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.

Exploring the interaction of a potent anti-cancer drug Selumetinib with bovine serum albumin: Spectral and computational attributes.

The binding of drugs to plasma proteins determines its fate within the physiological system, hence profound understanding of its interaction within the bloodstream is important to understand its pharmacodynamics and pharmacokinetics and thereby its therapeutic potential. In this regard, our work delineates the mechanism of interaction of Selumetinib (SEL), a potent anti-cancer drug showing excellent effect against multiple solid tumors, with plasma protein bovine serum albumin (BSA), using methods such as absorption, steady-state fluorescence, time-resolved, fluorescence resonance energy transfer, Fourier transform infrared spectra (FTIR), circular dichroism (CD), synchronous and 3D-fluorescence, salt fluorescence, molecular docking and molecular dynamic simulations. The BSA fluorescence intensity was quenched with increasing concentration of SEL which indicates interactions of SEL with BSA. Stern-Volmer quenching analysis and lifetime studies indicate the involvement of dynamic quenching. However, some contributions from the static quenching mechanism could not be ruled out unambiguously. The association constant was found to be 5.34 × 105 M-1 and it has a single binding site. The Förster distance (r) indicated probable energy transmission between the BSA and SEL. The positive entropy changes and enthalpy change indicate that the main interacting forces are hydrophobic forces, also evidenced by the results of molecular modeling studies. Conformation change in protein framework was revealed from FTIR, synchronous and 3D fluorescence and CD studies. Competitive binding experiments as well as docking studies suggest that SEL attaches itself to site I (subdomain IIA) of BSA where warfarin binds. Molecular dynamic simulations indicate the stability of the SEL-BSA complex. The association energy between BSA and SEL is affected in the presence of different metals differently.

An in-silico approach to identify bioactive phytochemicals from Houttuynia cordata Thunb. As potential inhibitors of human glutathione reductase.

Cellular infections are central to the etiology of various diseases, notably cancer and malaria. Counteracting cellular oxidative stress via the inhibition of glutathione reductase (GR) has emerged as a promising therapeutic strategy. Houttuynia cordata, a medicinal plant known for its potent antioxidant properties, has been the focus of our investigation. In this study, we conducted comprehensive in silico analyses involving the phytochemical constituents of H. cordata to identify potential natural GR inhibitors. Our methodological approach encompassed multiple in silico techniques, including molecular docking, molecular dynamics simulations, MMPBSA analysis, and dynamic cross-correlation analysis. Out of 13 docked phytochemicals, Quercetin, Quercitrin, and Sesamin emerged as particularly noteworthy due to their exceptional binding affinities for GR. Notably, our investigation demonstrated that Quercetin and Sesamin exhibited promising outcomes compared to the well-established pharmaceutical agent N-acetylcysteine (NAC). Molecular dynamics analyses provided insights into the ability of these phytochemicals to induce structural compaction and stabilization of the GR protein, as evidenced by changes in radius of gyration and solvent-accessible surface area. Moreover, MMPBSA analysis highlighted the crucial roles of specific residues, namely Gly27, Gly28, Ser51, His52, and Val61, in mediating essential interactions with these phytochemicals. Furthermore, an assessment of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME-Tox) profiles underscored the favourable drug-like attributes of these phytochemicals. Thus, the current findings underscore the immense potential of Houttuynia cordata phytochemicals as potent antioxidants with the capacity to combat a spectrum of maladies, including malaria and cancer. This study not only unveils novel therapeutic avenues but also underscores the distinctive outcomes and paramount significance of harnessing H. cordata phytochemicals for their efficacious antioxidant properties.Communicated by Ramaswamy H. Sarma.

Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective.

Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.

Computational Analysis of Bacopa monnieri (L.) Wettst. Compounds for Drug Development against Neurodegenerative Disorders.

