Mohamed Ismael

Applying ultra-accelerated quantum chemical molecular dynamics technique for the evaluation of ligand protein interactions

Ligand–protein interactions have been studied using several chemical information techniques including quantum chemical methods that are applied to truncated systems composed of the ligand molecule and the surrounding amino acids of the receptor. Fragmented quantum molecular chemical studies are also a choice to study the enzyme–ligand system holistically, however there are still restrictions on the number of water molecules that can be included in a study of this nature. In this work we adopt a completely different approach to study ligand–protein interactions accounting explicitly for as many solvent molecules as possible and without the need for a fragmented calculation. Furthermore, we embed our quantum chemical calculations within a molecular dynamics framework that enables a fundamentally fast system for quantum chemical molecular dynamic simulations (QCMD). Central to this new system for QCMD is the tight binding QC system, newly developed in our laboratories, which combined with the MD paradigm results in an ultra-accelerated QCMD method for protein–ligand interaction evaluations. We have applied our newly developed system to the dihydrofolate reductase (DHFR)–methotrexate (MTX) system. We show how the proposed method leads us to new insights into the main interactions that bind MTX to the enzyme, mainly the interaction between the amino group of MTX and Asp27 of DHFR, as well as MTX amino group with Thr113 of DHFR, which have been only elucidated experimentally to date.

The effect of R249S carcinogenic and H168R-R249S suppressor mutations on p53-DNA interaction, a multi scale computational study

In this study we have undertaken the theoretical analysis of the effect of R249S carcinogenic and H168R-R249S suppressor mutation at core domain of the tumor suppressor protein p53, on its natural interaction with DNA using a newly developed method. The results show that the carcinogenic mutation R249S affects the flexibility of L3 loop region in p53, inducing the loss of important hydrogen bonds observed at interaction in the wild-type with DNA, on the other hand the suppressor mutation H168R on the R249S assists in maintaining the wild-type like flexibility of the L3 region in p53 and thus recover the interaction terms lost in the carcinogenic mutation alone. The present study sets a new direction in the development of new drugs that may restore the interactions that lost as a consequence of the carcinogenic mutations in p53.

A graph theoretical approach to the effect of mutation on the flexibility of the DNA binding domain of p53 protein

Tumor suppressor protein p53 becomes inactive due to mutation on its DNA binding core domain leading to misbehavior of this protein and preventing its interaction with DNA. In the present study, changes of the protein conformation by five hot spot mutations of T-p53C were assessed preventing the mutants wild-type (WT) behavior. While studies of this nature were undertaken both experimentally and theoretically, the focus is fundamentally on the effects of the mutation on the dynamics of the protein. Hence, the basic concept underlying this study is the change in flexibility or rigidity of the protein. It was found that stable variant T-p53C (PDB-ID: 1uol) that is structurally and functionally very close to wild-type p53 is the most rigid structure and each single carcinogenic mutation on it makes the structure more flexible. We hypothesize that these changes of the molecule’s flexibility disrupt the network of hydrogen bonds associated with the interaction of WT not only at interaction but in the internal structures of the mutants as well, which prevents them from interacting in the WT fashion loosing the anti-cancer properties of WT.

An Evolving Automaton for RNA Secondary Structure Prediction

Conventional methods for RNA 2D structure prediction search for minimal free energy structures. RNAs, however, RNAs do not always adopt global minimum structures. Rather, their structure is the result of the folding pathway followed by the structure in nature, which adopts sub-optimal folds occurring along the pathway. Our algorithm consists of an automaton that generates RNA structures by searching for optimal folding pathways. The automaton is endowed of operations to travel throughout the hyperspace of conformers embedded in a base pairing matrix. Using genetic programming it evolves optimizing its ability to find optimal pathways and finally 2D structures. Comparing the evolving automaton with conventional methods shows its potential.