Mohammed Nooraldeen Al-Qattan

Mohammed Nooraldeen Al-Qattan
University of Science Malaysia | USM · Centre for Drugs Research (CDR)

Ph.D. (Computer-Aided Drug Design)

About

10
Publications
27,081
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175
Citations
Introduction
I am interested in computational simulation of GPCR mechanism of activity, and designing ligands for active and allosteric sites I am using neural networks in reproducing correlations instead of regression analyses. I did work on genetic cloning, protein expression, enzyme kinetics and protein crystallization followed by X-ray crystallography for Plasmodium falciparum Glutathione-S-Transferase.
Additional affiliations
October 2019 - present
Al-Noor University College - Iraq
Position
  • Professor (Assistant)
Description
  • teaching: physical pharmacy (theory) organic chemistry I
October 2017 - October 2019
Jerash University
Position
  • Professor (Assistant)
Description
  • medicinal chemistry I, II, III (theory) organic chemistry I, II (theory) medicinal chemistry II (practical) organic chemistry I (practical)
February 2016 - September 2017
Philadelphia University
Position
  • Professor (Assistant)
Description
  • medicinal chemistry I and II (theory)
Education
September 2009 - August 2015
University of Science Malaysia
Field of study
  • Drug Design, Biotechnology, Enzymology, X-ray crystallography
February 2007 - March 2009
University of Science Malaysia
Field of study
  • Computer-Aided Drug Design
September 1998 - November 2003
University of Mosul
Field of study
  • pharmacy

Publications

Publications (10)
Article
Context: Modulation of disease progression is frequently started by identifying biochemical pathway catalyzed by biomolecule that is prone to inhibition by small molecular weight ligands. Such ligands (leads) can be obtained from natural resources or synthetic libraries. However, de novo design based on fragments assembly and optimization is showi...
Article
Full-text available
In the context of drug design and development, phytochemical and synthetic libraries are starting places for searching leads. However, knowing crystal structure of receptor may help scratching molecules that complements available interaction sites using de novo molecular design approaches. This research describes the implementation of genetic algor...
Article
Full-text available
Manipulating intracellular signals by interaction with transmembranal G-protein-coupled receptors (GPCRs) is the way of action of more than 30% of available medicines. Designing molecules against GPCRs is most challenging due to their flexible binding orthosteric and allosteric pockets, a property that lead to different mode and extent of activatio...
Preprint
Full-text available
Modulation of disease progression is frequently started by identifying biochemical pathway catalyzed by biomolecule that is prone to inhibition by small molecular weight ligands. Such ligands (leads) can be obtained from natural resources or synthetic libraries. However de novo design based on fragments assembly and optimization is showing increasi...
Article
Modulating cellular processes through extracellular chemical stimuli is medicinally an attractive approach to control disease conditions. GPCRs are the most important group of transmembranal receptors that produce different patterns of activations using intracellular mediators (such as G-proteins and Beta-arrestins). Adenosine receptors (ARs) belon...
Article
The carbon nanotube (CNT)-based target-specific delivery of drugs, or other molecular cargo, has emerged as one of the most promising biomedical applications of nanotechnology. To achieve efficient CNT-based drug delivery, the interactions between the drug, CNT and biomolecular target need to be properly optimized. Recent advances in the computer-a...
Article
Background: Glutathione-s-transferases (GSTs) are enzymes that principally catalyze the conjugation of electrophilic compounds to the endogenous nucleophilic glutathione substrate, besides, they have other non-catalytic functions. The Plasmodium falciparum genome encodes a single isoform of GST (PfGST) which is involved in buffering the toxic heme...
Article
A molecular docking tool of AutoDock3.05 was evaluated for its ability to reproduce experimentally determined affinities of various sialic acid analogues toward hemagglutinin of influenza A virus. With the exception of those with a C6-modified glycerol side chain, the experimental binding affinities of most sialic acid analogues (C2, C4 and C5-subs...
Article
In this study fragment-based drug design is combined with molecular docking simulation technique, to design databases of virtual sialic acid (SA) analogues with new substitutions at C2, C5 and C6 positions of SA scaffold. Using spaces occupied by C2, C5 and C6 natural moieties of SA when bound to hemagglutinin (HA) crystallographic structure, new f...

