BLAST target To111 against the PDB >gi 1311141 pdb 1PDZ Mol id: 1; Molecule: Enolase; Chain: Null; Synonym 2-Phospho-D-Glycerate dehydratase; Ec: 4.2.1.11 rogen: Phosphoglycolate; Heterogen: Mn 2+ gi1311142 pdb 1PDY Mol id: 1; Molecule: Enolase; Chain: Null; Synonym: 2-Phospho-D-Glycerate Dehydratase; Ec: 4.2.1.11 Length =434 Score 384 bits (987), Expect e-107 Identities=220/432(50%),P。 natives=280/432(63%),Gaps=16/432(3%) Query: 3 IVKIIGREIIDSRGNPTVEAEVHLEGGFVGMAAAPSGASTGSREALELRDGDKSRFLGKG 62 I K+R I dsrgnPtve +++ g AA PSGastG Eale+RDGDKS++ GK Sbict: 3 ITKVFARTIFDSRGNPTVEVDLYTSKGLF-RAAVPSGASTGVHEALEMRDGDKSKYHGKS 61 Query: 63 VTKAVAAVNGPIAQALI--GKDAKDQAGIDKIMIDLDGTENKSKFGANAILAVSLANAKA 120 V AV VN工 Q D+ M LDGTENKS GANAIL VSLA KA Sbjct: 62 VFNAVKNVNDVIVPEIIKSGLKVTQQKECDEFMCKLDGTENKSSLGANAILGVSLAICKA 121 Query: 121 AAAAKGMPLYEHIAELNGTPGKYSMPVPMMNIINGGEHADNNVDIQEFMIQPVGAKTVKE 180 AA G+PLY HIA L ++PvP N+INGG hA n++qEFMi P Ga E Shjct: 122 GAAELGIPLYRHIANL-ANYDEVILPVPAFNVINGGSHAGNKLAMQEFMILPTGATSFTE 180 Query: 181 AIRMGSEVFHHLAKVLKAK-GMN-TAVGDEGGYAPNLGSNAEALAVIAEAVKAAGYELGK 238 A+RMG+ev+HHL V+KA+G++ TAVGDEGG+APN++N +AL +I EA+K AGY GK Sbjct: 181 AMRMGTEVYHHLKAVIKARFGLDATAVGDEGGFAPNILNNKDALDLIQEAIKKAGYT-GK 239 etc
BLAST target T0111 against the PDB >gi|1311141|pdb|1PDZ| Mol_id: 1; Molecule: Enolase; Chain: Null; Synonym: 2-Phospho-D-Glycerate Dehydratase; Ec: 4.2.1.11; Heterogen: Phosphoglycolate; Heterogen: Mn 2+ gi|1311142|pdb|1PDY| Mol_id: 1; Molecule: Enolase; Chain: Null; Synonym: 2-Phospho-D-Glycerate Dehydratase; Ec: 4.2.1.11 Length = 434 Score = 384 bits (987), Expect = e-107 Identities = 220/432 (50%), Positives = 280/432 (63%), Gaps = 16/432 (3%) Query: 3 IVKIIGREIIDSRGNPTVEAEVHLEGGFVGMAAAPSGASTGSREALELRDGDKSRFLGKG 62 I K+ R I DSRGNPTVE +++ G AA PSGASTG EALE+RDGDKS++ GK Sbjct: 3 ITKVFARTIFDSRGNPTVEVDLYTSKGLF-RAAVPSGASTGVHEALEMRDGDKSKYHGKS 61 Query: 63 VTKAVAAVNGPIAQALI--GKDAKDQAGIDKIMIDLDGTENKSKFGANAILAVSLANAKA 120 V AV VN I +I G Q D+ M LDGTENKS GANAIL VSLA KA Sbjct: 62 VFNAVKNVNDVIVPEIIKSGLKVTQQKECDEFMCKLDGTENKSSLGANAILGVSLAICKA 121 Query: 121 AAAAKGMPLYEHIAELNGTPGKYSMPVPMMNIINGGEHADNNVDIQEFMIQPVGAKTVKE 180 AA G+PLY HIA L + +PVP N+INGG HA N + +QEFMI P GA + E Sbjct: 122 GAAELGIPLYRHIANL-ANYDEVILPVPAFNVINGGSHAGNKLAMQEFMILPTGATSFTE 180 Query: 181 AIRMGSEVFHHLAKVLKAK-GMN-TAVGDEGGYAPNLGSNAEALAVIAEAVKAAGYELGK 238 A+RMG+EV+HHL V+KA+ G++ TAVGDEGG+APN+ +N +AL +I EA+K AGY GK Sbjct: 181 AMRMGTEVYHHLKAVIKARFGLDATAVGDEGGFAPNILNNKDALDLIQEAIKKAGYT-GK 239 etc…
Best prediction for to111 at CASP4 superimposed with the real structure For a description of results from CasP 4 homology modeling, see Tramontano, A, R Leplae, and V Morea. "Analysis and Assessment of Comparative Modeling Predictions in CASP4. Proteins Suppl 5 (2001 ) 22-38
Best prediction for T0111 at CASP4 superimposed with the real structure For a description of results from CASP 4 homology modeling, see… Tramontano, A, R Leplae, and V Morea. "Analysis and Assessment of Comparative Modeling Predictions in CASP4." Proteins Suppl 5 (2001): 22-38
Progress in Comparative Modeling Methods have not advanced significantly from CASP1 to CASP5 More template structures are available More sequences are available to help alignment More remotely related sequences can be detected using PSI-BLAST No new good solutions to the alignment OR refinement problem
Progress in Comparative Modeling Methods have not advanced significantly from CASP1 to CASP5 More template structures are available More sequences are available to help alignment More remotely related sequences can be detected using PSI-BLAST No new good solutions to the alignment OR refinement problem
The fold recognition/threading approach to protein structure prediction OBSERVATION: there appear to be a limited number of protein folds(a 1, 000? Instead of having to predict protein structure"from scratch", maybe we can just pick the correct answer out of a finite list This can be done using sequence-based techniques or by threading"the sequence onto different templates in turn and evaluating how good a match each one is
The fold recognition/threading approach to protein structure prediction OBSERVATION: there appear to be a limited number of protein folds (~1,000?) Instead of having to predict protein structure “from scratch”, maybe we can just pick the correct answer out of a finite list This can be done using sequence-based techniques, or by “threading” the sequence onto different templates in turn, and evaluating how good a match each one is