Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence
We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non‐redun...
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| Published in | Proteins, structure, function, and bioinformatics Vol. 49; no. 2; pp. 154 - 166 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Wiley Subscription Services, Inc., A Wiley Company
01.11.2002
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0887-3585 1097-0134 1097-0134 |
| DOI | 10.1002/prot.10181 |
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| Abstract | We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non‐redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508–519; Proteins 2000;40:502–511). We have introduced a variable size window that allowed us to include sequences as short as 20–30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI‐BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack‐knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI‐BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540–553; Kloczkowski A, Ting K‐L, Jernigan RL, Garnier J. Polymer 2002;43:441–449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220–223). Proteins 2002;49:154–166. © 2002 Wiley‐Liss, Inc. |
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| AbstractList | We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non‐redundant domains collected recently by Cuff and Barton (
Proteins 1999;34:508–519
;
Proteins 2000;40:502–511
). We have introduced a variable size window that allowed us to include sequences as short as 20–30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI‐BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack‐knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI‐BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B.
Methods Enzymol 1996;266:540–553
; Kloczkowski A, Ting K‐L, Jernigan RL, Garnier J.
Polymer 2002;43:441–449
). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (
Proteins 1999;34:220–223
). Proteins 2002;49:154–166. © 2002 Wiley‐Liss, Inc. We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non‐redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508–519; Proteins 2000;40:502–511). We have introduced a variable size window that allowed us to include sequences as short as 20–30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI‐BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack‐knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI‐BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540–553; Kloczkowski A, Ting K‐L, Jernigan RL, Garnier J. Polymer 2002;43:441–449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220–223). Proteins 2002;49:154–166. © 2002 Wiley‐Liss, Inc. We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non-redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508-519; Proteins 2000;40:502-511). We have introduced a variable size window that allowed us to include sequences as short as 20-30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI-BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack-knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI-BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540-553; Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Polymer 2002;43:441-449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220-223). We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non-redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508-519; Proteins 2000;40:502-511). We have introduced a variable size window that allowed us to include sequences as short as 20-30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI-BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack-knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI-BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540-553; Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Polymer 2002;43:441-449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220-223).We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non-redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508-519; Proteins 2000;40:502-511). We have introduced a variable size window that allowed us to include sequences as short as 20-30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI-BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack-knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI-BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540-553; Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Polymer 2002;43:441-449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220-223). |
