Classification protease pdf

These properties of bacterial alkaline proteases make them suitable for use in the detergent industry. Production from Pseudomonas sp. Partial purification and characterization of alkalophilic protease production from Pseudomonas aeruginosa was isolated from the gut of marine and coastal waters shrimp Penaeus monodon Kumar et al.

The enzyme was precipitated with ammonium sulphate and partially purified by ion exchange chromatography through DEAE Sephadex A column. The molecular weight of protease from P. Among the various surfactants tested for enzyme stability, maximum activity was retained in poly ethylene glycol.

The compatibility of protease enzyme with various commercial detergents; the enzyme retained maximum protease activity in tide. The results are indicated that all these properties make the bacterial proteases are most suitable for wide industrial applications.

Methodology a Protease producing organism The organism P. Hence, an attempt was made by the authors to isolate and characterize protease producing bacteria from marine shrimp; the gut was dissected out from 4 or 5 shrimps in aseptic condition. The dilutions from gut were spread on the Zobell marine agar and colonies with different morphology were selected and purified in nutrient agar plates Kumar et al.

The culture growth was determined by read at nm in UV- VIS Spectrometer and enzyme activity was estimated for every 6 hrs intervals until to reach a decline phase Kumar et al. Th e flask was kept in a shaker at rpm. Characterization of protease a Effect of pH on protease activity The effect of pH on protease activity was examined at various pH levels pH The residual activity of protease was estimated under standard assay conditions Doddapaneni et al. The enzyme was incubated without any surfactant was taken as control.

The residual activity of protease enzyme was estimated under standard assay conditions Doddapaneni et al. Production from Bacillus sp. The microbial alkaline protease production by using isolated Bacillus subtilis has been exercised industrially. Maximum enzyme activity was achieved when the bacterium was grown on corn steep liquor 2. The enzyme has an optimum pH of around Material and Methods a Microorganism and culture maintenance The bacteria was isolated from surface of meet samples, screened using a nutrient agar plates and later in alkaline broth.

Prior to each experiment, the bacterium was subcultured from the frozen stocks onto alkaline agar pH Alkaline broth was used as the basal medium for preliminary studies of the bacterial growth and protease production.

Equal amounts 0. Protease activity was determined as the amount of released tyrosine from the supernatants at nm. The flasks were inoculated by 0.

At the end of fermentation period, the culture medium was centrifuged to obtain the culture filtrate that was used as the enzyme source. In all experiments, the biomass dry weight, final pH and protease production were monitored Joo et al. The protease producing bacterial isolate and identified as Micrococcus luteus Amara et al. Generally, temperature and pH had more effect on the protease activity of M. The study gave evidence that these bacterial isolates could be potentially applied in biotechnological processes.

They were stored in ice and analyzed within one hour of collection. One gram of soil sample in a ml flask was homogenized with 10 ml of sterile water; it was later made up to ml with sterile water, mixed and shaken on a mechanical shaker for 45 min.

An aliquot of the homogenized sample 0. The isolates were identified based on their morphological and biochemical characteristics. Briefly, to ml of sterile water contained in a ml flask was added 3 g of skim milk and autoclaved for 15 min.

The mixture was gently stirred until completely homogenized and then distributed in petri dishes. Clear zones around the bacterial colonies indicate the presence of proteolytic activity which can be confirmed using coomassie blue staining method.

Ten milliliter of the staining solution was added to each of the plates and incubated at room temperature for 15 min. After the incubation the staining solution was removed from the plate and the plate gently washed with distilled water. Protease activity was determined after 24hrs of incubation Kalaiarasi, and Sunitha, The fungal proteases are active over a wide range of pH up to 4 to 11 and exhibit broad substrate specificity.

However, they have a lower reaction rate and worse heat tolerance than the bacterial enzymes. Fungal enzymes can be conveniently produced in a solid state fermentation process. Fungal acid proteases have an optimal pH 4 and 4. They are particularly useful in cheese making industry due to their narrow pH and temperature specificities. Fungal neutral proteases are metalloproteases that are active at pH 7. Due to their peptidase activity and their specific function in hydrolyzing hydrophobic amino acid bonds, fungal neutral proteases supplement the action of plant, animal and bacterial proteases in reducing the bitterness of food protein by hydrolysates.

Fungal alkaline proteases are also used in food protein modifications. Production from Aspergillus sp. French bean meals thus can be modified suitably for the growth of fungal species. Their study supports the fact that French beans are a moderate type of solid state fermentation substrate for protease production using Aspergillus sp.

The aim of this study was Isolation and identification of protease producing Aspergillus sp, solid state fermentation of French bean meal using isolated Aspergillus sp. French bean meal fermentation medium as a solid state system reveals encouraging results. Various vegetables and fruits were collected from the market showing spoilage to the extent that the integuments and the outer skin damaged extensively.

The sample was examined microscopically after staining fungal infected samples were screened and scrapings were inoculated on Potato Dextrose Agar PDA and grown at room temperature for 7 days.

