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Susan Jones

Bio: Susan Jones is an academic researcher from University of Sussex. The author has contributed to research in topics: Protein structure & Accessible surface area. The author has an hindex of 26, co-authored 36 publications receiving 9576 citations. Previous affiliations of Susan Jones include European Bioinformatics Institute & University College London.

Papers
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Journal ArticleDOI
TL;DR: Analysis of the structural families generated by CATH reveals the prominent features of protein structure space and a database of well-characterised protein structure families will facilitate the assignment of structure-function/evolution relationships to both known and newly determined protein structures.

2,551 citations

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TL;DR: This review examines protein complexes in the Brookhaven Protein Databank to gain a better understanding of the principles governing the interactions involved in protein-protein recognition.
Abstract: This review examines protein complexes in the Brookhaven Protein Databank to gain a better understanding of the principles governing the interactions involved in protein-protein recognition. The factors that influence the formation of protein-protein complexes are explored in four different types of protein-protein complexes--homodimeric proteins, heterodimeric proteins, enzyme-inhibitor complexes, and antibody-protein complexes. The comparison between the complexes highlights differences that reflect their biological roles.

2,515 citations

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TL;DR: For each type of complex, none of the parameters were definitive, but the majority showed trends for the observed interface to be distinguished from other surface patches.

623 citations

Journal ArticleDOI
TL;DR: A method for defining and analysing a series of residue patches on the surface of protein structures is used to predict the location of protein-protein interaction sites and is successful for 66% of the structures.

450 citations


Cited by
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Journal ArticleDOI
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Abstract: The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

34,239 citations

Journal ArticleDOI
TL;DR: A new method, based on chemical thermodynamics, is developed for automatic detection of macromolecular assemblies in the Protein Data Bank (PDB) entries that are the results of X-ray diffraction experiments, as found, biological units may be recovered at 80-90% success rate, which makesX-ray crystallography an important source of experimental data on macromolescular complexes and protein-protein interactions.

8,377 citations

Journal ArticleDOI
TL;DR: A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST and achieved an average Q3 score of between 76.5% to 78.3% depending on the precise definition of observed secondary structure used, which is the highest published score for any method to date.

5,512 citations

Journal ArticleDOI
TL;DR: There is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes and to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.
Abstract: ■ Abstract Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more pro- teins of known structure (templates). The prediction process consists of fold assign- ment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are in- creasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are nec- essary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

3,085 citations

Journal ArticleDOI
TL;DR: There exists a significant correlation between the correctness of the predicted structure and the structural similarity of the model to the other proteins in the PDB, which could be used to assist in model selection in blind protein structure predictions.
Abstract: We have developed TM-align, a new algorithm to identify the best structural alignment between protein pairs that combines the TM-score rotation matrix and Dynamic Programming (DP). The algorithm is approximately 4 times faster than CE and 20 times faster than DALI and SAL. On average, the resulting structure alignments have higher accuracy and coverage than those provided by these most often-used methods. TM-align is applied to an all-against-all structure comparison of 10 515 representative protein chains from the Protein Data Bank (PDB) with a sequence identity cutoff <95%: 1996 distinct folds are found when a TM-score threshold of 0.5 is used. We also use TM-align to match the models predicted by TASSER for solved non-homologous proteins in PDB. For both folded and misfolded models, TM-align can almost always find close structural analogs, with an average root mean square deviation, RMSD, of 3 A and 87% alignment coverage. Nevertheless, there exists a significant correlation between the correctness of the predicted structure and the structural similarity of the model to the other proteins in the PDB. This correlation could be used to assist in model selection in blind protein structure predictions. The TM-align program is freely downloadable at http://bioinformatics.buffalo.edu/TM-align.

2,582 citations