Community network analysis derived from molecular dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNA Glu and an archaeal leucyl-tRNA synthetase (LeuRS):tRNA Leu complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the physical network whose overall topology is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller number of critical edges or interactions. Consistent with this analysis, there are a large number of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.aminoacyl-tRNA synthetase ͉ communication networks ͉ community ͉ suboptimal paths

Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.

networkview@scs.illinois.edu

Background: Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million sequences and 35 thousand structures of proteins and nucleic acids are available in public databases. Finding correlations in and between these data to answer critical research questions is extremely challenging. This problem needs to be approached from several directions: information science to organize and search the data; information visualization to assist in recognizing correlations; mathematics to formulate statistical inferences; and biology to analyze chemical and physical properties in terms of sequence and structure changes.

The ribosomal L1 stalk is a mobile structure implicated in directing tRNA movement during translocation through the ribosome. This article investigates three aspects of L1 stalk:tRNA interaction. First, by combining through the molecular dynamics flexible fitting method data from cryo-electron microscopy, X-ray crystallography, and molecular dynamics simulations, atomic models of different tRNAs occupying the hybrid P/E state interacting with the L1 stalk are obtained. These models confirm the assignment of FRET states from previous single-molecule investigations of L1 stalk dynamics. Second, the models reconcile how initiator tRNAfMet interacts less strongly with the L1 stalk than elongator tRNAPhe, as seen in previous single-molecule experiments. Third, results from a simulation of the entire ribosome in which the L1 stalk is moved from a half-closed to its open conformation are found to support the hypothesis that L1 stalk opening is involved in tRNA release from the ribosome.

Multiple Alignment is a new interface for performing and analyzing multiple protein structure alignments. It enables viewing levels of sequence and structure similarity on the aligned structures and performing a variety of evolutionary and bioinformatic tasks, including the construction of structure-based phylogenetic trees and minimal basis sets of structures that best represent the topology of the phylogenetic tree. It is implemented as a plugin for VMD (Visual Molecular Dynamics), which is distributed by the NIH Resource for Macromolecular Modeling and Bioinformatics at the University of Illinois.

Elongation factor Tu (EF-Tu) binds to all twenty standard aminoacyl-transfer RNAs (aa-tRNAs) and transports them to the ribosome while protecting the ester linkage between the tRNA and its cognate amino acid. We use molecular dynamics simulations to investigate the dynamics of the EF-Tu-GTP-aa-tRNA Cys complex and the roles played by Mg 2+ ions and modified nucleosides on the free energy of protein-RNA binding. Individual modified nucleosides have pronounced effects on the structural dynamics of tRNA and the EF-Tu-Cys-tRNA Cys interface. Combined energetic and evolutionary analyses identify the coevolution of residues in EF-Tu and aa-tRNAs at the binding interface. Highly conserved EF-Tu residues are responsible for both attracting aa-tRNAs as well as providing nearby nonbonded repulsive energies which help fine-tune molecular attraction at the binding interface. In addition to the 3′ CCA end, highly conserved tRNA nucleotides G1, G52, G53, and U54 contribute significantly to EF-Tu binding energies. Modification of U54 to thymine affects the structure of the tRNA common loop resulting in a change in binding interface contacts. In addition, other nucleotides, conserved within certain tRNA specificities, may be responsible for tuning aa-tRNA binding to EF-Tu. The trend in EF-TuCys-tRNA Cys binding energies observed as the result of mutating the tRNA agrees with experimental observation. We also predict variations in binding free energies upon misacylation of tRNA Cys with D-cysteine or O-phosphoserine and upon changing the protonation state of Lcysteine. Principal components analysis in each case reveals changes in the communication network across the protein-tRNA interface and is the basis for the entropy calculations.

a b s t r a c tAs the molecular representation of the genetic code, tRNA plays a central role in the translational machinery where it interacts with several proteins and other RNAs during the course of protein synthesis. These interactions exploit the dynamic flexibility of tRNA. In this minireview, we discuss the effects of modified bases, ions, and proteins on tRNA structure and dynamics and the challenges of observing its motions over the cycle of translation.