# Patent

## Title
Computational method for identifying adhesin and adhesin-like proteins of therapeutic potential

## Abstract

A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of a neural network software wherein the attributes are (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv),charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ≧0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.

## Background
CROSS-REFERENCE TO OTHER APPLICATIONS
The present application claims the benefit of priority of each of the following applications: U.S., Provisional Application No. 06/589,220 filed on Jul. 20, 2004 and Indian Patent Application No. 173/DEL/2004 dated Feb. 6, 2004. The text of each of the aforementioned applications is specifically incorporated herein by reference.
The file copy of the sequence listing is submitted on a Compact-Disc Recordable (CD-R). The sequence listing is saved as an ASCII text file named 40359A.txt (2.8 1MB), which was created on May 18, 2005. The contents of the CD-R are incorporated herein by reference in its entirety.


FIELD OF THE PRESENT INVENTION
A computational method for identifying adhesin and adhesin-like proteins; computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.
BACKGROUND AND PRIOR ART OF THE PRESENT INVENTION
The progress in genome sequencing projects has generated a large number of inferred protein sequences from different organisms. It is expected that the availability of the information on the complete set of proteins from infectious human pathogens will enable us to develop novel molecular approaches to combat them. A necessary step in the successful colonization and subsequent manifestation of disease by microbial pathogens is the ability to adhere to host cells.
Microbial pathogens encode several proteins known as adhesins that mediate their adherence to host cell surface receptors, membranes, or extracellular matrix for successful colonization. Investigations in this primary event of host-pathogen interaction over the past decades have revealed a wide array of adhesins in a variety of pathogenic microbes. Presently, substantial information on the biogenesis of adhesins and the regulation of adhesin factors is available. One of the best understood mechanisms of bacterial adherence is attachment mediated by pili or fimbriae. Several afimbrial adhesins also have been reported. In addition, limited knowledge on the target host receptors also has been gained (Finlay, B. B. and Falkow, S 1997).
New approaches to vaccine development focus on targeting adhesins to abrogate the colonization process (Wizemann, et al 1999). However, the specific role of particular adhesins has been difficult to elucidate. Thus, prediction of adhesins or adhesin-like proteins and their functional characterization is likely to aid not only in deciphering the molecular mechanisms of host pathogen interaction but also in developing new vaccine formulations, which can be tested in suitable experimental model systems.
One of the best understood mechanisms of bacterial adherence is attachment mediated by pili or fimbriae. For example, FimH and PapG adhesins of Escherichia coli (Maurer, L., Orndorff, P. (1987), Bock, K., et al. (1985). Other examples of pili group adhesins include type IV pili in Pseudomonas aeruginosa, Neisseria species, Moraxella species, Enteropathogenic Escherichia coli and Vibrio cholerae (Sperandio V et al (1996). Several afimbrial adhesins are HMW proteins of Haemophilus influenzae (van Schilfgaarde 2000), the filamentous hemagglutinin, pertactin, of Bordetella pertussis (Bassinet et al 2000), the BabA of H. pylori (Yu J et al 2002) and the YadA adhesin of Yersinia enterocolitica (Neubauer et al 2000). The intimin receptor protein (Tir) of Enteropathogenic E. coli (EPEC) is another type of adhesin (Ide T et al 2003). Other class of adhesins includes MrkD protein of Kleibsella pneumoniae, Hia of H. influenzae (St Geme et al 2000), Ag I/II of Streptococcus mutans and SspA, SspB of Streptococcus gordonii (Egland et al 2001), FnbA, FnbB of Staphylococcus aureus and SfbI, protein F of Streptococcus pyogenes, the PsaA of Streptococcus pneumoniae (De et al 2003).
A known example of adhesins approved as vaccine is the acellular pertussis vaccine containing FHA and pertactin against B. pertussis the causative agent of whooping cough (Halperin, S et al 2003). Immunization with FimH is being evaluated for protective immunity against pathogenic E. coli (Langermann S et al 2000), in Streptococcus pneumoniae, PsaA is being investigated as a potential vaccine candidate against pneumococcal disease (Rapola, S et al 2003). Immunization results with BabA adhesin showed promise for developing a vaccine against H. pylori (Prinz, C et al 2003). A synthetic peptide sequence anti-adhesin vaccine is being evaluated for protection against Pseudomonas aeruginosa infections.
Screening for adhesin and adhesin like proteins by conventional experimental method is laborious, time consuming and expensive. As an alternative, homology search is used to facilitate the identification of adhesins. Although, this procedure is useful in the analysis of genome organization (Wolf et al 2001) and of metabolic pathways. (Peregrin-Alvarez et al 2003, Rison et al 2002), it is somewhat limited in allowing functional predictions when the homologues are not functionally characterized or the sequence divergence is high. Assignment of functional roles to proteins based on this technique has been possible for only about 60% of the predicted protein sequences (Fraser et al 2000). Thus, we explored the possibility of developing a non-homology method based on sequence composition properties combined with the power of the Artificial Neural Networks to identify adhesins and adhesin-like proteins in species belonging to wide phylogenetic spectrum.
Twenty years ago, Nishikawa et al carried out some of the early attempts to classify proteins into different groups based on compositional analysis (Nishikawa et al 1983). More recently, the software PropSearch was developed for analyzing protein sequences where conventional alignment tools fail to identify significantly similar sequences (Hobohm, U. and Sander, C 1995). PropSearch uses 144 compositional properties of protein sequences to detect possible structural or functional relationships between a new sequence and sequences in the database. Recently the compositional attributes of proteins have been used to develop softwares for predicting secretory proteins in bacteria and apicoplast targeted proteins in Plasmodium falciparum by training Artificial Neural Networks (Zuegge et al 2001).
Zuegge et al have used the 20 amino acid compositional properties. Their objective was to extract features of apicoplast targeted proteins in Plasmodium falciparum. This is distinct from our software SPAAN that focuses on adhesins and adhesin-like proteins involved in host-pathogen interaction.
Hobohm and Sander have used 144 compositional properties including isoelectric point and amino acid and dipeptide composition to generate hypotheses on putative functional role of proteins that are refractory to analysis using other sequence alignment based approaches like BLAST and FASTA. Hobohm and Sander do not specifically address the issue of adhesins and adhesin-like proteins, which is the focus of SPAAN. Nishikawa et al had originally attempted to classify proteins into various functional groups. This was a curiosity driven exercise but eventually lead to the development of a software to discriminate extra-cellular proteins from intracellular proteins. This work did not address the issue of adhesins and adhesin-like proteins, which is the focus of SPAAN.
Thus, none of the aforementioned research groups have been able to envisage the methodology of the instant application. The inventive method of this application provides novel proteins and corresponding gene sequences.
Adhesins and adhesin-like proteins mediate host-pathogen interactions. This is the first step in colonization of a host by microbial pathogens. Attempts Worldwide are focused on designing vaccine formulations comprising adhesin proteins derived from pathogens. When immunized, host will have its immune system primed against adhesins for that pathogen. When a pathogen is actually encountered, the surveillance mechanism will recognize these adhesins, bind them through antigen-antibody interactions and neutralize the pathogen through complement mediate cascade and other related clearance mechanisms. This strategy has been successfully employed in the case of Whooping cough and is being actively pursued in the case of Pneumonia, Gastric Ulcer and Urinary tract infections.
OBJECTS OF THE PRESENT INVENTION
The main object of the present invention is to provide a computational method for identifying adhesin and adhesin-like proteins of therapeutic potential.
Another object of invention is to provide a method for screening the proteins with unique compositional characteristics as putative adhesins in different pathogens.
Yet, another object of the invention is providing the use of gene sequences encoding the putative adhesin proteins useful as preventive therapeutics.
SUMMARY OF THE PRESENT INVENTION
A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of protein sequences using five attribute modules of software SPAAN, (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ≧0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins
DETAILED DESCRIPTION OF THE PRESENT INVENTION
Accordingly, the present invention relates to a computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of protein sequences using five attribute modules of software SPAAN, (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ≧0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins
In an embodiment of the present invention, wherein the invention relates to a computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of:

    
    
        a. computing the sequence-based attributes of protein sequences using five attribute modules of a neural network software, wherein the attributes are software, (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv) charge composition, and (v) hydrophobic composition,
        b. training the artificial neural Network (ANN) for each of the computed five attributes, and
        c. identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ≧0.51.
    
