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Now mainly molecular biological methods have made it possible to determine the phylogenetic and taxonomic relationships between phytoplasma strains, and those between phytoplasmas and other prokaryotes. Recently, the classification of phytoplasmas is mainly based on the nucleotide sequence of the 16S rRNA gene (Gundersen et al., 1994) .7 phytoplasma genomes have been sequenced to completion. These are strains Onion Yellows M (OY-M) (Oshima et al., 2004) and Aster yellows witches'-broom (AY-WB) (Bai et al., 2006), Candidatus (Ca.) Phytoplasma asteris, a strain of Ca. Phytoplasma australiense(Tran-Nguyen et al., 2008), and strain AT of Ca. Phytoplasma mali (Kube et al., 2008)
Phytoplasma have been associated with 45 plant species in India. Several fruits, vegetables, ornamentals, trees and other agriculturally important crop species are affected by phytoplasma diseases. Ten groups of phytoplasma have been identified in India and most of them have been reported from north - eastern parts of the country. Only few phytoplasmas have been recorded in Eastern, Western and Central India. These phytoplasmas affects sugarcane, sesame, ornamentals, oil crops, tree species, vegetables and many weed species. Aster yellow is the most prevalent group of phytoplasmas and has been associated with more than 31 diseases in India. Phytoplasmas have been characterized from as many as 45 plant species in India ( Rao et al., 2011).
Phytoplasmas are transmitted from plant to plant by insect vectors, mainly leafhoppers and psyllids (Ploaie, 1981). They traverse the wall of the intestinal tract, multiply in the hemolymph, and pass through the salivary glands, in which they multiply further. Then, the insect vectors introduce phytoplasmas along with salivary fluids into the phloem of a new host plant (Agrios, 1997). Usually these insect vectors do not transmit phytoplasmas transovarially, although two exceptions have been reported: aster yellows and mulberry dwarf phytoplasmas (Kawakita et al., 2000).
The symptoms shown by infected plants include: yellowing or reddening of the leaves, shortening of the internodes with stunted growth, smaller leaves, excessive proliferation of shoots resulting in a witches' broom, phyllody, virescence, sterile flowers, necrosis of the phloem tissues, dieback of the branches of woody plants, and the general decline and death of the plant (Agrios, 1997). Phytoplasma is a major threat to agriculture worldwide.
Phytoplasmas have been associated with diseases in several hundred plant species including many important food, vegetable, and fruit crops, ornamental plants, and timber and shade trees (Ahrens U et al., 1993)
These phytoplasmas affects sugarcane, sesame, ornamentals, oil crops, tree species, vegetables and many weed species. Aster yellows is the most prevalent group of phytoplasmas and has been associated with more than 31 diseasesin India.
The word effector typically denotes a protein that is secreted by a microbial pathogen or insect into a host cell to enhance colonization and facilitate multiplication of the pathogens/insects, but in a broader deï¬nition, effectors can also include elicitors, toxins, phytohormone analogs, cell wall-degradation enzymes, andother molecules that alter host plants .Previous studies have shown that immunodominant membrane protein accounts for a major portion of the total cellular membrane proteins in most phytoplasmas. 'Ca. Phytoplasma mali' genome has a regular GC skew, indicating that this genome is stable (Kube et al., 2008).
Phytoplasmas severely affect herbaceous and woody plants (Bertaccini, 2007), and are the primary limiting factors for many important crops all over the world. Phytoplasmas are associated with plant diseases in several hundred plant species, including many important food, vegetable, and fruit crops; ornamental plants; and timber and shade trees. Phytoplasma infected cells gain a bushy or witches broom that means appearance due to change in their normal growth pattern. Phytoplasma-infected plants may also show virescence, the development of green flowers due to the loss of pigment in the petal cells (Lee et al., 2000)
phytoplasma-infected poinsettia plants, which each have more than one flower and which are smaller, making it possible to grow them in pots (Bertaccini et al., 1996).
Citrus huanglongbing disease that is associated with aster yellows-related phytoplasmas in China (16SrI)(Teixeira et al.,2009)and with pigeon pea witches' broom-related phytoplasmas (16SrIX) in Brazil (Chen et al., 2008).
