Modelling of β-turns using Hidden Markov Model

Modelling of ÃŽ ²-turns using Hidden Markov Model Modelling of ÃŽ ²-turns using Hidden Markov Model Nivedita Rao Ms. Sunila Godara Abstract— One of the major tasks in predicting the secondary structure of a protein is to find the ÃŽ ²-turns. Functional and structural traits of a globular protein can be better understood by the turns as they play an important role in it. ÃŽ ²-turns play an important part in protein folding. ÃŽ ²-turns constitute on an average of 25% of the residues in all protein chains and are the most usual form of non-repetitive structures. It is already known that helices and ÃŽ ²-sheets are among the most important keys in stabilizing the structures in proteins. In this paper we have used hidden Markov model (HMM) in order to predict the ÃŽ ²-turns in proteins based on amino acid composition and compared it with other existing methods. Keywords- ÃŽ ²-turns, amino acid composition, hidden Markov model, residue. I. Introduction Bioinformatics has become a vital part of many areas of biology. In molecular biology, bioinformatics techniques such as signal processing or image processing allow mining of useful results from large volumes of raw data. In the field ofgeneticsandgenomics, it helps in sequencing and explaining genomes and their perceivedmutations. It plays an important part in the analysis of protein expression, gene expression and their regulation. It also helps in literal mining of biological prose and the growth of biological and gene ontologies for organizing and querying biological data. Bioinformatics tools aid in the contrast of genetic and genomic data and more commonly in the understanding of evolutionary facets of molecule based biology. At a more confederated level, bioinformatics helps in analyzing and categorizing the biological trails and networks that are an significant part of systems biology. In structural biology, bioinformatics helps in the understanding, simulation and modelling of RNA, DNA and protein structures as well as molecular bindings. The advancements in genome has increased radically over the recent years, thus resulting in the explosive growth of biological data widening the gap between the number of protein sequences stored in the databases and the experimental annotation of their functions. There are many types of tight turns. These turns may subject to the number of atoms form the turn [1]. Among them is ÃŽ ²-turn, which is one of the important components of protein structure as it plays an important part in molecular structure and protein folding. A ÃŽ ²-turn invokes four consecutive residues where the polypeptide chain bends back on itself for about 180 degrees [2]. Basically these chain reversals are the ones which provide a protein its globularity rather than linearity. Even ÃŽ ²-turns can be further classified into different types. According to Venkatachalam [3], ÃŽ ²-turns can be of 10 types based on phi, psi angles and also some other. Richardson[4] suggested only 6 distinct types(I,I,II,II,VIa and VIb) on the basis of phi, psi ranges, along with a new category IV. Presently, classification by Richardson is most widely used. Turns can be considered as an important part in globular proteins in respect to its structural and functional view. Without the component of turns, a polypeptide chain cannot fold itself into a compressed structure. Also, turns normally occur on the visible surface of proteins and therefore it possibly represents antigenic locations or involves molecular recognition. Thus, due to the above reasons, the prediction of ß-turns in proteins becomes an important element of secondary structure prediction. II. RELATED WORK A lot of work has been done for the prediction of ÃŽ ²-turns. To determine chain reversal regions of a globular protein, Chou at al. [5] used conformational parameters. Chou at al. [6] has given a residue-coupled model in order to predict the ÃŽ ²-turns in proteins. Chou at al. [7] used sequence of tetra peptide. Chou [8] again predicted tight turns and their types in protein using amino acid residues. Guruprasad K at al. [9] predicted ÃŽ ²-turn and ÃŽ ³-turn in proteins using a new set of amino acid and hydrogen bond. Hutchinson at al. [10] created a program called PROMOTIF to identify and analyse structural motifs in proteins. Shepherd at al. [11] used neural networks to predict the location and type of