Protein inherent structures by different minimization strategies

Inherent structure versus geometric metric for state space discretization

Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the microcluster level, the IS approach and root-mean-square deviation (RMSD)-based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the microclusters are similar. The discrepancy at the microcluster level leads to different macroclusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macrocluster level in terms of conformational features and kinetics.

3D-QSAR AND CONTOUR MAP ANALYSIS OF TARIQUIDAR ANALOGUES AS MULTIDRUG RESISTANCE PROTEIN-1 (MRP1) INHIBITORS

One of the major obstacles to the successful chemotherapy towards several cancers is multidrug resistance of human cancer cells to anti-cancer drugs. An important contributor to multidrug resistance is the human multidrug resistance protein-1 transporter (MRP1), which is an efflux pump of the ABC (ATP binding cassette) superfamily. Thus, highly efficacious, third generation MRP1 inhibitors, like tariquidar analogues, are promising inhibitors of multidrug resistance and are under clinical trials. To maximize the efficacy of MRP1 inhibitors and to reduce systemic toxicity, it is important to limit the exposure of MRP1 inhibitors and anticancer drugs to normal tissues and to increase their co-localization with tumor cells. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) associated with 3D-Quantitiative structure-activity relationship (3D-QSAR) studies were performed on a series of tariquidar analogues, as selective MDR modulators. Best predictability was obtained with CoMFA model r2 (non-cross-validated square of correlation coefficient) = 0.968, F value = 151.768 with five components, standard error of estimate = 0.107 while the CoMSIA yielded r2 = 0.982, F value = 60.628 with six components, and standard error of estimate = 0.154. These results indicate that steric, electrostatic, hydrophobic (lipophilic), and hydrogen bond donor substituents play significant roles in multidrug resistance modulation of tariquidar analogues upon MRP1. The tariquidar analogue and MRP1 binding and stability data generated from CoMFA and CoMSIA based 3D-contour maps may further aid in study and design of tariquidar analogues as novel, potent and selective MDR modulator drug candidates.

Accounting for the kinetics in order parameter analysis: lessons from theoretical models and a disordered peptide

Molecular simulations as well as single molecule experiments have been widely analyzed in terms of order parameters, the latter representing candidate probes for the relevant degrees of freedom. Notwithstanding this approach is very intuitive, mounting evidence showed that such descriptions are inaccurate, leading to ambiguous definitions of states and wrong kinetics. To overcome these limitations a framework making use of order parameter fluctuations in conjunction with complex network analysis is investigated. Derived from recent advances in the analysis of single molecule time traces, this approach takes into account the fluctuations around each time point to distinguish between states that have similar values of the order parameter but different dynamics. Snapshots with similar fluctuations are used as nodes of a transition network, the clusterization of which into states provides accurate Markov-state-models of the system under study. Application of the methodology to theoretical models with a noisy order parameter as well as the dynamics of a disordered peptide illustrates the possibility to build accurate descriptions of molecular processes on the sole basis of order parameter time series without using any supplementary information.