RNA's Dynamic Conformational Selection and Entropic Allosteric Mechanism in Controlling Cascade Protein Binding Events.

In the TAR RNA of immunodeficiency viruses, an allosteric communication exists between a distant loop and a bulge. The bulge interacts with the TAT protein vital for transactivating viral RNA, while the loop interacts with cyclin-T1, contingent on TAT binding. Through extensive atomistic and free energy simulations, we investigate TAR-TAT binding in nonpathogenic bovine immunodeficiency virus (BIV) and pathogenic human immunodeficiency virus (HIV). Thermodynamic analysis reveals enthalpically driven binding in BIV and entropically favored binding in HIV. The broader global basin in HIV is attributed to binding-induced loop fluctuation, corroborated by nuclear magnetic resonance (NMR), indicating classical entropic allostery onset. While this loop fluctuation affects the TAT binding affinity, it generates a binding-competent conformation that aids subsequent effector (cyclin-T1) binding. This study underscores how two structurally similar apo-RNA scaffolds adopt distinct conformational selection mechanisms to drive enthalpic and entropic allostery, influencing protein affinity in the signaling cascade.

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods.

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.

Hierarchical Hydration Dynamics of RNA with Nano-Water-Pool at Its Core.

Many functional RNAs fold into a compact, roughly globular shape by minimizing the electrostatic repulsion between their negatively charged phosphodiester backbone. The fold of such close, compact RNA architecture is often so designed that its outer surface and complex core both are predominately populated by phosphate groups loosely sequestering bases in the intermediate layers. A number of helical junctions maintain the RNA core and its nano-water-pool. While the folding of RNA is manifested by its counterion environment composed of mixed mono- and divalent salts, the concerted role of ion and water in maintaining an RNA fold is yet to be explored. In this work, detailed atomistic simulations of SAM-I and Add Adenine riboswitch aptamers, and subgenomic flavivirus RNA (sfRNA) have been performed in a physiological mixed mono- and divalent salt environment. All three RNA systems have compact folds with a core diameter of range 1-1.7 nm. The spatiotemporal heterogeneity of RNA hydration was probed in a layer-wise manner by distinguishing the core, the intermediate, and the outer layers. The layer-wise decomposition of hydrogen bonds and collective single-particle reorientational dynamics reveal a nonmonotonic relaxation pattern with the slowest relaxation observed at the intermediate layers that involves functionally important tertiary motifs. The slowness of this intermediate layer is attributed to two types of long-resident water molecules: (i) water from ion-hydration layers and (ii) structurally trapped water (distant from ions). The relaxation kinetics of the core and the surface water essentially exposed to the phosphate groups show well-separated time scales from the intermediate layers. In the slow intermediate layers, site-specific ions and water control the functional dynamics of important RNA motifs like kink-turn, observed in different structure-probing experiments. Most interestingly, we find that as the size of the RNA core increases (SAM1 core < sfRNAcore < Add adenine core), its hydration tends to show faster relaxation. The hierarchical hydration and the layer-wise base-phosphate composition uniquely portray the globular RNA to act like a soft vesicle with a quasi-dynamic nano-water-pool at its core.

Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method.

The emergence of SARS-CoV-2 and its variants that critically affect global public health requires characterization of mutations and their evolutionary pattern from specific Variants of Interest (VOIs) to Variants of Concern (VOCs). Leveraging the concept of equilibrium statistical mechanics, we introduce a new responsive quantity defined as "Mutational Response Function (MRF)" aptly quantifying domain-wise average entropy-fluctuation in the spike glycoprotein sequence of SARS-CoV-2 based on its evolutionary database. As the evolution transits from a specific variant to VOC, we find that the evolutionary crossover is accompanied by a dramatic change in MRF, upholding the characteristic of a dynamic phase transition. With this entropic information, we have developed an ancestral-based machine learning method that helps predict future domain-specific mutations. The feedforward binary classification model pinpoints possible residues prone to future mutations that have implications for enhanced fusogenicity and pathogenicity of the virus. We believe such MRF analyses followed by a statistical mechanics augmented ML approach could help track different evolutionary stages of such species and identify a critical evolutionary transition that is alarming.

Diurnal and Seasonal Variation of Atmospheric Particulate Matter and Trace Gases in Industrial Area of Delhi: A Study.