ÃŽ ²-turns. Wilmot at al. [12] analysed and predicted different types of ÃŽ ²-turn in proteins using phi, psi angles and central residues. Wilmot at al. [13] proposed a new nomenclature GORBTURN 1.0 for predicting ÃŽ ²-turns and their distortions. This study has used hidden Markov model to predict the ÃŽ ²-turns in the protein. HMM has been widely used as biological tools. (a) (b) Figure 1.1 (a) defines Type-I ß-turns and (b) defines Type-II ß-turns. The hydrogen bond is denoted by dashed lines. [14] III. Materials and methods A. Dataset The dataset used in the experiment is a non-redundant dataset which was previously described by Guruprasad and Rajkumar [9]. This dataset contains around 426 non-homologous protein chains. All protein chains do not have more than 25% sequence similarity. It is basically to ensure that there is very little correlation in the training set. In this dataset, each protein chain contains at least one beta turn and has X-ray crystallography with resolution 2 or more. The dataset shows there are mainly ten classes and other classes are made using the combination of these ten classes. Table 1 Datasets Description [14] B. Hidden markov model In our work, we have used the probabilistic feature of HMM for ÃŽ ²-turns prediction. A model is presumed that ruminate the protein sequence being generated with a stochastic process that alternates amid two hidden states: â€Å"turns† and â€Å"non-turns†. The HMM is trained using 20 protein sequences. The probability transition matrix is 2Ãâ€"2 for two states: turns and non-turns. The probability emission matrix is considered as 2Ãâ€"20 as there are 2 states and 20 amino acids. We prepared our probability transition matrix and probability emission matrix according to the knowledge that we have for dataset that is the probability of ÃŽ ²-non-turns is more than ÃŽ ²-turns in a protein sequence and by considering probabilities of each residue as the parameter taken from Chou [7] for calculating the emission and transition matrix. There are more than ten classes and this HMM model parameter is estimated in2 super states and the training was performed. Let P be a protein sequence of length n, which can also be expressed as Where ri is the amino acid residue at sequence position i. The sequence is considered to be generated from r1 to rn in hidden Markov model. The model is trained using Baum-Welch algorithm [15]. Baum-Welch algorithm is a standard method for finding the maximum likelihood estimation of HMMs, in which posterior probabilities were performed by using both forward and backward algorithms. These algorithms were used to compile the state transition probability and emission probability matrices. The initial probabilities are calculated, taking into account a correlation between residues in different position. The most probable path is calculated using Viterbi algorithm [16] as it automatically segments the protein into its component regions. The probability of residue in the protein sequence used to generate the emission matrix given by Where, m is the total number that of residue in the protein sequence and n is the total number of residues in the protein sequence. C. Accuracy measures Once the prediction of ÃŽ ²-turns is performed using the hidden Markov model, the problem arises of finding an appropriate measure for the quality of the prediction. Four different scalar measures are used to assess the models performance [17]. These measures can be derived four different quantities: TP (true positive), p, is the number of correctly classified ÃŽ ²-turn residues. TN (true negative), n, is the number of correctly classified non-ÃŽ ²-turn residues. FP (false positive), m, is the number of non-ÃŽ ²-turn residues incorrectly classified as ÃŽ ²-turn residues. FN( false negative), o, is the number of ÃŽ ²-turn residues incorrectly classified as non-ÃŽ ²-turn residues. The predictive performance of the HMM model can be expressed by the following parameters: Qtotal gives the percentage of correctly classified residues. MCC (Matthews Correlation Coefficient) [18] is a measure that counts for both over and under- predictions. Qpredicted , is the percentage of ÃŽ ²-turn predictions that are correct. Qobserved is the percentage of observed ÃŽ ²-turns that are correctly predicted. IV. results and discussions A. Results This model is used to predict the ÃŽ ²-turns and is based on hidden Markov model. There