The PDA plates showing intense mycelia growth with profuse brown to black spores were selected and repeatedly purified over PDA to get axenic cultures Bhalla et al. A wet mount was prepared on the slide with the cultur e from the PAD plates the mount was stained with a few drops of cotton blue and covered with a cover slip. The typical morphology of Aspergillus was observed under microscope Cappuccino and Sherman, The species of Aspergillus confirmed by subjecting it to standard slide culture technique.

A thick layer of PDA was laid over the slide aseptically and then they were inoculated with the fungal isolates. The slides were put in sterile Petri plates and incubated at room temperature for development of vertical hyphae, microscopic examination of the slides were done to confirm Aspergillus sp. The resulted pest was mixed with phosphate buffer and mineral solution.

The amount was spread into a smear and stained with lacto phenol cotton blue and Gram stained. The spores were suspended in 10mlbuffer mixed and poured into fermentation flask containing synthetic French bean meal fermentation medium. Flasks were grown at room temperature at static conditions for prolonged period of hrs. The sample were intermittently collected in an aliquot of 10ml and centrifuged at rpm for 10 min.

The supernatant was collected and considered as crude enzyme it will also suitably diluted and referred to as diluted enzyme. The amount of enzyme activity was determinant as further procedure described in methodology Paranthanam, The flasks were incubated at room temperature for 72 hrs. The fermented matter was centrifuged, as described earlier, and the supernatant was used for enzyme estimation. Production from Trichoderma sp. Mutants were screened as protease producers on the basis of zone of clearance on skimmed milk agar plates.

UV-8 mutant showed 9 mm clear zone diameter and activities of Compared to wild strain, NTG mutant was found to produce 2. Thus these findings have more impact on enzyme economy for biotechnological applications of microbial proteases. Materials and Methods a Microorganism and growth media The T. PDA slants, incubated for 7 days, were used for the preparation of the inoculum. The multilayer nature of neural networks Alternatively, the distribution of physical allows them to discover non-linear higher order properties of amino acids allows a measure of correlations among the data.

Neural networks distance to be used. The inputs are weighted, the weights be ordered and their differences may even being determined during fitting of the input vanish the physical property could be data. To assess the suitability of a given neural identical. Our features will be derived from network, the input data is divided into a physical properties of individual amino acids.

Multiple layers can be used where the outputs Classifying protein sequences according to from one layer are the inputs to the next. There function is not a new problem.

Sequence are many variables associated with developing databases have been used to train neural a neural network to model a problem. There are networks as classifiers see reference [5] for a choices in the number of neurons and number good review. The features used for of layers, the learning algorithm, and the classification included the complete primary activation functions of the neurons.

In this structures and N-gram encodings of primary problem, the incoming data has an associated structures [6,7,8]. Complete primary structure desired outcome so a supervised learning encodings involve translating each amino acid algorithm is desired.

Neural networks, once to a unique identifier. The identifier can be a properly trained, are fast classifiers. An important goal of the general field called The sequence data on the Swiss Protein bioinformatics is to utilize easy to obtain data, Sequence database use the single letter per such as protein sequences, to predict relatively amino acid notation. The primary structures harder to obtain data, such as structure or were preprocessed such that each amino acid biological function.

The rapid decrease in cost was replaced by experimentally determined for sequencing, DNA and protein, has created physical properties of the amino acids.

These this imbalance of data. While there is no doubt properties are described later in this section. BackPropagation We have been applying a signal model to the refers to the method of updating the weights amino acid sequence in order to extract features using a gradient descent method and updating for functional classification.

Three protein the weights going backward from the output families are considered along with the further toward the input during a training cycle. As restriction of only looking at primary structures there are many variations for function with to amino acids. The present paper is organized as follows: Regularization adds another parameter to the Section 2 describes the experimental optimization process.

In the optimization can be constrained to keep a Section 3, the results of each classification task smooth function at the expense of error or to are given and discussed. Section 4 concludes minimize the error only. The Bayesian this study with some directions for our future framework considers the neural network research.

Bayesian 2. This database is freely The data were randomly assigned to training or available over the World Wide Web. The numbers assigned to each group proteases and isomerases having to for each class of data are shown in Table 4. Many of these are just fragments so we removed the fragments Lipase Protease Isomerase before extracting features in this study. The input size range was chosen to use as test cases for the various features due to 64 37 69 the large number of samples available.

Table 4. Fractal dimension 6. Several methods feedforward-backpropagation network with of estimating the fractal dimension of a various numbers of hidden nodes in order to sequence were reported in the signal processing find the optimal network size.

All nodes use the literature. Among all these techniques, the tansig activation function. For each Higuchi algorithm [14] is known to be the most classification, Isomerase, Lipase, or Protease, a accurate and efficient method of estimating the neural network classifier was created and fractal dimension, and as a result, is used in this trained.

Alkaline, iii. Yxobacter I, iv. The neutral proteases show specificity for hydrophobic amino acids, while the alkaline proteases possess a very broad specificity. Myxobacter protease I is specific for small amino acid residues on either side of the cleavage bond, whereas protease II is specific for lysine residue on the amino side of the peptide bond. Later, it was found to be produced by the aerobic bacterium Achromobacter iophagus and other microorganisms including fungi.

