A Comparison Between Elastic Network Interpolation and MD Simulation of 16S Ribosomal RNA

Abstract

In this paper a coarse-grained method called elastic network interpolation (ENI) is used to generate feasible transition pathways between two given conformations of the core central domain of 16S Ribosomal RNA (16S rRNA). The two given conformations are the extremes generated by a molecular dynamics (MD) simulation, which differ from each other by 10Å in root-mean-square deviation (RMSD). It takes only several hours to build an ENI pathway on a 1.5GHz Pentium with 512 MB memory, while the MD takes several weeks on high-performance multi-processor servers such as the SGI ORIGIN 2000/2100. It is shown that multiple ENI pathways capture the essential anharmonic motions of millions of timesteps in a particular MD simulation. A coarse-grained normal mode analysis (NMA) is performed on each intermediate ENI conformation, and the lowest 1% of the normal modes (representing about 40 degrees of freedom (DOF)) are used to parameterize fluctuations. This combined ENI/NMA method captures all intermediate conformations in the MD run with 1.5Å RMSD on average. In addition, if we restrict attention to the time interval of the MD run between the two extreme conformations, the RMSD between the closest ENI/NMA pathway and the MD results is about 1Å. These results may serve as a paradigm for reducedDOF dynamic simulations of large biological macromolecules as well as a method for the reduced-parameter interpretation of massive amounts of MD data.     

IWS: integrated web server for protein sequence and structure analysis

Rapid increase in protein sequence information from genome sequencing projects demand the intervention of bioinformatics tools to recognize interesting gene-products and associated function. Often, multiple algorithms need to be employed to improve accuracy in predictions and several structure prediction algorithms are on the public domain. Here, we report the availability of an Integrated Web-server as a bioinformatics online package dedicated for in-silico analysis of protein sequence and structure data (IWS). IWS provides web interface to both in-house and widely accepted programs from major bioinformatics groups, organized as 10 different modules. IWS also provides interactive images for Analysis Work Flow, which will provide transparency to the user to carry out analysis by moving across modules seamlessly and to perform their predictions in a rapid manner. AVAILABILITY: IWS IS AVAILABLE FROM THE URL: http://caps.ncbs.res.in/iws.     

High-throughput modeling and analysis of protein structural dynamics

Protein function is a dynamic property closely related to the conformational mechanisms of protein structure in its physiological environment. To understand and control the function of target proteins, it becomes increasingly important to develop methods and tools for predicting collective motions at the molecular level. In this article, we review computational methods for predicting conformational dynamics and discuss software tools for data analysis. In particular, we discuss a high-throughput, web-based system called iGNM for protein structural dynamics. iGNM contains a database of protein motions for more than 20 000 PDB structures and supports online calculations for newly deposited PDB structures or user-modified structures. iGNM allows dynamics analysis of protein structures ranging from enzymes to large complexes and assemblies, and enables the exploration of protein sequence-structure-dynamics-function relations.     

Tensorial elastic network model for protein dynamics: Integration of the anisotropic network model with bond‐bending and twist elasticities

We present a tensorial elastic network model (TNM) to describe the equilibrium fluctuations of proteins near their native fold structure. The model combines the anisotropic network model (ANM), bond bending elasticity, and backbone twist elasticity, and can predict both the isotropic fluctuations, similar to the Gaussian network model (GNM), and anisotropic fluctuations, similar to the ANM. TNM performs equally well for B‐factor predictions as GNM and predicts the anisotropy of B‐factors better than ANM. The model also outperforms the ANM in its predictability of the complete anisotropic displacement parameters. Proteins 2012; © 2012 Wiley Periodicals, Inc.     

Relation between sequence and structure of HIV-1 protease inhibitor complexes: a model system for the analysis of protein flexibility.

The flexibility of different regions of HIV-1 protease was examined by using a database consisting of 73 X-ray structures that differ in terms of sequence, ligands or both. The root-mean-square differences of the backbone for the set of structures were shown to have the same variation with residue number as those obtained from molecular dynamics simulations, normal mode analyses and X-ray B-factors. This supports the idea that observed structural changes provide a measure of the inherent flexibility of the protein, although specific interactions between the protease and the ligand play a secondary role. The results suggest that the potential energy surface of the HIV-1 protease is characterized by many local minima with small energetic differences, some of which are sampled by the different X-ray structures of the HIV-1 protease complexes. Interdomain correlated motions were calculated from the structural fluctuations and the results were also in agreement with molecular dynamics simulations and normal mode analyses. Implications of the results for the drug-resistance engendered by mutations are discussed briefly.     

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.     

Combining protein sequence,structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily

PAS domains are widespread in archaea, bacteria, and eukaryota, and play important roles in various functions. In this study, we aim to explore functional evolutionary relationship among proteins in the PAS domain superfamily in view of the sequence‐structure‐dynamics‐function relationship. We collected protein sequences and crystal structure data from RCSB Protein Data Bank of the PAS domain superfamily belonging to three biological functions (nucleotide binding, photoreceptor activity, and transferase activity). Protein sequences were aligned and then used to select sequence‐conserved residues and build phylogenetic tree. Three‐dimensional structure alignment was also applied to obtain structure‐conserved residues. The protein dynamics were analyzed using elastic network model (ENM) and validated by molecular dynamics (MD) simulation. The result showed that the proteins with same function could be grouped by sequence similarity, and proteins in different functional groups displayed statistically significant difference in their vibrational patterns. Interestingly, in all three functional groups, conserved amino acid residues identified by sequence and structure conservation analysis generally have a lower fluctuation than other residues. In addition, the fluctuation of conserved residues in each biological function group was strongly correlated with the corresponding biological function. This research suggested a direct connection in which the protein sequences were related to various functions through structural dynamics. This is a new attempt to delineate functional evolution of proteins using the integrated information of sequence, structure, and dynamics.     

EviCor: Interactive Web Platform for Exploration of Molecular Features and Response to Anti-cancer Drugs

Experimental biologists are often left alone with the task to download, process, and analyze big datasets in order to perform correlation or other simpler analyses. To address these issues, we introduce EviCor, a handy toolbox for exploration of data from large public resources such as The Cancer Genome Atlas and The Cancer Cell Line Encyclopedia, complemented with follow-up information on same samples, which couples omics datasets with drug response profiles (https://www.evicor.org/). The data was processed for easy retrieval from the server-side database and includes pre-computed drug-feature correlation tables. Using information from multiple independent sources, the task-oriented web interface presents relations between phenotype, single-molecule, and pathway variables with graphical, statistical, and network analysis tools. Building custom multivariate models is enabled via user-friendly web interface and programmatic access via RESTinterface. Project code is available at https://github.com/aveviort/HyperSet.