1996 ◽  
Vol 320 (2) ◽  
pp. 585-587 ◽  
Author(s):  
Anna FILIPEK ◽  
Urszula WOJDA

A novel protein target of mouse calcyclin (S100A6) was detected by a gel overlay method with 125I-labelled calcyclin. Interaction of calcyclin with its 30 kDa target protein (p30) present in Ehrlich ascites tumour (EAT) cells depended on the presence of Ca2+ ions. The binding of p30, evidenced by the reaction with 125I-labelled calcyclin, was found to be of higher affinity than the binding between mouse calcyclin and annexin II or glyceraldehyde-3-phosphate dehydrogenase. Examination of tissue extracts by the gel overlay method has shown that p30 is present not only in the EAT cells but also in mouse brain and spleen. This novel target protein of mouse calcyclin was purified to homogeneity from EAT cells by means of Phenyl-Sepharose chromatography, affinity chromatography and CM-cellulose chromatography. Purified p30 was digested with α-chymotrypsin and a partial amino acid sequence of one of the resulting peptides was established. A database search analysis revealed that the sequence is unique, with a similarity of less than 55% to any other known protein sequence.


2021 ◽  
Vol 17 (3) ◽  
pp. 369-376
Author(s):  
Praveen Kumar Kumar ◽  
◽  
Shanmughavel Piramanayagam ◽  

Antimicrobial peptides (AMPs) play a prominent role in drug discovery due to the rapid increase in drug resistant infections. Hence, we report the molecular docking analysis of antimicrobial peptides MREEKKERKRD and MVQGAKRGGRLHRV with the target protein CXCL1 in the context of colorectal cancer for further consideration in drug discovery.


2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.


Author(s):  
Parth Sarthi Sen Gupta ◽  
Satyaranjan Biswal ◽  
Saroj Kumar Panda ◽  
Abhik Kumar Ray ◽  
Malay Kumar Rana

<p>While an FDA approved drug Ivermectin was reported to dramatically reduce the cell line of SARS-CoV-2 by ~5000 folds within 48 hours, the precise mechanism of action and the COVID-19 molecular target involved in interaction with this in-vitro effective drug are unknown yet. Among 12 different COVID-19 targets studied here, the RNA dependent RNA polymerase (RdRp) with RNA and Helicase NCB site show the strongest affinity to Ivermectin amounting -10.4 kcal/mol and -9.6 kcal/mol, respectively. Molecular dynamics of corresponding protein-drug complexes reveals that the drug bound state of RdRp with RNA has better structural stability than the Helicase NCB site, with MM/PBSA free energy of -135.2 kJ/mol, almost twice that of Helicase (-76.6 kJ/mol). The selectivity of Ivermectin to RdRp is triggered by a cooperative interaction of RNA-RdRp by ternary complex formation. Identification of the target and its interaction profile with Ivermectin can lead to more powerful drug designs for COVID-19 and experimental exploration. </p>


2004 ◽  
Vol 7 (5) ◽  
pp. 453-472 ◽  
Author(s):  
B. Li ◽  
Y. Liu ◽  
T. Uno ◽  
N. Gray

Author(s):  
Shikha Sharma ◽  
Shweta Sharma ◽  
Vaishali Pathak ◽  
Parwinder Kaur ◽  
Rajesh Kumar Singh

Aim: To investigate and validate the potential target proteins for drug repurposing of newly FDA approved antibacterial drug. Background: Drug repurposing is the process of assigning indications for drugs other than the one(s) that they were initially developed for. Discovery of entirely new indications from already approved drugs is highly lucrative as it minimizes the pipeline of the drug development process by reducing time and cost. In silico driven technologies made it possible to analyze molecules for different target proteins which are not yet explored. Objective: To analyze possible targets proteins for drug repurposing of lefamulin and their validation. Also, in silico prediction of novel scaffolds from lefamulin has been performed for assisting medicinal chemists in future drug design. Methods: A similarity-based prediction tool was employed for predicting target protein and further investigated using docking studies on PDB ID: 2V16. Besides, various in silico tools were employed for prediction of novel scaffolds from lefamulin using scaffold hopping technique followed by evaluation with various in silico parameters viz., ADME, synthetic accessibility and PAINS. Results: Based on the similarity and target prediction studies, renin is found as the most probable target protein for lefamulin. Further, validation studies using docking of lefamulin revealed the significant interactions of lefamulin with the binding pocket of the target protein. Also, three novel scaffolds were predicted using scaffold hopping technique and found to be in the limit to reduce the chances of drug failure in the physiological system during the last stage approval process. Conclusion: To encapsulate the future perspective, lefamulin may assist in the development of the renin inhibitors and, also three possible novel scaffolds with good pharmacokinetic profile can be developed into both as renin inhibitors and for bacterial infections.


1994 ◽  
Vol 91 (9) ◽  
pp. 3544-3548 ◽  
Author(s):  
M. Feese ◽  
D. W. Pettigrew ◽  
N. D. Meadow ◽  
S. Roseman ◽  
S. J. Remington

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Abdullahi Bello Umar ◽  
Adamu Uzairu ◽  
Gideon Adamu Shallangwa ◽  
Sani Uba

Abstract Background V600E-BRAF is a major protein target involved in various types of human cancers. However, the acquired resistance of the V600E-BRAF kinase to the vemurafenib and the side effects of other identified drugs initiate the search for efficient inhibitors. In the current paper, virtual docking screening combined with drug likeness and ADMET properties predictions were jointly applied to evaluate potent 2-(1H-imidazol-2-yl) pyridines as V600E-BRAF kinase inhibitors. Results Most of the studied compounds showed better docking scores and favorable interactions with theiV600E-BRAF target. Among the screened compounds, the two most potent (14 and 30) with good rerank scores (−124.079 and − 122.290) emerged as the most effective, and potent V600E-BRAF kinase inhibitors which performed better than vemurafenib (−116.174), an approved V600E-BRAF kinase inhibitor. Thus, the docking studies exhibited that these compounds have shown competing inhibition of V600E-BRAF kinase with vemurafenib at the active site and revealed better pharmacological properties based on Lipinski’s and Veber’s drug-likeness rules for oral bioavailability and ADMET properties. Conclusion The docking result, drug-likeness rules, and ADMET parameters identified compounds (14 and 30) as the best hits against V600E-BRAF kinase with better pharmacological properties. This suggests that these compounds may be developed as potent V600E-BRAF inhibitors.


Sign in / Sign up

Export Citation Format

Share Document