A threading approach to protein structure prediction: studies on TNF-like molecules, Rev proteins, and protein kinases

The main focus of this dissertation is the application of the threading approach to specific biological problems. The threading scheme developed in our group targets incorporating important structural features necessary for detecting structural similarity between the target sequence and the template structure. This enables us to use our threading method to solve problems for which sequence-based methods are not very much useful. We applied our threading method to predict the three-dimensional structures of lentivirus (EIAV, HIV-1, FIV, SIV) Rev proteins. Predicted structures of Rev proteins suggest that they share a structural similarity among themselves (four-helix bundle). Also, the threading approach has been utilized for screening for potential TNF-like molecules in Arabidopsis. The threading approach identified 35 potential TNF-like proteins in Arabidopsis, six of which are particularly interesting to be tested for the receptor kinase ligand activity. Threading method has also been used to identify potentially new protein kinases, which are not included in the protein kinase data base of C. elegans and Arabidopis. We identified eleven potentially new protein kinases and an additional protein worth investigating for protein kinase activity in C. elegans. Further, we identified ten potentially new protein kinases and additional four proteins worth investigating for the protein kinase activity in Arabidopsis

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems

Structural Model of the Rev Regulatory Protein from Equine Infectious Anemia Virus

Rev is an essential regulatory protein in the equine infectious anemia virus (EIAV) and other lentiviruses, including HIV-1. It binds incompletely spliced viral mRNAs and shuttles them from the nucleus to the cytoplasm, a critical prerequisite for the production of viral structural proteins and genomic RNA. Despite its important role in production of infectious virus, the development of antiviral therapies directed against Rev has been hampered by the lack of an experimentally-determined structure of the full length protein. We have used a combined computational and biochemical approach to generate and evaluate a structural model of the Rev protein. The modeled EIAV Rev (ERev) structure includes a total of 6 helices, four of which form an anti-parallel four-helix bundle. The first helix contains the leucine-rich nuclear export signal (NES). An arginine-rich RNA binding motif, RRDRW, is located in a solvent-exposed loop region. An ERLE motif required for Rev activity is predicted to be buried in the core of modeled structure where it plays an essential role in stabilization of the Rev fold. This structural model is supported by existing genetic and functional data as well as by targeted mutagenesis of residues predicted to be essential for overall structural integrity. Our predicted structure should increase understanding of structure-function relationships in Rev and may provide a basis for the design of new therapies for lentiviral diseases

A threading approach to protein structure prediction: studies on TNF-like molecules, Rev proteins, and protein kinases

The main focus of this dissertation is the application of the threading approach to specific biological problems. The threading scheme developed in our group targets incorporating important structural features necessary for detecting structural similarity between the target sequence and the template structure. This enables us to use our threading method to solve problems for which sequence-based methods are not very much useful. We applied our threading method to predict the three-dimensional structures of lentivirus (EIAV, HIV-1, FIV, SIV) Rev proteins. Predicted structures of Rev proteins suggest that they share a structural similarity among themselves (four-helix bundle). Also, the threading approach has been utilized for screening for potential TNF-like molecules in Arabidopsis. The threading approach identified 35 potential TNF-like proteins in Arabidopsis, six of which are particularly interesting to be tested for the receptor kinase ligand activity. Threading method has also been used to identify potentially new protein kinases, which are not included in the protein kinase data base of C. elegans and Arabidopis. We identified eleven potentially new protein kinases and an additional protein worth investigating for protein kinase activity in C. elegans. Further, we identified ten potentially new protein kinases and additional four proteins worth investigating for the protein kinase activity in Arabidopsis.</p

Microstructure-Dependent Gas Adsorption: Accurate Predictions of Methane Uptake in Nanoporous Carbons

We present a framework for rapidly predicting gas adsorption properties based on van der Waals density functional calculations and thermodynamic modeling. Utilizing this model and experimentally determined pore size distributions, we are able to accurately predict uptakes in five activated carbon materials without empirical potentials or lengthy simulations. Our results demonstrate that materials with smaller pores and higher heats of adsorption can still have poor adsorption characteristics due to relatively low densities of highly adsorbent pores

Structural Model of the Rev Regulatory Protein from Equine Infectious Anemia Virus

Rev is an essential regulatory protein in the equine infectious anemia virus (EIAV) and other lentiviruses, including HIV-1. It binds incompletely spliced viral mRNAs and shuttles them from the nucleus to the cytoplasm, a critical prerequisite for the production of viral structural proteins and genomic RNA. Despite its important role in production of infectious virus, the development of antiviral therapies directed against Rev has been hampered by the lack of an experimentally-determined structure of the full length protein. We have used a combined computational and biochemical approach to generate and evaluate a structural model of the Rev protein. The modeled EIAV Rev (ERev) structure includes a total of 6 helices, four of which form an anti-parallel four-helix bundle. The first helix contains the leucine-rich nuclear export signal (NES). An arginine-rich RNA binding motif, RRDRW, is located in a solvent-exposed loop region. An ERLE motif required for Rev activity is predicted to be buried in the core of modeled structure where it plays an essential role in stabilization of the Rev fold. This structural model is supported by existing genetic and functional data as well as by targeted mutagenesis of residues predicted to be essential for overall structural integrity. Our predicted structure should increase understanding of structure-function relationships in Rev and may provide a basis for the design of new therapies for lentiviral diseases.This article is from PloS ONE 4 (2009): e4178, doi: 10.1371/journal.pone.0004178. Posted with permission.</p

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.This article is from BMC Bioinformatics 5 (2005): 205, doi: 10.1186/1471-2105-5-205. Posted with permission.</p

Ultrafast Energy Transfer Process in Confined Gold Nanospheres Revealed by Femtosecond X‑ray Imaging and Diffraction

Femtosecond laser pulses drive nonequilibrium phase transitions via reaction paths hidden in thermal equilibrium. This stimulates interest to understand photoinduced ultrafast melting processes, which remains incomplete due to challenges in resolving accompanied kinetics at the relevant space–time resolution. Here, by newly establishing a multiplexing femtosecond X-ray probe, we have successfully revealed ultrafast energy transfer processes in confined Au nanospheres. Real-time images of electron density distributions with the corresponding lattice structures elucidate that the energy transfer begins with subpicosecond melting at the specimen boundary earlier than the lattice thermalization, and proceeds by forming voids. Two temperature molecular dynamics simulations uncovered the presence of both heterogeneous melting with the melting front propagation from surface and grain boundaries and homogeneous melting with random melting seeds and nanoscale voids. Supported by experimental and theoretical results, we provide a comprehensive atomic-scale picture that accounts for the ultrafast laser-induced melting and evaporation kinetics