Kelly M. Thayer

Molecular dynamics simulations of DNA curvature and flexibility: Helix phasing and premelting†

Recent studies of DNA axis curvature and flexibility based on molecular dynamics (MD) simulations on DNA are reviewed. The MD simulations are on DNA sequences up to 25 base pairs in length, including explicit consideration of counterions and waters in the computational model. MD studies are described for ApA steps, A‐tracts, for sequences of A‐tracts with helix phasing. In MD modeling, ApA steps and A‐tracts in aqueous solution are essentially straight, relatively rigid, and exhibit the characteristic features associated with the B′‐form of DNA. The results of MD modeling of A‐tract oligonucleotides are validated by close accord with corresponding crystal structure results and nuclear magnetic resonance (NMR) nuclear Overhauser effect (NOE) and residual dipolar coupling (RDC) structures of d(CGCGAATTCGCG) and d(GGCAAAAAACGG). MD simulation successfully accounts for enhanced axis curvature in a set of three sequences with phased A‐tracts studied to date. The primary origin of the axis curvature in the MD model is found at those pyrimidine/purine YpR “flexible hinge points” in a high roll, open hinge conformational substate. In the MD model of axis curvature in a DNA sequence with both phased A‐tracts and YpR steps, the A‐tracts appear to act as positioning elements that make the helix phasing more precise, and key YpR steps in the open hinge state serve as curvature elements. Our simulations on a phased A‐tract sequence as a function of temperature show that the MD simulations exhibit a premelting transition in close accord with experiment, and predict that the mechanism involves a B′‐to‐B transition within A‐tracts coupled with the prediction of a transition in key YpR steps from the high roll, open hinge, to a low roll, closed hinge substate. Diverse experimental observations on DNA curvature phenomena are examined in light of the MD model with no serious discrepancies. The collected MD results provide independent support for the “non‐A‐tract model” of DNA curvature. The “junction model” is indicated to be a special case of the non‐A‐tract model when there is a Y base at the 5′ end of an A‐tract. In accord with crystallography, the “ApA wedge model” is not supported by MD.

Evolutionary Covariance Combined with Molecular Dynamics Predicts a Framework for Allostery in the MutS DNA Mismatch Repair Protein

Mismatch repair (MMR) is an essential, evolutionarily conserved pathway that maintains genome stability by correcting base-pairing errors in DNA. Here we examine the sequence and structure of MutS MMR protein to decipher the amino acid framework underlying its two key activities-recognizing mismatches in DNA and using ATP to initiate repair. Statistical coupling analysis (SCA) identified a network (sector) of coevolved amino acids in the MutS protein family. The potential functional significance of this SCA sector was assessed by performing molecular dynamics (MD) simulations for alanine mutants of the top 5% of 160 residues in the distribution, and control nonsector residues. The effects on three independent metrics were monitored: (i) MutS domain conformational dynamics, (ii) hydrogen bonding between MutS and DNA/ATP, and (iii) relative ATP binding free energy. Each measure revealed that sector residues contribute more substantively to MutS structure-function than nonsector residues. Notably, sector mutations disrupted MutS contacts with DNA and/or ATP from a distance via contiguous pathways and correlated motions, supporting the idea that SCA can identify amino acid networks underlying allosteric communication. The combined SCA/MD approach yielded novel, experimentally testable hypotheses for unknown roles of many residues distributed across MutS, including some implicated in Lynch cancer syndrome.

Energetic Landscape of MDM2-p53 Interactions by Computational Mutagenesis of the MDM2-p53 Interaction

The ubiquitin ligase MDM2, a principle regulator of the tumor suppressor p53, plays an integral role in regulating cellular levels of p53 and thus a prominent role in current cancer research. Computational analysis used MUMBO to rotamerize the MDM2-p53 crystal structure 1YCR to obtain an exhaustive search of point mutations, resulting in the calculation of the ΔΔG comprehensive energy landscape for the p53-bound regulator. The results herein have revealed a set of residues R65-E69 on MDM2 proximal to the p53 hydrophobic binding pocket that exhibited an energetic profile deviating significantly from similar residues elsewhere in the protein. In light of the continued search for novel competitive inhibitors for MDM2, we discuss possible implications of our findings on the drug discovery field.

Chemical principles additive model aligns low consensus DNA targets of p53 tumor suppressor protein

Computational prediction of the interaction between protein transcription factors and their cognate DNA binding sites in genomic promoters constitutes a formidable challenge in two situations: when the number of discriminatory interactions is small compared to the length of the binding site, and when DNA binding sites possess a poorly conserved consensus binding motif. The transcription factor p53 tumor suppressor protein and its target DNA exhibit both of these issues. From crystal structure analysis, only three discriminatory amino acid side chains contact the DNA for a binding site spanning ten base pairs. Furthermore, our analysis of a dataset of genome wide fragments binding to p53 revealed many sequences lacking the expected consensus. The low information content leads to an overestimation of binding sites, and the lack of conservation equates to a computational alignment problem. Within a fragment of DNA known to bind to p53, computationally locating the position of the site equates to aligning the DNA with the binding interface. From a molecular perspective, that alignment implies a specification of which DNA bases are interacting with which amino acid side chains, and aligning many sequences to the same protein interface concomitantly produces a multiple sequence alignment. From this vantage, we propose to cast prediction of p53 binding sites as an alignment to the protein binding surface with the novel approach of optimizing the alignment of DNA fragments to the p53 binding interface based on chemical principles. A scoring scheme based on this premise was successfully implemented to score a dataset of biological DNA fragments known to contain p53 binding sites. The results illuminate the mechanism of recognition for the protein-DNA system at the forefront of cancer research. These findings implicate that p53 may recognize its target binding sites via several different mechanisms which may include indirect readout.

Dependence of prevalence of contiguous pathways in proteins on structural complexity

Allostery is a regulatory mechanism in proteins where an effector molecule binds distal from an active site to modulate its activity. Allosteric signaling may occur via a continuous path of residues linking the active and allosteric sites, which has been suggested by large conformational changes evident in crystal structures. An alternate possibility is that the signal occurs in the realm of ensemble dynamics via an energy landscape change. While the latter was first proposed on theoretical grounds, increasing evidence suggests that such a control mechanism is plausible. A major difficulty for testing the two methods is the ability to definitively determine that a residue is directly involved in allosteric signal transduction. Statistical Coupling Analysis (SCA) is a method that has been successful at predicting pathways, and experimental tests involving mutagenesis or domain substitution provide the best available evidence of signaling pathways. However, ascertaining energetic pathways which need not be contiguous is far more difficult. To date, simple estimates of the statistical significance of a pathway in a protein remain to be established. The focus of this work is to estimate such benchmarks for the statistical significance of contiguous pathways for the null model of selecting residues at random. We found that when 20% of residues in proteins are randomly selected, contiguous pathways at the 6 Å cutoff level were found with success rates of 51% in PDZ, 30% in p53, and 3% in MutS. The results suggest that the significance of pathways may have system specific factors involved. Furthermore, the possible existence of false positives for contiguous pathways implies that signaling could be occurring via alternate routes including those consistent with the energetic landscape model.