MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens.

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.

Complex network spectral moments for ATCUN motif DNA cleavage: first predictive study on proteins of human pathogen parasites.

The development of methods that can predict the metal-mediated biological activity based only on the 3D structure of metal-unbound proteins has become a goal of major importance. This work is dedicated to the amino terminal Cu(II)- and Ni(II)-binding (ATCUN) motifs that participate in the DNA cleavage and have antitumor activity. We have calculated herein, for the first time, the 3D electrostatic spectral moments for 415 different proteins, including 133 potential ATCUN antitumor proteins. Using these parameters as input for Linear Discriminant Analysis, we have found a model that discriminates between ATCUN-DNA cleavage proteins and nonactive proteins with 91.32% Accuracy (379 out of 415 of proteins including both training and external validation series). Finally, the model has predicted for the first time the DNA cleavage function of proteins from the pathogen parasites. We have predicted possible ATCUN-like proteins with a probability higher than 99% in nine parasite families such as Trypanosoma, Plasmodium, Leishmania, or Toxoplasma. The distribution by biological function of the ATCUN proteins predicted has been the following: oxidoreductases 70.5%, signaling proteins 62.5%, lyases 58.2%, membrane proteins 45.5%, ligases 44.4%, hydrolases 41.3%, transferases 39.2%, cell adhesion proteins 34.5%, metal binders 33.5%, translation proteins 25.0%, transporters 16.7%, structural proteins 9.1%, and isomerases 8.2%. The model is implemented at http://miaja.tic.udc.es/Bio-AIMS/ATCUNPred.php.