Some members of the subtilisin family from the yeasts Tritirachium and Metarhizium spp. Require thiol for their activity.

The thiol dependance is attributable to Cys near the active- site histidine. Although the majority of the serine proteases contain the catalytic triad Ser-His-Asp, a few use the Ser-base catalytic dyad. However, Xaa is Ala in most of the retropepsins. A marked conservation of cysteine residue is also evident in aspartic proteases.

The pepsins and the majority of other members of the family show specificity for the cleavage of bonds in peptides of at least six residues with hydrophobic amino acids in both the Pl and Pl9 positions.

In the next deacylation step, the acyl-enzyme reacts with a water molecule to release the second product, with the regeneration of free enzyme. Mechanisem of Action of Proteases Fig 3: Mechanism of action of proteases.

A Aspartic proteases. B Cysteine proteases. Im and 1HIm refer to the imidazole and protonated imidazole, respectively. The first detergent containing the bacterial enzyme was introduced in under the trade name BIO It is known that a protease is most suitable for this application if its pI coincides with the pH of the detergent solution.

Thank You. Total views 13, On Slideshare 0. From embeds 0. Number of embeds Ko and R. Reddy and S. Kumar are affiliated with the Department of Electrical environment. We evaluated the B. HIV-1 protease crystal structures. The most relevant chemical Corresponding author: R. Garg, email: rgarg mail. Flowchart of the model development scheme. The performance of the LDA and LR models are compared against the classification model of Random Forest used to perform the feature selection.

These structures have also been checked for completeness; HIV- classification models will provide an understanding of the 1 protease is a dimer structure which cleaves the nascent relationship between the most relevant quantitative chemical polypeptide at the dimer interface and is the target site of descriptors to the conformation of HIV-1 protease caused HIV-1 protease inhibitors [16].

Thus any pocket structures by the mutations present in the binding pockets and the which do not contain atoms from both dimers were elimi- complexed protease inhibitor. Descriptor Calculations A. Dataset To compute the chemical descriptors of the binding pocket, The PDB was searched for all HIV-1 protease crystal calculations of the molecular electronic structure must be structures complexed with FDA approved protease inhibitors.

The binding pocket was extracted using PyMOL [15] tures. Constitutional descriptors describe the non-geometric Fig. If multiple ligands were found to be complexed in molecular composition of the structure while geometric de- the structure, then multiple binding pockets will be extracted scriptors describe the 3D representation of the molecule.

Electrostatic binding pocket area and as such have two binding pockets descriptors describe the charge distribution of the molecule. Ensemble learners utilize mutliple models in combination which may result in an improved predictive model. Each of the classification tree models are grown fully without pruning as to keep bias at a minimum. In the tree growing steps of Random Forest, a small random sampling of the variables are considered for each nodal split.

The Gini measure of impurity is used to determine the variable uesed to make the nodal split. In the statistical computing environment R [20], there are two major parameters used to train the Random Forest classifier model: nT ree , the number of classification trees to train in the forest classifier, and mtry , the number of variables to randomly consider at each node of each tree.

Extraction of binding pocket. The OOB error measures the classification of the complexed ligand is considered to form the binding pocket and error over all of the trained classification trees in the Random is extracted from the HIV-1 protease structure. The complexed ligand is Forest model. To reduce the descriptor space, we eliminated any descrip- The Gini importance measures the improvement of each tors with null or constant values across the majority of the variable in the Gini criterion used to split the classification samples.

Null values occur because the descriptor is specific tree nodes. The mean decrease in accuracy measurement for atoms which are not present in the structure. This resulted involves measuring error of a trained classifier by randomly in a total of descriptors in the dataset. The mean decrease in accuracy values were recentered to have a zero mean and a standard measure for each variable i is defined as nX T ree deviation of one.

In short, the mean decrease C. Random Forest in accuracy of the variable i is the average difference Random Forest as a classification method is a classification between the OOB error of the full dataset and the error of tree based ensemble learning technique which consists of a the permuted variable over all the trees [22]. This combination of a classifier complexed ligands. Several combinations of distance metrics tool with an implicit variable relevance measurement makes and hierarchical clustering linkage methods in R were used Random Forest a highly desirable technique to be used as a on the set of descriptors determined by the best LDA and LR standalone classifier or in conjunction with other classifica- clustering models.

The Pearson correlation distance metric tion techniques as a preprocessing filter to improve models. HIV-1 protease inhibitors in order to determine an optimal The Pearson distance metric between two data samples x subset of descriptors to be used in other classification mod- and y for a given descriptor i is defined by one minus the eling techniques more suitable for small datasets.

In R, as each tree is generated in a single The Ward linkage method to produce the hierarchical tree Random Forest model, the OOB error is computed, enabling form clusters such that the sum of squares error of the the determination of the optimal tree size. A Forest models were generated from which the average OOB detailed explanation of the algorithm is provided by Ward error is determined at each tree size. The optimal value of [29].