    


In another embodiment of the present invention, wherein the invention relates to a method wherein the protein sequences is obtained from pathogens, eukaryotes, and multicellular organisms.
In an embodiment of the present invention, wherein the invention relates to a method, wherein the protein sequences are obtained from the pathogens selected from a group of organisms comprising Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Rickettsiae prowazekii, Porphyromonas gingivalis, Shigella flexneri, Streptococcus mutans, Streptococcus pneumoniae, Neisseria men ingitides, Streptococcus pyogenes, Treponema pallidum and Severe Acute Respiratory Syndrome associated human coronavirus (SARS).
In yet another embodiment of the present invention, wherein the method of the invention is a non-homology method.
In still another embodiment of the present invention, wherein the invention relates to the method using 105 compositional properties of the sequences.
In still another embodiment of the present invention, wherein the invention relates to a method showing sensitivity of at least 90%.
In still another embodiment of the present invention, wherein the invention relates to the method showing specificity of 100%.
In still another embodiment of the present invention, wherein the invention relates to a method identifying adhesins from distantly related organisms.
In still another embodiment of the present invention, wherein the invention relates to the neural network has multi-layer feed forward topology, consisting of an input layer, one hidden layer, and an output layer.
In still another embodiment of the present invention, wherein the invention relates to the number of neurons in the input layer are equal to the number of input data points for each attribute.
In still another embodiment of the present invention, wherein the invention relates to the “Pad” is a weighted linear sum of the probabilities from five computed attributes. In still another embodiment of the present invention, wherein the invention relates to each trained network assigns a probability value of being an adhesin for the protein sequence.
In still another embodiment of the present invention, wherein the invention relates to a computer system for performing the method of claim 1, said system comprising a central processing unit, executing SPAAN program, giving probabilities based on different attributes using Artificial Neural Network and in built other programs of assessing attributes, all stored in a memory device accessed by CPU, a display on which the central processing unit displays the screens of the above mentioned programs in response to user inputs; and a user interface device.
In still another embodiment of the present invention, wherein the invention relates to a set of 274 annotated genes encoding-adhesin and adhesin-like proteins, having SEQ ID Nos. 385 to 658.
In still another embodiment of the present invention, wherein the invention relates to a set of 105 hypothetical genes encoding adhesin and adhesin-like proteins, having SEQ. ID Nos. 659 to 763.
In still another embodiment of the present invention, wherein the invention relates to a set of 279 annotated adhesin and adhesin-like proteins of SEQ ID Nos. 1 to 279.
In still another embodiment of the present invention, wherein the invention relates to a set of 105 hypothetical adhesin and adhesin-like proteins of SEQ ID Nos. 280 to 384.
One more embodiment of the present invention, wherein the invention also relates to a fully connected multilayer feed forward Artificial Neural Network based on the computational method as claimed in claim 1, comprising of an input layer, a hidden layer and an output layer which are connected in the said sequence, wherein each neuron is a binary digit number and is connected to each neuron of the subsequent layer for identifying adhesin or adhesin like proteins, wherein the program steps comprise: [a] feeding a protein sequence in FASTA format; [b] processing the sequence obtained in step [a] through the 5 modules named A, C, D, H and M, wherein attribute. A represents an amino acid composition, attribute C represents a charge composition, attribute D represents a dipeptide composition of the 20 dipeptides [NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI and HR], attribute H represents a hydrophobic composition and attribute M represents amino acid frequencies in multiplets to quantify 5 types of compositional attributes of the said protein sequence to obtain numerical input vectors respectively for each of the said attributes wherein the sum of numerical input vectors is 105; [c] processing of; the numerical input vectors obtained in step [b] by,the input neuron layer to obtain signals, wherein the number of neurons is equal to the number of numerical input vectors for each attribute; [d] processing of signals obtained from step [c] by the hidden layer to obtain synaptic weighted signals, wherein the optimal number of neurons in the hidden layer was determined through experimentation for minimizing the error at the best epoch for each network individually; [e] delivering synaptic weighted signals obtained from step [d] to the output layer for assigning of a probability value for each protein sequence fed in step [a] as being an adhesin by each network module; [f] using the individual probabilities obtained from step [e] for computing the final probability of a protein sequence being an adhesin denoted by the Pad value, which is a weighted average of the individual probabilities obtained from step [e] and the associated fraction of correlation which is a measure of the strength of the prediction.
In still another embodiment of the present invention, wherein the input neuron layer consists of a total of 105 neurons corresponding to 105 compositional properties.
In still another embodiment of the present invention, wherein the hidden layer comprises of neurons represented as 30 for amino acid frequencies, 28 for multiplet frequencies, 28 for dipeptide frequencies, 30 for charge composition and 30 for hydrophobic composition.
In still another embodiment of the present invention, wherein the output layer comprises of neurons to deliver the output values as probability value for each protein sequence.
Identification of novel adhesins and their characterization are important for studying host-pathogen interactions and testing new vaccine formulations. We have employed Artificial Neural Networks to develop an algorithm SPAAN (Software for Prediction of Adhesin and Adhesin-like proteins using Neural Networks) that can identify adhesin proteins using 105 compositional properties of a protein sequence. SPAAN could correctly predict well characterized adhesins from several bacterial species and strains. SPAAN showed 89% sensitivity and 100% specificity in a test data set that did not contain proteins in the training set. Putative adhesins identified by the software can serve as potential preventive therapeutics.
The present invention provides a novel computational method for identifying adhesin and adhesin-like proteins of therapeutic potential. More particularly, the present invention relates to candidate genes for these adhesins. The invention further provides new leads for development of candidates genes, and their encoded proteins in their functional relevance to preventive approaches. This computational method involves calculation of several sequence attributes and their subsequent analyses lead to the identification of adhesin proteins in different pathogens. Thus, the present invention is useful for identification of the adhesin proteins in pathogenic organisms. The adhesin proteins from different genomes constitute a set of candidates for functional characterization through targeted gene disruption, microarrays and proteomics. Further, these proteins constitute a set of candidates for further testing in development of preventive therapeutics. Also, are provided the genes encoding the candidate adhesin proteins.
The present method offers novelty in the principles used and the power of Neural Networks to identify new adhesins compared to laborious and time consuming conventional methods. The present method is based on compositional properties of proteins instead of sequence alignments. Therefore this method has the ability to identify adhesin and adhesin like proteins from bacteria belonging to a wide phylogenetic spectrum. The predictions made from this method are readily verifiable through independent analysis and experimentation. The invention has the potential to accelerate the development of new preventive therapeutics, which currently requires high investment in terms of requirement of skilled labor and valuable time.
The present invention relates to a computational method for the identification of candidate adhesin proteins of therapeutic potential. The invention particularly describes a novel method to identify adhesin proteins in different genomes of pathogens. These adhesin proteins can be used for developing preventive therapeutics.
Accordingly, a computational method for identifying adhesin and adhesin-like proteins of therapeutic potential which comprises calculation of 105 compositional properties under the five sequence attributes, namely, Amino Acid frequency, Multiplet frequency, dipeptide frequency, charge composition and hydrophobic composition; and then training Artificial Neural Network (ANN, Feed Forward Error Back Propagation) using these properties for differentiating between adhesin and non-adhesin class of proteins. This computational method involves quantifying 105 compositional attributes of query proteins and qualifying them as adhesins or non-adhesins by a Pad value (Probability of being an adhesin). The present invention is useful for identification of adhesin and adhesin-like proteins in pathogenic organisms. These newly identified adhesin and adhesin-like proteins constitute a set-of candidates for development of new preventive therapeutics that can be tested in suitable experimental model systems readily. In addition, the genes encoding the candidate adhesin and adhesin-like proteins are provided.
The invention provides a set of candidate adhesin and adhesin-like proteins and their coding genes for further evaluation as preventive therapeutics. The method of invention is based on the analysis of protein sequence attributes instead of sequence patterns classified to functional domains. Present method is less dependent on sequence relationships and therefore offers the potential power of identifying adhesins from distantly related organisms. The invention provides a computational method, which involves prediction of adhesin and adhesin-like proteins using Artificial Neural Networks. The proteins termed adhesin were found to be predicted with a high probability (Pad ≧0.51) in various pathogens. Some adhesin sequences turned out to be identical or homologous to proteins that are antigenic or implicated in virulence. By this approach, proteins could be identified and short-listed for further testing in development of new vaccine formulations to eliminate diseases caused by various pathogenic organisms.
DESCRIPTION OF TABLES


    Table 1: Output file format given by SPAAN.
    Table 2: Organism Name, Accession number, Number of base pairs, Date of release and Total number of proteins.
    Table 3. Prediction of well characterized adhesins from various bacterial pathogens using SPAAN.
    Table 4. Analysis of predictions made by SPAAN on genome scans of a few selected pathogenic organisms.
    Table 5: GI numbers and Gene IDs of new putative adhesins predicted by SPAAN in the genomes listed in Table 2.
    Table 6: GI numbers and Gene IDs of hypothetical proteins predicted as putative adhesins by SPAAN in the genomes listed in Table 2.
    Table 7: The list of 198 adhesins found in bacteria





BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
FIG. 1, shows the Neural Network architecture
FIG. 2 shows assessment of SPAAN using defined test dataset.
FIG. 3(a) shows Histogram plots of the number of proteins in the various Pad value ranges are shown. (b) Pairwise sequence relationships among the adhesins were determined using CLUSTAL W and plotted on X-axis. Higher scores indicate similar pairs. (c) plot for non-adhesins. Data are plotted in the 4 quadrant format for clear inspection.



Software program was written in C Language and operated on Red Hat Linux 8.0 operating system. The computer program accepts input protein sequences in FastA format and produces a tabulated output. The output Table contains one row for each protein listing the probability outputs of each of the five modules, a weighted average probability of these five modules (Pad), and the function of the protein as described in the input sequence file. This software is called SPAAN (A Software for Prediction of Adhesins and Adhesin-like proteins using Neural Networks) and a software copyright has been filed. Although this software has multiple modules, the running of these modules have been integrated and automated. The user only needs to run one command.