Questions

Questions (6)
Question
Alsallam alaykom. I am using rdkit and py3dmol in pycharm, where I faced problem in making drawings visible.
it gives me for rdkit drawings:
<IPython.core.display.Image object>
and for py3dmol drawings gives nothing.
In jupyter everthing is going OK
the full code is provided below
import py3Dmol import copy from rdkit import Chem from rdkit.Chem import Draw from rdkit.Chem.Draw import IPythonConsole from rdkit.Chem import AllChem from rdkit.Chem import rdBase from rdkit.Chem import rdMolAlign from rdkit.Chem import rdMolDescriptors import numpy as np import streamlit as st p=AllChem.ETKDGv3() p.verbose=True mols=[m for m in Chem.SDMolSupplier('cdk2.sdf') if m !=None] [:2] for mol in mols: mol.RemoveAllConformers() hmols_1=[Chem.AddHs(m) for m in mols] hmols_2=copy.deepcopy(hmols_1) #generate 100 conformers per molcule for mol in hmols_1: AllChem.EmbedMultipleConfs(mol,100,p) for mol in hmols_2: AllChem.EmbedMultipleConfs(mol,100,p) writer=Chem.SDWriter('hmols_1.sdf') for mol in hmols_1: writer.write(mol) #import matplotlib.pyplot as plt #import PIL as pl #Draw.MolsToGridImage(mols) #fig.show() #from IPython.display import display, Image Draw.MolsToGridImage(mols) #IPythonConsole.display.display(fig) #fig.show() #.MolsToGridImage(mols) #st.image(Draw._drawerToImage().show()) # %% ### assignign force field scores crippen_contribs=[rdMolDescriptors._CalcCrippenContribs(mol) for mol in hmols_1] crippen_ref_contrib=crippen_contribs[0] crippen_prob_contribs=crippen_contribs[1:] ref_mol1=hmols_1[0] prob_mols_1=hmols_1[1:] mmff_params=[AllChem.MMFFGetMoleculeProperties(mol) for mol in hmols_2] mmff_ref_param=mmff_params[0] mmff_prob_params=mmff_params[1:] ref_mol2=hmols_2[0] prob_mols_2=hmols_2[1:] # %% ## allign using forcefield p_crippen=py3Dmol.view(width=600, height=400) p_crippen.addModel(Chem.MolToMolBlock(ref_mol1),'sdf') crippen_score=[] for idx, mol in enumerate(prob_mols_1): tempscore=[] for cid in range (100): crippenO3A=rdMolAlign.GetCrippenO3A(mol,ref_mol1,crippen_prob_contribs[idx],crippen_ref_contrib,cid,0) crippenO3A.Align() tempscore.append(crippenO3A.Score()) best=np.argmax(tempscore) p_crippen.addModel (Chem.MolToMolBlock(mol,confId=int(best)),'sdf') crippen_score.append(tempscore[best]) #p_crippen.setStyle({'stick': {}}) p_crippen.render() p_crippen p_crippen.show()
Question
How to do conformational search before DFT calculations in Gaussian WITHOUT using gmmx add-on.
I have tried using spartan and pcmodel but the conformation obtained cannot be used directly in DFT calculation; it gives error "no special actions if energy rises".
therefore, I tried using PM6 and AM1 semi-emiprical calculations before doing DFT but also gave errors "restarting incremental Fock formation" and "AX will form 1A0 Fock derivatives at one time"
Question
I have problem with simulating pr-lig complex where ligand has halogens like Cl or Br.
by preparing itp files using CgenFF server or Charmm-gui , i get an atom type LP1 and LP2 connected to the halgen. which makes me suspicious about the result.
Should I keep or remove those strange atoms (LP1 and LP2) ?
should I use another way to prepare the itp files ?
Question
I have 144 diffraction images for a crystal of high mosaicity about 2 and low resolution about 3.14.
the crystal has been prepared and soaked with ligand. I would like to solve the crystal structure looking merely for a substrate binding site.
I tried using IMosflm, however the results of integration showed that all reflections are partial and there is no full reflection at all.
Then I used scala from CCP4 to do 2D integration for partial reflections, then check the density map using molecular replacement (protein has been deposited in PDB) with Phoenix. I can Max have LLG 900 and TFZ of 11. I can only recognize coarse features like helices, however the map is not clear for many side chains as well as at the binding site.
Any suggestion to improve the results using parameters adjustment of iMosflm, Scala, Phoenix MR. OR even new suggested software.
I appreciate any suggestion.
thanks
Question
Although it is familiar in molecular biology, I could not found it in literature.
Using QM/MM is known to stimulate bond formation and breaking, however, how to drag this reaction to take place in AMBER.
If I am to use the simplified bond formation and breaking force field (Reaxff), how to incorporate it into Amber?

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