Diabetes
mellitus (DM) is a chronic metabolic disease, the third killer of
mankind. The finding of potent drugs against diabetes remains challenging.
In the present study, coumarin derivatives with known biological activity
against diabetic protein have been used to predict functional groups’
positions on coumarin derivatives. α-Glucosidase is a brush
border membrane-bound lysosomal enzyme from the hydrolase enzyme family.
It plays an important role in the metabolism of glycoproteins. Inhibitors
of lysosomal α-glucosidase can reduce postprandial hyperglycemia.
Due to this, lysosomal α-glucosidase is a good therapeutic target
for drugs. A total of 116 coumarin derivatives with IC50 values against
lysosomal α-glucosidase were selected for a CADD (computer-aided
drug design) approach to identify more potent drugs. Pharmacophore
modeling and atom-based 3-QSAR of 116 active compounds against lysosomal
α-glucosidase were performed and identified positions and types
of groups to increase activity. We performed molecular docking of
116 coumarin derivatives against the lysosomal α-glucosidase
enzyme, and three compounds (isorutarine, 10_, and 36) resulted in
a docking score of −7.64, −7.12, and −6.86 kcal/mol.
The molecular dynamics simulation of the above three molecules and
protein complex performed for 100 ns supported the interaction stability
of isorutarine, 10_, and 36 with the lysosomal binding site α-glucosidase.
Predicting the fraction unbound of a drug in plasma plays a significant
role in understanding its pharmacokinetic properties during in vitro
studies of drug design and discovery. Owing to the gaining reliability
of machine learning in biological predictive models and development
of automated machine learning techniques for the ease of nonexperts
of machine learning to optimize and maximize the reliability of the
model, in this experiment, we built an in silico prediction model
of a fraction unbound drug in human plasma using a chemical fingerprint
and a freely available AutoML framework. The predictive model was
trained on one of the largest data sets ever of 5471 experimental
values using four different AutoML frameworks to compare their performance
on this problem and to choose the most significant one. With a coefficient
of determination of 0.85 on the test data set, our best prediction
model showed better performance than other previously published models,
giving our model significant importance in pharmacokinetic modeling.
Background:
Antimicrobial peptides (AMPs) can defend the hosts against various pathogens and are found in
almost every life form from microorganisms to humans. As the rapid increase of drug-resistant strains in recent years is
presenting a serious challenge to healthcare, antimicrobial peptides (AMPs) can revolutionize the antimicrobial
development against the drug-resistant microbes.
Objective:
The objective was to encourage the study on the human microbiome towards inhibition of drug-resistant
bacteria by the development of a database containing antimicrobial peptides from the human microbiome.
Method:
This database is an outcome of an extended analysis of Human metagenome, involving the prediction of coding
regions, extraction of peptides, prediction of antimicrobial peptides, and modeling their structure utilizing different in
silico tools. Further, an intelligent hash function-based query engine was designed to validate the novelty of specific
candidate peptide over the reported knowledgebase.
Result and Discussion:
This knowledgebase currently focuses on antimicrobial peptide sequences (AMPs) predicted from
the human microbiome along with 3D their structures modeled using various modeling and molecular dynamics
approaches. It includes a total of 1087 unique AMPs from various body sites, with 454 AMPs from the oral cavity, 180
AMPs from the gastrointestinal tract, 42 AMPs from the skin, 12 AMPs from the airway, 6 AMPs from the urogenital
tract and 393 AMPs from undefined body locations. A scoring matrix has been generated based on the similarity scores of
the sequences that have been incorporated into the knowledgebase. Further, a Jmol applet is included in the website to
help users visualize the 3D structures.
Conclusion:
The information and functions of the knowledgebase can offer great help in finding novel antimicrobial
drugs, especially towards finding inhibitors for drug-resistant bacteria. The HAMP is freely available at
https://bioserver.iiita.ac.in/amp/index.html.
The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has turned into a pandemic with about a million confirmed cases worldwide. Being an airborne infection, it can be highly fatal to populous countries like India. This study sets to identify potential cytotoxic T lymphocyte (CTL) epitopes in the SARS-CoV-2 Indian isolate which can acts act as an effective vaccine candidate for the majority of the Indian population. The immunogenicity and the foreignness of the epitopes towards the human body have to studies to further confirm their candidacy. The top-scoring epitopes were subjected to molecular docking studies to study their interactions with the corresponding human leukocyte antigen (HLA) system. The CTL epitopes were observed to bind at the peptide-binding groove of the corresponding HLA system, indicating their potency as a vaccine candidate. The identified epitopes can be subjected to further studies for the development of SARS-CoV-2 vaccine.
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