    
    
        AAcompo.c:
        Input: File containing protein sequences in the fasta format.
        Output: File containing frequencies of all 20 AAs for each protein in one row.
        charge.c:
        Input: File containing protein sequences in the fasta format.
        Output: File containing frequency of charged amino acids (R, K, E and D) and moments (up to 18th order) of the positions of charged amino acids.
        hdr.c:
        Input: File containing protein sequences in the fasta format.
        Output: File containing frequencies of 5 groups of amino acids formed on the basis their Hydrophobicity and moments of their positions up to 5th order.
        multiplets.c:
        Input: File containing protein sequences in the fasta format.
        Output: File containing fractions of multiplets of each of the 20 amino acids.
        quetydipep.c:
        Input: File. 1 containing protein sequences in the fasta format.
        
            File.2 containing list of the significant dipeptides in dipeptide analysis.
        
        
        Output: File containing frequencies of the dipeptides listed in the input File.2 for each protein in the input File. 1.
        train.c:
        Input: File containing following specifications
        
            1. Number of input and output parameters.
            2. Number of nodes in the hidden layers.
            3. Names of the training, validate and test data files.
            4. Learning rate, coefficient of moment.
            5. Maximum number of cycles for training.
        
        
        Output: Outputs are as follows.
        
            1. Output of the trained NN for the test data set.
            2. Values of the weight connections in the trained NN.
            3. Some extra information about training.
        
        
        recognize.c:
        Input: File containing following specifications
        
            1. Number of input and output parameters.
            2. Number of nodes in the hidden layers.
            3. Names of the query input file.
            4. Name of the file containing values of the weight connections for trained NN.
            5. Name of the output file.
        
        
        Output: Outputs for the query entries calculated by the trained NN.
        standard.c:
        Input: File containing protein sequences in fasta format.
        Output: File containing protein sequences in fasta format with all the new line characters removed lying within a sequence.
        filter.c:
        Input: File containing protein sequences in fasta format.
        Output: File containing protein sequences from the input except those which are short in length (<50 AAs) and which contain any amino acid other than the 20 known amino acids.

The Five Attributes:

Amino Acid Frequencies

    
    


Amino acid frequency fi=(counts of ith amino acid in the sequence)/1; i, =1 . . . 20, 1 is the length of the protein.
Multiplet Frequency
Multiplets are defined as homopolymeric stretches (X)n where X is any of the 20 amino acids and n is an integer >2. After identifying all the multiplets, the frequencies of the amino acids in the multiplets were computed as

fi(m)=(counts of ith amino acid occurring as multiplet)/1

Dipeptide Frequencies

The frequency of a dipeptide (i, j) fij=(counts of ijth dipeptide)/(total dipeptide counts); i, j ranges from 1 to 20.
It has been found that dipeptide repeats in proteins are important for functional expression of the clumping factor present on Staphylococcus aureus cell surface that binds to fibrinogen (Hartford et al 1999). Thus we included the dipeptide frequency module. The total number of dipeptides is 400. For optimal training of Neural Network, the ratio of total number of input vectors to the total number of weight connections must be around 2 to avoid over fitting (Andrea et al). Therefore, we identified the dipeptides whose frequencies in the adhesin data set (469 proteins, see database construction) were significantly different from that in the non-adhesin dataset (703 proteins) using t-test. The frequencies of top 20 dipeptides (when arranged in the descending order of the p-values of t-test), were fed to the Neural Network. These dipeptides were (using single letter IUPAC-IUB code) NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI, AND HR. With frequency inputs for 20 dipeptides and 28 neurons in the 2nd layer, the total number of weight connections is 588, and is in keeping with the criterion of avoiding over fitting.
Charge Composition
The input frequency of charged amino acids (R, K, E and D considering the ionization properties of the side chains at pH 7.2) given by fc=(counts of charged amino acids)/1 Further, information on the characteristics of the distribution of the charged amino acids in a given protein sequence was provided by computing the moments of the positions of the occurrences of the charged amino acids. Since moments characterize the patterns of distribution such as skewness and kurtosis (sharpness of the peak) we have used them to represent the distribution patterns of the charged residues in the sequence.
The general expression to compute moments of a given order; say ‘i’ is




  
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        Xi=position of ith charged amino acid
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The moments 2nd to 19th order were used to train the ANN constituting a total 20 inputs in addition to frequency of charged amino acids and the length of the protein. The upper limit of 19th order was set based on assessments of sensitivity and specificity on a small dataset of adhesins and non-adhesins. Moments of order greater than 19 were not useful in improvement of performance.
Hydrophobic Composition
A given protein sequence was digitally transformed using the hydrophobic scores of the amino acids according to Brendel et al. (43). The scores for five groups of amino acids: (−8 for K, E, D, R), (−4 for S, T, N, Q), (−2 for P, H), (+1 for A, G, Y, C, W), (+2 for L, V, I, F, M).
Following inputs were given for each of the group

    (a) fi=(counts of ith group)/(total counts in the protein); i ranges from 1 to 5
    (b) mji=jth order moment of positions of amino acids in ith group; j ranges from 2 to 5.


A total of 25 inputs representing the hydrophobic composition of a protein were fed to the Neural Network. The rationale for using moments was same as described in the section on charge composition inputs.
Taken together a total of 105 compositional properties of a given protein sequence Were used to predict their adhesin characteristics.
The software PropSearch uses 144. compositional properties of protein sequences to detect possible structural or functional relationships between a new sequence and sequences in the database (Hobohm and Sander 1995). The approach defines protein sequence dissimilarity (or distance) as a weighted sum of differences of compositional properties such as singlet and doublet amino acid composition, molecular weight, isoelectric point (protein property search or PropSearch). Compositional properties of proteins have also been used for predicting secretory proteins in bacteria and apicoplast targeted proteins in Plasmodium falciparum (Zuegge, et al. 2001). The properties used here are statistical methods, principal component analysis, self-organizing maps, and supervised neural networks. In SPAAN, we have used 105 compositional properties in the five modules viz. Amino Acid frequencies, Multiplet frequencies, Dipeptide frequencies, Charge composition, Hydrophobic composition. The total of 105 properties used in SPAAN are 20 for Amino acid frequencies, 20 for Multiplets frequencies, 20 for Dipeptide frequencies (Top 20 significant dipeptides are used, based on t-test), 20 for Charge composition (frequency of charged amino acids (R, K, E and D) and moments of 2nd to 19th order), and 25 for Hydrophobic composition (Amino acids were classified into five groups (−8 for K, E, D, R), (−4 for S, T, N, Q), (−2 for P, H), (+1 for A, G, Y, C, W), (+2 for L, V, I, F, M). A total of 25 inputs consisted of the following: Frequency of each group, Moments of positions of amino acids in each group from 2nd to 5th order.
Neural Network
A feed forward error back propagation Neural Network was used. The program is a kind gift from Charles W. Anderson, Department of Computer Science, Colorado State University, Fort Collins, Colo. 80523, anderson@cs.colostate.edu
Neural Network Architecture
The Neural Network used here has a multi-layer feed-forward topology. It consists of an input layer, one hidden layer and an output layer. This is a ‘fully-connected’ Neural Network where each neuron i is connected to each unit j of the next layer (FIG. 1). The weight of each-connection is denoted by wij. The state Ii of each neuron in the input layer is assigned directly from the input data, whereas the states of hidden layer neurons are computed by the sigmoid function,

hj=1/(1+exp −(wj0+ΣwijIi)),

where, wj0 is the bias weight

The back propagation algorithm was used to minimize the differences between the computed output and the desired output. Ten thousand cycles (epochs) of iterations are performed. Subsequently, the best epoch with minimum error was identified. At this point the network produces approximate target values for a given input in the training set.
A network was trained optimally for each attribute. Thus five networks were prepared. The schematic diagram (FIG. 1) shows the procedure adopted. The number of neurons in the input layer was equal to the number of input data points for each attribute (for example 20 neurons for 20 numerical input vectors of the amino acid composition attribute). The optimal number of neurons in the hidden layer was determined through experimentation for minimizing the error at the best epoch for each network individually. An upper limit for the total number of weight connections was set to half of the total number of input vectors to avoid over fitting as suggested previously (Andrea et al).
Computer programs to compute individual compositional attributes were written in C and executed on a PC under Red Hat Linux ver 7.3 or 8.0. The network was trained on the training set, checks error and optimizes using the validate set through back propagation. The validate set was different from the training set. Since, the number of well annotated adhesins were not many, we used the ‘validate set’ itself as test set for preliminary evaluation of the performance and to obtain the fraction of correlation to compute the weighted average probability (Pad value) described in the next section. The training set had 367 adhesins and 580 non-adhesins. The validate set had 102 adhesins and 123 non-adhesins. The adhesins were qualified with a digit ‘1’ and the non-adhesins were qualified with a digit ‘0’.
During predictions, the network is fed with new data from the sequences that were not part of training set. Each network assigns a probability value of being an adhesin to a given sequence. The final probability is computed as described in the next section.
Probability of being an Adhesin, the Pad Value
Query proteins are processed modularly through network trained for each attribute. Thus, five probability outputs are obtained. Final prediction was computed using the following expression which is a weighted linear sum of the probabilities from five modules:




  
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        +
        
          
            P
            M
          
          *
          
            fc
            M
          
        
      
