pubmed.ncbi.nlm.nih.gov

Protein fold recognition using sequence-derived predictions - PubMed

Protein fold recognition using sequence-derived predictions

D Fischer et al. Protein Sci. 1996 May.

Abstract

In protein fold recognition, one assigns a probe amino acid sequence of unknown structure to one of a library of target 3D structures. Correct assignment depends on effective scoring of the probe sequence for its compatibility with each of the target structures. Here we show that, in addition to the amino acid sequence of the probe, sequence-derived properties of the probe sequence (such as the predicted secondary structure) are useful in fold assignment. The additional measure of compatibility between probe and target is the level of agreement between the predicted secondary structure of the probe and the known secondary structure of the target fold. That is, we recommend a sequence-structure compatibility function that combines previously developed compatibility functions (such as the 3D-1D scores of Bowie et al. [1991] or sequence-sequence replacement tables) with the predicted secondary structure of the probe sequence. The effect on fold assignment of adding predicted secondary structure is evaluated here by using a benchmark set of proteins (Fischer et al., 1996a). The 3D structures of the probe sequences of the benchmark are actually known, but are ignored by our method. The results show that the inclusion of the predicted secondary structure improves fold assignment by about 25%. The results also show that, if the true secondary structure of the probe were known, correct fold assignment would increase by an additional 8-32%. We conclude that incorporating sequence-derived predictions significantly improves assignment of sequences to known 3D folds. Finally, we apply the new method to assign folds to sequences in the SWISSPROT database; six fold assignments are given that are not detectable by standard sequence-sequence comparison methods; for two of these, the fold is known from X-ray crystallography and the fold assignment is correct.

PubMed Disclaimer

Cited by

Amino acids 257 to 288 of mouse p48 control the cooperation of polyomavirus large T antigen, replication protein A, and DNA polymerase alpha-primase to synthesize DNA in vitro.
Kautz AR, Weisshart K, Schneider A, Grosse F, Nasheuer HP. Kautz AR, et al. J Virol. 2001 Sep;75(18):8569-78. doi: 10.1128/jvi.75.18.8569-8578.2001. J Virol. 2001. PMID: 11507202 Free PMC article.
Characterization of structural and functional role of selenocysteine in selenoprotein H and its impact on DNA binding.
Barage SH, Deobagkar DD, Baladhye VB. Barage SH, et al. Amino Acids. 2018 May;50(5):593-607. doi: 10.1007/s00726-018-2543-5. Epub 2018 Feb 26. Amino Acids. 2018. PMID: 29480333
Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.
Shapovalov M, Dunbrack RL Jr, Vucetic S. Shapovalov M, et al. PLoS One. 2020 May 6;15(5):e0232528. doi: 10.1371/journal.pone.0232528. eCollection 2020. PLoS One. 2020. PMID: 32374785 Free PMC article.
Understanding the sequence determinants of conformational switching using protein design.
Dalal S, Regan L. Dalal S, et al. Protein Sci. 2000 Sep;9(9):1651-9. doi: 10.1110/ps.9.9.1651. Protein Sci. 2000. PMID: 11045612 Free PMC article.
Pcons: a neural-network-based consensus predictor that improves fold recognition.
Lundström J, Rychlewski L, Bujnicki J, Elofsson A. Lundström J, et al. Protein Sci. 2001 Nov;10(11):2354-62. doi: 10.1110/ps.08501. Protein Sci. 2001. PMID: 11604541 Free PMC article.

References

1. Protein Sci. 1994 Aug;3(8):1315-28 - PubMed
1. J Mol Biol. 1981 Mar 25;147(1):195-7 - PubMed
1. Proc Natl Acad Sci U S A. 1988 Apr;85(8):2444-8 - PubMed
1. Nucleic Acids Res. 1992 May 11;20 Suppl:2019-22 - PubMed
1. Proteins. 1992 Jul;13(3):258-71 - PubMed

Protein fold recognition using sequence-derived predictions - PubMed