| Author | Ting, K.-L. Kloczkowski, A. Garnier, J. Jernigan, R.L. |
| Author_xml | – sequence: 1 givenname: A. surname: Kloczkowski fullname: Kloczkowski, A. organization: Laboratory of Experimental and Computational Biology, NCI, NIH, Bethesda, Maryland – sequence: 2 givenname: K.-L. surname: Ting fullname: Ting, K.-L. organization: Laboratory of Experimental and Computational Biology, NCI, NIH, Bethesda, Maryland – sequence: 3 givenname: R.L. surname: Jernigan fullname: Jernigan, R.L. email: jernigan@iastate.edu organization: Laboratory of Experimental and Computational Biology, NCI, NIH, Bethesda, Maryland – sequence: 4 givenname: J. surname: Garnier fullname: Garnier, J. organization: Mathematical and Statistical Computing Laboratory, CIT, NIH, Bethesda, Maryland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/12210997$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1002/(SICI)1097-0134(19990201)34:2<220::AID-PROT7>3.0.CO;2-K 10.1002/bip.360221211 10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4 10.1016/S0076-6879(96)66033-9 10.1002/1097-0134(20000815)40:3<502::AID-PROT170>3.0.CO;2-Q 10.1016/S0032-3861(01)00425-6 10.1016/0167-4838(88)90206-3 10.1002/prot.340230303 10.1016/S0022-2836(05)80007-5 10.1006/jmbi.1994.0116 10.1006/jmbi.1999.3091 10.1016/S0022-2836(05)80333-X 10.1016/0022-2836(92)90927-C 10.1016/0022-2836(88)90564-5 10.1016/S0378-1119(01)00461-9 10.1002/1097-0134(20001001)41:1<17::AID-PROT40>3.0.CO;2-F 10.1073/pnas.86.1.152 10.1006/jmbi.1993.1413 10.1016/0022-2836(87)90501-8 10.1093/protein/6.8.849 10.1016/0014-5793(95)00245-5 10.1006/jsbi.2001.4336 10.1002/(SICI)1097-0134(199710)29:2<172::AID-PROT5>3.0.CO;2-F 10.1007/978-1-4613-1571-1_10 10.1002/(SICI)1097-0134(199703)27:3<329::AID-PROT1>3.0.CO;2-8 10.1016/0022-2836(78)90297-8 10.1093/bioinformatics/14.10.846 10.1016/0022-2836(92)90892-N 10.1002/(SICI)1097-0134(199606)25:2<169::AID-PROT3>3.0.CO;2-D 10.1385/1-59259-368-2:71 10.1016/0022-2836(74)90405-7 10.1093/nar/25.17.3389 10.1016/0005-2795(75)90109-9 10.1006/jmbi.1997.0958 10.1016/0959-440X(95)80099-9 10.1002/pro.5560050113 10.1016/S0076-6879(96)66034-0 10.1093/protein/2.3.185 10.1006/jmbi.1993.1649 10.1016/0022-2836(87)90292-0 10.1006/jmbi.1993.1464 10.1002/pro.5560010312 10.1110/ps.9.6.1162 10.1021/bi00699a001 10.1002/(SICI)1097-0134(199606)25:2<157::AID-PROT2>3.0.CO;2-F 10.1016/0014-5793(86)80917-6 10.1016/0022-2836(74)90404-5 |
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| References | Zemla A, Venclovas C, Fidelis K, Rost B. A modified definition of Sov, a segment-based measure of protein secondary structure prediction assessment. Proteins 1999; 34: 220-223. Petersen TN, Lundegaard C, Nielsen M, et al. Prediction of protein secondary structure at 80% accuracy. Proteins 2000; 41: 17-20. Biou V, Gibrat JF, Levin J, Robson B, Garnier J. Secondary structure prediction: combination of three different methods. Protein Eng 1988; 2: 185-191. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993; 232: 584-599. Karplus K, Barrett C, Hughley R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998; 14: 846-856. Levin JM, Robson B, Garnier J. An algorithm for secondary structure determination in proteins based on sequence similarity. FEBS Lett 1986; 205: 303-308. Ouali M, King RD. Cascaded multiple classifiers for secondary structure prediction. Protein Sci 2000; 9: 1162-1176. Rost B. Review: protein secondary structure prediction continues to rise. J Struct Biol 2001; 134: 204-218. Chou PY, Fasman GD. Prediction of protein conformation. Biochemistry 1974; 13: 211-215. Cuff JA, Barton GJ. