ARL15, a GTPase implicated in rheumatoid arthritis, potentially repositions its truncated N-terminus as a function of guanine nucleotide binding.

The ADP ribosylation factor like protein 15 (ARL15) gene encodes for an uncharacterized GTPase associated with rheumatoid arthritis (RA) and other metabolic disorders. Investigation of the structural and functional attributes of ARL15 is important to position the protein as a potential drug target. Using spectroscopy, we demonstrated that ARL15 exhibits properties inherent of GTPases. The Km and Vmax of the enzyme were calculated to be 100 µM and 1.47 µmole/min/µL, respectively. The equilibrium dissociation constant (Kd) of GTP binding with ARL15 was estimated to be about eight-fold higher than that of GDP. Small Angle X-ray Scattering (SAXS) data indicated that in solution, the apo state of monomeric ARL15 adopts a shape characterized by a globe of maximum linear dimension (Dmax) of 6.1 nm, and upon binding to GTP or GDP, the vector distribution profile changes to peak-n-tail shoulder with Dmax extended to 7.6 and 7.7 nm, respectively. Structure restoration using a sequence-based template and experimental SAXS data provided the first visual insight revealing that the folded N-terminal in the unbound state of the protein may toggle open upon binding to guanine nucleotides. The conformational dynamics observed in the N-terminal region offer a scope to develop drugs that target this unique GTPase, potentially providing treatments for a range of metabolic disorders.

An in silico discovery of potential 3CL protease inhibitors of SARS-CoV-2 based upon inactivation of the cysteine 145-Histidine 41 catalytic dyad.

Coronavirus disease 19 (COVID19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, several countries are at risk of the pandemic caused by this virus. In the absence of any vaccine or virus-specific antiviral treatments, the need is to fast track search for potential drug candidates to combat the virus. Though there are known drugs that are being repurposed to fight against the SARS-CoV-2, there is a requirement for the virus-specific drugs at the earliest. One of the main drug targets of SARS-CoV-2 is an essential non-structural protein, 3CL protease, critical for the life cycle of the virus. We have used molecular docking studies to screen a chemically diverse set of small molecules to identify potential drug candidates to target this protein. Of the 22,630 molecules from varied small molecule libraries, based on the binding affinities and physicochemical properties, we finalized 30 molecules to be potential drug candidates. Eight of these molecules bind in a manner allowing for the scope of a nearly orthogonal backside nucleophilic attack on their suitably placed electrophilic carbonyl groups by the thiol group of cysteine residue 145, while remaining inside a 4 Ǻ distance range. It is interesting since carbonyl groups are known to be attacked in a similar fashion by external nucleophiles and can be relevant when considering these molecules as potential mechanism-based irreversible inhibitors of the 3CLPro. Further, ADMET analysis and Molecular dynamics simulations and available bioactive assays led to the identification of three molecules with high potential to be explored as drug candidates/lead molecules to target 3CLPro of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Dengue outbreak and severity prediction: current methods and the future scope.

Dengue virus (DENV) is the causative agent of dengue fever and severe dengue. Every year, millions of people are infected with this virus. There is no vaccine available for this disease. Dengue virus is present in four serologically varying strains, DENV 1, 2, 3, and 4, and each of these serotypes is further classified into various genotypes based on the geographic distribution and genetic variance. Mosquitoes play the role of vectors for this disease. Tropical countries and some temperate parts of the world witness outbreaks of dengue mainly during the monsoon (rainy) seasons. Several algorithms have been developed to predict the occurrence and prognosis of dengue disease. These algorithms are mainly based on epidemiological data, climate factors, and online search patterns in the infected area. Most of these algorithms are based on either machine learning or deep learning techniques. We summarize the different software tools available for predicting the outbreaks of dengue based on the aforementioned factors, briefly outline the methodology used in these algorithms, and provide a comprehensive list of programs available for the same in this article.

