural enzymes involve multiple types of bonding, such as hydrogen, ionic, covalent, and π-π, etc. In this work, we explore a Cytc-protein chemically modified glassy carbon electrode (GCE/CB@NQ/Cytc) prepared via π-π bonding using graphitic carbon as an underlying surface and an aromatic organic molecule, naphthoquinone (NQ), as a cofactor for an effective ET reaction. A simple drop-casting technique-based preparation of GCE/CB@NQ showed a distinct surface-confined redox peak at a standard electrode potential (E°) = -0.2 V vs Ag/AgCl (surface excess = 21.3 nmol cm-2) in pH 7 phosphate buffer solution. A control experiment of modification of NQ on an unmodified GCE failed to show any such unique feature. For the preparation of GCE/CB@NQ/Cytc, a dilute solution of Cytc-pH 7 phosphate buffer was drop-cast on the GCE/CB@NQ surface, wherein the protein folding and denaturalization-based complication and its associated ET functionalities were avoided. Molecular dynamics simulation studies show the complexation of NQ with Cytc at the protein binding sites. The protein-bound surface shows an efficient and selective bioelectrocatalytic reduction performance of H2O2, as demonstrated using cyclic voltammetry and amperometric i-t techniques. Finally, the redox-competition scanning electrochemical microscopy (RC-SECM) technique was adopted for in situ visualization of the electroactive adsorbed surface. The RC-SECM images clearly show the regions of highly bioelectrocatalytic active sites of Cytc-proteins bound to NQ molecules on a graphitic carbon surface. The binding of Cytc with NQ has significant implications for studying the biological electron transport mechanism, and the proposed method provides the requisite framework for such a study.

Next generation sequencing exome data analysis aids in the discovery of SNP and INDEL patterns in Parkinson's disease.

Whole exome sequencing is an adept method to reveal novel and disease-related SNPs and INDELs as it screen the actionable areas of the genome. We evaluated the exome sequenced datasets of patients with Parkinson's disease (PD) in South African ethnic origin. The primary focus of this study was to discover the SNPs and INDELs patterns responsible for PD. The variant discovery was performed with genome analysis tool kit best practices variant detection pipelines. The SNPs were linked to the genes and categorized based on the filter-based annotation from ANNOVAR. We identified a total of 7955 SNPs and 9952 INDELs in all seven datasets together. A total of 130 missense nsSNPs were prioritized based on its damaging effect predicted from SIFT and Polyphen2 annotation. We noticed a novel nsSNP rs111655870 in gene LRRK2 that shows the mutation of a Leucine to Phenylalanine at position 208 which can alter the protein function. The study also filtered seven nsSNPs in genes NAGA, SULT4A1, MYH8, FLNA, TPM3, ATP13A1, CLN8 that have potentially deleterious effects predicted by various computational tools. This analysis suggested that the above filtered nsSNPs and INDELs have a functional impact and provide the footing for genetic studies related to PD. Further screening of these variations provides deeper insight for molecular mechanism of disease progression.

Exploring genetic targets of psoriasis using genome wide association studies (GWAS) for drug repurposing.

Psoriasis is a chronic inflammatory disease causing itching in the body and pain in the joints. Currently, no permanent cure is available at a commercial level for this disease. Genome wide association studies (GWAS) provide a deeper insight that helps in better understanding this disease and further possible cure of this disease. The major goal of the present study is to identify potent genetic targets of psoriasis disease using GWAS approach and identify drugs for repurposing. The methods used include GWAS catalogue, GeneAnalytics, canSAR protein annotation tool, VarElect, Drug bank, Proteomics database, ProTox software. By exploring GWAS catalogue, 126 psoriasis associated genes (PAG) were identified. 68 genes found to be druggable were obtained from canSAR protein annotation tool. Localization results depict that maximum genes are present in cytoplasmic cellular components. The superpathways obtained from GeneAnalytics resulted in involvement of these genes in the immune system, Jak/Stat pathway, Th17 and Wnt pathways. Two genes Interleukin 13 (IL13) and POLI are Food and Drug Administration (FDA) approved targets. Small compounds for these targets were analysed for drug-likeliness, toxicity and mutagenecity properties. The FDA approved drug pandel was found to possess desirable properties. The medications used for psoriasis causes mild to severe side effects and does not work well always. Hence we propose drug repurposing strategy to use existing drugs for new therapies. Therefore, the drug pandel could be explored further and repurposed to treat psoriasis.