Plants infected with phytoplasmas exhibit symptoms suggesting a profound disturbance in the normal balance of growth regulators, leading to virescence/phyllody (development of green leaf-like structures instead of flowers),sterility of flowers, proliferation of axillary buds result- A. Bertaccini and B. Duduk ing in "witches' broom" symptoms, abnormal internode elongation is due to the effect of phytoplasma that the plant show apical dominace, proliferation of auxiliary shoots and generalized stunting. (Bertaccini, 2007).
Some phytoplasma transmissions in insects have been reported to be transovarial, such as the insect/disease combinations Scaphoideus titanus/aster yellows (Danielli et al., 1996). Hishimonoides sellatiformis/mulberry dwarf (Kawakita et al., 2000), Matsumuratettix hiroglyphicus (Matsumura)/sugarcane white leaf (Hanboonsong et al., 2002), and Cacopsylla melanoneura apple (Tedeschi et al., 2006). Phytoplasmas were transmitted by seed has also been investigated. This type of transmission was first suspected in the spread of coconut lethal yellowing (Cordova et al., 2003).
Lethal yellowing (LY) is a devastating phytoplasmaassociated disease of coconut (Cocos nucifera) and at least 35 other palm species in the Americas ( Harrison et al., 1999).
Lethal phytoplasma-associated diseases of coconut have also been reported in East Africa, West Africa and Indonesia (Ashburner et al., 1996) .In coconut, LY causes different visual symptoms such as nut drop,leaf yellowing and senescence, and palm death(McCoy et al.,1983).
Sandal spike was the first phytoplasma disease reported in India (Varma et al., 1969). Thereafter a large number of phytoplasma diseases were described, which included Brinjal little leaf disease (Varma et al., 1969), grassy shoot disease of sugarcane(Chona et al., 1960), Rice yellow dwarf disease (Reddy et al., 1990), Sesamum phyllody (Vasudeva et al., 1955), white leaf disease of Cynodon dactylon (Singh et al., 1978),little leaf disease of Acanthospermum hispidium(Raju et al., 1981) and yellowing disease of Urtochloa panicoides (Muniyappa et al., 1982).Phytoplasmal infections are the primary limiting factors for production of many important crops all over the world ( Lee I-M.,et al.,1992)
Peach rosette, peach yellows, little peach, and red suture are reported in peach orchards of the Southeast. However, except for peach rosette, they occur infrequently. Peach yellows disease was first observed in 1791 in Pennsylvania, rosette in 1891 in Georgia, while little peach and red suture were first seen in 1896 and 1911, respectively, in Michigan. Other diseases of peach associated with infection by phytoplasmas occur in other areas of the United States and the world.
Phytoplasma- infected poinsettia plants, which each have more than one flower and which are smaller, making it possible to grow them in pots (Bertaccini et al., 2007).
Membrane proteins are crucial for survival. They constitute key components for cell-cell signalling, mediate the transport of ions and solutes across the membrane, and are crucial for recognition of self (Stack et al., 1995). Transport across biological membranes is fundamental to any form of life. Referring to the source of energy used by transport proteins, one can distinguish channels, primary and secondary transporters (Saier, 2000). Whereas secondary transporters couple transport to electrochemical gradients across the membrane, for example a proton or a sodium potential, primary transporters such as proton-pumping ATPases or ATP-binding cassette (ABC)-transporters harness the free energy of ATP hydrolysis.
PhytoplasmaÂ membrane proteins are in direct contact with hosts and are presumably involved in determining vector specificity. There are different types of transporter proteins are present. They are ATP-binding cassette (ABC) transporter systems for dipeptides/oligopeptides (DppDFCBA, OppA), spermidine/putrescine (PotABCD), cobalt (CbiOQ), Mn/Zn (ZnuBCA), and D-methionine (permease and solute binding components). MalE is a maltose -binding proteinFor the import of sugars, only the components of the ABC transporter for maltose (MalKFGE) are present.
ATP-binding cassette (ABC) import systems are known as binding protein-dependent (BPD) transporters (SaurinÂ etÂ al., 1999).What is known today as binding protein-dependent ABC transporters was historically distinguished from other classes of transport systems by their susceptibility to coldosmotic shock (Neu & Heppel, 1965).In relation to genome size, the highest number of ABC systems is found in bacteria (Davidson et al., 2008).
potD- ABC type spermidine /putrescine transport ,Â a periplasmic binding protein
oppA- ABC type oligopeptide transport system,periplasmic component,solute binding protein family. The function of OppA as substrate-binding protein (oligopeptide recognition) is well recognized in bacteria ( Detmers et al., 1998).
dppA-Bacterial extracellular solute binding protein.Dipeptide/oligopeptide transport system.It is a periplasmic component.
zunA- ABC type Mn/Zn transport system ,periplasmic component,solute binding.
metQ- ABC type methionine transport system,periplasmic component.It is a putative compound.
