Improving the Performance of Protein Model Synthesis from Electron-Density Maps

Proteins are large biological molecules and the building blocks of all cells in living organisms. Modelling their structure supports the understanding of their role in key biological processes, including the onset, evolution and cure of diseases. Nevertheless, protein model building is extremely challenging. Although the computational tools for protein model building (e.g., from crystallographic data sets) have improved significantly in recent years, they still perform poorly for protein structures for which only data sets with low resolution and affected by poor phase distributions are available. This thesis introduces new methods that support and improve model building for such protein structures. We start with a systematic evaluation of all major automated crystallographic model-building pipelines using 1211 protein structures (202 at original resolution and 1009 at truncated resolutions). Using the results of this study as a baseline, we then propose and show the effectiveness of using pairwise pipeline combinations to build better protein models for many crystallographic data sets. As the performance of individual pipelines and pipeline combinations depends on the input data set, we introduce a predictive machine learning model that recommends pipelines or pipeline combinations suitable for a given data set, helping researchers avoid the time-consuming running of pipelines likely to perform poorly. The model bases its predictions on statistical features calculated from the electron-density map, and is available as a freely accessible web application. Finally, we introduce a neural network trained to recognise incorrect parts of a protein model during the building process. Developed using large training data sets newly created for this purpose, and integrated into the protein model building software Buccaneer, the neural networks enables Buccaneer to avoid these incorrect parts and to produce protein models with significantly improved completeness and fitting measures to crystallography data

Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening

Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library preparation have restrictive and/or expensive licenses. Freely available alternatives, though often effective, do not fully account for all possible ionization, tautomeric, and ring-conformational variants. We here present Gypsum-DL, a free, robust open-source program that addresses these challenges. As input, Gypsum-DL accepts virtual compound libraries in SMILES or flat SDF formats. For each molecule in the virtual library, it enumerates appropriate ionization, tautomeric, chiral, cis/trans isomeric, and ring-conformational forms. As output, Gypsum-DL produces an SDF file containing each molecular form, with 3D coordinates assigned. To demonstrate its utility, we processed 1558 molecules taken from the NCI Diversity Set VI and 56,608 molecules taken from a Distributed Drug Discovery (D3) combinatorial virtual library. We also used 4463 high-quality protein-ligand complexes from the PDBBind database to show that Gypsum-DL processing can improve virtual-screening pose prediction. Gypsum-DL is available free of charge under the terms of the Apache License, Version 2.0

Crystal structure of the 3C protease from South African Territories type 2 foot-and-mouth disease virus

The replication of foot-and-mouth disease virus (FMDV) is dependent on the virus-encoded 3C protease (3Cpro). As in other picornaviruses, 3Cpro performs most of the proteolytic processing of the polyprotein expressed from the single open reading frame in the RNA genome of the virus. Previous work revealed that the 3Cpro from serotype A – one of the seven serotypes of FMDV – adopts a trypsin-like fold. Phylogenetically the FMDV serotypes are grouped into two clusters, with O, A, C, and Asia 1 in one, and the three South African Territories serotypes, (SAT-1, SAT-2 and SAT-3) in another. We report here the cloning, expression and purification of 3C proteases from four SAT serotype viruses (SAT2/GHA/8/91, SAT1/NIG/5/81, SAT1/UGA/1/97, and SAT2/ZIM/7/83) and the crystal structure at 3.2 Å resolution of 3Cpro from SAT2/GHA/8/91)

Arabidopsis thaliana dehydroascorbate reductase 2 : conformational flexibility during catalysis

Dehydroascorbate reductase (DHAR) catalyzes the glutathione (GSH)-dependent reduction of dehydroascorbate and plays a direct role in regenerating ascorbic acid, an essential plant antioxidant vital for defense against oxidative stress. DHAR enzymes bear close structural homology to the glutathione transferase (GST) superfamily of enzymes and contain the same active site motif, but most GSTs do not exhibit DHAR activity. The presence of a cysteine at the active site is essential for the catalytic functioning of DHAR, as mutation of this cysteine abolishes the activity. Here we present the crystal structure of DHAR2 from Arabidopsis thaliana with GSH bound to the catalytic cysteine. This structure reveals localized conformational differences around the active site which distinguishes the GSH-bound DHAR2 structure from that of DHAR1. We also unraveled the enzymatic step in which DHAR releases oxidized glutathione (GSSG). To consolidate our structural and kinetic findings, we investigated potential conformational flexibility in DHAR2 by normal mode analysis and found that subdomain mobility could be linked to GSH binding or GSSG release

eBank UK: linking research data, scholarly communication and learning

This paper includes an overview of the changing landscape of scholarly communication and describes outcomes from the innovative eBank UK project, which seeks to build links from e-research through to e-learning. As introduction, the scholarly knowledge cycle is described and the role of digital repositories and aggregator services in linking data-sets from Grid-enabled projects to e-prints through to peer-reviewed articles as resources in portals and Learning Management Systems, are assessed. The development outcomes from the eBank UK project are presented including the distributed information architecture, requirements for common ontologies, data models, metadata schema, open linking technologies, provenance and workflows. Some emerging challenges for the future are presented in conclusion

Protein structural variation in computational models and crystallographic data

Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. Simple models defined by contact topology, known as elastic network models, have been used to model a variety of systems, but the validation is typically limited to individual modes for a single protein. We use anisotropic displacement parameters from crystallography to test the quality of prediction of both the magnitude and directionality of conformational variance. Normal modes from four simple elastic network model potentials and from the CHARMM forcefield are calculated for a data set of 83 diverse, ultrahigh resolution crystal structures. While all five potentials provide good predictions of the magnitude of flexibility, the methods that consider all atoms have a clear edge at prediction of directionality, and the CHARMM potential produces the best agreement. The low-frequency modes from different potentials are similar, but those computed from the CHARMM potential show the greatest difference from the elastic network models. This was illustrated by computing the dynamic correlation matrices from different potentials for a PDZ domain structure. Comparison of normal mode results with anisotropic temperature factors opens the possibility of using ultrahigh resolution crystallographic data as a quantitative measure of molecular flexibility. The comprehensive evaluation demonstrates the costs and benefits of using normal mode potentials of varying complexity. Comparison of the dynamic correlation matrices suggests that a combination of topological and chemical potentials may help identify residues in which chemical forces make large contributions to intramolecular coupling.Comment: 17 pages, 4 figure