Effect of ACGT motif in spatiotemporal regulation of AtAVT6D, which improves tolerance to osmotic stress and nitrogen-starvation.

KEY MESSAGE: Plasma membrane-localized AtAVT6D importing aspartic acid can be targeted to develop plants with enhanced osmotic and nitrogen-starvation tolerance. AtAVT6D promoter can be exploited as a stress-inducible promoter for genetic improvements to raise stress-resilient crops. The AtAVT6 family of amino acid transporters in Arabidopsis thaliana has been predicted to export amino acids like aspartate and glutamate. However, the functional characterization of these amino acid transporters in plants remains unexplored. The present study investigates the expression patterns of AtAVT6 genes in different tissues and under various abiotic stress conditions using quantitative Real-time PCR. The expression analysis demonstrated that the member AtAVT6D was significantly induced in response to phytohormone ABA and stresses like osmotic and drought. The tissue-specific expression analysis showed that AtAVT6D was strongly expressed in the siliques. Taking together these results, we can speculate that AtAVT6D might play a vital role in silique development and abiotic stress tolerance. Further, subcellular localization study showed AtAVT6D was localized to the plasma membrane. The heterologous expression of AtAVT6D in yeast cells conferred significant tolerance to nitrogen-deficient and osmotic stress conditions. The Xenopus oocyte studies revealed that AtAVT6D is involved in the uptake of Aspartic acid. While overexpression of AtAVT6D resulted in smaller siliques in Arabidopsis thaliana. Additionally, transient expression studies were performed with the full-length AtAVT6D promoter and its deletion constructs to study the effect of ACGT-N24-ACGT motifs on the reporter gene expression in response to abiotic stresses and ABA treatment. The fluorometric GUS analyses revealed that the promoter deletion construct-2 (Pro.C2) possessing a single copy of ACGT-N24-ACGT motif directed the strongest GUS expression under all the abiotic conditions tested. These results suggest that Pro.C2 can be used as a stress-inducible promoter to drive a significant transgene expression.

The RNA Secondary Structure Analysis Reveals Potential for Emergence of Pathogenic Flaviviruses.

The Flavivirus genus is divided into four groups: Mosquito-borne flaviviruses, Tick-borne flaviviruses, no-known vector flaviviruses, and Insect specific flaviviruses. Millions of people are affected worldwide every year due to the flaviviral infections. The 5' UTR of the RNA genome plays a critical role in the biology of flaviviruses. To explore any correlation between the topology of the 5' UTR and pathogenesis, a global scale study of the RNA secondary structure of different groups of flaviviruses has been conducted. We found that most of the pathogenic flaviviruses, irrespective of their mode of transmission, tend to form a Y shaped topology in the Stem loop A of the 5' UTR. Some of the current non-pathogenic flaviviruses were also observed to form Y shaped structure. Based on this study, it has been proposed that the flaviviruses having the Y shaped topology in their 5' UTR regions may have the potential to become pathogenic.

LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets.

Hyperparameter tuning, specifically tuning of learning rate, can often be a time-consuming process, especially when dealing with large data sets. A mathematical foundation in the choice of learning rate can minimize tuning efforts. We propose the application of a novel adaptive learning rate paradigm, guided by Lipschitz continuity of the loss functions (LipGene), to the task of Gene Expression Inference using shallow neural networks. We utilize Mean Absolute Error and Quantile loss separately for training. Our adaptive learning rate, which is dynamically computed for each epoch, is based on the principle of Lipschitz constant and requires no tuning. Experimentally, we prove that our proposed approach greatly surpasses conventional choices of learning rates in terms of both speed of convergence and generalizability. Advocating the principle of Parsimonious Computing, our method can reduce compute infrastructure required for training by using smaller networks with a minimal compromise on the prediction error.

Global analysis of RNA-protein interactions in TNF-α induced alternative splicing in metabolic disorders.

