There are a lot of studies that resulted in finding the new potential inhibitor. A lot of NAMPT inhibitors have been found that are coming from different sources. However, there is still lack of research that compares the binding of each one of them. On the other hand, GRID@LIPI ( provides computer environment for a computational system to facilitate the advanced computer-based research development in different fields, such as mathematics, physics, chemistry, astronomy, mechanics, biology, and geology. GRID@LIPI consists of computer cluster system that includes: computing nodes (~1000 processors), computing nodes with a high internal memory, GPU-based computing nodes, and storage server. The facility that GRID@LIPI provided would close the gap between the computing services and the researcher, and therefore support the field of drug discovery to find the best NAMPT inhibitor derived from different sources.

virtual screening, molecular docking, molecular dynamics

The objective of this research is to get an experimental results recommendation in silico and find a new bioactive compound from local microbes or plants for anti-cancer drug, using virtual screening approach.


1. Program yang akan digunakan:
o AutoDock Vina untuk Molecular Docking
o NAMD untuk Molecular Dynamic

1. Receptor and ligand preparation (Data gathering)
The three-dimensional (3D) structure of NAMPT (protein databank [PDB] ID: 4LVF.A) was obtained from the PDB. [RSCB Protein Databank (]. The ligands were derived from PUBCHEM or DRUG BANK [PUBCHEM (] [DRUG BANK (]. This method will be resulted in a list of NAMPT inhibitors that have been known, especially the one that is from microbes and plants.
List of Ligands:
- Cyanoguanidine derivatives (PDB ID: 4LTS)
A co-crystal structure of amide-containing compound (4) in complex with the nicotinamide phosphoribosyltransferase (Nampt) protein and molecular modeling were utilized to design and discover a potent novel cyanoguanidine-containing inhibitor bearing a sulfone moiety (5, Nampt Biochemical IC50=2.5nM, A2780 cell proliferation IC50=9.7nM).
- Substrate competitive FK866 inhibitor (PDB ID: 2GVJ)
- Urea containing derivatives (PDB ID: 4JNM)
- Amide containing derivatives (PDB ID: 4KFN + 4LWW)
- Curcumin (PubChem CID:969516)
- CHS-828 (PubChem CID:148198 )
- GNE-618 (PDB ID:4O13)
Any co-crystallized ligands were identified and removed from the structure. Crystallographic water molecules were also excluded from the 3D coordinated file. PyMOL can be used to remove unnecessary compounds.
OpenBabel is used to convert the SDF file from PubChem to PDB file

2. Active Site Prediction
A small region or cleft where the ligand molecule can bind to the receptor protein and produce the preferred outcome is termed as an active site/catalytic site. Identification of this active site residue in the target protein structure has a great range of applications in molecular docking and de novo drug designing. Accurate
identification of this catalytic binding site is difficult due to the constant conformational changes of the target protein.

3. Molecular Docking using AutoDock Vina
The type of docking of this experiment is flexible docking study and was carried out using Autodock Vina. The 3D structure NAMPT (PDB ID: 4LVF.A) and the alkaloids were submitted in PDB format with default parameters.
The virtual screening program will be run using computer facilities provided by GRID LIPI (

4. Protein Ligand Complex Simulation
Ligand-enzyme complexes with the lowest binding energy were selected for molecular dynamics (MD) simulation using NAMD program. MD simulations are carried out to determine the structural stability within a nanosecond time scale.
Output: Binding energy of each complexes

5. Analysis
The best ligand conformation is chosen based on binding free energy value, hydrogen bonding, and hydrophobic interaction. The comparison between each of the interaction then will be analyzed and generate the best inhibitor. The analysis will be done in both quantitatively and qualitatively.


Dr. Arli Aditya Parikesit
Annisa Muthiah S

6.Computation plan (required processor core hours, data storage, software, etc)

Nodes/Cores/Memory yang dibutuhkan:

Nodes: 4 nodes
Cores: 8 cores
Memory: 32GB

7.Source of funding
thesis report, publication
9.Date of usage
02/03/2018 - 25/05/2018
10.Gpu usage
11.Supporting files
12.Created at
13.Approval status