Properties and prediction of mitochondrial transit peptides from Plasmodium falciparum

A neural network approach for the prediction of mitochondrial transit peptides (mTPs) from the malaria-causing parasite Plasmodium falciparum is presented. Nuclear-encoded mitochondrial protein precursors of P.fialciparum were analyzed by statistical methods, principal component analysis and supervised neural networks, and were compared to those of other eukaryotes. A distinct amino acid usage pattern has been found in protein encoding regions of P. falciparum: glycine, alanine, tryptophan and arginine are under-represented, whereas isoleucine, tyrosine, asparagine and lysine are over-represented compared to the SwissProt average. Similar patterns were observed in mTPs of R falciparum. Using principal component analysis (PCA), mTPs from P. falciparum were shown to differ considerably from those of other organisms. A neural network system (PlasMit) for prediction of mTPs in R falciparum sequences was developed, based on the relative amino acid frequency in the first 24 N-terminal amino acids, yielding a Matthews correlation coefficient of 0.74 (90% correct prediction) in a 20-fold cross-validation study. This system predicted 1177 (22%) mitochondrial genes, based on 5334 annotated genes in the R falciparum genome. A second network with the same topology was trained to give more conservative estimate. This more stringent network yielded a Matthews correlation coefficient of 0.51 (84% correct prediction) in a 10-fold cross-validation study. It predicted 381 (7.1%) mitochondrial genes, based on 5334 annotated genes in the P.falciparum genome. (C) 2003 Elsevier B.V. All rights reserved.