EasyModeller: A graphical interface to MODELLER

<p>Abstract</p> <p>Background</p> <p>MODELLER is a program for automated protein Homology Modeling. It is one of the most widely used tool for homology or comparative modeling of protein three-dimensional structures, but most users find it a bit difficult to start with MODELLER as it is command line based and requires knowledge of basic Python scripting to use it efficiently.</p> <p>Findings</p> <p>The study was designed with an aim to develop of "EasyModeller" tool as a frontend graphical interface to MODELLER using Perl/Tk, which can be used as a standalone tool in windows platform with MODELLER and Python preinstalled. It helps inexperienced users to perform modeling, assessment, visualization, and optimization of protein models in a simple and straightforward way.</p> <p>Conclusion</p> <p>EasyModeller provides a graphical straight forward interface and functions as a stand-alone tool which can be used in a standard personal computer with Microsoft Windows as the operating system.</p

Local Alignment Refinement Using Structural Assessment

Homology modeling is the most commonly used technique to build a three-dimensional model for a protein sequence. It heavily relies on the quality of the sequence alignment between the protein to model and related proteins with a known three dimensional structure. Alignment quality can be assessed according to the physico-chemical properties of the three dimensional models it produces

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

A quality metric for homology modeling: the H-factor

<p>Abstract</p> <p>Background</p> <p>The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, <it>in silico </it>protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and <it>in silico </it>methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.</p> <p>Results</p> <p>In this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.</p> <p>Conclusions</p> <p>We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at <url>http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor</url>.</p

Localization of the Drosophila Rad9 Protein to the Nuclear Membrane Is Regulated by the C-Terminal Region and Is Affected in the Meiotic Checkpoint

Rad9, Rad1, and Hus1 (9-1-1) are part of the DNA integrity checkpoint control system. It was shown previously that the C-terminal end of the human Rad9 protein, which contains a nuclear localization sequence (NLS) nearby, is critical for the nuclear transport of Rad1 and Hus1. In this study, we show that in Drosophila, Hus1 is found in the cytoplasm, Rad1 is found throughout the entire cell and that Rad9 (DmRad9) is a nuclear protein. More specifically, DmRad9 exists in two alternatively spliced forms, DmRad9A and DmRad9B, where DmRad9B is localized at the cell nucleus, and DmRad9A is found on the nuclear membrane both in Drosophila tissues and also when expressed in mammalian cells. Whereas both alternatively spliced forms of DmRad9 contain a common NLS near the C terminus, the 32 C-terminal residues of DmRad9A, specific to this alternative splice form, are required for targeting the protein to the nuclear membrane. We further show that activation of a meiotic checkpoint by a DNA repair gene defect but not defects in the anchoring of meiotic chromosomes to the oocyte nuclear envelope upon ectopic expression of non-phosphorylatable Barrier to Autointegration Factor (BAF) dramatically affects DmRad9A localization. Thus, by studying the localization pattern of DmRad9, our study reveals that the DmRad9A C-terminal region targets the protein to the nuclear membrane, where it might play a role in response to the activation of the meiotic checkpoint

Early Evolution of Ionotropic GABA Receptors and Selective Regimes Acting on the Mammalian-Specific Theta and Epsilon Subunits

BACKGROUND: The amino acid neurotransmitter GABA is abundant in the central nervous system (CNS) of both invertebrates and vertebrates. Receptors of this neurotransmitter play a key role in important processes such as learning and memory. Yet, little is known about the mode and tempo of evolution of the receptors of this neurotransmitter. Here, we investigate the phylogenetic relationships of GABA receptor subunits across the chordates and detail their mode of evolution among mammals. PRINCIPAL FINDINGS: Our analyses support two major monophyletic clades: one clade containing GABA(A) receptor alpha, gamma, and epsilon subunits, and another one containing GABA(A) receptor rho, beta, delta, theta, and pi subunits. The presence of GABA receptor subunits from each of the major clades in the Ciona intestinalis genome suggests that these ancestral duplication events occurred before the divergence of urochordates. However, while gene divergence proceeded at similar rates on most receptor subunits, we show that the mammalian-specific subunits theta and epsilon experienced an episode of positive selection and of relaxed constraints, respectively, after the duplication event. Sites putatively under positive selection are placed on a three-dimensional model obtained by homology-modeling. CONCLUSIONS: Our results suggest an early divergence of the GABA receptor subunits, before the split from urochordates. We show that functional changes occurred in the lineages leading to the mammalian-specific subunit theta, and we identify the amino acid sites putatively responsible for the functional divergence. We discuss potential consequences for the evolution of mammals and of their CNS

