Quantitative Biology

One algorithm to predict protein structure is the residual dipolar coupling based residue assembly and filter tool (REDCRAFT). This algorithm exploits an exponential reduction of the search space of all possible structures to find a structure that best fits a set of experimental residual dipolar couplings. However, the minimum amount of data required to successfully determine a protein's structure using REDCRAFT has not been previously investigated. Here we explore the effect of reducing the amount of data used to fold proteins. Our goal is to reduce experimental data collection times while retaining the accuracy levels previously achieved with larger amounts of data. We also investigate incorporating a priori secondary structure information into REDCRAFT to improve its structure prediction ability.

Hydrogenase occupy a central place in the energy metabolism of anaerobic bacteria. Although the structure of mitochondrial complex I is similar to that of hydrogenase, whether it has hydrogen metabolic activity remain unclear. Here, we show that a H2 evolving activity exists in higher plants mitochondria and is closely related to complex I, especially around ubiquinone binding site. The H2 production could be inhibited by rotenone and ubiquinone. Hypoxia could simultaneously promote H2 evolution and succinate accumulation. Redox properties of quinone pool, adjusted by NADH or succinate according to oxygen concentration, acts as a valve to control the flow of protons and electrons and the production of H2. The coupling of H2 evolving activity of mitochondrial complex I with metabolic regulation reveals a more effective redox homeostasis regulation mechanism. Considering the ubiquity of mitochondria in eukaryotes, H2 metabolism might be the innate function of higher organisms. This may serve to explain, at least in part, the broad physiological effects of H2.

Presently, India bears amongst the highest burden of non-communicable diseases such as diabetes mellitus (DM), hypertension (HT), and cardio vascular disease (CVD) and thus represents a vulnerable target to the SARS-CoV-2/COVID-19 pandemic. Involvement of the angiotensin converting enzyme 2 (ACE2) in susceptibility to infection and pathogenesis by SARS-CoV-2 is currently an actively pursued research area. An increased susceptibility to infection in individuals with DM, HT and CVD together with higher levels of circulating ACE2 in these settings presents a scenario where interaction with soluble ACE2 may result in disseminated virus-receptor complexes that could enhance virus acquisition and pathogenesis. Thus, understanding the SARS-CoV-2 receptor binding domain-ACE2 interaction, both membrane bound and in the cell free context may contribute to elucidating the role of co-morbidities in increased susceptibility to infection and pathogenesis. Both Azithromycin and Hydroxychloroquine (HCQ) have shown efficacy in mitigating viral carriage in infected individuals. Furthermore, each of these compounds generate active metabolites which in turn may also modulate virus-receptor interaction and thus influence clinical outcomes. In this study, we model the structural interaction of S1 with both full-length and soluble ACE2. Additionally, therapeutic drugs and their active metabolites were docked with soluble ACE2 protein. Our results show that S1 from either of the reported Indian sequences can bind both full-length and soluble ACE2, albeit with varying affinity that can be attributed to a reported substitution in the RBD. Furthermore, both Azythromycin and HCQ together with their active metabolites can allosterically affect, to a range of extents, binding of S1 to ACE2.

As the number of solved protein structures increases, the opportunities for meta-analysis of this dataset increase too. Protein structures are known to be formed of domains; structural and functional subunits that are often repeated across sets of proteins. These domains generally form compact, globular regions, and are therefore often easily identifiable by inspection, yet the problem of automatically fragmenting the protein into these compact substructures remains computationally challenging. Existing domain classification methods focus on finding subregions of protein structure that are conserved, rather than finding a decomposition which spans the full protein structure. However, such a decomposition would find ready application in coarse-graining molecular dynamics, analysing the protein's topology, in de novo protein design and in fitting electron microscopy maps. Here, we present a tool for performing this modular decomposition using the Infomap community detection algorithm. The protein structure is abstracted into a network in which its amino acids are the nodes, and where the edges are generated using a simple proximity test. Infomap can then be used to identify highly intra-connected regions of the protein. We perform this decomposition systematically across 4000 distinct protein structures, taken from the Protein Data Bank. The decomposition obtained correlates well with existing PFAM sequence classifications, but has the advantage of spanning the full protein, with the potential for novel domains. The coarse-grained network formed by the communities can also be used as a proxy for protein topology at the single-chain level; we demonstrate that grouping these proteins by their coarse-grained network results in a functionally significant classification.

Visual Molecular Dynamics (VMD) is one of the most widely used molecular graphics software in the community of theoretical simulations. So far, however, it still lacks a graphical user interface (GUI) for molecular manipulations when doing some modeling tasks. For instance, translation or rotation of a selected molecule(s) or part(s) of a molecule, which are currently only can be achieved using tcl scripts. Here, we use tcl script develop a user-friendly GUI for VMD, named Molcontroller, which is featured by allowing users to quickly and conveniently perform various molecular manipulations. This GUI might be helpful for improving the modeling efficiency of VMD users.

