ABSTRACT
Proteins of the Heterochromatin Protein 1 (HP1) family are regulators of chromatin structure and genome function in eukaryotes. Post-translational modifications expand the repertoire of the chemical diversity of HP1 proteins and regulate their activity. Here, we investigated the effect of phosphorylation by Casein kinase 2 (CK2) on the structure, dynamics and binding activity of human HP1ß. We show that Ser89 in the hinge region is the most effective substrate, followed by Ser175 at the C-terminal tail. Phosphorylation at these sites results in localized conformational changes in HP1ß that do not compromise the ability of the protein to bind chromatin.
Subject(s)
Casein Kinase II/chemistry , Chromosomal Proteins, Non-Histone/chemistry , Protein Processing, Post-Translational , Amino Acid Sequence , Binding Sites , Chromobox Protein Homolog 5 , Chromosomal Proteins, Non-Histone/metabolism , Consensus Sequence , Heterochromatin/metabolism , Humans , Kinetics , Models, Molecular , Molecular Sequence Data , Nuclear Magnetic Resonance, Biomolecular , Phosphorylation , Protein BindingABSTRACT
We propose a novel fragment assembly method for low-resolution modeling of RNA and show how it may be used along with small-angle X-ray solution scattering (SAXS) data to model low-resolution structures of particles having as many as 12 independent secondary structure elements. We assessed this model-building procedure by using both artificial data on a previously proposed benchmark and publicly available data. With the artificial data, SAXS-guided models show better similarity to native structures than ROSETTA decoys. The publicly available data showed that SAXS-guided models can be used to reinterpret RNA structures previously deposited in the Protein Data Bank. Our approach allows for fast and efficient building of de novo models of RNA using approximate secondary structures that can be readily obtained from existing bioinformatic approaches. We also offer a rigorous assessment of the resolving power of SAXS in the case of small RNA structures, along with a small multimetric benchmark of the proposed method.
Subject(s)
Models, Molecular , RNA/chemistry , Algorithms , Base Sequence , Computer Simulation , Humans , Inverted Repeat Sequences , Molecular Sequence Data , Nucleic Acid Conformation , Scattering, Small Angle , Software , X-Ray DiffractionABSTRACT
BACKGROUND: Protein DataBank file format is used for the majority of biomolecular data available today. Haskell is a lazy functional language that enjoys a high-level class-based type system, a growing collection of useful libraries and a reputation for efficiency. FINDINGS: I present a fast library for processing biomolecular data in the Protein Data Bank format. I present benchmarks indicating that this library is faster than other frequently used Protein Data Bank parsing programs. The proposed library also features a convenient iterator mechanism, and a simple API modeled after BioPython. CONCLUSION: I set a new standard for convenience and efficiency of Protein Data Bank processing in a Haskell library, and release it to open source.