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 1999; 34: 508-519. Zvelebil MJ, Barton GJ, Taylor WR, Sternberg MJE. Prediction of protein secondary structure and active sites using alignment of homologous sequences. J Mol Biol 1987; 195: 957-961. Eisenhaber F, Frommel C, Argos P. Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class. Proteins 1996; 25: 169-179. Kabsch W, Sander C. A dictionary of protein secondary structure. Biopolymers 1983; 22: 2577-2637. Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol 1994; 235: 13-26. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000; 40: 502-511. King RD, Sternberg MJ. Machine learning approach for prediction of protein secondary structure. J Mol Biol 1990; 216: 441-457. Lim VI. Algorithm for prediction of α-helical and β-structural regions in globular proteins. J Mol Biol 1974; 88: 873-894. Holley LH, Karplus M. Protein secondary structure prediction with a neural network. Proc Natl Acad Sci USA 1989; 86: 152-156. Rost B. PHD: predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol 1996; 266: 525-539. Salamov AA, Solovyev VV. Protein secondary structure prediction using local alignments. J Mol Biol 1997; 268: 31-36. Barton GJ. Protein secondary structure prediction. Curr Opin Struct Biol 1995; 5: 372-376. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 1996; 266: 540-553. Di Francesco V, Garnier J, Munson PJ. Improving protein secondary structure prediction with aligned homologous sequences. Protein Sci 1996; 5: 106-113. Stolorz P, Lapedes A, Xia Y. Predicting protein secondary structure using neural net and statistical methods. J Mol Biol 1992; 225: 1049-1063. Yi T-M, Lander ES. Protein secondary structure prediction using nearest-neighbor methods. J Mol Biol 1993; 232: 1117-1129. Zhang CT, Chou KC. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci 1992; 1: 401-408. Salzberg S, Cost S. Predicting protein secondary structure with nearest-neighbors algorithm. J Mol Biol 1992; 227: 371-374. Matthews BB. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975; 405: 442-451. Russell RB, Barton GJ. The limits of protein secondary structure prediction accuracy from multiple sequence alignment. J Mol Biol 1993; 234: 951-957. Eisenhaber F, Imperiale F, Argos P, Frommel C. Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods. Proteins 1996; 25: 157-168. Jones TD. Protein secondary structure prediction based on position specific scoring matrices. J Mol Biol 1999; 292: 195-202. Rost B. Sander C, Schneider R. PHD: an automatic mail server for protein secondary structure prediction. Comput Appl Biosci 1994; 10: 53-60. Levin JM, Pascarella S, Argos P, Garnier J. Quantification of secondary structure prediction improvement using distantly related proteins. Protein Eng 1993; 6: 849-854. Lecompte O, Thompson JD, Plewniak F, Thierry JC, Poch O. Multiple alignment of complete sequences (MACS) in the post-genomic era. Gene 2001; 270: 17-30. Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Protein secondary structure prediction based on the GOR algorithm incorporating multiple sequence alignment information. Polymer 2002; 43: 441-449. Lim VI. Structural principles of the globular organization of protein chains: a stereochemical theory of globular protein secondary structure. J Mol Biol 1974; 88: 857-872. Bahar I, Altigan AR, Jernigan R, Erman B. Understanding the recognition of protein structural classes by amino acid composition. Proteins 1997; 29: 172-185. Qian N, Sejnowski TJ. Predicting the secondary structure of a globular proteins using neural network models. J Mol Biol 1989; 202: 865-884. Levin JM, Garnier J. Improvements in secondary structure prediction method based on a search for local sequence homologies and its use as a model building tool. Biochim Biophys Acta 1988; 955: 283-295. Chou KC. Does the folding type of protein depend on its amino acid composition? FEBS Lett 1995; 363: 127-131. Salamov AA, Solovyev VV. Prediction of protein secondary