Abstract
Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.
Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and Tar (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals. (C) 2011 Elsevier Inc. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 311 |
| Number of pages | 9 |
| Journal | Journal of Neuroscience Methods |
| Volume | 55 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Sep 2011 |
Keywords
- Target selection
- Crystallisation
- Structural genomics
- Structural biology
- Bioinformatics
- Construct design
- MULTIPLE SEQUENCE ALIGNMENT
- STRUCTURE PREDICTION SERVER
- HIGH-THROUGHPUT
- WEB SERVER
- PROTEIN CRYSTALLIZATION
- PHOSPHORYLATION SITES
- THERMOTOGA-MARITIMA
- INTRINSIC DISORDER
- ESCHERICHIA-COLI
- DRUG DISCOVERY
Cite this
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Computational approaches to selecting and optimising targets for structural biology. / Overton, Ian M.; Barton, Geoffrey J.
In: Journal of Neuroscience Methods, Vol. 55, No. 1, 09.2011, p. 311.Research output: Contribution to journal › Article
TY - JOUR
T1 - Computational approaches to selecting and optimising targets for structural biology
AU - Overton, Ian M.
AU - Barton, Geoffrey J.
PY - 2011/9
Y1 - 2011/9
N2 - Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and Tar (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals. (C) 2011 Elsevier Inc. All rights reserved.
AB - Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and Tar (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals. (C) 2011 Elsevier Inc. All rights reserved.
KW - Target selection
KW - Crystallisation
KW - Structural genomics
KW - Structural biology
KW - Bioinformatics
KW - Construct design
KW - MULTIPLE SEQUENCE ALIGNMENT
KW - STRUCTURE PREDICTION SERVER
KW - HIGH-THROUGHPUT
KW - WEB SERVER
KW - PROTEIN CRYSTALLIZATION
KW - PHOSPHORYLATION SITES
KW - THERMOTOGA-MARITIMA
KW - INTRINSIC DISORDER
KW - ESCHERICHIA-COLI
KW - DRUG DISCOVERY
U2 - 10.1016/j.ymeth.2011.08.014
DO - 10.1016/j.ymeth.2011.08.014
M3 - Article
C2 - 21906678
VL - 55
SP - 311
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
IS - 1
ER -