Development of GMP-1 a molecular chaperone network modulator protecting mitochondrial function and its assessment in fly and mice models of Alzheimer's disease.

Mitochondrial dysfunction is an early feature of Alzheimer's disease (AD) and may play an important role in the pathogenesis of disease. It has been shown that amyloid beta peptide (Aß) and amyloid precursor protein (APP) interact with mitochondria contributing to the mitochondrial dysfunction in AD. Prevention of abnormal protein targeting to mitochondria can protect normal mitochondrial function, increase neuronal survival and at the end, ameliorate symptoms of AD and other neurodegenerative disorders. First steps of mitochondrial protein import are coordinated by molecular chaperones Hsp70 and Hsp90 that bind to the newly synthesized mitochondria-destined proteins and deliver them to the protein import receptors on the surface of organelle. Here, we have described the development of a novel compound named GMP-1 that disrupts interactions between Hsp70/Hsp90 molecular chaperones and protein import receptor Tom70. GMP-1 treatment of SH-SY5Y cells results in decrease in mitochondria-associated APP and protects SH-SY5Y cells from toxic effect of Aß1-42 exposure. Experiments in drosophila and mice models of AD demonstrated neuroprotective effect of GMP-1 treatment, improvement in memory and behaviour tests as well as restoration of mitochondrial function.

Ligand Release Pathways Obtained with WExplore: Residence Times and Mechanisms.

The binding of ligands with their molecular receptors is of tremendous importance in biology. Although much emphasis has been placed on characterizing binding sites and bound poses that determine the binding thermodynamics, the pathway by which a ligand binds importantly determines the binding kinetics. The computational study of entire unbiased ligand binding and release pathways is still an emerging field, made possible only recently by advances in computational hardware and sampling methodologies. We have developed one such method (WExplore) that is based on a weighted ensemble of trajectories, which we apply to ligand release for the first time, using a set of three previously characterized interactions between low-affinity ligands and the protein FKBP-12 (FK-506 binding protein). WExplore is found to be more efficient that conventional sampling, even for the nanosecond-scale unbinding events observed here. From a nonequilibrium ensemble of unbinding trajectories, we obtain ligand residence times and release pathways without using biasing forces or a Markovian assumption of transitions between regions. We introduce a set of analysis tools for unbinding transition pathways, including using von Mises-Fisher distributions to model clouds of ligand exit points, which provide a quantitative proxy for ligand surface diffusion. Differences between the transition pathway ensembles of the three ligands are identified and discussed.

Assessment and acceleration of binding energy calculations for protein-ligand complexes by the fragment molecular orbital method.

In the field of drug discovery, it is important to accurately predict the binding affinities between target proteins and drug applicant molecules. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics-based force fields, although they cannot fully describe protein-ligand interactions. A noteworthy computational method in development involves large-scale electronic structure calculations. Fragment molecular orbital (FMO) method, which is one of such large-scale calculation techniques, is applied in this study for calculating the binding energies between proteins and ligands. By testing the effects of specific FMO calculation conditions (including fragmentation size, basis sets, electron correlation, exchange-correlation functionals, and solvation effects) on the binding energies of the FK506-binding protein and 10 ligand complex molecule, we have found that the standard FMO calculation condition, FMO2-MP2/6-31G(d), is suitable for evaluating the protein-ligand interactions. The correlation coefficient between the binding energies calculated with this FMO calculation condition and experimental values is determined to be R = 0.77. Based on these results, we also propose a practical scheme for predicting binding affinities by combining the FMO method with the quantitative structure-activity relationship (QSAR) model. The results of this combined method can be directly compared with experimental binding affinities. The FMO and QSAR combined scheme shows a higher correlation with experimental data (R = 0.91). Furthermore, we propose an acceleration scheme for the binding energy calculations using a multilayer FMO method focusing on the protein-ligand interaction distance. Our acceleration scheme, which uses FMO2-HF/STO-3G:MP2/6-31G(d) at R(int) = 7.0 Å, reduces computational costs, while maintaining accuracy in the evaluation of binding energy.