The MetNPQ transporter of B. subtilis, which is also distributed among other gram-positive bacteria, was shown to also transport methionine sulfoxide (Hullo et al., 2004).
malE- ABC type sugar transport system periplasmic component. The maltose-binding protein (MalE) may have affinity to maltose, trehalose, sucrose, and palatinose (Silva Z et al.,2005)
hflB- hflB protease,ATP dependent mettaloprotease. It is a pleiotropic protein required for correct cell division in bacteria.
PROTIEN SEQUENCE ANALYSIS
Protiens are the polypeptide chains of amino acids. The biochemical compounds are compost of two or more structures folded in specific manner. There are many tools for primary protein sequence analysis. Protparam , ComputepI are the online servers available sequence analysis.These analysis tool gives all the compute parameters of a protein ,it can either be specified as a Swiss-Prot/TrEMBL accession number or ID, or in the form of a raw sequence.
Analysis tools include Compute pI/Mw, a tool for predicting protein isoelectric point (pI) and molecular weight (Mw). ProtParam, include the molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index, and grand average of hydropathicity (GRAVY). The protein identification and analysis software that is available through the ExPASy World Wide Web server http://www.expasy.org/tools/ (Gasteiger et al., 2003).
PROTEIN STRUCTURE PREDICTION
The prediction of protein secondary structure is a step toward the goal of understanding protein folding. A variety of methods have been proposed that make use of the physicochemical characteristics of the amino acids (Lim, V.1974), sequence homology (B. & Gamier, J. (1986), pattern matching (Kuntz et al., (1986), and statistical analyses (Chou, P. & Fasman, G. 1974) of proteins of known structure .These prediction methods are used to classify known structures in the Brookhaven Protein Data Bank as helices (H) and sheets (E); residues that are neither H nor E are classified as "coil.
CHOU FASMAN METHOD
The Chou-Fasman method has been widely used for predicting protein secondary structure. It is based on knowledge of the potential of amino acid residues to form a-helical or b-sheet regions in proteins. The Chou-Fasman method was among the first secondary structure prediction algorithms developed and relies predominantly on probability parameters determined from relative frequencies of each amino acids appearance in each type of secondary structure.( Jari et al., 1998)
GOR is method for using the prediction of secondary structures in proteins. This method uses both information theory and Bayesian statistics for predicting the secondary structure of proteins. (Garnier et al., 1996)
Accurate prediction of protein secondary structure is a step toward the goal of understanding protein folding. A variety of methods have been proposed that make use of the physicochemical characteristics of the amino acids (Lim, V. (1974), sequence homology (B. & Gamier, J. 1986), pattern matching (Kuntz et al., 1986), and statistical analyses (Chou, P. & Fasman, G. 1974) of proteins of known structure.
Secondary structure prediction servers are Jpred, Psipred, APSSP2, SABLE and GOR V SERVER
TERITIARY STRUCTURE PREDICTION
The prediction of the three-dimensional structure of a protein from its amino acid sequence.Structure prediction is often divided into three areas: ab initio prediction, fold recognition, and homology modeling. The distinction between these areas is usuallybased on the extent to which information in sequence and structural databases is used in the construction of a model. Ab initio prediction in its purest form makes no use of information in databases (Nanias et al., 2005), and the goal is to predict the structure of a protein based entirely on the laws of physics and chemistry.
Most successful methods for structure prediction have been homology-based comparative modeling and fold recognition (Moult J et al., 1999) When homologous or weakly homologous sequences of known structure are not available, the most successful structure prediction methods have been those that predict secondary structure and local structure motifs; these methods have been available for some time (Bystroff C et al., 1998 ).
The homology modeling is a simplest and most reliable approach. The observation that proteins with similar sequences tend to fold into similar structures forms the basis for this method. It has been observed that even proteins with 25% sequence identity fold into similar structures. This method does not work for remote homologs (< 25% pair wise identity).
Homology modeling servers are ROBETTA, MODELLER, and Protinfo CM.