      )
    
    
      (
      
        
          fc
          A
        
        +
        
          fc
          C
        
        +
        
          fc
          D
        
        +
        
          fc
          H
        
        +
        
          fc
          M
        
      
      )
    
  




Pi=Probability from i module,

fci=fraction of correlation of i module of the trained Neural Network,

Where i=A (Amino acid frequencies), C (Charge composition), D (Dipeptide frequencies), H (Hydrophobic composition), or M (Multiplet frequencies).
The fraction of correlation fci represents the fraction of total entries that were correctly predicted. (Pi,adhesin>0.5 and Pi, non-adhesin<0.5) by the trained network on the test set used in preliminary evaluation (Charles Anderson).
Neural Network
A feed forward error back propagation Neural Network was used. The program was downloaded from the web site with permission from the author, Charles W. Anderson, Department of Computer Science, Colorado State University, Fort Collins, Colo. 80523, anderson@cs.colostate.edu
Statistical Analysis
All statistical procedures were carried out using Microsoft Excel (Microsoft Corporation Inc. USA).
Sequence Analysis
Homology analysis was carried out using CLUSTAL W (Thompson et al 1994), BLAST (Altschul et al 1990), CDD (conserved domain database) search (Marchler-Bauer et al 2002).
The whole genome sequences of microbial pathogens present new opportunities for the development of clinical applications such as diagnostics and vaccines. The present invention provides new leads for the development of candidate genes, and their encoded proteins in their functional relevance to preventive therapeutics.
The protein sequences of both the classes, i.e. adhesin and non-adhesin, were downloaded from the existing database (National Centre for Biotechnology Information (NCBI), USA). A total of 105 compositional properties under the five sequence attributes namely, amino acid composition, multiplet composition, dipeptide composition, charge composition and hydrophobic composition were computed by computer programs written in C language. The attributes were computed for all the proteins in both the databases. The sequence-based attributes were then used to train Artificial Neural Network for each of the protein attributes. Adhesins were qualified,by the digit ‘1’ and non-adhesins were qualified by the digit ‘0’. Finally each trained Artificial Neural Network was used to identify potential adhesins which can be envisaged to be useful for the development of preventive therapeutics against pathogenic infections. Accordingly, the invention provides a computational method for identifying adhesin and adhesin-like proteins of therapeutic potential, which comprises:

    1. preparing two comprehensive data-sets of adhesin and non-adhesin proteins from publicly available information on protein sequences,
    2. calculating computationally the sequence based attributes of the protein sequences in the publicly available protein datasets using specially developed Software for Prediction of Adhesins and Adhesin-like proteins using Neural Networks (SPAAN),
    3. training the Artificial Neural Network (ANN) for the selected attributes,
    4. assigning probability value suitable for an adhesin, “Pad” to the query protein and identifying adhesin like property in the query proteins with the help of trained Artificial Neural Network implemented in SPAAN,
    5. validating computationally the protein sequences as therapeutic potentials by comparing with the known protein sequences that are biochemically characterized in the pathogen genome.


In an embodiment of the invention the protein sequence data may be taken from an organism, specifically but not limited to organisms such as Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Rickettsiae prowazekii, Porphyromonas gingivalis, Shigella flexneri, Streptococcus mutans, Streptococcus pneumoniae, Neisseria meningitides, Streptococcus pyogenes, Treponema pallidum, Severe Acute Respiratory Syndrome associated coronavirus.
In another embodiment to the present invention different sequence-based attributes used for identification of proteins of therapeutic potential, comprise amino acid composition, charge composition, hydrophobicity composition, multiplets frequencies, and dipeptide frequencies.
In an embodiment, the non-homologous adhesin protein sequence may be compared with that of known sequences of therapeutic applications in the selected pathogens.
In an embodiment of the invention, the sequences of adhesin or adhesin like proteins comprise sequences of sequences IDs listed in Tables 5 and.6 identified by the method of invention.
Another embodiment of the invention the computer system comprises a central processing unit, executing SPAAN program, giving probabilities based on different attributes using Artificial Neural Network and in built other programs of assessing attributes, all stored in a memory device accessed by CPU, a display on which the central processing unit displays the screens of the above mentioned programs in response to user inputs; and a user interface device.
In One embodiment of the present invention, the particulars of the organisms such as their name, strain, accession number in NCBI database and other details are given in Table 2:
The invention is further explained with the help of the following examples, which are given by illustration and should be construed to limit the scope of the present invention in any manner.
EXAMPLE 1 
Operating SPAAN:
The purpose of the program is to computationally calculate various sequence-based attributes of the protein sequences.
The program works as follows:
The internet downloaded FASTA format files obtained from http://www.ncbi.nlm.nih.gov were saved by the name <organism_name>.faa are converted in the standard format by C program and passed as input to another set of C programs which computes the 5 different attributes of protein sequences (a total of 105 compositional properties in all 5 modules).
The computed properties were fed as input to the 5 different Neural Networks. Each trained network assigns a probability value of being an adhesin for a query protein. The final probability (Pad) was calculated as weighted average of these five individual probabilities. The weights were determined from a correlation value of correct; prediction during test runs of each of the five modules.
Input/Output format:
Downloaded Files and their format:

    
    
        <organism name>.faa: file which stores the annotation and the protein sequence.
    
    


Input file Format: FASTA
“>gi.vertline.”<annotation>
For example,
>gi.vertline.2314605.vertline.gb.vertline.AAD08472.vertline.histidine and glutamine-rich protein








MAHHEQQQQQQANSQHHHHHHHAHHHHYYGGEHHHHNAQQHAEQQAEQQ



 


AQQQQQQQAHQQQQQKAQQQNQQY






>gi.vertline.3261822.vertline.gnl.vertline.PID.vertline.e328405 PE_PGRS








MIGDGANGGPGQPGGPGGLLYGNGGHGGAGAAGQDRGAGNSAGLIGNGGA



 


GGAGGNGGIGGAGAPGGLGGDGGKGGFADEFTGGFAQGGRGGFGGNGNTG


 


ASGGMGGAGGAGGAGGAGGLLIGDGGAGGAGGIGGAGGVGGGGGAGGTGG


 


GGVASAFGGGNAFGGRGGDGGDGGDGGTGGAGGARGAGGAGGAGGWLSGH


 


SGAHGAMGSGGEGGAGGGGGARGEAGAGGGTSTGTNPGKAGAPGTQGDSG


 


DPGPPG






    
    
        >gi.vertline . . . .
    
    









TABLE 1







Output file format given by SPAAN


<organism_name>.out














SN
PA
PC
PD
PH
PM
Pad-value
Protein Name

















1
0.05683
0.290803
0.441338
0.50304
0.029503
0.260485
>gi.vertline.32454344.vertline.









gb.vertline.AAP82966.1.









vertline.orf1a polyprotein









[SARS coronavirus Hong









Kong ZY-2003]


2
0.639235
0.166721
0.054583
0.935385
0.453498
0.462452
>gi.vertline.32454345.vertline.









gb.vertline.AAP82967.1.









vertline.orf1ab polyprotein









[SARS coronavirus Hong









Kong ZY-2003]


3
0.651111
0.911504
0.438696
0.543944
0.924044
0.690247
>gi.vertline.32454346.vertline.









gb.vertline.AAP82968.1.









vertline.spike glycoprotein









[SARS coronavirus Hong









Kong ZY-2003]


4
0.464324
0.655003
0.179503
0.008700
0.241573
0.300970
>gi.vertline.32454347.vertline.









gb.vertline.AAP82969.1.









vertline.Orf3a [SARS









coronavirus Hong Kong









ZY-2003]









Where PA, PC, PD, PH, PM are the outputs of the five Neural Networks.
EXAMPLE 2 
Organisms and Sequence Numbers