structure by combining nearest-neighbor algorithm and multiple sequence alignments. J Mol Biol 1995; 247: 11-15. Gibrat JF, Garnier J, Robson B. Further developments of protein secondary structure prediction using information theory: new parameters and consideration of residue pairs. J Mol Biol 1987; 198: 425-443. Garnier J, Osguthorpe DJ, Robson B. Analysis and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 1978; 120: 97-120. Moult J, Judson R, Fidelis K, Pedersen JT. A large scale experiment to assess protein structure prediction. Proteins 1995; 23: I-IV. Altschul SF, Madden TL, Schaffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-3402. Frishman D, Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins 1997; 27: 329-335. 1974; 13 1994; 235 1989; 86 1999; 292 2000; 9 1997; 25 2000; 41 1992; 225 1992; 227 1997; 29 1997; 27 1996; 266 1995; 5 1993; 6 1988; 2 1986; 205 1997; 268 2001; 134 1987; 195 1974; 88 1990; 216 1987; 198 2000 1989; 202 2001; 270 1995; 23 2002; 43 1978; 120 1999; 34 2000; 40 1995; 247 1995; 363 1996; 25 1996; 5 1993; 234 1988; 955 1992; 1 1975; 405 1983; 22 1989 1994; 10 1998; 14 1993; 232 e_1_2_4_40_2 e_1_2_4_44_2 e_1_2_4_21_2 e_1_2_4_42_2 e_1_2_4_23_2 e_1_2_4_48_2 e_1_2_4_25_2 e_1_2_4_46_2 e_1_2_4_27_2 e_1_2_4_29_2 e_1_2_4_3_2 e_1_2_4_5_2 e_1_2_4_7_2 e_1_2_4_50_2 e_1_2_4_9_2 e_1_2_4_31_2 e_1_2_4_10_2 e_1_2_4_33_2 e_1_2_4_52_2 e_1_2_4_12_2 e_1_2_4_35_2 e_1_2_4_14_2 e_1_2_4_37_2 e_1_2_4_39_2 e_1_2_4_16_2 e_1_2_4_18_2 Rost B (e_1_2_4_20_2) 1994; 10 e_1_2_4_43_2 e_1_2_4_22_2 e_1_2_4_41_2 e_1_2_4_24_2 e_1_2_4_47_2 e_1_2_4_26_2 e_1_2_4_45_2 e_1_2_4_28_2 e_1_2_4_49_2 e_1_2_4_2_2 e_1_2_4_4_2 e_1_2_4_6_2 e_1_2_4_8_2 e_1_2_4_51_2 e_1_2_4_30_2 e_1_2_4_11_2 e_1_2_4_32_2 e_1_2_4_13_2 e_1_2_4_34_2 e_1_2_4_15_2 e_1_2_4_36_2 e_1_2_4_17_2 e_1_2_4_38_2 e_1_2_4_19_2 |
| References_xml | – reference: Levin JM, Robson B, Garnier J. An algorithm for secondary structure determination in proteins based on sequence similarity. FEBS Lett 1986; 205: 303-308. – reference: Levin JM, Pascarella S, Argos P, Garnier J. Quantification of secondary structure prediction improvement using distantly related proteins. Protein Eng 1993; 6: 849-854. – reference: Bahar I, Altigan AR, Jernigan R, Erman B. Understanding the recognition of protein structural classes by amino acid composition. Proteins 1997; 29: 172-185. – reference: Salzberg S, Cost S. Predicting protein secondary structure with nearest-neighbors algorithm. J Mol Biol 1992; 227: 371-374. – reference: Karplus K, Barrett C, Hughley R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998; 14: 846-856. – reference: Levin JM, Garnier J. Improvements in secondary structure prediction method based on a search for local sequence homologies and its use as a model building tool. Biochim Biophys Acta 1988; 955: 283-295. – reference: Frishman D, Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins 1997; 27: 329-335. – reference: Rost B. Sander C, Schneider R. PHD: an automatic mail server for protein secondary structure prediction. Comput Appl Biosci 1994; 10: 53-60. – reference: Petersen TN, Lundegaard C, Nielsen M, et al. Prediction of protein secondary structure at 80% accuracy. Proteins 2000; 41: 17-20. – reference: Qian N, Sejnowski TJ. Predicting the secondary structure of a globular proteins using neural network models. J Mol Biol 1989; 202: 865-884. – reference: Biou V, Gibrat JF, Levin J, Robson B, Garnier J. Secondary structure prediction: combination of three different methods. Protein Eng 1988; 2: 185-191. – reference: Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993; 232: 584-599. – reference: Chou KC. Does the folding type of protein depend on its amino acid composition? FEBS Lett 1995; 363: 127-131. – reference: Chou PY, Fasman GD. Prediction of protein conformation. Biochemistry 1974; 13: 211-215. – reference: Holley LH, Karplus M. Protein secondary structure prediction with a neural network. Proc Natl Acad Sci USA 1989; 86: 152-156. – reference: Russell RB, Barton GJ. The limits of protein secondary structure prediction accuracy from multiple sequence alignment. J Mol Biol 1993; 234: 951-957. – reference: Kabsch W, Sander C. A dictionary of protein secondary structure. Biopolymers 1983; 22: 2577-2637. – reference: Matthews BB. