ABSTRACT
Nanomaterials in biological solutions are known to interact with proteins and have been documented to affect protein function, such as enzyme activity. Understanding the interactions of nanoparticles with biological components at the molecular level will allow for rational designs of nanomaterials for use in medical technologies. Here we present the first detailed molecular mechanics model of functionalized gold nanoparticle (NP) interacting with an enzyme (L-lactate dehydrogenase (LDH) enzyme). Molecular dynamics (MD) simulations of the response of LDH to the NP binding demonstrate that although atomic motions (dynamics) of the main chain exhibit only a minor response to the binding, the dynamics of side chains are significantly constrained in all four active sites that predict alteration in kinetic properties of the enzyme. It is also demonstrated that the 5 nm gold NPs cause a decrease in the maximal velocity of the enzyme reaction (V(max)) and a trend towards a reduced affinity (increased K(m)) for the ß-NAD binding site, while pyruvate enzyme kinetics (K(m) and V(max)) are not significantly altered in the presence of the gold NPs. These results demonstrate that modeling of NP:protein interactions can be used to understand alterations in protein function.
Subject(s)
Gold/chemistry , L-Lactate Dehydrogenase/chemistry , Metal Nanoparticles/chemistry , Metal Nanoparticles/ultrastructure , Models, Chemical , Molecular Dynamics Simulation , Computer Simulation , Enzyme Activation , Enzymes, Immobilized/chemistry , Enzymes, Immobilized/ultrastructure , L-Lactate Dehydrogenase/ultrastructure , Nanoconjugates/chemistry , Nanoconjugates/ultrastructure , Particle Size , Protein BindingABSTRACT
BACKGROUND: Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. PRINCIPAL FINDINGS: To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. CONCLUSIONS: Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).
