ARP/wARP for crystallographic model building and drug discovery

The ARP/wARP software project combines automated model building and refinement into an unified approach for macromolecular crystal structure determination. The project is based on two decades of extensive research and development in the areas of macromolecular X-ray crystallography, informatics, data mining and statistical pattern recognition. ARP/wARP collects a vast amount of computationally efficient methods and provides easy-to-use pipelines for building models of proteins, nucleotides, ligands, as well as their complexes. All methods are intuitively accessible from the ArpNavigator [1], which grants direct visualisation and real-time interaction with model building results. Structures determined using ARP/wARP include histones, hsp70, viral proteases, an insect antifreeze protein, transferases, deadenylases, synthases, kinases, photolyases and the spliceosome. The novel release of ARP/wARP, version 7.4, comes with notable innovations for determining structures at medium-to-low resolution such as exploitation of non-crystallographic symmetry, improved protocols for model update and estimation of validity of built models. Joint releases with the CCP4 suite improve software development and integration, and make the installation and updates fast and convenient for the user. A novel procedure for the automatic identification of ligands in electron density maps is introduced. It is based on the sparse parameterisation of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. The integration of the ViCi web-server for in-silico ligand-based drug design and updated stereo-chemical restraints for ligand fitting make ARP/wARP an asset for crystallographic drug discovery pipelines.

Towards structural characterization of H. polymorpha telomerase components

Telomeres are regions of non-coding DNA that cap the chromosomes, preventing the loss of coding DNA during cell division and contributing to chromosomal stability. In actively dividing cells, such as embryonic stem cells, the telomeres need to elongated by telomerase. The telomerase complex consist of the enzyme telomerase reverse transcriptase (TERT), telomerase RNA (TR) and additional proteins. TERT and TR are required for the telomerase activity in vitro. Telomerase is active in vast majority of the cancer cells ensuring continuous cell division and tumor growth. Syndromes leading to premature aging are often associated with short telomeres. Finding ways to regulate the telomerase activity would help to advance therapies for these conditions. However, the structural information available of the telomerase complex is very limited. We have chosen thermophilic yeast Hansenula polymorpha as a model system due to the stability of its proteins. The N-terminal domain of the TERT is essential for telomerase activity and possibly is involved in binding of TR, telomeric DNA and additional protein components of the telomerase complex. We have crystallised the N-terminal domain of H. polymorpha TERT and, in lack of a homologious structure, produced a seleno-methionine derivative of the protein. MAD data on N-terminal domain has been collected to resolution of 2.0 Å at the PETRA-III beamline P13 (EMBL/DESY) in Hamburg. We will discuss the structure-function relationship of the N-domain and the whole TERT component.

ValiFrag: Evaluating fragment quality during automated protein model building

X-ray diffraction data from flexible macromolecules and their complexes can rarely be measured to a resolution better than 3 Å. Due to a loss of detectable atomic features, the determination of low-resolution structures is beyond the current operational range of crystallographic software and requires a large amount of manual intervention. ARP/wARP [1] v7.4 generates structures that are up to 80% complete at 3.0 Å, but the completeness drops sharply as the resolution gets worse. Reduction of the model completeness is accompanied with an increase in the number of fragments built, which become shorter. Such fragments are applicable for further model building if they are correct. Though, if they are wrong they may cause the formation of incorrectly built regions in the final model. Thus, there is a need to improve fragment quality before automated model completion is applied. We exploit the vast amount of structural information deposited in the Protein Data Bank (PDB) [2], to make use of it for structural validation of built fragments. Precisely, we evaluate the conformation of each fragment. If the conformation is present in several different protein models in the PDB, it is likely to be modelled correctly in the built model and is accepted. If, on the contrary, it cannot be found in any PDB model, it is probably incorrect. Here we present the software implementation of this validation, called ValiFrag, which checks the validity of automatically built protein chain fragments by evaluating their occurrence in the PDB. Protein models from the PDB were broken into dipeptides and conformational parameters for each of these were then stored in a database. For each automatically built fragment, ValiFrag computes the probability of it to be correct according to the conformation of all possible dipeptides. It can, therefore, assess which fragments are likely to be structurally incorrect and should possibly be modified, or even removed, to improve the final model.