Polymorphisms and disease: hotspots of inactivation in methyltransferases

Insights from molecular dynamics simulations for computational protein design

A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions. Although much success on this front has been achieved, design success rates remain low, an ever-present reminder of our limited understanding of the relationship between amino acid sequences and the structures they adopt. In addition to experimental techniques and rational design strategies, computational methods have been employed to aid in the design and engineering of proteins. Molecular dynamics (MD) is one such method that simulates the motions of proteins according to classical dynamics. Here, we review how insights into protein dynamics derived from MD simulations have influenced the design of proteins. One of the greatest strengths of MD is its capacity to reveal information beyond what is available in the static structures deposited in the Protein Data Bank. In this regard simulations can be used to directly guide protein design by providing atomistic details of the dynamic molecular interactions contributing to protein stability and function. MD simulations can also be used as a virtual screening tool to rank, select, identify, and assess potential designs. MD is uniquely poised to inform protein design efforts where the application requires realistic models of protein dynamics and atomic level descriptions of the relationship between dynamics and function. Here, we review cases where MD simulations was used to modulate protein stability and protein function by providing information regarding the conformation(s), conformational transitions, interactions, and dynamics that govern stability and function. In addition, we discuss cases where conformations from protein folding/unfolding simulations have been exploited for protein design, yielding novel outcomes that could not be obtained from static structures.

New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

Most rotamer libraries are generated from subsets of the PDB and do not fully represent the conformational scope of protein side chains. Previous attempts to rectify this sparse coverage of conformational space have involved application of weighting and smoothing functions. We resolve these limitations by using physics-based molecular dynamics simulations to determine more accurate frequencies of rotameric states. This work forms part of our Dynameomics initiative and uses a set of 807 proteins selected to represent 97% of known autonomous protein folds, thereby eliminating the bias toward common topologies found within the PDB. Our Dynameomics derived rotamer libraries encompass 4.8 × 10(9) rotamers, sampled from at least 51,000 occurrences of each of 93,642 residues. Here, we provide a backbone-dependent rotamer library, based on secondary structure ϕ/ψ regions, and an update to our 2011 backbone-independent library that addresses the doubling of our dataset since its original publication.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Methionine synthase A2756G transition might be a risk factor for male infertility: Evidences from seven case-control studies

Methionine synthase (MTR) has a crucial role in DNA synthesis and methylation reactions. The aim of this study was to investigate the association of the MTR-A2756G polymorphism with idiopathic male infertility. Blood samples were collected from 217 idiopathic infertile- and 233 healthy-men, and MTR-A2756G genotyping was performed by PCR-RFLP. Meta-analysis was conducted by pooling our data with the data obtained from 6 previous studies. Also, the effects of this substitution on protein structure were evaluated by bioinformatics tools. Our study revealed the association of AG-genotype, GG-genotype, and G-allele with male infertility. Meta-analysis showed a significant association between A2756G transition and male infertility. In addition, structural analysis of the transition effect on protein revealed a significant influence on MTR function (with score: 38; expected accuracy: 66%). These findings suggest that the A2756G substitution might be a genetic risk factor and a potential biomarker for idiopathic male infertility.

Recognition of methylated DNA through methyl-CpG binding domain proteins

DNA methylation is a key regulatory control route in epigenetics, involving gene silencing and chromosome inactivation. It has been recognized that methyl-CpG binding domain (MBD) proteins play an important role in interpreting the genetic information encoded by methylated DNA (mDNA). Although the function of MBD proteins has attracted considerable attention and is well characterized, the mechanism underlying mDNA recognition by MBD proteins is still poorly understood. In this article, we demonstrate that the methyl-CpG dinucleotides are recognized at the MBD–mDNA interface by two MBD arginines through an interplay of hydrogen bonding and cation-π interaction. Through molecular dynamics and quantum-chemistry calculations we investigate the methyl-cytosine recognition process and demonstrate that methylation enhances MBD–mDNA binding by increasing the hydrophobic interfacial area and by strengthening the interaction between mDNA and MBD proteins. Free-energy perturbation calculations also show that methylation yields favorable contribution to the binding free energy for MBD–mDNA complex.

Structural effects of the L145Q, V157F, and R282W cancer-associated mutations in the p53 DNA-binding core domain

The p53 tumor suppressor is a transcription factor involved in many important signaling pathways, such as apoptosis and cell-cycle arrest. In over half of human cancers, p53 function is compromised by a mutation in its gene. Mutations in the p53 DNA-binding core domain destabilize the structure and reduce DNA-binding activity. We performed molecular dynamics simulations at physiological temperature to study the structural and dynamic effects of the L145Q, V157F, and R282W cancer-associated mutations in comparison to the wild-type protein. While there were common regions of destabilization in the mutant simulations, structural changes particular to individual mutations were also observed. Significant backbone deviations of the H2 helix and S7-S8 loop were observed in all mutant simulations; the H2 helix binds to DNA. In addition, the L145Q and V157F mutations, which are located in the β-sandwich core of the domain, disrupted the β-sheet structure and the loop-sheet-helix motif. The R282W mutation caused distortion of the loop-sheet-helix motif, but otherwise this mutant was similar to the wild-type structure. The introduction of these mutations caused rearrangement of the DNA-binding surface, consistent with their reduced DNA-binding activity. The simulations reveal detailed effects of the mutations on the stability and dynamics of p53 that may provide insight for therapeutic approaches.

Dynameomics: protein dynamics and unfolding across fold space

All currently known structures of proteins together define ‘protein fold space’. To increase the general understanding of protein dynamics and protein folding, we selected a set of 807 proteins and protein domains that represent 95% of the currently known autonomous folded domains present in globular proteins. Native state and unfolding simulations of these representatives are now complete and accessible via a novel database containing over 11 000 simulations. Because protein folding is a microscopically reversible process, these simulations effectively sample protein folding across all of protein fold space. Here, we give an overview of how the representative proteins were selected and how the simulations were performed and validated. We then provide examples of different types of analyses that can be performed across our large set of simulations, made possible by the database approach. We further show how the unfolding simulations can be used to compare unfolding of structural elements in isolation and in different structural contexts, using as an example a short, triple stranded β-sheet that forms the WW domain and is present in several larger unrelated proteins.