In this report, using the database of RNA-binding protein specificities (RBPDB) and our previously published RNA-seq data, we analyzed the interactions between RNA and RNA-binding proteins to decipher the role of alternative splicing in metabolic disorders induced by TNF-α. We identified 13 395 unique RNA-RBP interactions, including 385 unique RNA motifs and 35 RBPs, some of which (including MBNL-1 and 3, ZFP36, ZRANB2, and SNRPA) are transcriptionally regulated by TNF-α. In addition to some previously reported RBPs, such as RBMX and HuR/ELAVL1, we found a few novel RBPs, such as ZRANB2 and SNRPA, to be involved in the regulation of metabolic syndrome-associated genes that contain an enrichment of tetrameric RNA sequences (AUUU). Taken together, this study paves the way for novel RNA-protein interaction-based therapeutics for treating metabolic syndromes.

The RNA secondary structural variation in the cyclization elements of the dengue genome and the possible implications in pathogenicity.

Dengue virus (DENV), the causative agent of dengue fever and severe dengue, exists as four antigenically different serotypes. These serotypes are further classified into genotypes and have varying degrees of pathogenicity. The 5' and 3' ends of the genomic RNA play a critical role in the viral life cycle. A global scale study of the RNA structural variation among the sero- and genotypes was carried out to correlate RNA structure with pathogenicity. We found that the GC rich stem and rigid loop structure of the 5' end of the genomic RNA of DENV 2 differs significantly from the others. The observed variation in base composition and base pairing may confer structural and functional advantage in highly virulent strains. This variation in the structure may influence the ease of cyclization and recruitment of viral RNA polymerase, NS5 RdRp, thereby affecting the pathogenicity of these strains.

Finding small molecules with pan-serotype activity to target Dengue non-structural protein 1.

Dengue virus (DENV) is a mosquito-borne flavivirus which causes Dengue fever and severe Dengue. It exists as four antigenically different serotypes that are further classified into genotypes with varying degrees of pathogenicity. The non-structural protein 1 (NS1) of DENV has an important role in viral replication and its pathogenesis. NS1 is also considered as an important diagnostic marker for Dengue pathogenesis. To the best of our knowledge, there are no attempts to explore small molecule drugs to target the NS1 of all the serotypes. Here, we have taken the DENV 2 NS1 crystal structure as a reference to model the NS1 structure of the other three serotypes. Once the active site of the NS1 is identified, virtual screening of plant flavonoids is carried out against the NS1 of all the four serotypes. The top 200 molecules in the library with high binding affinities are further analysed to find the common ones having comparable affinities to all the four serotypes. The predicted common flavonoids are subjected to ADMET profiling to further select the most potential molecules that can be used to target NS1 of all the four serotypes.

Type II diabetes mellitus and obesity: Common links, existing therapeutics and future developments.

Type II diabetes mellitus (T2DM) and obesity are two common pathophysiological conditions of metabolic syndrome (MetS), a collection of similar metabolic dysfunctions due to sedentary lifestyle and overnutrition. Obesity arises from improper adipogenesis which otherwise has a crucial role in maintaining proper metabolic functions. Downstream events arising from obesity have been linked to T2DM. The nuclear receptor peroxisome proliferator activator gamma (PPAR-γ), responsible for maintaining lipid and glucose homeostasis, is down-regulated under obesity leading to a weakened insulin sensitivity of the human body. In course of our review we will outline details of the down-regulation mechanism, provide an overview of the current clinical therapeutics and their shortcomings. Toxicity studies on the seminal drug troglitazone, belonging to the most effective glitazone anti-diabetic category, is also discussed. This will lead to an overview about structural adaptations on the existing glitazones to alleviate their side effects and toxicity. Finally, we forward a concept of novel therapeutics mimicking the glitazone framework, based on some design concepts and preliminary in silico studies. These could be later developed into dual acting drugs towards alleviating the deleterious effects of obesity on normal glucose metabolism, and address obesity in itself.