The diurnal and seasonal variation of PM10, SO2, NO2, NH3 and water-soluble compounds were studied in Naraina industrial area; Delhi from January to December, 2017. It was observed that annual average concentrations of PM10, SO2, NO2, NH3, SO42-, NO3-, NH4+,Na+, K+, Ca2+, Mg2+, Cl- and F- in the day time were 227 ± 91, 9 ± 5, 59 ± 22, 65 ± 15, 17.45 ± 5.14, 17.60 ± 4.94, 8.66 ± 2.94, 4.05 ± 1.08, 3.46 ± 0.91, 10.38 ± 4.48, 3.15 ± 0.99, 43.06 ± 5.20 and 0.50 ± 0.12 µg m-3, respectively and night time were 320 ± 127, 14 ± 7, 82 ± 25, 83 ± 20, 22.64 ± 5.22, 21.66 ± 5.0, 11.81 ± 3.47, 3.29 ± 0.87, 3.02 ± 1.19, 7.55 ± 3.16, 2.49 ± 0.95, 31.86 ± 4.70 and 0.37 ± 0.12 µg m-3, respectively. PM10 and sometimes NO2 concentrations exceeded the Indian National Ambient Air Quality Standards. SO2, and NH3 concentrations were within the standard. The selected parameters varied from season to season. In the night time, selected parameters concentrations were high in comparison to day time might be due to formation of inorganic secondary particulate matters and low wind speed in the ambient air.

Miniature ß-Hairpin Mimetic by Intramolecular Hydrogen Bond and C-H···π Interactions.

Canonically, protein ß-hairpin motifs are stabilized by intramolecular hydrogen bonds. Here, we attempt to develop a rational design recipe for a miniature hairpin structure stabilized by hydrogen bonding as well as C-H···π interaction and try to understand how such a stabilization effect varies with different functional groups at each terminus. Database analysis shows that the α-amino acids with an aromatic side chain will not favor that kind of C-H···π stabilized hairpin structure. However, hybrid tripeptides with an N-terminal Boc-Trp-Aib corner residue and C-terminal aromatic ω-amino acids fold into the hairpin conformation with a central ß-turn/open-turn that is reinforced by a C-H···π interaction. The CCDC database analysis further confirms that this C-H···π stabilized hairpin motif is general for Boc-protected tripeptides containing Aib in the middle and aromatic functionality at the C-terminus. The different α-amino acids like Leu/Ala/Phe/Pro/Ser at the N-terminus have a minor influence on the C-H···π interaction and stabilities of the folded structures in solid-state. However, the hybrid peptides exhibit different degrees of conformational heterogeneity both in the solid and solution phase, which is common for this kind of flexible small molecule. Conformational heterogeneity in the solution phase including the C-H···π stabilized ß-hairpin structures are characterized by the molecular dynamics (MD) simulations explaining their plausible origin at an atomistic level.

Correlation in Domain Fluctuations Navigates Target Search of a Viral Peptide along RNA.

Biological macromolecules often exhibit correlations in fluctuations involving distinct domains. This study decodes their functional implications in RNA-protein recognition and target-specific binding. The target search of a peptide along RNA in a viral TAR-Tat complex is closely monitored using atomistic simulations, steered molecular dynamics simulations, free energy calculations, and a machine-learning-based clustering technique. An anticorrelated domain fluctuation is identified between the tetraloop and the bulge region in the apo form of TAR RNA that sets a hierarchy in the domain-specific fluctuations at each binding event and that directs the succeeding binding footsteps. Thus, at each binding footstep, the dynamic partner selects an RNA location for binding where it senses a higher fluctuation, which is conventionally reduced upon binding. This event stimulates an alternate domain fluctuation, which then dictates sequential binding footstep/s and thus the search progresses. Our cross-correlation maps show that the fluctuations relay from one domain to another specific domain until the anticorrelation between those interdomain fluctuations sustains. Artificial attenuation of that hierarchical domain fluctuation inhibits specific RNA binding. The binding is completed with the arrival of a few long-lived water molecules that mediate slightly distant RNA-protein sites and finally stabilize the overall complex. The study underscores the functional importance of naturally designed fluctuating RNA motifs (bulge, tetraloop) and their interplay in dictating the directionality of the search in a highly dynamic environment.

