Prediction of potential cysteine synthase inhibitors of Leishmania braziliensis and Leishmania major parasites by computational screening.
Acta Trop
; 225: 106182, 2022 Jan.
Article
in En
| MEDLINE
| ID: mdl-34627756
ABSTRACT
Leishmaniasis is a neglected tropical disease considered a public health problem that requires innovative strategies for its chemotherapeutic control. In the present investigation, a molecular docking approach was carried out using the protein cysteine synthase (CS) of Leishmania braziliensis (CSLb) and Leishmania major (CSLm) parasites to identify new compounds as potential candidates for the development of selective leishmaniasis therapy. CS protein sequence similarity, active site, structural modeling, molecular docking, and ADMET properties of compounds were analyzed using bioinformatics tools. Molecular docking analyses identified 1000 ligands with highly promising binding affinity scores for both CS proteins. A total of 182 compounds for CSLb and 173 for CSLm were selected for more detailed characterization based on the binding energy and frequency values and ADMET properties. Based on Principal Component Analysis (PCA) and K-means clusterization for both CS proteins, we classified compounds into 5 clusters for CSLb and 7 for CSLm, thus providing an excellent starting point for verification of enzyme inhibition in in vitro studies. We found the ZINC16524774 compound predicted to have a high affinity and stability for both CSLb and CSLm proteins, which was also evaluated through molecular dynamics simulations. Compounds within each of the five clusters also displayed pharmacological and structural properties that make them attractive drug candidates for the development of selective cutaneous leishmaniasis chemotherapy.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Parasites
/
Leishmania braziliensis
/
Leishmania major
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limits:
Animals
Country/Region as subject:
America do sul
/
Brasil
Language:
En
Year:
2022
Type:
Article