Small protein-protein interfaces rich in electrostatic are often linked to regulatory function.

Protein-protein interaction (PPI) is critical for several biological functions in living cells through the formation of an interface. Therefore, it is of interest to characterize protein-protein interfaces using an updated non-redundant structural dataset of 2557 homo (identical subunits) and 393 hetero (different subunits) dimer protein complexes determined by X-ray crystallography. We analyzed the interfaces using van der Waals (vdW), hydrogen bonding and electrostatic energies. Results show that on average homo and hetero interfaces are similar. Hence, we further grouped the 2950 interfaces based on percentage vdW to total energies into dominant (≥60%) and sub-dominant (<60%) vdW interfaces. Majority (92%) of interfaces have dominant vdW energy with large interface size (146 ± 87 (homo) and 137 ± 76 (hetero) residues) and interface area (1622 ± 1135 Å2 (homo) and 1579 ± 1060 Å2 (hetero)). However, a proportion (8%) of interfaces have sub-dominant vdW energy with small interface size (85 ± 46 (homo) and 88 ± 36 (hetero) residues) and interface area (823 ± 538 Å2 (homo) and 881 ± 377 Å2 (hetero)). It is found that large interfaces have two-fold more interface area and interface size than small interfaces with increasing hydrogen bonding energy to interface size. However, small interfaces have three-fold more electrostatics energy than large interfaces with increasing electrostatics to interface size. Thus, 8% of complexes having small interfaces with limited interface area and sub-dominant vdW energy are rich in electrostatics. It is interesting to observe that complexes having small interfaces are often associated with regulatory function. Hence, the observed structural features with known molecular function provide insights for the better understanding of PPI.Communicated by Ramaswamy H. Sarma.

Protein-protein interfaces are vdW dominant with selective H-bonds and (or) electrostatics towards broad functional specificity.

Several catalysis, cellular regulation, immune function, cell wall assembly, transport, signaling and inhibition occur through Protein- Protein Interactions (PPI). This is possible with the formation of specific yet stable protein-protein interfaces. Therefore, it is of interest to understand its molecular principles using structural data in relation to known function. Several interface features have been documented using known X-ray structures of protein complexes since 1975. This has improved our understanding of the interface using structural features such as interface area, binding energy, hydrophobicity, relative hydrophobicity, salt bridges and hydrogen bonds. The strength of binding between two proteins is dependent on interface size (number of residues at the interface) and thus its corresponding interface area. It is known that large interfaces have high binding energy (sum of (van der Waals) vdW, H-bonds, electrostatics). However, the selective role played by each of these energy components and more especially that of vdW is not explicitly known. Therefore, it is important to document their individual role in known protein-protein structural complexes. It is of interest to relate interface size with vdW, H-bonds and electrostatic interactions at the interfaces of protein structural complexes with known function using statistical and multiple linear regression analysis methods to identify the prominent force. We used the manually curated non-redundant dataset of 278 hetero-dimeric protein structural complexes grouped using known functions by Sowmya et al. (2015) to gain additional insight to this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck (Anshul and Sowdhamini, 2015). This dataset consists of obligatory (enzymes, regulator, biological assembly), immune and nonobligatory (enzyme and regulator inhibitors) complexes. Results show that the total binding energy is more for large interfaces. However, this is not true for its individual energy factors. Analysis shows that vdW energies contribute to about 75% ± 11% on average among all complexes and it also increases with interface size (r2 ranging from 0.67 to 0.89 with p<0.01) at 95% confidence limit irrespective of molecular function. Thus, vdW is both dominant and proportional at the interface independent of molecular function. Nevertheless, H bond energy contributes to 15% ± 6.5% on average in these complexes. It also moderately increases with interface size (r2 ranging from 0.43 to 0.61 with p<0.01) only among obligatory and immune complexes. Moreover, there is about 11.3% ± 8.7% contribution by electrostatic energy. It increases with interface size specifically among non-obligatory regulator-inhibitors (r2 = 0.44). It is implied that both H-bonds and electrostatics are neither dominant nor proportional at the interface. Nonetheless, their presence cannot be ignored in binding. Therefore, H-bonds and (or) electrostatic energy having specific role for improved stability in complexes is implied. Thus, vdW is common at the interface stabilized further with selective H-bonds and (or) electrostatic interactions at an atomic level in almost all complexes. Comparison of this observation with residue level analysis of the interface is compelling. The role by H-bonds (14.83% ± 6.5% and r2 = 0.61 with p<0.01) among obligatory and electrostatic energy (8.8% ± 4.77% and r2 = 0.63 with p <0.01) among non-obligatory complexes within interfaces (class A) having more non-polar residues than surface is influencing our inference. However, interfaces (class B) having less non-polar residues than surface show 1.5 fold more electrostatic energy on average. The interpretation of the interface using inter-atomic (vdW, H-bonds, electrostatic) interactions combined with inter-residue predominance (class A and class B) in relation to known function is the key to reveal its molecular principles with new challenges.

