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Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network. The computational prediction of protein allostery can guide experimental studies of protein function and cellular activity. Here, the authors develop a network-based method to detect allosteric coupling within proteins solely based on their structures, and set up a webserver for allostery prediction.

Hippocampal pyramidal neurons are endowed with signature excitability characteristics, exhibit theta-frequency selectivity — manifesting as impedance resonance and as a band-pass structure in the spike-triggered average (STA) — and coincidence detection tuned for gamma-frequency inputs. Are there specific constraints on molecular-scale (ion channel) properties in the concomitant emergence of cellular-scale encoding (feature detection and selectivity) and excitability characteristics? Here, we employed a biophysically-constrained unbiased stochastic search strategy involving thousands of conductance-based models, spanning 11 active ion channels, to assess the concomitant emergence of 14 different electrophysiological measurements. Despite the strong biophysical and physiological constraints, we found models that were similar in terms of their spectral selectivity, operating mode along the integrator-coincidence detection continuum and intrinsic excitability characteristics. The parametric combinations that resulted in these functionally similar models were non-unique with weak pair-wise correlations. Employing virtual knockout of individual ion channels in these functionally similar models, we found a many-to-many relationship between channels and physiological characteristics to mediate this degeneracy, and predicted a dominant role for HCN and transient potassium channels in regulating hippocampal neuronal STA. Our analyses reveals the expression of degeneracy, that results from synergistic interactions among disparate channel components, in the concomitant emergence of neuronal excitability and encoding characteristics.

Alphaviruses pose a growing global health threat, with Chikungunya virus (CHIKV) epidemics ongoing. Although several CHIKV vaccine candidates have progressed to late-stage clinical evaluation, none have yet achieved licensure or widespread availability. The CHIKV nonstructural proteins nsP2 and nsP4 encode essential enzymatic activities that represent key targets for antiviral development, yet the biochemical basis of nsP4 RNA-dependent RNA polymerase (RdRp) activity remains poorly understood. Here, we identify a minimal, functional precursor form of nsP4 derived from the nsP3-nsP4 polyprotein (P34) that is active in a cell-based RNA replicon system. Using synthetic, capped mRNAs, we show that cleavage of P34 by the nsP2 protease is required for robust reporter expression, and that a truncated form retaining only the C-terminal 50 residues of nsP3 (CT50-P34) supports near-wild-type replication. Unexpectedly, ubiquitin-nsP4 fusions failed to substitute for P34, likely reflecting the transient expression supported by our RNA-based system. We propose that precursor forms of nsP4 interact with the nsP1 dodecamer at the site of genome replication, where cleavage activates the RdRp and localization within the nsP1 dodecamer maintains nsP4 in its active conformation. Dissociation from the nsP1 dodecamer triggers a conformational switch to an inactive state. Together, these findings establish a tractable framework for interrogation of the assembly, activation, and regulation of the alphavirus polymerase.

Apoptosis is the elimination of functionally non-essential, neoplastic, and infected cells via the mitochondrial pathway or death receptor pathway. The process of apoptosis is highly regulated through membrane channels and apoptogenic proteins. Apoptosis maintains cellular balance within the human body through cell cycle progression. Loss of apoptosis control prolongs cancer cell survival and allows the accumulation of mutations that can promote angiogenesis, promote cell proliferation, disrupt differentiation, and increase invasiveness during tumor progression. The apoptotic pathway has been extensively studied as a potential drug target in cancer treatment. However, the off-target activities of drugs and negative implications have been a matter of concern over the years. Phytochemicals (PCs) have been studied for their efficacy in various cancer cell lines individually and synergistically. The development of nanoparticles (NPs) through green synthesis has added a new dimension to the advancement of plant-based nanomaterials for effective cancer treatment. This review provides a detailed insight into the fundamental molecular pathways of programmed cell death and highlights the role of PCs along with the existing drugs and plant-based NPs in treating cancer by targeting its programmed cell death (PCD) network.

The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.

COVID-19 pandemic has affected the patients to a large extent who are in immediate need of medical care. Now-a-days people rely on online reviews shared on review websites to gather information about hospitals like the availability of beds, availability of ventilators etc. However, these reviews are large in number and unstructured which makes it difficult to interpret them. Hence, the authors have proposed a methodology that will apply an aspect based sentiment analysis on the reviews to gain meaningful insights of the hospital based on different aspects like doctor, staff, facilities etc. For the purpose of empirical validation, a total of 26,071 reviews pertaining to 325 hospitals were scrapped. The authors concluded that the study can be useful for patients as it helps them to select the hospital which best suits them. The study also helps hospital administration to improve the current services according to needs of the patients.

Crystal structure of a vicilin, SM80.1, was determined towards exploring its possible physiological functions. The protein was purified from Solanum melongena by combination of ammonium sulphate fractionation and size exclusion chromatography. Structure was determined ab initio at resolution of 1.5 Å by X-ray crystallography showing the three-dimensional topology of the trimeric protein. Each monomer of SM80.1 consists of two similar domains with hydrophobic binding pocket and each accommodating different ligands, i.e. acetate and pyroglutamate. The relatively high stability of these independent anionic ligands in similar pockets indicated a strict requirement of stabilization by hydrogen bonds with the charged residues, suggesting a degree of plasticity within the binding pocket. Comparison of SM80.1 structure with those of other 7S vicilins indicated conservation of putative binding pocket for anionic ligands. Here we propose the possibility of trapping of these ligands in the protein for their requirement in the metabolic processes.

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the best of our knowledge and have tried to summarize all existing information in a comprehensive and succinct manner. The studies have been summarized in a tabular form according to the publication year considering numerous key perspectives. The main emphasis is laid on various steps involved in text classification process viz. document representation methods, feature selection methods, data mining methods and the evaluation technique used by each study to carry out the results on a particular dataset.

Sequence and structural homology suggests that MP-4 protein from Mucuna pruriens belongs to Kunitz-type protease inhibitor family. However, biochemical assays showed that this protein is a poor inhibitor of trypsin. To understand the basis of observed poor inhibition, thermodynamics and molecular dynamics (MD) simulation studies on binding of MP-4 to trypsin were carried out. Molecular dynamics simulations revealed that temperature influences the spectrum of conformations adopted by the loop regions in the MP-4 structure. At an optimal temperature, MP-4 achieves maximal binding while above and below the optimum temperature, its functional activity is hampered due to unfavourable flexibility and relative rigidity, respectively. The low activity at normal temperature is due to the widening of the conformational spectrum of the Reactive Site Loop (RSL) that reduces the probability of formation of stabilizing contacts with trypsin. The unique sequence of the RSL enhances flexibility at ambient temperature and thus reduces its ability to inhibit trypsin. This study shows that temperature influences the function of a protein through modulation in the structure of functional domain of the protein. Modulation of function through appearance of new sequences that are more sensitive to temperature may be a general strategy for evolution of new proteins.