AIM: With several experimental studies establishing the role of Bacopa monnieri as an effective neurological medication, less focus has been employed to explore how effectively Bacopa monnieri brings about this property. The current work focuses on understanding the molecular interaction of the phytochemicals of the plant against different neurotrophic factors to explore their role and potential as potent anti-neurodegenerative drugs. BACKGROUND: Neurotrophins play a crucial role in the development and regulation of neurons. Alterations in the functioning of these Neurotrophins lead to several Neurodegenerative Disorders. Albeit engineered medications are accessible for the treatment of Neurodegenerative Disorders, due to their numerous side effects, it becomes imperative to formulate and synthesize novel drug candidates. OBJECTIVE: This study aims to investigate the potential of Bacopa monnieri phytochemicals as potent antineurodegenerative drugs by inspecting the interactions between Neurotrophins and target proteins. METHODS: The current study employs molecular docking and molecular dynamic simulation studies to examine the molecular interactions of phytochemicals with respective Neurotrophins. Further inspection of the screened phytochemicals was performed to analyze the ADME-Tox properties in order to classify the screened phytochemicals as potent drug candidates. RESULTS: The phytochemicals of Bacopa monnieri were subjected to in-silico docking with the respective Neurotrophins. Vitamin E, Benzene propanoic acid, 3,5-bis (1,1- dimethylethyl)- 4hydroxy-, methyl ester (BPA), Stigmasterol, and Nonacosane showed an excellent binding affinity with their respective Neurotrophins (BDNF, NT3, NT4, NGF). Moreover, the molecular dynamic simulation studies revealed that BPA and Stigmasterol show a very stable interaction with NT3 and NT4, respectively, suggesting their potential role as a drug candidate. Nonacosane exhibited a fluctuating binding behavior with NGF which can be accounted for by its long linear structure. ADME-Tox studies further confirmed the potency of these phytochemicals as BPA violated no factors and Vitamin E, Stigmasterol and Nonacosane violated 1 factor for Lipinski's rule. Moreover, their high human intestinal absorption and bioavailability score along with their classification as non-mutagen in the Ames test makes these compounds more reliable as potent antineurodegenerative drugs. CONCLUSION: Our study provides an in-silico approach toward understanding the anti-neurodegenerative property of Bacopa monnieri phytochemicals and establishes the role of four major phytochemicals which can be utilized as a replacement for synthetic drugs against several neurodegenerative disorders.

Computational approach to attenuate virulence of Pseudomonas aeruginosa through bioinspired silver nanoparticles.

In this study we aim to investigate the computational docking approach of biofabricated silver nanoparticles against P. aeruginosa virulent exoenzymes, such as ExoS and ExoY. Therefore, the synthesis and characterization of biofabricated silver nanoparticles using Piper betle leaves (Pb-AgNPs) were carried out. The surface topology and functional group attachment on the surface of Pb-AgNPs were analyzed using UV-visible spectroscopy, Scanning Electron Microscopy, Fourier Transformed Infrared Spectroscopy (FTIR), and X-Ray Diffraction. The FTIR analysis revealed that the synthesized silver nanoparticles were capped with P. betle phytochemicals importantly Eugenol and Hydroxychavicol. These are the major bioactive compounds present in P. betle leaves; therefore, computational docking of Eugenol-conjugated AgNPs (PbEu-AgNPs) and Hydroxychavicol-conjugated AgNPs (PbHy-AgNPs) against ExoS and ExoY was performed. The active residues of PbEu-AgNPs and PbHy-AgNPs interacted with the active site of ExoS and ExoY exoenzymes. Biofabricated AgNP-mediated inhibition of these virulent exoenzymes blocked the adverse effect of P. aeruginosa on the host cell. The computational analysis provides new approach into the design of biofabricated AgNPs as promising anti-infective nanomedicine agents. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03367-0.

Exploratory Data Analysis of Genomic Sequence of Variants of SARS-CoV-2 Reveals Sequence Divergence and Mutational Localization.

Whole genome sequencing has rapidly progressed in recent years, with sequencing the SARS-CoV-2 genomes, making it a more reliable clinical tool for public health surveillance. This development has resulted in the production of a large amount of genomic data used for various types of genomic exploration. However, without a proper standard protocol, the usage of genomic data for analyzing various biological phenomena, such as mutation and evolution, may result in a propagating risk of using an unvalidated data set. This process could lead to irregular data being generated along with a high risk of altered analysis. Thus, the current study lays out the foundation for a preprocess pipeline using data analysis to analyze the genomic data set for its accuracy. We have used the recent example of SARS-CoV-2 to demonstrate the process overflow that can be utilized for various kinds of biological exploration such as understanding mutational events, evolutionary divergence, and speciation. Our analysis reveals a significant amount of sequence divergence in the gamma variant as compared with the reference genome thereby making the variant less infective and deadly. Moreover, we found regions in the genomic sequence that is more prone to mutational localization thereby altering the structural integrity of the virus resulting in a more reliable molecular viral mechanism. We believe that the current work will help for an initial check of the genomic data followed by the biological assessment of the process overflow which will be beneficial for the variant analysis and mutational uprising.

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.