TABLE 2







Organism Name, Accession number, Number of base pairs, Date of release


and Total number of proteins analyzed












Accession
Number of

Total


Organism Name
Number
base pairs
Date of release
no. of proteins














E. coli O157 H7
NC_002695
5498450
7 Mar. 2001
5361


H. influenzae Rd
NC_000907
1830138
30 Sep. 1996
1709


H. pylori J99
NC_000921
1643831
10 Sep. 2001
1491


M. pneumoniae
NC_000912
816394
2 Apr. 2001
689


M. tuberculosis H37Rv
NC_000962
4411529
7 Sep. 2001
3927


R. prowazekii strain Madrid E
NC_000963
1111523
10 Sep. 2001
835


P. gingivalis W83
NC_002950
2343476
9 Sep. 2003
1909


S. flexneri 2a str. 2457T
NC_004741
4599354
23 Apr. 2003
4072


S. mutans UA159
NC_004350
2030921
25 Oct. 2002
1960


S. pneumoniae R6
NC_003098
2038615
6 Sep. 2001
2043


N. meningitidis
NC_003116
2184406
27 Sep. 2001
2065


serogroup A strain Z2491


S. pyogenes MGAS8232
NC_003485
1895017
Jan. 31, 2002
1845


T. pallidum subsp.
NC_000919
1138011
7 Sep. 2001
1036


pallidum str. Nichols


Severe Acute
AY291315
29727
11 JUN. 2003
14


Respiratory Syndrome


(SARS) associated


coronavirus Frankfurt 1


SARS coronavirus HSR1
AY323977
29751
15 OCT. 2003
14


SARS coronavirus ZJ01
AY297028
29715
19 MAY 2003
3


SARS coronavirus TW1
AY291451
29729
14 MAY 2003
11


SARS coronavirus
AY282752
29736
07 MAY 2003
4


CUHK-Su10


SARS coronavirus Urbani
AY278741
29727
12 AUG. 2003
12


SARS coronavirus
NC_004718
29751
9 Sep. 2003
29


SARS coronavirus Tor2
AY274119
29751
16 MAY 2003
15


SARS coronavirus GD01
AY278489
29757
18 AUG. 2003
12


SARS coronavirus CUHK-W1
AY278554
29736
31 JUL. 2003
11


SARS coronavirus BJ01
AY278488
29725
01 MAY 2003
11









EXAMPLE 3 
The multi-layered feed forward Neural Network architecture implemented in SPAAN (FIG. 1). A given protein sequence in FASTA format is first processed through the 5 modules A, C, D, H, and M to quantify the five types of compositional attributes. A: Amino acid composition, C: Charge composition, D: dipeptide composition of the 20 dipeptides (NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI, HR), H: Hydrophobic composition, M: Amino acid frequencies as Multiplets. The sequence shown is part of the FimH precursor (gi 5524634) of E. coli. Subsequently, these numerical data are input to the input neuron layer. The directions of arrows show data flow. The number of neurons chosen in the input layer was equal to the number of the numerical input vectors of each module. The network was optimally trained through minimization of error of detection based on validate set through back propagation. The details are described in the methods. Each network module assigns a probability value of the protein being an adhesin based on the corresponding attribute. The final probability of a protein sequence being an adhesin is the Pad value a weighted average of the individual probabilities and the associated fraction of correlation which is a measure of the strength of the prediction.
EXAMPLE 4 
Performance of SPAAN assessed using a test set of 37 adhesins and 37 non-adhesins that were not part of the training set. Matthew's correlation coefficient (Mcc, plotted on Y-axis) for all the proteins with Pad values above a given threshold (plotted on X-axis) (FIG. 2). The Matthew's correlation is defined as:




  Mcc
  =
  
    
      
        (
        
          TP
          *
          TN
        
        )
      
      -
      
        (
        
          FP
          *
          FN
        
        )
      
    
    
      
        
          (
          
            TN
            +
            FN
          
          )
        
        ⁢
        
          (
          
            TN
            +
            FP
          
          )
        
        ⁢
        
          (
          
            TP
            +
            FN
          
          )
        
        ⁢
        
          (
          
            TP
            +
            FP
          
          )
        
      
    
  




Where TP=True Positives, TN=True Negatives, FP=False Positives, FN=False Negatives.
Here TPs are adhesins, TNs are non-adhesins. In general, adhesins have high Pad value, whereas non-adhesins have low Pad value. Thus known adhesins with Pad value above a given threshold are true positives whereas known non-adhesins with Pad value below the given threshold are true negatives. The sensitivity, Sn is given by




  (
  
    TP
    
      TP
      +
      FN
    
  
  )




and specificity, Sp is given by





  
    (
    
      TP
      
        TP
        +
        FP
      
    
    )
  
  .




False negatives are those cases, wherein a known adhesin had Pad value lower than the chosen threshold. Similarly, a known non-adhesin with a Pad value higher than the chosen threshold was taken as false positive. A theoretical polynomial curve of second order (dashed line) was fitted to the observed curve (smooth line) with a Karl-Pearson correlation coefficient R2=0.9799. The maximum point of the theoretical curve (where first derivative vanishes and second derivative is negative) was chosen as reference (vertical dotted line) to identify the maximum Mcc=0.94 on the observed curve (shown by arrow). The corresponding Pad value threshold was 0.51. At this Pad value threshold, Sn and Sp were 0.89 and 1.0 respectively. Note that the Mcc does not drop down to the x-axis because the highest Pad value attained by adhesins was 0.939 in comparison to the theoretical attainable limit of 1.0.

EXAMPLE 5 
Assessment of SPAAN on well known adhesins from various bacterial pathogens.







TABLE 3







Prediction of well characterized adhesins from various bacterial pathogens


using SPAAN.












Disease


Pad valueb


Species
caused
Adhesina
Host ligand
(Range)





E. coli
Diarrhoea
PapG (27)
α-D-gal(1-4) β-D-Gal-
0.84-0.76





containing receptors




SfaS (5)
alpha-sialyl-beta-2,3-b-
0.94-0.94





galactose




FimH (63)
D-mannosides
0.96-0.23c




Intimin (12)
tyrosine-phosphorylated
0.95-0.78





form of host cell receptor





Hp90




PrsG (5)
Gal(alpha1-4)Gal
0.86-0.85


Nontypeable H. influenza
Influenza
HMW1,
Human epithelial cells
0.97




HMW2




Hia (8)
human conjuctival cells
0.93-0.90


H. influenzae
bacterial
HifE (18)
Sialylyganglioside-GM1
0.85-0.73



meningitisd


K. pneumoniae
Pneumonia
MrkD
type V collagen
0.82


B. pertussis
Whooping
FHA
Sulphated sugars on cell-
0.85



cough

surface glycoconjugates




Pertactin
Integrins
0.43


Y. enterocolitica
Enterocolitis
YadA (5)
β1 integrins
0.88-0.79


S. mutans
Dental
SpaP (2)
Salivary glycoprotein
0.88, 0.87



Caries
PAc
Salivary glycoprotein
0.88


Streptococcus
Oral cavity
SspA (2)
Salivary glycoprotein
0.85, 0.84


gordonii

CshA
Fibronectin
0.78




CshB
Fibronectin
0.63




ScaA
Co-aggregation
0.71




SspB (2)
Salivary glycoprotein
0.85, 0.84


Streptococcus
Tooth decay
SpaA
Salivary glycoprotein
0.89


sobrinus

PAg (2)
Salivary glycoprotein
0.89, 0.73


Streptococcus
Scarlet
Protein F
Fibronectin
0.49


pyogenes
Fever


Streptococcus
Bacterial
PsaA (5)
Human nasopharyngeal
0.82-0.78


pneumoniae
Pneumonia

cells




CbpAe/
phosphorylcholine of the
0.81-0.49




SpsA/
teichoic acid.




PbcA/PspC


Streptococcus
Valve
FimA
Salivary glycoprotein fibrin
0.76


parasanguis
endocarditis


Streptococcus
Tooth Decay
SsaB
Salivary glycoprotein
0.71


sanguis


Enterococcus
Empyma in
EfaA
Unknown
0.83


faecalis
patients with



liver disease


Staphylococcus
Food
FnbA
Fibronectin
0.8


aureus
Poisoning
FnbB (3)
Fibronectin
0.78, 0.77,






0.69


Helicobacter
Peptic
BabA (17)
difucosylated Lewisb blood
0.87-0.68


pylori
Ulcers

group antigen





aThe number of sequences from different strains and homologs from related species analyzed are shown in parantheses.


bRounded off to the second decimal.


cOut of 63 FimH proteins, 54 were from E. coli, 6 from Shigella flexineri, 2 from Salmonella enterica and 1 was from Salmonella typhimurium. Except 2 FimH proteins, the rest had Pad ≧ 0.51. The 2 exceptions (gi numbers: 5524636, 1778448) were from E. coli.The gi: 5524636 protein is annotated as a FimH precursor but is much shorter (129 amino acids) than other members of the family. The gi: 1778448 protein is a S. typhimurium homolog in E. coli.


dOther ailments include pneumonia, epiglottitis, osteomyelitis, septic arthritis and sepsis in infants and older children.


eThe adhesin CbpA is also known by alternative names SpsA, PbcA and PspC. A total of seven sequences were analyzed. Except 1 PspC sequence, the rest all had Pad ≧ 0.51.






