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Available Genome/proteome databases will be mined for the top prevalent enlisted pathogen and/or their known symbiont partners.
Novel and known protein targets will be discovered by exhaustive mining of available protein databases/Literature. A protein target must have minimal homology (less than 30%) with any of the human protein/s. The identified novel or known targets will be classified depending on their structure and function.
Protein targets will be subjected to virtual screening against MeGx natural compound database using Libdock.
The protocol of virtual screening will be same as described above in methodology. Known antibiotics (against which the pathogen has developed resistance) such as Penicillin tetracycline, macrolide and metronidazole frequently used for periodontal diseases will be considered as controls in virtual screening studies. Only those natural products (from MeGx) which may outperform known antibiotic binding scores (in virtual screening) will be selected for further interaction studies. Hence possibly overcoming the developing antibiotic resistance. The natural products will be carefully chosen so that they may not contain groups (lactam etc) against whom the pathogen has already developed resistance.
Wherever possible the selected natural products will be further screened on the basis of the mechanism of known antibiotic resistance.
Quantum Mechanical/Moolecular mechanical Docking strategy will be applied to determine the interaction profile and the degree of affinity between the pathogen targets and promising leads (top binders from virtual screening). Here the known commercial antibiotic will serve as control. The protocol will be same as discussed above. In order to correlate the effect of biofilm on antibiotic resistance. The hydrodynamic radius of the selected natural product should fall within the known porosity thresholds of the oral biofilms. Identification of the common oral biofilm producers found in hail region will be obtained from the samples of healthy and periodontitis subjects. Porosity data will be obtained from published data (subject to availability). Only those promising leads having hydrodynamic radii lesser than average pore size of the biofilm will be subjected to exhaustive interaction studies.
Matrix generation: The key to all stoichiometric methods is the automatic generation of the stoichiometric matrix N from a textual input in a familiar chemical reaction notation. Several modeling tools have been developed (Vallino, 1991; Pfeiffer et al., 1999).
Metabolic flux analysis: Stoichiometry-based MFA complements the stoichiometric relations by the measured fluxes. If the flux measurements are nonredundant and if not too many degrees of freedom remain all intracellular metabolic fluxes can be estimated from the data. This turns out to be a classical linear estimation exercise for which all relevant problems can be solved: the structural identifiability of the fluxes can be decided, all fluxes can be efficiently computed, the available redundant information is fully used, a confidence region for the estimated fluxes can be computed, the set of redundancy relations for the measured data can be derived explicitly, and gross measurement errors can be detected.
Extreme flux patterns: Forward and backward fluxes are now considered separately in the stoichiometric relations. Additionally some fluxes can be assumed to be unidirectional based on thermodynamic considerations, i.e. the back flux is set to zero. If the substrate uptake is scaled to one (i.e. 100%) the arising inequality v >=?0 then further restricts the set of possible flux patterns to a polyhedron in the flux space (Figure 3) (Clarke, 1988). Any point in this polyhedron represents a feasible flux pattern of the system. The corner points of the polyhedron - called the extreme points or the extreme flux patterns - are of special interest because they exactly determine the feasible flux space. Algorithms for the calculation of extreme points are available (Hohenbalken et al., 1987). An extreme flux pattern is characterized by the fact that the number of fluxes which vanish in this flux pattern is at a local maximum. Thus the extreme flux patterns can be interpreted as basic metabolic operation modes where only some of the reaction steps are active (Figure 4). Any other flux pattern can be represented by a weighted combination of the extreme flux patterns. This gives an understanding of the metabolic network in terms of certain distinctive physiologically patterns.
Optimal flux patterns: A rather speculative extension to the extreme point analysis is the complementation of the stoichiometric relations by a linear metabolic optimization criterion. Several criteria like maximal growth rate, maximal product formation or a minimal ATP production for a given substrate uptake have been investigated (Edwards and Palsson, 1998). They all lead to a classical linear programming problem which can be solved by the simplex algorithm. Optimality criteria have also been used to solve the metabolic flux balances in the case where the measured data (even with energy balancing) is still not sufficient to compute a unique solution (Bonarius et al., 1997). Except for degenerate cases the optimization result is always an extreme flux pattern. However, such a result need not represent a reasonable solution. For example the maximal lycine production rate for C. glutamicum has been computed to more than 60% of the glucose uptake rate in (Vallino, 1991) while current values from production processes are below 30%. The difference comes from the fact that in order to reach maximal product formation the organism must stop any "waste" of energy for growth or maintenance.
Elementary flux modes: Extreme flux patterns are not always convincing solutions to the problem of finding physiologically meaningful pathways in a metabolic network. Several approaches have been undertaken to obtain a biologically meaningful and yet mathematically precise definition of a metabolic pathway. A recent definition of such elementary flux modes requires that an elementary mode has a maximal number of vanishing fluxes and cannot be decomposed into smaller pathways (Schuster et al., 1999). Thus elementary modes are the smallest functioning subunits of a metabolic network. This motivates the hypothesis that they are also genetically regulated as a unit which in turn is a promising approach to the development of functional genomics tools. Elementary modes can be efficiently calculated by a newly designed algorithm that has some similarity to the simplex algorithm (Pfeiffer et al., 1999). They have several promising applications in metabolic design, drug development or functional genomics (Schuster et al., 1999).
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