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975; 405: 442-451. – reference: Cuff JA, Barton GJ. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 1999; 34: 508-519. – reference: Rost B. PHD: predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol 1996; 266: 525-539. – reference: Eisenhaber F, Imperiale F, Argos P, Frommel C. Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods. Proteins 1996; 25: 157-168. – reference: Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Protein secondary structure prediction based on the GOR algorithm incorporating multiple sequence alignment information. Polymer 2002; 43: 441-449. – reference: Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 1996; 266: 540-553. – reference: Salamov AA, Solovyev VV. Prediction of protein secondary structure by combining nearest-neighbor algorithm and multiple sequence alignments. J Mol Biol 1995; 247: 11-15. – reference: Gibrat JF, Garnier J, Robson B. Further developments of protein secondary structure prediction using information theory: new parameters and consideration of residue pairs. J Mol Biol 1987; 198: 425-443. – reference: Jones TD. Protein secondary structure prediction based on position specific scoring matrices. J Mol Biol 1999; 292: 195-202. – reference: Yi T-M, Lander ES. Protein secondary structure prediction using nearest-neighbor methods. J Mol Biol 1993; 232: 1117-1129. – reference: Barton GJ. Protein secondary structure prediction. Curr Opin Struct Biol 1995; 5: 372-376. – reference: Lim VI. Algorithm for prediction of α-helical and β-structural regions in globular proteins. J Mol Biol 1974; 88: 873-894. – reference: Rost B. Review: protein secondary structure prediction continues to rise. J Struct Biol 2001; 134: 204-218. – reference: Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000; 40: 502-511. – reference: King RD, Sternberg MJ. Machine learning approach for prediction of protein secondary structure. J Mol Biol 1990; 216: 441-457. – reference: Zhang CT, Chou KC. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci 1992; 1: 401-408. – reference: Lim VI. Structural principles of the globular organization of protein chains: a stereochemical theory of globular protein secondary structure. J Mol Biol 1974; 88: 857-872. – reference: Di Francesco V, Garnier J, Munson PJ. Improving protein secondary structure prediction with aligned homologous sequences. Protein Sci 1996; 5: 106-113. – reference: Eisenhaber F, Frommel C, Argos P. Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class. Proteins 1996; 25: 169-179. – reference: Moult J, Judson R, Fidelis K, Pedersen JT. A large scale experiment to assess protein structure prediction. Proteins 1995; 23: I-IV. – reference: Salamov AA, Solovyev VV. Protein secondary structure prediction using local alignments. J Mol Biol 1997; 268: 31-36. – reference: Lecompte O, Thompson JD, Plewniak F, Thierry JC, Poch O. Multiple alignment of complete sequences (MACS) in the post-genomic era. Gene 2001; 270: 17-30. – reference: Stolorz P, Lapedes A, Xia Y. Predicting protein secondary structure using neural net and statistical methods. J Mol Biol 1992; 225: 1049-1063. – reference: Zemla A, Venclovas C, Fidelis K, Rost B. A modified definition of Sov, a segment-based measure of protein secondary structure prediction assessment. Proteins 1999; 34: 220-223. – reference: Garnier J, Osguthorpe DJ, Robson B. Analysis and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 1978; 120: 97-120. – reference: Ouali M, King RD. Cascaded multiple classifiers for secondary structure prediction. Protein Sci 2000; 9: 1162-1176. – reference: Altschul SF, Madden TL, Schaffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-3402. – reference: Zvelebil MJ, Barton GJ, Taylor WR, Sternberg MJE. Prediction of protein secondary