EXAMPLE 6 
Ability of SPAAN to discriminate adhesins from non-adhesins at Pad ≧0.51 (FIG. 3-a).
EXAMPLE 7 
The non-homology character of SPAAN assesses in both adhesins and non-adhesins (FIGS. 3b and 3c).
FIG. 3(a-c). SPAAN is non-homology based software. A total of 130 adhesins and 130 non-adhesins were analyzed to assess whether the predictive power of SPAAN could be influenced by the sequence relationships. (a) Histogram plots of the number of proteins in the various Pad value ranges are shown. Shaded bars represent adhesins whereas open bars represent non-adhesins. Note the SPAAN's ability to segregate adhesins and non-adhesins into two distinct cohesive groups. (b) Pairwise sequence relationships among the adhesins were determined using CLUSTAL W and plotted on X-axis. Higher scores indicate similar pairs. The corresponding differences in Pad values in the same protein pair was plotted on the Y-axis. Each point in the diagram represents a pair. Arrow points to protein pairs of the FimH family with high ΔPad values in spite of high similarity. Since one of the FimH proteins (gi: 5524636) had very low Pad value all pairs with this false negative protein show high ΔPad values. The protein (gi: 5524636) is of much shorter length compared with other members of the same family. (c) plot for non-adhesins. Data are plotted in the 4 quadrant format for clear inspection. Note that among protein pairs with CLUSTAL W score <20 the majority (82% in adhesins and 86% in non-adhesins) have ΔPad<0.2. These data support the non-homology character of SPAAN.
EXAMPLE 8 
Genomescan of pathogens by SPAAN identifies well known adhesins and new adhesins and adhesin-like proteins







TABLE 4







Analysis of predictions made by SPAAN on genome scans of a


few selected pathogenic organismsa









Species











Escherichia
Mycobacterium
SARS associated


Protein
coli
tuberculosis
corona virus


Class
O157:H7
H37Rv
(11 strains)





Total number of
575
435
5


proteins with


Pad ≧ 0.51


Known adhesins
 17b
—
—


Putative proteins with
 92c
105j
—


adhesin like


characteristics


Hypothetical proteins
 22d
—
—


with adhesin-like


characteristics


Proteins likely to be
190e
191k
5m


extracytoplasmic or


located at surface


Phage proteins
 30f
—
—


Others
 13g
 6l
—


Hypothetical proteins
157h
 86h
—


Wrong predictions
 54i
 47i
—





aSPAAN has general applicability. The three pathogens chosen here are those in which intense investigations are being conducted presently. M. tuberculosis is of special importance to developing countries.


bFimbrial adhesins, AidA-I, gamma intimin, curlin, translocated intimin receptor, putative adhesin and transport, Iha, prepilin peptidase dependent protein C.


cThese proteins have been annotated as proteins with a putative function. These sequences were analyzed using CDD (Conserved domain database, NCBI) and BLAST searches. Adhesin like domains were found in these proteins.


dThese proteins have been annotated as ‘hypothetical’. These sequences were analyzed using CDD and BLAST searches. Adhesin like domains were found in these proteins.


eThese proteins are outer membrane, extracellular, transport, surface, exported, flagellar, periplasmic lipoprotein, and proteins annotated as ‘hypothetical’ but found to have similar functions listed here using BLAST and CDD searches.


fThe phage proteins were of the following functional roles - tail fiber, head decoration, DNA injection, tail, major capsid, host specificity, endolysin.


gProteins predicted by SPAAN but not readily classifiable into the classes listed here have been collectively grouped as ‘Others’. However, some of these proteins are known to participate in host-pathogen interactions.The annotated functional roles are typeIII secretion, antibiotic resistance, heat shock, acid shock, structural, tellurium resistance, terminase, Hcp-like, Sec-independent translocase, uncharacterized nucleoprotein, HicB-like.


hThese proteins have been annotated as hypothetical. Re-analyses of these proteins using BLAST and CDD failed identify any function for these proteins.


iThese proteins have been annotated with functional roles that are very likely to occur within the cell. Hence these proteins may have remote possibility of functioning as adhesins or adhesin-like proteins.Therefore this set of proteins have been incorrectly predicted as adhesins or adhesin-like by SPAAN.


jThese proteins are PE_PGRS, PE proteins. Several reports (for example Brennan et al.) indicate that PE_PGRS proteins may be localized to cell surface and aid in host-pathogen interaction.


kLipoproteins (lpp, lpq, lpr), PPE, outer membrane, surface, transport, secreted, periplasmic, extracellular, ESAT-6, peptidoglycan binding, exported, mpt (with extracellular domains), and proteins annotated as ‘hypothetical’ but found to have similar functions listed here using BLAST and CDD searches.


lThese proteins were of the following functions - glutaredoxin-like thioltransferase, putative involvement in molybdate uptake, ATP synthase chain, sulphotransferases, S. erythraea rhodanese-like protein M29612|SERCYSA_5, unknown function.


mThese proteins were the spike glycoprotein with antigenic properties, and nsp2, nsp5, nsp6 and nsp7.













TABLE 5







New putative adhesins predicted by SPAAN in the genomes listed in table


2 -


(Total number = 279)









Protein GI




Number
Gene ID
Protein name










Escherichia coli O157:H7









13360742
912619
hemagglutinin/hemolysin-related protein


13362986
914770
putative ATP-binding component of a transport system


13361114
913228
putative tail fiber protein


13364757
913676
minor fimbrial subunit/D-mannose specific adhesin


13362687
915687
putative fimbrial-like protein


13360856
912599
AidA-I adhesin-like protein


13364140
915374
putative fimbrial protein


13359793
914435
putative invasin


13364768
913650
putative invasin


13364034
915471
Gamma intimin


13362703
915668
putative DNA transfer protein precursor


13364141
915376
putative fimbrial protein


13359819
914463
AidA-I adhesin-like protein


13360480
917768
putative fimbrial-like protein


13362692
915681
putative fimbrial-like protein


13362585
916824
putative ATP-binding component of a transport system


13359881
914526
putative flagellin structural protein


13361579
917311
putative type 1 fimbrial protein precursor


13360880
913991
curlin major subunit CsgA


13364036
915465
translocated intimin receptor Tir


13360740
912615
putative major pilin protein


13361582
917317
putative ATP-binding component of a transport system and




adhesin protein


13364754
913683
export and assembly outer membrane protein of type 1




fimbriae


13360484
917767
homolog of Salmonella FimH protein


13364751
913688
major type 1 subunit fimbrin


13359597
913742
putative fimbrial protein


13362550
916787
putative ATP-binding component of a transport system


13359595
913739
putative fimbrial protein


13359599
913748
probable outer membrane porin protein involved in fimbrial




assembly


13363900
915704
putative fimbrial protein precursor


13361575
917307
putative fimbrial-like protein


13364756
913678
fimbrial morphology


13363496
916142
truncated putative fimbrial protein


13359601
913761
putative fimbrial-like protein


13364145
915368
putative type 1 fimbrial protein


13363902
915708
putative outer membrane usher protein precursor


13361576
917309
putative outer membrane protein


13361013
913353
putative major tail subunit


13364755
913682
fimbrial morphology


13360738
912793
putative outer membrane usher protein


13363928
915608
alpha-amylase


13363495
916144
putative outer membrane protein


13362383
916617
putative type-1 fimbrial protein


13364373
914972
outer membrane vitamin B12 receptor protein BtuB


13360879
912479
minor curlin subunit precursor CsgB


13360739
912756
putative chaperone protein


13361574
917314
putative fimbrial-like protein


13361127
913212
outer membrane protease precursor


13363210
916442
putative lipoprotein


13361104
913238
major tail protein


13361709
917446
putative major tail subunit


13359725
914366
outer membrane pore protein PhoE


13360875
913765
curli production assembly/transport component CsgF


13362170
913927
putative outer membrane protein


13361473
917203
putative BigB-like protein


13364025
915286
EspF protein


13360081
916982
outer membrane receptor for ferric enterobactin (enterochelin)




and colicins B and D


13362977
914779
hypothetical lipoprotein


13360351
917632
outer membrane protein X


13360696
914208
putative outer membrane precursor


13361456
917206
putative outer membrane protein


13361626
917374
putative outer host membrane protein precursor


13361698
917449
putative outer membrane protein


13362186
913421
putative outer membrane protein precursor


13362697
915676
long-chain fatty acid transport protein FadL


13360918
914188
flagellar hook protein FlgE


13360737
912506
putative outer membrane protein


13360342
917629
putative outer membrane receptor for iron transport


13363396
916248
outer membrane channel TolC


13361958
912705
putative scaffolding protein in the formation of a murein-




synthesizing holoenzyme


13359921
914566
nucleoside-specific channel-forming protein TSX


13360944
913890
outer membrane receptor for ferric iron uptake


13359998
914644
putative outer membrane transport protein


13363390
916251
putative ferrichrome iron receptor precursor


13364227
915153
outer membrane phospholipase A


13361982
912846
putative outer membrane protein


13360129
917032
a minor lipoprotein


13361817
912692
putative outer membrane protein


13360233
917507
membrane spanning protein TolA


13362837
915218
putative outer membrane lipoprotein


13362328
912985
putative colanic acid biosynthesis glycosyl transferase







Haemophilus influenzae Rd









16272254
949521
prepilin peptidase-dependent protein D


16272928
950762
immunoglobin A1 protease


16272129
951072
lipoprotein


16273251
950616
hemoglobin-binding protein


30995429
950130
opacity protein


16272854
949634
protective surface antigen D15


16272283
950648
opacity associated protein


16272604
949701
hemoglobin-binding protein







Helicobacter pylori J99









4155101
889167
putative vacuolating cytotoxin (VacA) paralog


4154798
890022
putative vacuolating cytotoxin (VacA) paralog


4155426
890036
putative vacuolating cytotoxin (VacA) paralog


4155390
890075
vacuolating cytotoxin


4155400
890058
outer membrane protein - adhesin


4155681
889718
putative Outer membrane protein


4155420
890042
Outer membrane protein/porin


4155775
889799
outer membrane protein - adhesin


4155419
890044
Outer membrane protein/porin


4154526
889066
putative Outer membrane protein


4154724
889419
putative Outer membrane protein


4155862
890404
putative Outer membrane protein


4156048
889958
putative IRON(III) DICITRATE TRANSPORT PROTEIN


4154510
889297
putative Outer membrane protein


4155432
889515
putative outer membrane protein


4155623
889671
putative Outer membrane protein


4155700
889739
putative Outer membrane function


4154740
889426
Outer membrane protein/porin


4155692
889743
putative Outer membrane protein


4155594
889648
putative outer membrane protein


4155680
889719
putative Outer membrane protein


4155217
890243
putative Outer membrane protein


4155958
889905
putative Outer membrane protein


4155201
890259
putative Outer membrane protein


4155013
889232
cag island protein


4154974
889032
putative Outer membrane protein


4155214
890244
putative Outer membrane protein


4154973
889042
Outer membrane protein


4155344
890115
putative Outer membrane protein


4155099
889160
FLAGELLIN A


4155023
888978
cag island protein


4155035
889201
cag island protein, CYTOTOXICITY ASSOCIATED




IMMUNODOMINANT ANTIGEN


4155289
890164
NEURAMINYLLACTOSE-BINDING HEMAGGLUTININ




PRECURSOR







Mycoplasma pneumoniae









13507881
877207
involved in cytadherence


13507880
877268
ADP1_MYCPN adhesin P1


13508228
877211
species specific lipoprotein


13508181
877124
species specific lipoprotein


13508179
877071
Mollicute specific lipoprotein, MG307 homolog, from M. genitalium


