PITRM1 interaction studies with amyloidogenic nonapeptide mutants of familial Alzheimer's disease.

Amyloid ß-protein (ABP) is found to be the major cause for the development of neurodegeneration which leads to Alzheimer's. The Aß nonapeptide segment, QKLVFFAED (amino acids 15-23) is the highly amyloidogenic central region of Aß. Familial mutation in Aß increases the aggregation property of the peptide compared to the Native (Wild) amyloid-beta (Aß) and these mutations fall on the Aß nonapeptide segment. The catalytic activity of pitrilysin metallopeptidase 1(PITRM1) with familial mutant Aß (Flemish, Arctic, Dutch, Italian and Iowa) during interaction is examined using molecular dynamic simulation. The molecular dynamics simulation of PITRM1 and the Aß nonapeptide segment showed similar RMSD with respect to stability. The active site amino acid (AA) H108, hydrophobic pocket AA residues L111, F123, F124, and L127 and the basic pocket AA residues R888 and H896 showed similar interactions with both wild and familial Aß. The molecular level interaction between amyloid beta and PITRM1 were similar in the wild and familial mutants except for the Arctic mutant. The hydrophobic interaction was commonly observed between the S1 hydrophobic pocket and the LVFF region, the Arctic mutant showed less hydrogen bond formation consistently when compared to other complexes. This molecular information on catalytic activity suggests that modulating inactive PITRM1 or an increase in expression of PITRM1 can help in eliminating different kinds of familial mutant Aß in neurodegenerative cells.Communicated by Ramaswamy H. Sarma.

Molecular interaction of human acetylcholinesterase with trans-tephrostachin and derivatives for Alzheimer's disease.

Alzheimer's disease (AD), a neurodegenerative disorder affects more than 35 million people globally. Acetylcholinesterase suppression is the common approach to enhance the well-being of AD patients by increasing the duration of acetylcholine in the cholinergic synapses. Generally, herbal secondary metabolites are reported to be a major resource for acetylcholinesterase inhibitors (AChEIs). Trans-tephrostachin was reported from Tephrosia purpurea for AChE inhibition. Here, we report on the design, synthesis, and assessment of human acetylcholinesterase inhibitory activity from trans-tephrostachin derivatives or analogs as anti-AD agents. The five newly synthesized compounds 4a. 4b, 4c, 4d and 4e displayed potent inhibitory activities with IC50 values of 35.0, 35.6, 10.6, 10.3, and 28.1 µM respectively. AChE enzyme kinetic study was performed for the five derived compounds using the Ellman's method. The Lineweaver-Burk and the secondary plots revealed the mixed inhibition for 4a, 4c and 4d whereas 4b and 4e demonstrated competitive inhibition. Molecular docking and molecular dynamics simulations showed the derivatives or analogs of trans-tephrostachin attained a high binding affinity and efficacy than the standard drug. In conclusion, trans-tephrostachin and its derivative compounds could become effective agents for further drug development to treat AD.

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

In silico screening of sugar alcohol compounds to inhibit viral matrix protein VP40 of Ebola virus.

Ebola virus is a virulent pathogen that causes highly lethal hemorrhagic fever in human and non-human species. The rapid growth of this virus infection has made the scenario increasingly complicated to control the disease. Receptor viral matrix protein (VP40) is highly responsible for the replication and budding of progeny virus. The binding of RNA to VP40 could be the crucial factor for the successful lifecycle of the Ebola virus. In this study, we aimed to identify the potential drug that could inhibit VP40. Sugar alcohols were enrich with antiviral properties used to inhibit VP40. Virtual screening analysis was perform for the 48 sugar alcohol compounds, of which the following three compounds show the best binding affinity: Sorbitol, Mannitol and Galactitol. To understand the perfect binding orientation and the strength of non-bonded interactions, individual molecular docking studies were perform for the best hits. Further molecular dynamics studies were conduct to analyze the efficacy between the protein-ligand complexes and it was identify that Sorbitol obtains the highest efficacy. The best-screened compounds obtained drug-like property and were less toxic, which could be use as a potential lead compound to develop anti-Ebola drugs.

Zebrafish bio-assay guided isolation of human acetylcholinesterase inhibitory trans-tephrostachin from Tephrosia purpurea (L.) Pers.

An acetylcholinesterase inhibitory compound was isolated from Tephrosia purpurea (L.) Pers. by zebrafish brain based bioassay guided isolation and predicted as trans-tephrostachin.Enzyme kinetics studies (Line weaver-Burk plots and Michaelis Menten equation) favored the reversible / mixed type, with the inhibition constant (Ki) of 53.0 ± 7.4 µM in zebrafish brain (IC50 value of 39.0 ± 1.4 µM). However, the inhibition constant (Ki) was found to be 36.0 ± 0.4 µM with IC50 value of 20.0 ± 1.0 µM, whereas donepezil showed 3.2 ± 0.3 µM with the IC50 value of 0.12 ± 0.04 µM for human acetylcholinesterase. Further, the molecular docking, dynamics and simulation for trans-tephrostachin obtained better binding affinity and efficacy than commercial drugs donepezil and galanthamine. Hence, the isolated compound trans - tephrostachin from T. purpurea shall be further considered for the development of potential drug for the counteraction of Alzheimer's disease progression.

Exploring the functional impact of mutational drift in LRRK2 gene and identification of specific inhibitors for the treatment of Parkinson disease.

Parkinson's disease (PD) is a disorder of the central nervous system that is caused due to the death of the dopaminergic neurons in the region of the brain called substantia nigra. Mutations in LRRK2 genes are associated with disease condition and it's been reported as crucial factor for drug resistance. Identification of deleterious mutations and studying the structural and functional impact of such mutations may lead to the identification of potential selective inhibitors. In this study, we analyzed 52 PD associated mutations, among that 20 were identified as highly deleterious. The deleterious mutations G2019S and I2020T in the kinase domain were playing a key role in causing resistance to drug levedopa. Molecular docking analyses have been performed to understand the binding affinity of levodapa with LRRK2 in wild and mutant condition. Molecular docking results show that levedopa binds differentially and obtained less number of hydrogen bonds in compared with wild type LRRK2. In addition, molecular dynamics simulations were performed to study the efficacy of docked complexes and it was observed that the efficacy of the mutant complexes (G2019S-Levodopa and I2020T-Levodopa) affected in the presence of mutation. Finally, through virtual screening approach specific inhibitors SCHEMBL6473053 and SCHEMBL1278779 have been identified that could potentially inhibit LLRK2 mutants G2019S and I2020T respectively. Over all this computational investigation correlates the impact of genotypic modulation in structure and function of drug target which enhanced in the identification of precision medicine to treat PD.