The human checkpoint sensor Rad9–Rad1–Hus1 interacts with and stimulates DNA repair enzyme TDG glycosylase

Human (h) DNA repair enzyme thymine DNA glycosylase (hTDG) is a key DNA glycosylase in the base excision repair (BER) pathway that repairs deaminated cytosines and 5-methyl-cytosines. The cell cycle checkpoint protein Rad9–Rad1–Hus1 (the 9-1-1 complex) is the surveillance machinery involved in the preservation of genome stability. In this study, we show that hTDG interacts with hRad9, hRad1 and hHus1 as individual proteins and as a complex. The hHus1 interacting domain is mapped to residues 67–110 of hTDG, and Val74 of hTDG plays an important role in the TDG–Hus1 interaction. In contrast to the core domain of hTDG (residues 110–308), hTDG(67–308) removes U and T from U/G and T/G mispairs, respectively, with similar rates as native hTDG. Human TDG activity is significantly stimulated by hHus1, hRad1, hRad9 separately, and by the 9-1-1 complex. Interestingly, the interaction between hRad9 and hTDG, as detected by co-immunoprecipitation (Co-IP), is enhanced following N-methyl-N′-nitro-N-nitrosoguanidine (MNNG) treatment. A significant fraction of the hTDG nuclear foci co-localize with hRad9 foci in cells treated with methylating agents. Thus, the 9-1-1 complex at the lesion sites serves as both a damage sensor to activate checkpoint control and a component of the BER

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

Human RAD18 Interacts with Ubiquitylated Chromatin Components and Facilitates RAD9 Recruitment to DNA Double Strand Breaks

RAD18 is an ubiquitin ligase involved in replicative damage bypass and DNA double-strand break (DSB) repair processes. We found that RPA is required for the dynamic pattern of RAD18 localization during the cell cycle, and for accumulation of RAD18 at sites of γ-irradiation-induced DNA damage. In addition, RAD18 colocalizes with chromatin-associated conjugated ubiquitin and ubiquitylated H2A throughout the cell cycle and following irradiation. This localization pattern depends on the presence of an intact, ubiquitin-binding Zinc finger domain. Using a biochemical approach, we show that RAD18 directly binds to ubiquitylated H2A and several other unknown ubiquitylated chromatin components. This interaction also depends on the RAD18 Zinc finger, and increases upon the induction of DSBs by γ-irradiation. Intriguingly, RAD18 does not always colocalize with regions that show enhanced H2A ubiquitylation. In human female primary fibroblasts, where one of the two X chromosomes is inactivated to equalize X-chromosomal gene expression between male (XY) and female (XX) cells, this inactive X is enriched for ubiquitylated H2A, but only rarely accumulates RAD18. This indicates that the binding of RAD18 to ubiquitylated H2A is context-dependent. Regarding the functional relevance of RAD18 localization at DSBs, we found that RAD18 is required for recruitment of RAD9, one of the components of the 9-1-1 checkpoint complex, to these sites. Recruitment of RAD9 requires the functions of the RING and Zinc finger domains of RAD18. Together, our data indicate that association of RAD18 with DSBs through ubiquitylated H2A and other ubiquitylated chromatin components allows recruitment of RAD9, which may function directly in DSB repair, independent of downstream activation of the checkpoint kinases CHK1 and CHK2