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Cited by 1,452

The DOI matches its Crossref record; 104 of the 104 references it cites were found in a registry or an index.

Combining evolutionary information and neural networks to predict protein secondary structure

Rost, Burkhard, Sander, Chris·1994·Proteins: Structure, Function, and Bioinformatics
DOI 10.1002/prot.340190108
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Abstract

Using evolutionary information contained in multiple sequence alignments as input to neural networks, secondary structure can be predicted at significantly increased accuracy. Here, we extend our previous three‐level system of neural networks by using additional input information derived from multiple alignments. Using a position‐specific conservation weight as part of the input increases performance. Using the number of insertions and deletions reduces the tendency for overprediction and increases overall accuracy. Addition of the global amino acid content yields a further improvement, mainly in predicting structural class. The final network system has a sustained overall accuracy of 71.6% in a multiple cross‐validation test on 126 unique protein chains. A test on a new set of 124 recently solved protein structures that have no significant sequence similarity to the learning set confirms the high level of accuracy. The average cross‐validated accuracy for all 250 sequence‐unique chains is above 72%. Using various data sets, the method is compared to alternative prediction methods, some of which also use multiple alignments: the performance advantage of the network system is at least 6 percentage points in three‐state accuracy. In addition, the network estimates secondary structure content from multiple sequence alignments about as well as circular dichroism spectroscopy on a single protein and classifies 75% of the 250 proteins correctly into one of four protein structural classes. Of particular practical importance is the definition of a position‐specific reliability index. For 40% of all residues the method has a sustained three‐state accuracy of 88%, as high as the overall average for homology modelling. A further strength of the method is greatly increased accuracy in predicting the placement of secondary structure segments. © 1994 Wiley‐Liss, Inc.

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Metadata

Authors
Rost, B., & Sander, C.
Venue
Proteins: Structure, Function, and Bioinformatics
Publisher
Wiley
Published
1994-05
Volume
19
Issue
1
Pages
55-72
ISSN
  • 0887-3585 (ISSN-L)
  • 1097-0134
DOI
10.1002/prot.340190108
OpenAlex
W2013136212
Cited by
1,452
References
104
FWCI
37.01
Type
journal article
Language
en
Open access
closed

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