Group Leader
ACADEMIC PROFILES
SOCIAL
REPOSITORIES
CONTACTS
+39 049 827 6260
+39 049 827 6269
2003
Journal Articles
221.
Eckart Bindewald; Alessandro Cestaro; Jürgen Hesser; Matthias Heiler; Silvio C. E. Tosatto
MANIFOLD: Protein fold recognition based on secondary structure, sequence similarity and enzyme classification Journal Article
In: Protein Engineering, vol. 16, no. 11, pp. 785-789, 2003, (Cited by: 35).
Abstract | Links | Altmetric | Dimensions | PlumX
@article{SCOPUS_ID:0345600222,
title = {MANIFOLD: Protein fold recognition based on secondary structure, sequence similarity and enzyme classification},
author = {Eckart Bindewald and Alessandro Cestaro and Jürgen Hesser and Matthias Heiler and Silvio C. E. Tosatto},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-0345600222&origin=inward},
doi = {10.1093/protein/gzg106},
year = {2003},
date = {2003-01-01},
journal = {Protein Engineering},
volume = {16},
number = {11},
pages = {785-789},
publisher = {Oxford University Press},
abstract = {We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13-14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.},
note = {Cited by: 35},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13-14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.
222.
Mario Albrecht; Silvio C. E. Tosatto; Thomas Lengauer; Giorgio Valle
Simple consensus procedures are effective and sufficient in secondary structure prediction Journal Article
In: Protein Engineering, vol. 16, no. 7, pp. 459-462, 2003, (Cited by: 67; Open Access).
Abstract | Links | Altmetric | Dimensions | PlumX
@article{SCOPUS_ID:0042379959,
title = {Simple consensus procedures are effective and sufficient in secondary structure prediction},
author = {Mario Albrecht and Silvio C. E. Tosatto and Thomas Lengauer and Giorgio Valle},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-0042379959&origin=inward},
doi = {10.1093/protein/gzg063},
year = {2003},
date = {2003-01-01},
journal = {Protein Engineering},
volume = {16},
number = {7},
pages = {459-462},
publisher = {Oxford University Press},
abstract = {We have analyzed the performance of majority voting on minimal combination sets of three state-of-the-art secondary structure prediction methods in order to obtain a consensus prediction. Using three large benchmark sets from the EVA server, our results show a significant improvement in the average Q3 prediction accuracy of up to 1.5 percentage points by consensus formation. The application of an additional trivial filtering procedure for predicted secondary structure elements that are too short, does not significantly affect the prediction accuracy. Our analysis also provides valuable insight into the similarity of the results of the prediction methods that we combine as well as the higher confidence in consistently predicted secondary structure.},
note = {Cited by: 67; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We have analyzed the performance of majority voting on minimal combination sets of three state-of-the-art secondary structure prediction methods in order to obtain a consensus prediction. Using three large benchmark sets from the EVA server, our results show a significant improvement in the average Q3 prediction accuracy of up to 1.5 percentage points by consensus formation. The application of an additional trivial filtering procedure for predicted secondary structure elements that are too short, does not significantly affect the prediction accuracy. Our analysis also provides valuable insight into the similarity of the results of the prediction methods that we combine as well as the higher confidence in consistently predicted secondary structure.
2002
Journal Articles
223.
Silvio C. E. Tosatto; Eckart Bindewald; Jürgen Hesser; Reinhard Männer
A divide and conquer approach to fast loop modeling Journal Article
In: Protein Engineering, vol. 15, no. 4, pp. 279-286, 2002, (Cited by: 63; Open Access).
Abstract | Links | Altmetric | Dimensions | PlumX
@article{SCOPUS_ID:0036096608,
title = {A divide and conquer approach to fast loop modeling},
author = {Silvio C. E. Tosatto and Eckart Bindewald and Jürgen Hesser and Reinhard Männer},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-0036096608&origin=inward},
doi = {10.1093/protein/15.4.279},
year = {2002},
date = {2002-01-01},
journal = {Protein Engineering},
volume = {15},
number = {4},
pages = {279-286},
publisher = {Oxford University Press},
abstract = {We describe a fast ab initio method for modeling local segments in protein structures. The algorithm is based on a divide and conquer approach and uses a database of precalculated look-up tables, which represent a large set of possible conformations for loop segments of variable length. The target loop is recursively decomposed until the resulting conformations are small enough to be compiled analytically. The algorithm, which is not restricted to any specific loop length, generates a ranked set of loop conformations in 20-180 s on a desktop PC. The prediction quality is evaluated in terms of global RMSD. Depending on loop length the top prediction varies between 1.06 Å RMSD for three-residue loops and 3.72 Å RMSD for eight-residue loops. Due to its speed the method may also be useful to generate alternative starting conformations for complex simulations.},
note = {Cited by: 63; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We describe a fast ab initio method for modeling local segments in protein structures. The algorithm is based on a divide and conquer approach and uses a database of precalculated look-up tables, which represent a large set of possible conformations for loop segments of variable length. The target loop is recursively decomposed until the resulting conformations are small enough to be compiled analytically. The algorithm, which is not restricted to any specific loop length, generates a ranked set of loop conformations in 20-180 s on a desktop PC. The prediction quality is evaluated in terms of global RMSD. Depending on loop length the top prediction varies between 1.06 Å RMSD for three-residue loops and 3.72 Å RMSD for eight-residue loops. Due to its speed the method may also be useful to generate alternative starting conformations for complex simulations.