are basically two classes: turns and non-turns. It is used to predict one protein sequence at a time. It has been observed that it performs better than some existing prediction methods. B. Comparison with other methods In order to examine of this method, it has been compared with other existing methods as shown in table 2. For now, the comparison is done on a single protein sequence. The comparison is for protein sequence with PDB code 1ah7. Figure 2 shows comparison of Qtotal using different algorithms. Figure 3 shows comparison of Qpredicted using different algorithms. Figure 4 shows comparison of Qobserved using different algorithms. Figure 5 shows comparison of MCC using different algorithms. The HMM based method shows better results than some of the already existing algorithms of the prediction. Figure 2. comparison of Qtotal with different algorithms Figure 4. comparison of Qobserved with different algorithms Figure 3. comparison of QPredicted with different algorithms Figure 5. comparison of MCC with different algorithms Table 2 Comparison with other methods V. conclusion In this paper, we presented a way in which HMM can be used to predict ÃŽ ²-turns in a protein chain. Our method is used to predict turns and non-turns of single protein sequence at a time. The results thus obtained are better than some of the other existing methods. The performance of the ÃŽ ²-turns can further be improved by considering other techniques such as using predicted secondary structures and dihedral angles from multiple predictors or by using feature selection technique [19] or by considering combination of many features together. We can also combine different machine learning techniques together to improve the performance of the prediction. References Chou, Kuo-Chen. Prediction of tight turns and their types in proteins.Analytical biochemistry286.1 (2000): 1-16. Chou, P.Y. and Fasman, G.D. (1974) Conformational parameters for amino acids in helical, beta-sheet and random coil regions calculated from proteins.Biochemistry, 13, 211-222. Venkatachalam, C. M. Stereochemical criteria for polypeptides and proteins. V. Conformation of a system of three linked peptide units.Biopolymers6.10 (1968): 1425-1436. Richardson, Jane S. The anatomy and taxonomy of protein structure. Advances in protein chemistry34 (1981): 167-339. Chou, P. Y., and G. D. Fasman. Prediction of beta-turns.Biophysical journal 26.3 (1979): 367-383. Chou, K.C. â€Å"Prediction of beta-turns† Journal of Peptide Research(1997): 120-144. Chou, Kou-Chen, and James R. Blinn. Classification and prediction of ÃŽ ²-turn types.Journal of protein chemistry16.6 (1997): 575-595. Chou, Kuo-Chen. Prediction of tight turns and their types in proteins.Analytical biochemistry286.1 (2000): 1-16. Guruprasad, Kunchur, and Sasidharan Rajkumar. Beta-and gamma-turns in proteins revisited: a new set of amino acid turn-type dependent positional preferences and potentials.Journal of biosciences25.2 (2000): 143. Hutchinson, E. Gail, and Janet M. Thornton. PROMOTIF—a program to identify and analyze structural motifs in proteins.Protein Science5.2 (1996): 212-220. Shepherd, Adrian J., Denise Gorse, and Janet M. Thornton. Prediction of the location and type of ÃŽ ²-turns in proteins using neural networks.Protein Science8.5 (1999): 1045-1055. Wilmot, C. M., and J. M. Thornton. Analysis and prediction of the different types of ÃŽ ²-turn in proteins.Journal of molecular biology203.1 (1988): 221-232. Wilmot, C. M., and J. M. Thornton. ÃŽ ²-Turns and their distortions: a proposed new nomenclature.Protein engineering3.6 (1990): 479-493. Available from :http://imtech.res.in/raghava/betatpred/intro.html Welch, Lloyd R. Hidden Markov models and the Baum-Welch algorithm.IEEE Information Theory Society Newsletter53.4 (2003): 10-13. Lou, Hui-Ling. Implementing the Viterbi algorithm.Signal Processing Magazine, IEEE12.5 (1995): 42-52. Fuchs, Patrick FJ, and Alain JP Alix. High accuracy prediction of ÃŽ ²Ãƒ ¢Ã¢â€š ¬Ã‚ turns and their types using propensities and multiple alignments.Proteins: Structure, Function, and Bioinformatics59.4 (2005): 828-839. Matthews, Brian W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme.Biochimica et Biophysica Acta (BBA)-Protein Structure405.2 (1975): 442-451. Saeys, Yvan, Ià ±aki Inza, and Pedro Larraà ±aga. A review of feature selection techniques in bioinformatics.bioinformatics23.19 (2007): 2507-2517.

