Protein structure (Part 2 of 2)

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Many databases explore protein structures SCOP CATH Dali Domain Dictionary FSSP Page 293

Structural Classification of Proteins (SCOP) SCOP describes protein structures using a hierarchical classification scheme: Classes Folds Superfamilies (likely evolutionary relationship) Families Domains Individual PDB entries http://scop.mrc.lmb.cam.ac.uk/scop/ Page 293

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SCOP statistics (October, 2002) Class # folds # superfamilies # families All a 151 252 393 All b 110 205 337 a/b 113 185 438 a+b 208 295 454 … Total 686 1073 1827 Page 298

Class, Architecture, Topology, and Homologous Superfamily (CATH) database CATH clusters proteins at four levels: C Class (a, b, a&b folds) A Architecture (shape of domain, e.g. jelly roll) T Topology (fold families; not necessarily homologous) H Homologous superfamily http://www.biochem.ucl.ac.uk/basm/cath_new Page 293

Fig. 9.23 Page 298

Fig. 9.24 Page 299

Fig. 9.24 Page 299

Fig. 9.25 Page 300

Fig. 9.25 Page 300

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Fig. 9.27 Page 302

Fig. 9.28 Page 303

Dali Domain Dictionary Dali contains a numerical taxonomy of all known structures in PDB. Dali integrates additional data for entries within a domain class, such as secondary structure predictions and solvent accessibility. Page 302

Fig. 9.29 Page 303

Fig. 9.30 Page 304

Fig. 9.30 Page 304

Fig. 9.30 Page 304

Fold classification based on structure-structure alignment of proteins (FSSP) FSSP is based on a comprehensive comparison of PDB proteins (greater than 30 amino acids in length). Representative sets exclude sequence homologs sharing > 25% amino acid identity. The output includes a “fold tree.” http://www.ebi.ac.uk/dali/fssp Page 293

Fig. 9.31 Page 305

FSSP: fold tree Fig. 9.32 Page 306

Fig. 9.33 Page 307

Fig. 9.34 Page 307

Approaches to predicting protein structures There are about >20,000 structures in PDB, and about 1 million protein sequences in SwissProt/ TrEMBL. For most proteins, structural models derive from computational biology approaches, rather than experimental methods. The most reliable method of modeling and evaluating new structures is by comparison to previously known structures. This is comparative modeling. An alternative is ab initio modeling. Page 303-305

Approaches to predicting protein structures obtain sequence (target) fold assignment comparative modeling ab initio modeling build, assess model Page 308

Comparative modeling of protein structures [1] Perform fold assignment (e.g. BLAST, CATH, SCOP); identify structurally conserved regions [2] Align the target (unknown protein) with the template. This is performed for >30% amino acid identity over a sufficient length [3] Build a model [4] Evaluate the model Page 305

Errors in comparative modeling Errors may occur for many reasons [1] Errors in side-chain packing [2] Distortions within correctly aligned regions [3] Errors in regions of target that do not match template [4] errors in sequence alignment [5] use of incorrect templates Page 306

Comparative modeling In general, accuracy of structure prediction depends on the percent amino acid identity shared between target and template. For >50% identity, RMSD is often only 1 Å. Page 306

Baker and Sali (2000) Page 308

Comparative modeling Many web servers offer comparative modeling services. Examples are SWISS-MODEL (ExPASy) Predict Protein server (Columbia) WHAT IF (CMBI, Netherlands) Page 309

Ab initio protein structure prediction Ab initio prediction can be performed when a protein has no detectable homologs. Protein folding is modeled based on global free-energy minimum estimates. The “Rosetta Stone” methods was applied to sequence families lacking known structures. For 80 of 131 proteins, one of the top five ranked models successfully predicted the structure within 6.0 Å RMSD (Bonneau et al., 2002). Page 309-310

Protein structure and human disease In some cases, a single amino acid substitution can induce a dramatic change in protein structure. For example, the DF508 mutation of CFTR alters the a helical content of the protein, and disrupts intracellular trafficking. Other changes are subtle. The E6V mutation in the gene encoding hemoglobin beta causes sickle- cell anemia. The substitution introduces a hydrophobic patch on the protein surface, leading to clumping of hemoglobin molecules. Page 311

Protein structure and human disease Disease Protein Cystic fibrosis CFTR Sickle-cell anemia hemoglobin beta “mad cow” disease prion protein Alzheimer disease amyloid precursor protein Table 9.5 Page 312