Wiggle—Predicting Functionally Flexible Regions from Primary Sequence

The Wiggle series are support vector machine–based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity

Molecular dynamics study of naturally existing cavity couplings in proteins

Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results

Molecular Dynamics Simulation Studies of DNA and Proteins: Force Field Parameter Development of Small Ligands and Convergence Analysis for Simulations of Biomolecules

In the first part of this dissertation, CHARMM force field parameters for DNA minor groove-binding polyamides were developed. The parameterization involved the subdivision of the polyamides into model compounds, which were calibrated against MP2/6-31G(d) data. To test the new parameters, fourteen 10 ns molecular dynamics crystal simulations have been carried out on a DNA/polyamide complex at low (113K) and high (300K) temperatures. Of the 18 helical parameters examined, only one (stagger) is found to be statistically significant from the crystal structure with a t-test at the 95% confidence level. For the high temperature, stagger is non-significant at the 97% confidence level, which underscores the importance of running multiple trajectories. It is observed that when the simulations are run at 300K, the DNA fragment begins to distort; however, better sampling is achieved. Competition between water and polyamides for hydrogen bonding to DNA is found to explain weak or unpredictable binding. In the second part, force field parameters for retinoids were developed. The retinoids were divided into model compounds and calibrated against MP2/6-31G(d) data. To test the parameters, five molecular dynamics crystal simulations of reported x-ray structures of protein/retinoid complexes were performed. The structural and geometric analysis of these simulations compares well to experiment, and some dynamics that could be important to ligand binding were discovered. The new parameters can now be used in simulations of retinoid-binding proteins to better understand these systems and in drug design to make new retinoids with therapeutic and anticancer potential. The last part explores the convergence of structural parameters in biomolecular systems. A simple statistical test was applied to the different parameters from a few long and many short simulations to observe which strategy is best. For the protein, both the long and short simulations gave similar results with respect to convergence. For the DNA, it was found that fraying effects penetrate four base pairs in from the ends of the helix. Structural parameters converge more quickly for the middle four bases than for all bases, and the long simulations yielded better results with respect to convergence than the short simulations