structure and active sites using alignment of homologous sequences. J Mol Biol 1987; 195: 957-961. – reference: Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol 1994; 235: 13-26. – volume: 86 start-page: 152 year: 1989 end-page: 156 article-title: Protein secondary structure prediction with a neural network publication-title: Proc Natl Acad Sci USA – volume: 43 start-page: 441 year: 2002 end-page: 449 article-title: Protein secondary structure prediction based on the GOR algorithm incorporating multiple sequence alignment information publication-title: Polymer – volume: 88 start-page: 873 year: 1974 end-page: 894 article-title: Algorithm for prediction of α‐helical and β‐structural regions in globular proteins publication-title: J Mol Biol – volume: 232 start-page: 1117 year: 1993 end-page: 1129 article-title: Protein secondary structure prediction using nearest‐neighbor methods publication-title: J Mol Biol – volume: 266 start-page: 525 year: 1996 end-page: 539 article-title: PHD: predicting one‐dimensional protein structure by profile based neural networks publication-title: Methods Enzymol – volume: 232 start-page: 584 year: 1993 end-page: 599 article-title: Prediction of protein secondary structure at better than 70% accuracy publication-title: J Mol Biol – volume: 202 start-page: 865 year: 1989 end-page: 884 article-title: Predicting the secondary structure of a globular proteins using neural network models publication-title: J Mol Biol – start-page: 71 year: 2000 end-page: 95 – volume: 225 start-page: 1049 year: 1992 end-page: 1063 article-title: Predicting protein secondary structure using neural net and statistical methods publication-title: J Mol Biol – volume: 270 start-page: 17 year: 2001 end-page: 30 article-title: Multiple alignment of complete sequences (MACS) in the post‐genomic era publication-title: Gene – volume: 22 start-page: 2577 year: 1983 end-page: 2637 article-title: A dictionary of protein secondary structure publication-title: Biopolymers – start-page: 417 year: 1989 end-page: 465 – volume: 216 start-page: 441 year: 1990 end-page: 457 article-title: Machine learning approach for prediction of protein secondary structure publication-title: J Mol Biol – volume: 227 start-page: 371 year: 1992 end-page: 374 article-title: Predicting protein secondary structure with nearest‐neighbors algorithm publication-title: J Mol Biol – volume: 266 start-page: 540 year: 1996 end-page: 553 article-title: GOR method for predicting protein secondary structure from amino acid sequence publication-title: Methods Enzymol – volume: 955 start-page: 283 year: 1988 end-page: 295 article-title: Improvements in secondary structure prediction method based on a search for local sequence homologies and its use as a model building tool publication-title: Biochim Biophys Acta – volume: 25 start-page: 157 year: 1996 end-page: 168 article-title: Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods publication-title: Proteins – volume: 25 start-page: 3389 year: 1997 end-page: 3402 article-title: Gapped BLAST and PSI‐BLAST: a new generation of protein database search programs publication-title: Nucleic Acids Res – volume: 41 start-page: 17 year: 2000 end-page: 20 article-title: Prediction of protein secondary structure at 80% accuracy publication-title: Proteins – volume: 9 start-page: 1162 year: 2000 end-page: 1176 article-title: Cascaded multiple classifiers for secondary structure prediction publication-title: Protein Sci – volume: 234 start-page: 951 year: 1993 end-page: 957 article-title: The limits of protein secondary structure prediction accuracy from multiple sequence alignment publication-title: J Mol Biol – volume: 198 start-page: 425 year: 1987 end-page: 443 article-title: Further developments of protein secondary structure prediction using information theory: new parameters and consideration of residue pairs publication-title: J Mol Biol – volume: 23 start-page: I year: 1995 end-page: IV article-title: A large scale experiment to assess protein structure prediction