Author
Tom L. Blundell
Other affiliations: Agricultural and Food Research Council, University of Oxford, Medical Research Council ...read more
Bio: Tom L. Blundell is an academic researcher from University of Cambridge. The author has contributed to research in topics: Protein structure & Protein secondary structure. The author has an hindex of 86, co-authored 687 publications receiving 56613 citations. Previous affiliations of Tom L. Blundell include Agricultural and Food Research Council & University of Oxford.
Papers published on a yearly basis
Papers
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TL;DR: A comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures, which is automated and illustrated by the modelling of trypsin from two other serine proteinases.
12,386 citations
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TL;DR: A novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development and performs as well or better than current methods.
Abstract: Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.
1,866 citations
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TL;DR: It is demonstrated that the combination of these three key features implemented in FUGUE improves both homology recognition performance and alignment accuracy.
1,263 citations
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TL;DR: The TRANSPARENT TESTA GLABRA1 (TTG1) locus regulates several developmental and biochemical pathways in Arabidopsis, including the formation of hairs on leaves, stems, and roots, and the production of seed mucilage and anthocyanin pigments.
Abstract: The TRANSPARENT TESTA GLABRA1 ( TTG1 ) locus regulates several developmental and biochemical pathways in Arabidopsis, including the formation of hairs on leaves, stems, and roots, and the production of seed mucilage and anthocyanin pigments. The TTG1 locus has been isolated by positional cloning, and its identity was confirmed by complementation of a ttg1 mutant. The locus encodes a protein of 341 amino acid residues with four WD40 repeats. The protein is similar to AN11, a regulator of anthocyanin biosynthesis in petunia, and more distantly related to those of the β subunits of heterotrimeric G proteins, which suggests a role for TTG1 in signal transduction to downstream transcription factors. The 1.5-kb TTG1 transcript is present in all major organs of Arabidopsis. Sequence analysis of six mutant alleles has identified base changes producing truncations or single amino acid changes in the TTG1 protein.
856 citations
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TL;DR: The physical, chemical, and biological properties of insulin in the light of the atomic arrangement found in insulin crystals are reviewed in this paper, where the relation of the three-dimensional arrangement of the atoms in the molecule of 2-zinc insulin crystal to the solution properties of the insulin (particularly its states of aggregation), to the chemical reaction and chemical modification of the molecule, and to its primary biological activity is discussed.
Abstract: Publisher Summary This chapter reviews the physical, chemical, and biological properties of insulin in the light of the atomic arrangement found in insulin crystals. It also describes the relation of the three-dimensional arrangement of the atoms in the molecule of 2-zinc insulin crystal to the solution properties of insulin (particularly its states of aggregation), to the chemical reaction and chemical modification of the molecule, and to its primary biological activity. Normally the insulin crystals contain two zinc ions to every six molecules of insulin—a hexamer. The slow solution of the crystals provides a method of delaying the action of insulin that closely parallels the methods adopted in the pancreas itself for the storage and release of insulin. Within many β granules, grains can be seen that almost certainly contain zinc insulin hexamers packed in a crystalline array, and in experimental animals diabetes has been induced by chelating agents, such as EDTA, perhaps simply by interfering with normal insulin storage. It, therefore, seems plausible that ready crystallization of insulin in the presence of zinc is a reflection of the storage processes in the β cell.
815 citations
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TL;DR: Coot is a molecular-graphics program designed to assist in the building of protein and other macromolecular models and the current state of development and available features are presented.
Abstract: Coot is a molecular-graphics application for model building and validation of biological macromolecules. The program displays electron-density maps and atomic models and allows model manipulations such as idealization, real-space refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers and Ramachandran idealization. Furthermore, tools are provided for model validation as well as interfaces to external programs for refinement, validation and graphics. The software is designed to be easy to learn for novice users, which is achieved by ensuring that tools for common tasks are `discoverable' through familiar user-interface elements (menus and toolbars) or by intuitive behaviour (mouse controls). Recent developments have focused on providing tools for expert users, with customisable key bindings, extensions and an extensive scripting interface. The software is under rapid development, but has already achieved very widespread use within the crystallographic community. The current state of the software is presented, with a description of the facilities available and of some of the underlying methods employed.
22,053 citations
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TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
Abstract: Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.
12,489 citations
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TL;DR: An overview of the CCP4 software suite for macromolecular crystallography is given.
Abstract: The CCP4 (Collaborative Computational Project, Number 4) software suite is a collection of programs and associated data and software libraries which can be used for macromolecular structure determination by X-ray crystallography. The suite is designed to be flexible, allowing users a number of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by standard file formats, data objects and graphical interfaces. Structure solution by macromolecular crystallography is becoming increasingly automated and the CCP4 suite includes several automation pipelines. After giving a brief description of the evolution of CCP4 over the last 30 years, an overview of the current suite is given. While detailed descriptions are given in the accompanying articles, here it is shown how the individual programs contribute to a complete software package.
11,023 citations
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TL;DR: An environment for comparative protein modeling is developed that consists of SWISS‐MODEL, a server for automated comparativeprotein modeling and of the SWiss‐PdbViewer, a sequence to structure workbench that provides a large selection of structure analysis and display tools.
Abstract: Comparative protein modeling is increasingly gaining interest since it is of great assistance during the rational design of mutagenesis experiments. The availability of this method, and the resulting models, has however been restricted by the availability of expensive computer hardware and software. To overcome these limitations, we have developed an environment for comparative protein modeling that consists of SWISS-MODEL, a server for automated comparative protein modeling and of the SWISS-PdbViewer, a sequence to structure workbench. The Swiss-PdbViewer not only acts as a client for SWISS-MODEL, but also provides a large selection of structure analysis and display tools. In addition, we provide the SWISS-MODEL Repository, a database containing more than 3500 automatically generated protein models. By making such tools freely available to the scientific community, we hope to increase the use of protein structures and models in the process of experiment design.
10,713 citations
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TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
10,601 citations