Molecular profile to cancer cell line matchmaking

Profile-to-cell line matchmaking is a computational protocol to identify cancer cell lines that are genomically similar to a patient’s case profile. In doing so, high-throughput drug screens applied to the same cancer cell lines may be used for therapeutic hypothesis generation in research settings and potentially in clinical settings. To evaluate the metrics of the matchmaking, a hold-one-out approach of the considered cancer cell lines is applied, and molecular similarity models are assessed based on their ability to identify cancer cell lines that share therapeutic sensitivity.

Entropic Path Sampling: Computational Protocol to Evaluate Entropic Profile along a Reaction Path

Fleeting intermediates constitute dynamically-stepwise mechanisms. They have been characterized in molecular dynamics trajectories, but whether these intermediates form a free energy minimum to become entropic intermediate remains elusively defined. We developed a computational protocol known as entropic path sampling to evaluate the entropic variation of reacting species along a reaction path based on an ensemble of trajectories. Using cyclopentadiene dimerization as a model reaction, we observed a shallow entropic trap (–T∆S = –0.8 kcal/mol) along the reaction path which originates from an enhanced conformational flexibility as the reacting species enter into a flat energy region. As the reacting species further approach product formation, unfavorable entropic restriction fails to offset the potential energy drop, resulting in no free energy minimum along the post-TS pathway. Our results show that cyclopentadiene dimerization involves an entropic trap that leads to dynamic intermediates with elongated lifetime, but the reaction does not involve entropic intermediates.

MDO: A Computational Protocol for Accurate Prediction of Full Flexibility Receptor-Ligand Binding Mode Structures

Predicting the binding structure of bio-complex is essential for understanding its properties, functions, and mechanisms, but is rather difficult due to the huge sampling space involved. A new computational protocol, MDO, for finding the ligand binding structure is proposed. MDO consists of global sampling via MD simulation and clustering of the receptor configurations, local sampling via molecular docking and clustering of the ligand conformations, and binding structure optimization by the ONIOM (QM/QM) method. MDO is tested on 15 protein-ligand complexes with known accurate structures. The success rate of MDO predictions, with RMSD < 2 Å, is found to be 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities. The MDO protocol is a promising tool for the structure based drug design.

Computational Prediction of the Supramolecular Self-Assembling Properties of Organic Molecules: Flexibility vs Rigidity

<p>Two families of organic molecules with different backbones have been considered. The first family, composed by a substituted central phenyl is considered as flexible. The second one, based on a macrolactam-like unit, is considered as rigid. They have however a common feature, three amide moieties (as substituents for the phenyl and within the cycle for the macrolactam-like molecule) that allow hydrogen bonding when molecules are stacked. In this study we propose a computational protocol to unravel the ability of the different families to self-assemble into organic nanotubes. Starting from the monomer and going towards larger assemblies like dimers, trimers, and pentamers we applied different theoretical protocols to rationalize the behavior of the different assemblies. Both structures and thermodynamics were investigated to give a complete picture of the process. Thanks to the combination of a quantum mechanics approach and molecular dynamics simulations along with the use of tailored tools (non covalent interaction visualization) and techniques (umbrella sampling), we have been able to differentiate the two families and highlight the best candidate for self-assembling purposes.</p>

Computational Prediction of the Supramolecular Self-Assembling Properties of Organic Molecules: Flexibility vs Rigidity

<p>Two families of organic molecules with different backbones have been considered. The first family, composed by a substituted central phenyl is considered as flexible. The second one, based on a macrolactam-like unit, is considered as rigid. They have however a common feature, three amide moieties (as substituents for the phenyl and within the cycle for the macrolactam-like molecule) that allow hydrogen bonding when molecules are stacked. In this study we propose a computational protocol to unravel the ability of the different families to self-assemble into organic nanotubes. Starting from the monomer and going towards larger assemblies like dimers, trimers, and pentamers we applied different theoretical protocols to rationalize the behavior of the different assemblies. Both structures and thermodynamics were investigated to give a complete picture of the process. Thanks to the combination of a quantum mechanics approach and molecular dynamics simulations along with the use of tailored tools (non covalent interaction visualization) and techniques (umbrella sampling), we have been able to differentiate the two families and highlight the best candidate for self-assembling purposes.</p>