publication-title: Proteins – volume: 205 start-page: 303 year: 1986 end-page: 308 article-title: An algorithm for secondary structure determination in proteins based on sequence similarity publication-title: FEBS Lett – volume: 120 start-page: 97 year: 1978 end-page: 120 article-title: Analysis and implications of simple methods for predicting the secondary structure of globular proteins publication-title: J Mol Biol – volume: 1 start-page: 401 year: 1992 end-page: 408 article-title: An optimization approach to predicting protein structural class from amino acid composition publication-title: Protein Sci – volume: 195 start-page: 957 year: 1987 end-page: 961 article-title: Prediction of protein secondary structure and active sites using alignment of homologous sequences publication-title: J Mol Biol – volume: 2 start-page: 185 year: 1988 end-page: 191 article-title: Secondary structure prediction: combination of three different methods publication-title: Protein Eng – volume: 268 start-page: 31 year: 1997 end-page: 36 article-title: Protein secondary structure prediction using local alignments publication-title: J Mol Biol – volume: 29 start-page: 172 year: 1997 end-page: 185 article-title: Understanding the recognition of protein structural classes by amino acid composition publication-title: Proteins – volume: 13 start-page: 211 year: 1974 end-page: 215 article-title: Prediction of protein conformation publication-title: Biochemistry – volume: 6 start-page: 849 year: 1993 end-page: 854 article-title: Quantification of secondary structure prediction improvement using distantly related proteins publication-title: Protein Eng – volume: 292 start-page: 195 year: 1999 end-page: 202 article-title: Protein secondary structure prediction based on position specific scoring matrices publication-title: J Mol Biol – volume: 88 start-page: 857 year: 1974 end-page: 872 article-title: Structural principles of the globular organization of protein chains: a stereochemical theory of globular protein secondary structure publication-title: J Mol Biol – volume: 235 start-page: 13 year: 1994 end-page: 26 article-title: Redefining the goals of protein secondary structure prediction publication-title: J Mol Biol – volume: 134 start-page: 204 year: 2001 end-page: 218 article-title: Review: protein secondary structure prediction continues to rise publication-title: J Struct Biol – volume: 27 start-page: 329 year: 1997 end-page: 335 article-title: Seventy‐five percent accuracy in protein secondary structure prediction publication-title: Proteins – volume: 5 start-page: 372 year: 1995 end-page: 376 article-title: Protein secondary structure prediction publication-title: Curr Opin Struct Biol – volume: 40 start-page: 502 year: 2000 end-page: 511 article-title: Application of multiple sequence alignment profiles to improve protein secondary structure prediction publication-title: Proteins – volume: 25 start-page: 169 year: 1996 end-page: 179 article-title: Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class publication-title: Proteins – volume: 34 start-page: 220 year: 1999 end-page: 223 article-title: A modified definition of Sov, a segment‐based measure of protein secondary structure prediction assessment publication-title: Proteins – volume: 14 start-page: 846 year: 1998 end-page: 856 article-title: Hidden Markov models for detecting remote protein homologies publication-title: Bioinformatics – volume: 247 start-page: 11 year: 1995 end-page: 15 article-title: Prediction of protein secondary structure by combining nearest‐neighbor algorithm and multiple sequence alignments publication-title: J Mol Biol – volume: 10 start-page: 53 year: 1994 end-page: 60 article-title: PHD: an automatic mail server for protein secondary structure prediction publication-title: Comput Appl Biosci – volume: 405 start-page: 442 year: 1975 end-page: 451 article-title: Comparison of the predicted and observed secondary structure of T phage lysozyme publication-title: Biochim Biophys Acta – volume: 363 start-page: 