Fold recognition, often referred to as ''Threading,''.Which corresponds to the case where one or more structures (templates) similar to a given target sequence exist in the PDB but are not easily identified.This is used for sequences with sequence identity â‰¤ 30%. In this approach, given a sequence and the set of folds available in the Protein Data Bank (PDB) the aim is to see if the sequence can adopt one of the folds of known structure. This method takes advantage of the knowledge of existing structures and the principles by which they are stabilized. Fold assignment and alignment are achieved by threading the sequence through each of the structures in a library of all known folds( Christian M.-R. Lemer et al.,1995).
I - TASSER (S.Wu , J. Skolnick,2007) is a hierarchical protein structure modeling approach based on the multiple threading alignments and an iterative implementation of the Threading Assembly Refinement (TASSER) program (Y. Zhang and J. Skolnick,2004). It consists of four consecutive steps of threading, structure assembly, structure refinement, and function prediction.
It is a meta -server technique.one of the major tool in the field of protein tertiary structure prediction during recent years (Lundstrom J et al., 2001).This tool generate 3D structure predictions by taking the consensus models from a variety of individual (mainly threading/fold-recognition) servers.Various benchmarking and blind test experiments demonstrate that the consensus meta-server predictions outperform the best individual threading server It generates protein structure predictions by ranking and selecting models from 8 state-of-the-art threading programs. (Fischer D et al., 2003).
AB INITIO METHOD
The goal of Ab initio structure prediction is simple: Given a protein's amino acid sequence predict the structure of its native state. Ab initio methods are well-suited for adding some missing regions to homology models, thereby producing much more complete sets of models. The ab initio structure prediction problem would most probably be solved too late to be applied to any real biological problems.The term ab initio or de novo is also applied to the prediction of the structure of proteins for which there is no similar structure in the Protein Database (PDB) but where local sequence and structural relationships involving short protein fragments, as well as secondary structure prediction, are incorporated into the prediction process (Bradley et al., 2005). PEP -FOLD, QUARK are the available servers for Ab initio method.
Docking is a computer simulation method. It is a special term used to find out the best match between the receptor and ligand molecules .These simulation process is used to predict receptor- ligand complex .The receptor is usually a protein molecule or a nucleic acid(DNA OR RNA) and the ligand is either a small molecule or another protein .It is a simulation process of exact binding of ligand molecule in the pre- defined binding site of the receptor molecule(Raquel Dias et al., 2008).The binding of small molecule in to the large receptor molecule leads to form a biological function .The are different types docking are present. They are (a) Rigid body docking (b) Flexible ligand docking (c) Flexible docking.
(a)Rigid-body docking simulations were able to predict the correct position of ligand, when compared to crystallographic structures (Perozzo,R et al., 2002).Where as Flexible ligand docking methods the receptor remain rigid and the ligand treated as flexible. In flexible docking can consider several possible conformations of ligand or receptor, as well as for both molecules at the same time, at a higher computational time cost. Compared to other methods the rigid-body docking simulations were able to predict the correct position of ligand, when compared to crystallographic structures (Pereira, J.H et al.,2004).
MECHANICS OF DOCKING
Before going for the docking process ,the first requirement is structure prediction. Structures are obtained either from the Bioinformatics structure prediction tools or by X-ray crystallography or nuclear magnetic resonance. The success of a docking program depends on two components namely the search algorithm and the scoring function (De Wit , 2010).
THE SEARCH ALGORITHM
The docking algorithm is classified into different algorithm.They are the set of parameters or rules for the conformation. While considering the flexibility of the ligand or the receptor docking algorithm is divided into two large groups,Rigid body docking and Flexible docking. Mainly the Docking programs are depends upon the different search algorihms . Some of them are Monte Carlo simulations, distance geometry, genetic algorithms (Taylor, R et al., 1997).
THE SCORING FUNCTION
Scoring functions, which are able to evaluate intermolecular binding affinity or binding free energy, are employed in order to optimize and rank results, obtaining the best orientation after the docking procedure .The scoring function scan the possible solution for obtaining the good candidates for the conformation(Hoffmann D et al.,1999)
The Glide searches the favourable interaction between the receptor and the ligand molecules. It searches the possible position of the ligand molecule in the active- site of the receptor. Glide provides high throughput virtual screening of the ligand molecule. It compare the ligand scores to other ligands and compare the ligand geometries from other reference ligand.
Glide approximate the complete conformational, orientation ,and the positional space of the ligand molecule (Friesner et al., 2004).