Identification of the most potent acetylcholinesterase inhibitors from plants for possible treatment of Alzheimer’s disease: a computational approach

Background Being one of the rapidly growing dementia type diseases in the world, Alzheimer’s disease (AD) has gained much attention from researchers in the recent decades. Many hypotheses have been developed that describe different reasons for the development of AD. Among them, the cholinergic hypothesis depicts that the degradation of an important neurotransmitter, acetylcholine by the enzyme acetylcholinesterase (AChE), is responsible for the development of AD. Although, many anti-AChE drugs are already available in the market, their performance sometimes yields unexpected results. For this reason, research works are going on to find out potential anti-AChE agents both from natural and synthetic sources. In this study, 50 potential anti-AChE phytochemicals were analyzed using numerous tools of bioinformatics and in silico biology to find out the best possible anti-AChE agents among the selected 50 ligands through molecular docking, determination of the druglikeness properties, conducting the ADMET test, PASS and P450 site of metabolism prediction, and DFT calculations. Result The predictions of this study suggested that among the selected 50 ligands, bellidifolin, naringenin, apigenin, and coptisine were the 4 best compounds with quite similar and sound performance in most of the experiments. Conclusion In this study, bellidifolin, naringenin, apigenin, and coptisine were found to be the most effective agents for treating the AD targeting AChE. However, more in vivo and in vitro analyses are required to finally confirm the outcomes of this research.

In Silico Analysis of Some Phytochemicals as Potent Anti-tubercular Agents Targeting Mycobacterium tuberculosis RNA Polymerase and InhA Protein

Tuberculosis (TB) is a contagious disease, caused by Mycobacterium tuberculosis (MTB) that has infected and killed a lot of people in the past. At present treatments against TB are available at a very low cost. Since these chemical drugs have many adverse effects on health, more attention is now given on the plant-derived phytochemicals as potential agents to fight against TB. In this study, 5 phytochemicals, 4-hydroxybenzaldehyde, benzoic acid, bergapten, psoralen, and p-hydroxybenzoic acid, are selected to test their potentiality, safety, and efficacy against two potential targets, the MTB RNA polymerase and enoyl-acyl carrier protein (ACP) reductase, the InhA protein, using various tools of in silico biology. The molecular docking experiment, drug-likeness property test, ADME/T-test, P450 SOM prediction, pharmacophore mapping, and modeling, solubility testing, DFT calculations, and PASS prediction study had confirmed that all the molecules had the good potentiality to inhibit the two targets. However, two agents, 4-hydroxybenzaldehyde and bergapten were considered as the best agents among the five selected agents and they also showed far better results than the two currently used drugs, that function in these pathways, rifampicin (MTB RNA polymerase) and isoniazid (InhA protein). These two agents can be used effectively to treat tuberculosis.

Things and Data in Recent Biology

There is much talk about data-driven and in silico biology, but how exactly does it work? This essay reflects on the relation of data practices to the biological things from which they are abstracted. Looking at concrete examples of computer use in biology, the essay asks: How are biological things turned into data? What organizes and limits the combination, querying, and re-use of data? And how does the work on data link back to the organismic or biological world? Considering the life cycle of data, the essay suggests that data remain linked to the biological material and the concrete context from which they are extracted and to which they always refer back. Consequently, the transition to data science is never complete. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

In Silico Biology of H1N1: Molecular Modelling of Novel Receptors and Docking Studies of Inhibitors to Reveal New Insight in Flu Treatment

Influenza is an infectious disease caused by RNA viruses of the family Orthomyxoviridae. The new influenza H1N1 viral stain has emerged by the genetic combination of genes from human, pig, and bird’s H1N1 virus. The influenza virus is roughly spherical and is enveloped by a lipid membrane. There are two glycoproteins in this lipid membrane; namely, hemagglutinin (HA) which helps in attachment of the viral strain on the host cell surface and neuraminidase (NA) that is responsible for initiation of viral infection. We have developed homology models of both Hemagglutinin and Neuraminidase receptors from H1N1 strains in eastern India. The docking studies of B-Sialic acid and O-Sialic acid in the optimized and energy-minimized homology models show important H-bonding interactions with ALA142, ASP230, GLN231, GLU232, and THR141. This information can be used for structure-based and pharmacophore-based new drug design. We have also calculated ADME properties (Human Oral Absorption (HOA) and % HOA) for Oseltamivir which have been subject of debate for long.

In Silico Biology

Biological systems are typically complex and adaptive, involving large numbers of entities, or organisms, and many-layered interactions between these. System behaviour evolves over time, and typically benefits from previous experience by retaining memory of previous events. Given the dynamic nature of these phenomena, it is non-trivial to provide a comprehensive description of complex adaptive systems and, in particular, to define the importance and contribution of low-level unsupervised interactions to the overall evolution process. In this chapter, the authors focus on the application of the agent-based paradigm in the context of the immune response to HIV. Explicit implementation of lymph nodes and the associated lymph network, including lymphatic chain structure, is a key objective, and requires parallelisation of the model. Steps taken towards an optimal communication strategy are detailed.