13508178
877118
Mollicute specific lipoprotein, MG307 homolog, from M. genitalium,


13508176
876797
Mollicute specific lipoprotein, MG307 homolog, from M. genitalium


13508175
876848
Mollicute specific lipoprotein, MG307 homolog, from M. genitalium


13508106
876953
involved in cytadherence


13508350
877112
similar to phosphate binding protein Psts







Mycobacterium tuberculosis H37 Rv









15607496
886491
PPE


15607445
886592
PPE


15610644
888270
PE_PGRS


15608588
886605
PE_PGRS


15609627
887941
PE_PGRS


15610643
888256
PE_PGRS


15607718
887725
PE_PGRS


15609054
885362
PPE


15610486
888113
PPE


15610483
888120
PPE


15610479
888033
PPE


15609771
888573
PE_PGRS


15610648
888306
PE_PGRS


15610481
888114
PE_PGRS


15608117
885264
PE_PGRS


15607973
885391
PE_PGRS


15608231
885258
PE_PGRS


15608906
885429
PE_PGRS


15608891
885544
PPE


15609990
888171
PE_PGRS


15609055
885506
PPE


15608227
887094
PE_PGRS


15610524
888151
PE_PGRS


15609490
886003
PPE


15607886
888664
PE_PGRS


15609624
887909
PE_PGRS


15607420
886621
PE_PGRS


15608897
885325
PE_PGRS(wag22)


15608590
886595
PE_PGRS


15609728
887992
PE_PGRS


15608012
885742
PE_PGRS


15608534
886745
PE_PGRS


15608940
885730
PE_PGRS


15607887
888662
PE_PGRS


15609235
888312
PE_PGRS


15610694
887822
PPE


15609533
885517
PE_PGRS


15610480

PE_PGRS







Rickettsia prowazekii strain Madrid E









15604316
883411
CELL SURFACE ANTIGEN (sca3)


15604546
883694
CELL SURFACE ANTIGEN (sca5)







Porphyromonas gingivalis W83









34541453
2551934
hemagglutinin protein HagA


34540040
2551409
lipoprotein, putative


34540364
2552375
extracellular protease, putative


34541613
2552074
hemagglutinin protein HagE


34540183
2551891
internalin-related protein







Shigella flexneri 2a str. 2457T









30065424
1080663
minor fimbrial subunit, D-mannose specific adhesin


30062726
1077662
putative adhesion and penetration protein


30063758
1078834
putative fimbrial-like protein


30065431
1080671
major type 1 subunit fimbrin (pilin)


30063366
1078379
flagellar protein FliD


30064308
1079668
outer membrane fluffing protein


30062613
1077555
flagellar hook protein FlgE


30061954
1076843
conserved hypothetical lipoprotein


30065173
1080393
putative lipase


30065425
1080664
minor fimbrial subunit, precursor polypeptide


30064485
1079637
putative fimbrial protein


30062615
1077558
flagellar basal body L-ring protein FlgH


30064307
1079452
outer membrane fluffing protein


30065601
1080859
putative glycoprotein/receptor


30062118
1077025
putative fimbrial-like protein


30064099
1079223
lipoprotein


30062616
1077559
flagellar basal body P-ring protein FlgI


30063546
1078596
putative fimbrial-like protein


30062940
1077910
putative outer membrane protein


30065426
1080665
minor fimbrial subunit, precursor polypeptide


30062779
1077721
putative outer membrane protein


30064194
1079329
putative lipoprotein


30063365
1078378
flagellin


30062298
1077222
outer membrane protein X


30064968
1080175
putative major fimbrial subunit


30061858
1076740
outer membrane pore protein E (E, Ic, NmpAB)


30062178
1080410
minor lipoprotein


30062479
1077412
putative fimbrial-like protein


30062565
1077506
minor curlin subunit precursor


30063880
1078972
putative outer membrane lipoprotein


30064531
1079686
cytoplasmic membrane protein


30065033
1080243
putative receptor protein







Streptococcus mutans UA159









24378550
1029610
putative secreted antigen GbpB/SagA; putative peptidoglycan




hydrolase


24379087
1028055
cell surface antigen SpaP


24380463
1029310
putative membrane protein


24379075
1028046
penicillin-binding protein 2b


24378955
1027967
penicillin-binding protein 1a; membrane carboxypeptidase


24379801
1028662
glucan-binding protein C, GbpC


24379528
1029536
hypothetical protein; possible cell wall protein, WapE


24379231
1028158
putative glucan-binding protein D; BglB-like protein


24380488
1029325
conserved hypothetical protein; possible transmembrane




protein


24380291
1029139
putative amino acid binding protein


24379342
1028247
putative penicillin-binding protein, class C; fmt-like protein


24380047
1028904
putative ABC transporter, branched chain amino acid-binding




protein


24378698
1029755
putative ABC transporter, metal binding lipoprotein; surface




adhesin precursor; saliva-binding protein; lipoprotein receptor




LraI (LraI family)


24378708
1029768
putative transfer protein


24379427
1028331
cell wall-associated protein precursor WapA


24379272
1028196
putative amino acid transporter, amino acid-binding protein


24379641
1028511
putative ABC transporter, amino acid binding protein







Streptococcus pneumoniae R6









15902395
934801
Choline-binding protein


15902381
934810
Choline-binding protein F


15902165
932894
Surface protein pspA precursor


15904047
934859
Choline binding protein D


15904036
933487
Choline binding protein A


15903986
933069
Choline-binding protein


15903796
933669
Autolysin (N-acetylmuramoyl-L-alanine amidase)







Neisseria meningitidis Z2491









15794121
907145
putative membrane protein


15794144
907168
putative surface fibril protein


15793284
906275
truncated pilin


15793460
906456
IgA-specific serine endopeptidase


15793282
906273
fimbrial protein precursor (pilin)


15793337
906332
adhesin


15793253
906243
putative lipoprotein


15794356
907848
putative lipoprotein


15793684
906699
putative membrane protein


15793290
906281
truncated pilin


15793283
906274
truncated pilin


15793475
906471
haemoglobin-haptoglobin-utilization protein


15793406
906401
porin, major outer membrane protein P.I


15794985
907333
adhesin MafA2


15794344
907836
putative lipoprotein


15794622
908118
hypothetical outer membrane protein


15793599
906604
pilus-associated protein


15793763
906779
putative periplasmic binding protein







Streptococcus pyogenes MGAS8232









19745214
995235
putative secreted protein


19746570
994224
putative penicillin-binding protein 1a


19745593
994771
putative 42 kDa protein


19745813
993958
putative adhesion protein


19745225
994839
putative choline binding protein


19745828
995250
streptolysin S associated protein


19746229
995021
putative minor tail protein


19746909
994105
putative laminin adhesion


19745560
995061
putative cell envelope proteinase







Treponema pallidum subsp. pallidum str. Nichols









15639714
2611034
flagellar hook protein (flgE)


15639609
2611657
tpr protein J (tprJ)


15639111
2610909
tpr protein C (tprC)


15639125
2610968
tpr protein D (tprD)







SARS coronavirus









31581505

spike protein S [SARS coronavirus Frankfurt 1]


32187357

spike protein S [SARS coronavirus HSR 1]


32187342

spike glycoprotein [SARS coronavirus ZJ01]


30698329

putative spike glycoprotein S [SARS coronavirus TW1]


30421454

putative spike glycoprotein [SARS coronavirus CUHK-Su10]


30027620

S protein [SARS coronavirus Urbani]


29836496
1489668
E2 glycoprotein precursor; putative spike glycoprotein [SARS




coronavirus]


30795145

spike glycoprotein [SARS coronavirus Tor2]


31416295

spike glycoprotein S [SARS coronavirus GD01]


30023954

putative E2 glycoprotein precursor [SARS coronavirus




CUHK-W1]


30275669

spike glycoprotein S [SARS coronavirus BJ01]


29837498

3C-like proteinase nsp5-pp1a/pp1ab (3CL-PRO) [SARS




coronavirus]


29837501

putative nsp8-pp1a/pp1ab [SARS coronavirus]


29837503

putative nsp10-pp1a/pp1ab; formerly known as growth-factor-




like protein [SARS coronavirus]


