Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0348321 (Haemophilus)
 15,372 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Previously, we introduced a neural network system predicting locations of transmembrane helices (HTMs) based on evolutionary profiles (PHDhtm, Rost B, Casadio R, Fariselli P, Sander C, 1995, Protein Sci 4:521-533). Here, we describe an improvement and an extension of that system. The improvement is achieved by a dynamic programming-like algorithm that optimizes helices compatible with the neural network output. The extension is the prediction of topology (orientation of first loop region with respect to membrane) by applying to the refined prediction the observation that positively charged residues are more abundant in extra-cytoplasmic regions. Furthermore, we introduce a method to reduce the number of false positives, i.e., proteins falsely predicted with membrane helices. The evaluation of prediction accuracy is based on a cross-validation and a double-blind test set (in total 131 proteins). The final method appears to be more accurate than other methods published: (1) For almost 89% (+/-3%) of the test proteins, all HTMs are predicted correctly. (2) For more than 86% (+/-3%) of the proteins, topology is predicted correctly. (3) We define reliability indices that correlate with prediction accuracy: for one half of the proteins, segment accuracy raises to 98%; and for two-thirds, accuracy of topology prediction is 95%. (4) The rate of proteins for which HTMs are predicted falsely is below 2% (+/-1%). Finally, the method is applied to 1,616 sequences of Haemophilus influenzae. We predict 19% of the genome sequences to contain one or more HTMs. This appears to be lower than what we predicted previously for the yeast VIII chromosome (about 25%).
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PMID:Topology prediction for helical transmembrane proteins at 86% accuracy. 884 59

We present here computer generated model of N-terminal fragment, amino acids (aa) 36-245, of a Plasmodium vivax heat shock metalloprotease called PVHSP28, whose gene was cloned and characterised earlier. The fragment showed homology with HSPs from many organisms, including Escherichia coli and Haemophilus influenzae. PVHSP28 had the signature sequence 'HEXXH' and 'EXXXD' of Zinc metalloproteases. Being the first malarial HSP possessing metalloprotease activity, PVHSP28 is an ideal target for the design of new anti-malarial drugs. However, except for a small region (aa 62-132) which had 24.6% sequence similarity with 1TAQ (a DNA polymerase), it did not show sequence similarity with any published structures in protein data bank. Hence it could not be modelled using any automated modeling programs. We modelled 36-245 aa of PVHSP28 using predicted secondary structure as well as experimentally determined and predicted properties of the protein on the basis of its amino acid sequence, using various Internet tools and in-house package MODEL. The model was energy minimised using Sander's module of AMBER 5.0, working on a Silicon Graphics machine, with all atom force field.
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PMID:Computer modeling of small heat-shock metalloprotease of the human malaria parasite Plasmodium vivax. 1169 26