Protein Structure Analysis Online
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- Abstract
- Table of Contents
- Figures
- Literature Cited
Abstract
Computational biology/chemistry tools are used in most areas of life/health science research. These methods are continually being developed and their use can present difficulties for both experienced and novice investigators. To facilitate the use of these applications, many packages have been implemented online during these last 5 years. This unit focuses on online computational methods with a special emphasis on structural refinement/atomic simulations, protein electrostatic calculations, searches for functional sites, searches for druggable pockets, protein docking and small molecule docking, and prediction of potential impact of amino acid variations on the structure and function of the protein molecules. Curr. Protoc. Protein Sci. 50:2.13.1?2.13.23. © 2007 by John Wiley & Sons, Inc.
Keywords: structural bioinformatics; electrostatics; simulations; docking; on?line tools; druggable pocket
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online LibraryTable of Contents
- Introduction
- Visualization, Structural Refinement, Simulations
- Electrostatics
- Finding Hot Spots and Functional Sites
- Prediction of Druggable Pocket
- Protein‐Protein and Protein–Small Molecule Interactions
- Analysis of Point Mutations
- Conclusion
- Acknowledgments
- Literature Cited
- Figures
- Tables
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Figure 2.13.1 Flowchart of the RECON algorithm. The RECON (REsidue CONtacts) Web server is intended to provide the scientific community with a publicly available tool capable of predicting intra‐residue contacts by a correlated mutation method. The user submits an amino acid sequence, and, after several steps as outlined in the flowchart, the RECON server outputs a list of predicted contacting residues. The right panel summarizes the goal, which is to predict residue contacts within the 3‐D structure of a protein without prior knowledge of the 3‐D structure. CD‐HIT is a tool for clustering large protein databases at different sequence identify levels. View Image -
Figure 2.13.2 The Protein Continuum Electrostatics (PCE) Web tool computes the electrostatic potentials and p K a values in a protein. Initiation of a calculation job starts with uploading a pdb file and selecting the dielectric constants and the ionic strength, as well as the titratable groups. Users can obtain various data, including electrostatic potential values mapped onto the molecular surface. An example of electrostatic potential distributions on the lysozyme surface (PDB code 7lyz) from 3.0 kcal/mol/electron (red) to +3.0 kcal/mol/electron (blue) is shown here; these images are returned by the PCE server. View Image -
Figure 2.13.3 Flowchart of computations performed by the H++ server. The input is a structure file in either PDB or PQR (PDB + charges + radii) format. The output includes computed p K 1/2 values for all titratable groups, as well as titration curves, isoelectric point, and the original structure, in which the protonation states of all ionization groups have been made consistent with the calculated p K values. The generated structure is available in several formats used by popular molecular modeling packages. View Image -
Figure 2.13.4 Pocket prediction with Q‐SiteFinder. This method implements an energy‐based approach for the prediction of druggable pockets. A geometry‐based method is also implemented. Here the prediction is carried out on the X‐ray structure of coagulation factor X (PDB entry 1fjs, shown as cartoon in magenta), a serine protease, cocrystallized with a small ligand (yellow spheres). The top predicted binding pocket is shown in dark blue. This predicted region is indeed the catalytic site of the protein. This site is predicted to be at least 340 Å3 , a size well suited for binding drugs. The figure was generated using PyMOL. View Image -
Figure 2.13.5 The PROTCOM database provides users with a searchable database of protein complexes enhanced with domain‐domain structure information. The 3‐D structures are extracted from the Protein Databank and are subjected to a parser to separately treat two‐chain, multiple‐chain, and single‐chain entries. The result is a binary complex (A:B) made from the original entries (in the case of a two‐chain complex), from all possible combinations of interacting monomers (in case of multi‐chains complexes), or from the parsed domains (in the case of single‐chain proteins). The entries are further parsed to remove cases with very large or very small interfacial areas. View Image -
Figure 2.13.6 PolyPhen processing flowchart. PolyPhen is a tool to predict possible impacts of amino acid substitution on the structure/function of a protein. It combines information on sequence features, multiple alignment with homologous proteins, and structural parameters to make a prediction. View Image
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