29837502

putative nsp9-pp1a/pp1ab [SARS coronavirus]
















TABLE 6







Hypothetical proteins predicted as putative adhesins by SPAAN in the


genomes listed in table 2 -


(Total number of proteins = 105)










Protein GI




number
Gene ID











Escherichia coli O157:H7










13363955
915578



13360000
914929



13362244
912369



13359999
914888



13361583
917316



13361172
913156



13361131
913207



13359780
914422



13360571
912499



13362197
912893



13362260
912399



13360947
913505



13361464
917196



13361635
917367



13362421
916655



13361463
917195







Haemophilus influenzae Rd










16272115
951058



30995442
950581







Helicobacter pylori J99










4155526
889586



4155712
889748



4155632
889684



4156035
889468



4155499







Mycoplasma pneumoniae










13507870
877230



13508239
877245



13508109
876868



13508025
877084



13507838
876784



13507883
877183



13507871
877239



13507944
877056



13508241
876750



13507942
877055



13507840
877387



13507867
877242



13508201
877044



13507941
876985



13508114
877397







Mycobacterium tuberculosis H37Rv










15611014
886198



15610173
887320



15609513
885515



15608094
885411



15610958
886155



15607528
886436



15607678
887473



15609587
885760



15610708
887227



15609526
885246



15611033
886225



15609028
885094



15607730
887771



15609121
885813



15608255
885951



15608409
887039



15609124
885815



15607734
887797







Rickettsia prowazekii strain Madrid E










15604649
883964



15604322
883472



15604659
883996



15604417
883217







Porphyromonas gingivalis W83










34540233
2551594







Shigella flexneri 2a str. 2457T










30062687
1077638



30062956
1080449



30063681
1078754



30065435
1080675



30063891
1078983



30063211
1078195



30065233
1080463



30064387
1079531



30062638
1077590



30065236
1080466



30061839
1076721







Streptococcus mutans UA159










24378864
1029452



24380475
1029319



24380237
1029088



24379203
1028139



24380480
1029320



24379275
1029489



24379291
1028216



24379295
1028215



24379804
1028663



24379162
1029417



24378987
1029363



24379179
1028118



24379166
1028107



24378827
1029444



24380216
1029067







Streptococcus pneumoniae R6










15902140
932867



15903446
934616



15903916
934001



15903848
933609



15902832
934332



15902372
934804



15902152
932889







Neisseria meningitidis Z2491










15793668
906680



15794714
907603







Streptococcus pyogenes MGAS8232










19747011
993608



19747024
994165



19747012
994373



19746396
995057



19746651
993824



19745883
995045



19745912
994077







Treponema pallidum subsp. pallidum str. Nichols










15639844
2611061



15639720
2611059

















TABLE 7





The list of 198 adhesins found in bacteria


















PapG (E. coli)
12837502




7407201




7407207




7407205




147096




4240529




7407203




42308




7443327




78746




18265934




26111419




26250987




26109826




26249418




13506767




42301




78745




129622




147092




13506906




7407209




147080




281926




7407199




147100




78744



SfaS (E. coli)
477910




264035




42959




134449




96425



FimH (E. coli)
26251208




26111640




5524634




29422425




5524630




29422435




29422415




10946257




29422419




11120564




29422457




11120562




29422459




5524632




29422455




29422453




29422451




29422449




29422447




29422445




29422443




29422437




29422433




29422431




29422429




29422427




29422423




29422421




29422417




729494




1361011




1790775




3599571




29422441




12620398




29422439




5524628




1787779




1742472




1742463




15801636




25321294




12515169




11120566




24051859




24112911




13360484




15800801




15830279




25392018




25500156




12514120




1787173




16128908




16501811




16759519




24051219




24112354




30040724




30062478




6650093




5524636




1778448



Intimin (E. coli)
17384659




4388530




1389879




15723931




4323336




4323338




4323340




4323342




4323344




4323346




4323348




4689314



PrsG (E. coli)
42523




42529




7443328




7443329




1172645



HMW1 (Nontypeable H. influenzae)
282097



HMW2 (Nontypeable H. influenzae)
5929966



Hia (Nontypeable H. influenzae)
25359682




25359489




25359709




25359628




25359414




25359389




21536216




25359445



HifE (H. influenzae)
13506868




13506870




13506872




13506874




13506876




3688787




3688790




3688793




2126301




1170264




1170265




533127




535169




3025668




3025670




3025672




3025674




642038



MrkD (K. pneumoniae)
127307



FHA (B. pertussis)
17154501



Pertactin (B. pertussis)
33571840



YadA (Y. enterocolitica)
10955604




4324391




28372996




23630568




32470319



SpaP (S. mutans)
26007028




47267



PAc (S. mutans)
129552



SspA (Streptococcus gordonii)
25990270




1100971



CshA (Streptococcus gordonii)
457707



CshB (Streptococcus gordonii)
18389220



ScaA (Streptococcus gordonii)
310633



SspB (Streptococcus gordonii)
25055226




3220006



SpaA (Streptococcus sobrinus)
546643



PAg (Streptococcus sobrinus)
217036




47561



Protein F (Streptococcus pyogenes)
19224134



PsaA (Streptococcus pneumoniae)
18252614




7920456




7920458




7920460




7920462



CbpAe/SpsA/PbcA/PspC
14718654



(Streptococcus pneumoniae)
2425109




2576331




2576333




3153898




9845483




19548141



FimA (Streptococcus parasanguis)
97883



SsaB (Streptococcus sanguis)
97882



EfaA (Enterococcus faecalis)
493017



FnbA (Staphylococcus aureus)
120457



FnbB (Staphylococcus aureus)
581562




21205592




13702452



BabA (Helicobacter pylori)
13309962




13309964




13309966




13309968




13309970




13309972




13309974




13309976




13309978




13309980




13309982




13309984




13309986




13309988




13309990




13309992




13309994










Advantages:

    1. The method helps in discovering putative adhesins, which are of great importance in drug discoveries and preventive therapeutics.
    2. The method is useful in predicting the adhesive nature of even unique proteins, because it is independent of the homology of the query proteins with other proteins.
    3. This method is easy to use. For calculating the output, only the amino acid sequence is required as input. No other information is required to get the information about its adhesive nature.


REFERENCES MAY BE MADE TO


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## Claims

1. A computational method for identifying adhesin proteins, said method comprising steps of:
a. computing the sequence-based defined attributes of protein sequences using five attribute modules of Software for Prediction of Adhesin and Adhesin-like proteins using Neural Networks (SPAAN), (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies of NG, RE, TN, NT, GT, TT, DE, ER, RR, RK, RI, AT, TS, IV, SG, GS, TG, GN, VI and HR, (iv) charge composition, and (v) hydrophobic composition,
b. training artificial neural Network (ANN), one network for each of the computed five attributes by feeding the input data into the input layer of neurons,
c. identifying as an adhesin a protein having a weighted average probability of being an adhesin (Pad) value (optimal threshold) of ≧0.51, and
d. outputting sequence of identified protein to the user.

2. A method as claimed in claim 1, wherein the protein sequences are obtained from pathogens, eukaryotes, and multicellular organisms.

3. A method as claimed in claim 1, wherein the protein sequences are obtained from the pathogens selected from a group of organisms comprising Escherichia coli, Haemophilus influenzae, Helicobacterpylori, Mycoplasma pneumoniae, Mycobacterium tuberculosis, Rickettsiae prowazekii, Porphyromonas gin givalis, Shigellaflexneri, Streptococcus mutans, Streptococcus pneumoniae, Neisseria meningitides, Streptococcus pyo genes, Treponema pallidum and Severe Acute Respiratory Syndrome associated human coronavirus (SARS).

4. A method as claimed in claim 1, wherein the method is a non-homology method incorporating compositional properties and trained artificial neural network.

5. A method as claimed in claim 1, wherein the method uses 105 compositional properties of the protein sequences consisting of 20 properties each of amino acid frequencies, of multiplet frequencies, of dipeptide frequencies, of charge composition and 25 properties of hydrophobic composition.

6. A method as claimed in claim 1, wherein the method shows sensitivity of about 90% wherein sensitivity is greater than 90% for a Pad value above the optimal threshold.

7. A method as claimed in claim 1, wherein the method shows specificity of 100% wherein specificity is 100% for a Pad value above the optimal threshold.

8. A method as claimed in claim 1, wherein the method identifies adhesins from phylogenetically distantly related organisms.

9. A method as claimed in claim 1, wherein the neural network has multi-layer feed forward topology, consisting of an input layer, one hidden layer, and an output layer.

10. A method as claimed in claim 9, wherein the number of neurons in the input layer are equal to the number of input data points for each attribute.

11. A method as claimed in claim 1, wherein the “Pad” is a weighted linear sum of the probabilities one each from five networks.

12. A method as claimed in claim 1, wherein each trained network assigns a probability value of being an adhesin for the protein sequence.

13. A computer system for performing the method of claim 1, said system comprising a central processing unit, executing SPAAN program, giving probabilities based on different attributes using Artificial Neural Network and in built other programs of assessing attributes, all stored in a memory device accessed by CPU, a display on which the central processing unit displays the screens of the above mentioned programs in response to user inputs; and a user interface device.