The Coronavirus Disease (COVID-19) pandemic caused by the SARS-coronavirus 2 (SARS-CoV-2) urgently calls for the design of drugs directed against this new virus. Given its essential role in proteolytic processing, the main protease Mpro has been identified as an attractive candidate for drugs against SARS-CoV-2 and similar coronaviruses. Recent high-throughput screening studies have identified a set of existing, small-molecule drugs as potent Mpro inhibitors. Amongst these, Ebselen (2-Phenyl-1,2-benzoselenazol-3-one), a glutathione peroxidase mimetic seleno-organic compound, is particularly attractive. Recent experiments suggest that its effectiveness is higher than that of other molecules that also act at the enzyme catalytic site. By relying on extensive simulations with all-atom models, in this study we examine at a molecular level the potential of Ebselen to decrease Mpro catalytic activity. Our results indicate that Ebselen exhibits a distinct affinity for the catalytic site cavity of Mpro. In addition, our molecular models reveal a second, previously unkown binding site for Ebselen in the dimerization region localized between the II and III domains of the protein. A detailed analysis of the free energy of binding indicates that the affinity of Ebselen to this second binding site is in fact significantly larger than that to the catalytic site. A strain analysis indicates that Ebselen bound between the II-III domains exerts a pronounced allosteric effect that regulates catalytic site access through surface loop interactions, and induces a displacement and reconfiguration of water hotspots, including the catalytic water, which could interfere with normal enzymatic function. Taken together, these findings provide a framework for the future design of more potent and specific Mpro inhibitors, based on the Ebselen scaffold, that could lead to new therapeutic strategies for COVID-19.

Levan is a bacterial homopolysaccharide, which consists of beta-2,6 linked beta-D-fructose monomers. Because of its structural properties and its health promoting effects, levan is a promising functional ingredient for the food, cosmetic and pharma industry. The properties of levan have been reported to be linked to its molecular weight. For a better understanding of how its molecular weight determines its polymer conformation in aqueous solution, levan produced by the food grade acetic acid bacterium Gluconobacter albidus TMW 2.1191 was analysed over a broad molecular weight range using dynamic and static light scattering and viscometry. Levan, with low molecular weight, exhibited a compact random coil structure. As the molecular weight increased, the structure transformed into a compact non-drained sphere. The density of the sphere continued to increase with increasing molecular weight. This resulted in a negative exponent in the Mark-Houwink-Sakurada Plot. For the first time, an increase in molecular density with increasing molecular weight, as determined by a negative Mark-Houwink-Sakurada exponent, could be shown for biopolymers. Our results reveal the unique properties of high-molecular weight levan and indicate the need of further systematic studies on the structure-function relationship of levans for their targeted use in food, cosmetic and pharmaceutical applications.

Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features); thus, it can consider information of nonadjacent atoms. However, the correlation between the distance on the graph and the three-dimensional coordinate distance is uncertain. In this paper, we propose three improvements for modifying the weave module. First, the distances between ring atoms on the graph were modified to bring the distances on the graph closer to the coordinate distance. Second, different weight matrices were used depending on the distance on the graph in the convolution layers of the pair features. Finally, a weighted sum, by distance, was used when converting pair features to atom features. The experimental results show that the performance of the proposed method is slightly better than that of the weave module, and the improvement in the distance representation might be useful for compound activity prediction.

The emergence of COVID-19 has severely compromised the arsenal of antiviral and antibiotic drugs. Drug discovery is a multistep process with a high failure rate, high cost and it takes approximately 10-12 years for the development of new molecules into the clinical candidate. On the other side, drug repurposing also called old drugs for new uses, is an attractive alternative approach for a new application of marketed FDA approved or investigational drugs. In the current pandemic situation raised due to COVID-19, repurposing of existing FDA approved drugs are emerging as the first line of the treatment. The causative viral agent of this highly contagious disease and acute respiratory syndrome coronavirus (SARS-CoV) shares high nucleotide similarity. Therefore, many existing viral targets are structurally expected to be similar to SARS-CoV and likely to be inhibited by the same compounds. Here, we selected three viral key proteins based on their vital role in viral life cycle: ACE2 (helps in entry into the human host), viral nonstructural proteins RNA-dependent RNA polymerase (RdRp) NSP12, and NSP16 which helps in replication, and viral latency (invasion from immunity). The FDA approved drugs chloroquine (CQ), hydroxychloroquine (HCQ), remdesivir (RDV) and arbidol (ABD) are emerging as promising agents to combat COVID-19. Our hypothesis behind the docking studies is to determine the binding affinities of these drugs and identify the key amino acid residues playing a key role in their mechanism of action. The docking studies were carried out through Autodock and online COVID-19 docking server. Further studies on a broad range of FDA approved drugs including few more protein targets, molecular dynamics studies, in-vitro and in-vivo biological evaluation are required to identify the combination therapy targeting various stages of the viral life cycle.

COVID-19, a member of corona virus family is spreading its tentacles across the world due to lack of drugs at present. However, the main viral proteinase (Mpro/3CLpro) has recently been regarded as a suitable target for drug design against SARS infection due to its vital role in polyproteins processing necessary for coronavirus reproduction. The present in silico study was designed to evaluate the effect of Jensenone, a essential oil component from eucalyptus oil, on Mpro by docking study. In the present study, molecular docking studies were conducted by using 1-click dock and swiss dock tools. Protein interaction mode was calculated by Protein Interactions Calculator.The calculated parameters such as binding energy, and binding site similarity indicated effective binding of Jensenone to COVID-19 proteinase. Active site prediction further validated the role of active site residues in ligand binding. PIC results indicated that, Mpro/ Jensenone complexes forms hydrophobic interactions, hydrogen bond interactions and strong ionic interactions. Therefore, Jensenone may represent potential treatment potential to act as COVID-19 Mpro inhibitor. However, further research is necessary to investigate their potential medicinal use.