127 year: 1995 end-page: 131 article-title: Does the folding type of protein depend on its amino acid composition? publication-title: FEBS Lett – volume: 5 start-page: 106 year: 1996 end-page: 113 article-title: Improving protein secondary structure prediction with aligned homologous sequences publication-title: Protein Sci – volume: 34 start-page: 508 year: 1999 end-page: 519 article-title: Evaluation and improvement of multiple sequence methods for protein secondary structure prediction publication-title: Proteins – ident: e_1_2_4_10_2 doi: 10.1002/(SICI)1097-0134(19990201)34:2<220::AID-PROT7>3.0.CO;2-K – ident: e_1_2_4_2_2 doi: 10.1002/bip.360221211 – ident: e_1_2_4_31_2 doi: 10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4 – ident: e_1_2_4_40_2 doi: 10.1016/S0076-6879(96)66033-9 – ident: e_1_2_4_32_2 doi: 10.1002/1097-0134(20000815)40:3<502::AID-PROT170>3.0.CO;2-Q – ident: e_1_2_4_45_2 doi: 10.1016/S0032-3861(01)00425-6 – ident: e_1_2_4_3_2 – ident: e_1_2_4_27_2 doi: 10.1016/0167-4838(88)90206-3 – ident: e_1_2_4_5_2 doi: 10.1002/prot.340230303 – ident: e_1_2_4_11_2 doi: 10.1016/S0022-2836(05)80007-5 – ident: e_1_2_4_8_2 doi: 10.1006/jmbi.1994.0116 – ident: e_1_2_4_25_2 doi: 10.1006/jmbi.1999.3091 – ident: e_1_2_4_33_2 doi: 10.1016/S0022-2836(05)80333-X – ident: e_1_2_4_46_2 – ident: e_1_2_4_23_2 doi: 10.1016/0022-2836(92)90927-C – ident: e_1_2_4_22_2 doi: 10.1016/0022-2836(88)90564-5 – ident: e_1_2_4_44_2 doi: 10.1016/S0378-1119(01)00461-9 – ident: e_1_2_4_24_2 doi: 10.1002/1097-0134(20001001)41:1<17::AID-PROT40>3.0.CO;2-F – ident: e_1_2_4_21_2 doi: 10.1073/pnas.86.1.152 – ident: e_1_2_4_19_2 doi: 10.1006/jmbi.1993.1413 – ident: e_1_2_4_38_2 doi: 10.1016/0022-2836(87)90501-8 – ident: e_1_2_4_39_2 doi: 10.1093/protein/6.8.849 – ident: e_1_2_4_49_2 doi: 10.1016/0014-5793(95)00245-5 – volume: 10 start-page: 53 year: 1994 ident: e_1_2_4_20_2 article-title: PHD: an automatic mail server for protein secondary structure prediction publication-title: Comput Appl Biosci – ident: e_1_2_4_37_2 doi: 10.1006/jsbi.2001.4336 – ident: e_1_2_4_52_2 doi: 10.1002/(SICI)1097-0134(199710)29:2<172::AID-PROT5>3.0.CO;2-F – ident: e_1_2_4_17_2 doi: 10.1007/978-1-4613-1571-1_10 – ident: e_1_2_4_6_2 doi: 10.1002/(SICI)1097-0134(199703)27:3<329::AID-PROT1>3.0.CO;2-8 – ident: e_1_2_4_15_2 doi: 10.1016/0022-2836(78)90297-8 – ident: e_1_2_4_35_2 doi: 10.1093/bioinformatics/14.10.846 – ident: e_1_2_4_47_2 – ident: e_1_2_4_30_2 doi: 10.1016/0022-2836(92)90892-N – ident: e_1_2_4_51_2 doi: 10.1002/(SICI)1097-0134(199606)25:2<169::AID-PROT3>3.0.CO;2-D – ident: e_1_2_4_4_2 doi: 10.1385/1-59259-368-2:71 – ident: e_1_2_4_14_2 doi: 10.1016/0022-2836(74)90405-7 – ident: e_1_2_4_41_2 doi: 10.1093/nar/25.17.3389 – ident: e_1_2_4_9_2 doi: 10.1016/0005-2795(75)90109-9 – ident: e_1_2_4_28_2 doi: 10.1006/jmbi.1997.0958 – ident: e_1_2_4_34_2 doi: 10.1016/0959-440X(95)80099-9 – ident: e_1_2_4_42_2 doi: 10.1002/pro.5560050113 – ident: e_1_2_4_18_2 doi: 10.1016/S0076-6879(96)66034-0 – ident: e_1_2_4_7_2 doi: 10.1093/protein/2.3.185 – ident: e_1_2_4_43_2 doi: 10.1006/jmbi.1993.1649 – ident: e_1_2_4_16_2 doi: 10.1016/0022-2836(87)90292-0 – ident: e_1_2_4_29_2 doi: 10.1006/jmbi.1993.1464 – ident: e_1_2_4_48_2 doi: 10.1002/pro.5560010312 – ident: e_1_2_4_36_2 doi: 10.1110/ps.9.6.1162 – ident: e_1_2_4_12_2 doi: 10.1021/bi00699a001 – ident: e_1_2_4_50_2 doi: 10.1002/(SICI)1097-0134(199606)25:2<157::AID-PROT2>3.0.CO;2-F – ident: e_1_2_4_26_2 doi: 10.1016/0014-5793(86)80917-6 – ident: e_1_2_4_13_2 doi: 10.1016/0022-2836(74)90404-5 |
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| SubjectTerms | Algorithms Animals Evolution, Molecular GOR algorithm Information Theory multiple sequence alignment protein secondary structure Protein Structure, Secondary Proteins - chemistry PSI-BLAST secondary structure prediction Sensitivity and Specificity Sequence Alignment Sequence Analysis, Protein - methods |
| Title | Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence |
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