Comparative QSAR analysis of cyclo-oxygenase2 inhibiting drugs.

Cyclo-oxygenase 2 (COX2) inhibiting drugs were subjected to comparative quantitative structure activity relationship (QSAR) analysis with an attempt to derive and to understand the relationship between the biological activity and molecular descriptors by multiple regression analysis. The different drugs that inhibit cyclo-oxygenase 2 enzyme were compared instead of subjecting one drug and its derivatives to QSAR analysis. The study was conducted to look for the common structural features between the drugs which confer to a good biological activity. Based on the regression analysis the following descriptors were finalized as the components fitting best in the regression equations: Ss, SCBO, RBN, nN, SIC0, IC1, and H-055. These descriptors belong to constitution (Ss, SCBO, RBN, nN), information indices (SIC0, IC1) and atom centered fragments (H-055) category. Based on these descriptors QSAR models were generated and evaluated for best structure-activity correlation. The model generated from constitution and information indices descriptors corresponds to the essential structural features of the drugs and are found to have significant correlation with COX2 inhibiting activity. This study shall help in rational drug design and synthesis of new selective cyclo-oxygenase 2 inhibitors with predetermined affinity and activity.

A HLA-DRB supertype chart with potential overlapping peptide binding function.

HLA-DRB alleles are class II alleles that are associated with CD4+ T-cell immune response. DRB alleles are polymorphic and currently there are about 622 named in the IMGT/HLA sequence database. Each allele binds short peptides with high sensitivity and specificity. However, it has been suggested that majority of HLA alleles can be covered within few HLA supertypes, where different members of a supertype bind similar peptides showing distinct repertoires. Definition of DRB supertypes using binding data is limited to few (about 29) known alleles (< 5% of all known DRB alleles). Hence, we describe a strategy using structurally defined virtual pockets to group all known DRB alleles with regard to their overlapping peptide binding specificity.

Class II HLA-peptide binding prediction using structural principles.

The precise prediction of class II human leukocyte antigen (HLA) peptide binding finds application in epitope design for the development of vaccines and diagnostics of diseases associated with CD4+ T-cellular immunity. HLA II binding peptides have an extended conformation at the binding groove unlike class I. This increases peptide binding combinations of varying length at the groove, having an eventual effect in the host immune response to infectious agents. Here we describe the development of a prediction model using information gleaned from HLA II-peptide (HLA II-p) structural data. We created a manually curated dataset of 15 HLA II-p structural complexes from Protein databank (PDB). The dataset was used to develop virtual binding pockets for accommodating HLA-II-specific short peptides. The binding of peptides to the virtual pockets is estimated using the Q matrix (a quantitative matrix based on amino acid residue properties). Internal cross-validation of the model using the 15 HLA II-p structural complexes produced an accuracy of 53% with a sensitivity of 53%. The model was further evaluated using a dataset of 3676 class II-specific peptides consisting of 1188 binders and 2488 nonbinders derived from MHCBN (a database of HLA binders and nonbinders). The model produced an accuracy of 53% with 70.8% specificity and 27.6% sensitivity. The positive predictive value (PPV) was 62% and the negative predictive value (NPV) 58%. A 62% PPV suggests that the model fairly predicts a good number of binders among predicted binders and thus that the success rate among predicted binder for further verification is good. The described model is simple and rapid, with large HLA allele coverage representing the sampled global population, despite weak prediction accuracy. The ability of the model to predict a wide array of defined class II alleles is found to be applicable for proteome-wide scanning of parasitic genomes.