Raising the Age of Purchase and Use of Tobacco Product in Bangladesh: Benefits and Challenges in Protecting Youth from the Tobacco Epidemic.

BACKGROUND: Tobacco use among young and adolescents is the biggest threat to public health globally. In Bangladesh, every one in 14 youth (13-15 years) uses tobacco in some form. While this problem is growing in the country, we estimate the underage initiation of tobacco use and present evidence that policy measures like increasing the age of purchase and use from the current 18 years to 21 years in the country backed with current tobacco control efforts and adopting vendor licensing will significantly reduce future tobacco burden. METHOD: We analysed the two rounds of nationally representative Global Adult Tobacco Survey (GATS) data: GATS-1 (2009-10) and GATS-2 (2016-17) and segregated the data for two categories of tobacco consumption (smokers and smokeless tobacco users) based on the age of initiation (<18, 18-21 and >21 years). Consumption patterns were also analyaed by using the GATS-2 data. Projections from sub-national level analysis for youth initiating tobacco use before 21 years and change in the prevalence of overall underage tobacco users were calculated based on weighted value. RESULT: According to GATS-2, around 89% of current tobacco users initiated tobacco use into daily use before the age of 18 years in Bangladesh. Whereas, striking differences were observed (statistically significant) for the average age of initiation of smoking among smokers aged 20-34 increased from 17.4 in 2009 to 19.3 years; and 20.1 to 22 years for SLT.  Moreover, more than 24% of them initiated into regular smoking before the age of 15 years. CONCLUSION: There is an increasing trends of tobacco initiation among the underage youth of Bangladesh. By increasing the age of access, sale, purchase, and use of tobacco from current 18 years to 21 years will significantly reduce youth initiation and taper down the overall adult tobacco use prevalence over the long run in Bangladesh.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi.

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

Habitat vulnerability in slum areas of India - What we learnt from COVID-19?

UN-Habitat identified the present COVID-19 pandemic as 'city-centric'. In India, more than 50% of the total cases were documented in megacities and million-plus cities. The slums of cities are the most vulnerable due to its unhygienic environment and high population density that requires an urgent implementation of public healthcare measures. This study aims to examine habitat vulnerability in slum areas to COVID-19 in India using principal component analysis and Fuzzy AHP based technique to develop slum vulnerability index to COVID-19 (SVIcovid-19). Four slum vulnerability groups (i.e. principal components) were retained with eigen-values greater than 1 based on Kaiser criterion - poor slum household status; lack of social distance maintenance; high concentrations of slum population and towns and mobility of the households. This study also mapped composite SVIcovid-19 on the basis of PCA and Fuzzy AHP method at the state level for a better understanding of spatial variations. The result shows that slums located in the eastern and central parts of India (particularly Uttar Pradesh, Bihar, Jharkhand, Odisha, West Bengal) were more vulnerable to COVID-19 transmission due to lack of availability as well as accessibility to the basic services and amenities to slum dwellers. Thus, the findings of the study may not only help to understand the habitat vulnerability in slum areas to COVID-19 but it will also teach a lesson to implement effective policies for enhancing the quality of slum households (HHs) and to reduce the health risk from any infectious disease in future.

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms.

Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may-support landslide prediction and management.

Chelated Magnesium Logic Gate Regulates Riboswitch Pseudoknot Formation.

Magnesium plays a critical role in the structure, dynamics, and function of RNA. The precise microscopic effect of chelated magnesium on RNA structure is yet to be explored. Magnesium is known to act through its diffuse cloud around RNA, through the outer sphere (water-mediated), inner sphere, and often chelated ion-mediated interactions. A mechanism is proposed for the role of experimentally discovered site-specific chelated magnesium ions on the conformational dynamics of SAM-I riboswitch aptamers in bacteria. This mechanism is observed with atomistic simulations performed in a physiological mixed salt environment at a high temperature. The simulations were validated with phosphorothioate interference mapping experiments that help to identify crucial inner-sphere Mg2+ sites prescribing an appropriate initial distribution of inner- and outer-sphere magnesium ions to maintain a physiological ion concentration of monovalent and divalent salts. A concerted role of two chelated magnesium ions is newly discovered since the presence of both supports the formation of the pseudoknot. This constitutes a logical AND gate. The absence of any of these magnesium ions instigates the dissociation of long-range pseudoknot interaction exposing the inner core of the RNA. A base triple is the epicenter of the magnesium chelation effect. It allosterically controls RNA pseudoknot by bolstering the direct effect of magnesium chelation in protecting the functional fold of RNA to control ON and OFF transcription switching.