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.

Structure modulation of helix 69 from Escherichia coli 23S ribosomal RNA by pseudouridylations.

Helix 69 (H69) is a 19-nt stem-loop region from the large subunit ribosomal RNA. Three pseudouridine (Ψ) modifications clustered in H69 are conserved across phylogeny and known to affect ribosome function. To explore the effects of Ψ on the conformations of Escherichia coli H69 in solution, nuclear magnetic resonance spectroscopy was used to reveal the structural differences between H69 with (ΨΨΨ) and without (UUU) Ψ modifications. Comparison of the two structures shows that H69 ΨΨΨ has the following unique features: (i) the loop region is closed by a Watson-Crick base pair between Ψ1911 and A1919, which is potentially reinforced by interactions involving Ψ1911N1H and (ii) Ψ modifications at loop residues 1915 and 1917 promote base stacking from Ψ1915 to A1918. In contrast, the H69 UUU loop region, which lacks Ψ modifications, is less organized. Structure modulation by Ψ leads to alteration in conformational behavior of the 5' half of the H69 loop region, observed as broadening of C1914 non-exchangeable base proton resonances in the H69 ΨΨΨ nuclear magnetic resonance spectra, and plays an important biological role in establishing the ribosomal intersubunit bridge B2a and mediating translational fidelity.

Molecular determinants of HIV-1 NCp7 chaperone activity in maturation of the HIV-1 dimerization initiation site.

Human immunodeficiency virus genome dimerization is initiated through an RNA-RNA kissing interaction formed via the dimerization initiation site (DIS) loop sequence, which has been proposed to be converted to a more thermodynamically stable linkage by the viral p7 form of the nucleocapsid protein (NC). Here, we systematically probed the role of specific amino acids of NCp7 in its chaperone activity in the DIS conversion using 2-aminopurine (2-AP) fluorescence and nuclear magnetic resonance spectroscopy. Through comparative analysis of NCp7 mutants, the presence of positively charged residues in the N-terminus was found to be essential for both helix destabilization and strand transfer functions. It was also observed that the presence and type of the Zn finger is important for NCp7 chaperone activity, but not the order of the Zn fingers. Swapping single aromatic residues between Zn fingers had a significant effect on NCp7 activity; however, these mutants did not exhibit the same activity as mutants in which the order of the Zn fingers was changed, indicating a functional role for other flanking residues. RNA chaperone activity is further correlated with NCp7 structure and interaction with RNA through comparative analysis of nuclear magnetic resonance spectra of NCp7 variants, and complexes of these proteins with the DIS dimer.

Pseudouridines in rRNA helix 69 play a role in loop stacking interactions.

The (1)H NMR spectra of RNAs representing E. coli 23S rRNA helix 69 with [1,3-(15)N]pseudouridine modification at specific sites reveal unique roles for pseudouridine in stabilizing base-stacking interactions in the hairpin loop region.

AMBER Force Field Parameters for the Naturally Occurring Modified Nucleosides in RNA.

Classical molecular dynamics (MD) simulations are useful for characterizing the structure and dynamics of biological macromolecules, ultimately, resulting in elucidation of biological function. The AMBER force field is widely used and has well-defined bond length, bond angle, partial charge, and van der Waals parameters for all the common amino acids and nucleotides, but it lacks parameters for many of the modifications found in nucleic acids and proteins. Presently there are 107 known naturally occurring modifications that play important roles in RNA stability, folding, and other functions. Modified nucleotides are found in almost all transfer RNAs, ribosomal RNAs of both the small and large subunits, and in many other functional RNAs. We developed force field parameters for the 107 modified nucleotides currently known to be present in RNA. The methodology used for deriving the modified nucleotide parameters is consistent with the methods used to develop the Cornell et al. force field. These parameters will improve the functionality of AMBER so that simulations can now be readily performed on diverse RNAs having post-transcriptional modifications.