Regional scenario of air pollution in lockdown due to COVID-19 pandemic: Evidence from major urban agglomerations of India.

Air pollution in India during COVID-19 lockdown, which imposed on 25th March to 31st May 2020, has brought a significant improvement in air quality. The present paper mainly focuses on the scenario of air pollution level (PM2.5, PM10, SO2, NO2 and O3) across 57 urban agglomerations (UAs) of India during lockdown. For analysis, India has been divided into six regions - Northern, Western, Central, Southern, Eastern and North-Eastern. Various spatial statistical modelling with composite air quality index (CAQI) have been utilised to examine the spatial pattern of air pollution level. The result shows that concentration of all air pollutants decreased significantly (except O3) during lockdown. The maximum decrease is the concentration of NO2 (40%) followed by PM2.5 (32%), PM10 (24%) and SO2 (18%). Among 57 UA's, only five - Panipat (1.00), Ghaziabad (0.76), Delhi (0.74), Gurugram (0.72) and Varanasi (0.71) had least improvement in air pollution level considering entire lockdown period. The outcome of this study has an immense scope to understand the regional scenario of air pollution level and to implement effective strategies for environmental sustainability.

Examining the impact of lockdown (due to COVID-19) on ambient aerosols (PM2.5): A study on Indo-Gangetic Plain (IGP) Cities, India.

The outbreak of COVID-19 pandemic has impacted all the aspects of environment. The numbers of COVID-19 cases and deaths are increasing across the globe. In many countries lockdown has been imposed at local, regional as well as national level to combat with this global pandemic that caused the improvement of air quality. In India also lockdown was imposed on 25th March, 2020 and it was further extended in different phases. The lockdown due to outbreak of COVID-19 pandemic has showed substantial reduction of PM2.5 concentrations across the cities of India. The present study aims to assess concentration of PM2.5 across 12 cities located in different spatial segments Indo-Gangetic Plain (IGP). The result showed that there was substantial decrease of PM2.5 concentrations across the cities located in IGP after implementation of lockdown. Before 30 days of lockdown, average PM2.5 across cities was 65.77 µg/m3 that reached to 42.72 µg/m3 during lockdown periods (decreased by 35%). Maximum decrease of PM2.5 concentrations has been documented in Lower Gangetic Plain (LGP) cities (57%) followed by Middle Gangetic Plain (MGP) cities (34%) and Upper Gangetic Plain (UGP) cities (27%) respectively. Among all the cities of IGP, maximum decrease of PM2.5 concentrations was recorded in Kolkata (64%) (LGP) followed by Muzaffarpur (53%) (MGP), Asansol (51%) (LGP), Patna (43%) (MGP) and Varanasi (33%) (MGP).Therefore, this study has an immense potentiality to understand the impact of lockdown on a physical region (Ganga River Basin) and it may be also helpful for planners and policy makers to implement effective measures at regional level to control air pollution.

Dynamic Asymmetry Exposes 2019-nCoV Prefusion Spike.

The novel coronavirus (2019-nCoV) spike protein is a smart molecular machine that instigates the entry of coronavirus to the host cell causing the COVID-19 pandemic. In this study, a symmetry-information-loaded structure-based Hamiltonian is developed using recent Cryo-EM structural data to explore the complete conformational energy landscape of the full-length prefusion spike protein. The study finds the 2019-nCoV prefusion spike to adopt a unique strategy by undertaking a dynamic conformational asymmetry that results in two prevalent asymmetric structures of spike where one or two spike heads rotate up to provide better exposure to the host-cell receptor. A few unique interchain interactions are identified at the interface of closely associated N-terminal domain (NTD) and receptor binding domain (RBD) playing a crucial role in the thermodynamic stabilization of the up conformation of the RBD in the case of the 2019-nCoV spike. The interaction-level information decoded in this study may provide deep insight into developing effective therapeutic targets.