JC Penney Advertising Essay

As time progresses, the world, in terms of business is rather contracting. There is growing communication, interaction and exchange between different parts of the world. Technologies that were once thought of as a far sighted notion are now being used like household commodities and communication mediums that were once considered luxuries available to few are now necessities needed to prosper. And as the world is becoming more integrated and countries are becoming more and more dependent on one another in terms of trade and business the concept of branding, advertising and promotion is becoming more prevalent and widespread. This paper will focus on the marketing of JC penny. It would elaborate on the print as well as online marketing. Moreover it would compare and contrast advertising of online and bricks and mortar companies. Discussion The demand for accountability of marketing is rising and also the pressure of having less absolute dollars to work with so there is utmost need to be sharper, more purposeful and more targeted with marketing. JC Penney is a general retail brand that specializes in clothing, accessories and home furnishing. It has been around for decades and has been catering the needs of consumers of all ages and backgrounds. JC Penney’s main target audience at the present times is women as well as youngsters. JC Penney faced criticism for being a brand that accommodated only the high-end and older generation. However, they have been changing their brand image and have been successful in implementing an image that is portraying a more young and trendy vibe. It now focuses on providing products that have the characteristics of being conservative, traditional, modern or trendy. Fundamentally JC Penney has been escalated in the last years is through the fact that it has moved from mass marketing to a more targeted approach. The few reasons for this change is that JC Penney believes that when business is difficult there is a lot greater chance of success with getting the arms around the best customers and increasing frequency share of wallet and trips with the best customers then trying to recruit new customers in tough times that may not shopping the brand. JC Penney has managed to find ways to develop formats that allowed it to get more productivity out the money that is spend. It has also become more targeted in terms of customer selection through becoming much sharper about making sure the right customers get the right format in the right piece. (Fetterman, 2006) JC Penney is one of the brands that hold the significance of being a brick and mortar store as well as an online retailer. Hence, it follows branding through all of the sources of mass media. It publishes magazines and postcards for the promotion of its products. It also advertises it products and offers through newspapers, television ads and online ads. When comparing the online and print media usage it quoted by Mike Boylson the Executive Vice President and Chief Marketing Officer of JC Penney that In the postcard you can deliver more of a sales message, or more of a discount message these postcards may drive the customers online to see the full assortment online where as the larger brand books of JC Penny show a much richer sense of the style that they have and they portray fundamentally completely different messages. The postcard includes the offer and a link to the website to go see the expanded content where as the book itself that goes out shows the product, the customer can then either come in the store or they can go online or place their order on the phone. Direct mail is very important because through versioning and through customer segmentation the company is able to send out more targeted messages that are highly accountable and are also able to track the results in direct mail to a degree that cannot be possible in a lot of the other traditional mass media used by JC Penney. JC Penney has been focusing a great deal over its brand image and has been trying to diversify and broaden its target audience. It has recently changed to a new brand motif; ‘Every day matters’ along with the new tag line, the company has been working on enhancing its customer’s services and the opening of several temporary promotional stores. JC Penney is focusing on increasing the popularity of its brick and mortar stores as well as its online retailing through providing customers with latest offers, discounts and showcasing their product line online for ease of access. Sloan, 2007) As the world is advancing so are the technologies and the ease with which communication is possible among all parts of the world. With the advent of the internet and the upscale increase in its popularity, there has been almost nothing that is not available on the World Wide Web. The phenomenon of e-shopping emerged with the internet. The fact that customers could get what they want in the ease of their homes, increased the recognition of the internet and also of online shopping. There are numerous differences and similarities between online shopping and traditional shopping. But what holds more importance is the way the companies market their product online and how different it is from the marketing and promotion of brick and mortar companies. (Lowrey, 2008) Marketing over the internet is considered less costly, as it is holds a lower cost of distributing information on a global platform. More and more business are moving towards online retailing due to its outnumbered advantages in terms of cost, convenience and mobility of information over a great distance. One of the major focuses that companies including JC Penney is on the website. The success of online marketing is highly dependent over the outlook, design and the information provided by the website. Both the online companies as well as brick and mortar companies need to identify their target audience before they implement any marketing strategies. This lets them focus on the type of marketing tool they would benefit them. Brick and mortar companies offer a more traditional aspect of shopping and they also follow a traditional approach of marketing. This is mostly through mass media such as newspapers, television broadcast and magazines. With the passage of time, there are less and less companies that focus solely over brick and mortar business. Most companies are now available online as there is less overheads and larger audience prone to response through the internet. Conclusion In the end it is imperative to recognize the increasing importance of internet in business. Both, online retailing and brick and mortar companies hold their own set of characteristics that make them distinct. As the progress of online shopping is increasing there is still need for brick and mortar stores for traditional shoppers. Most companies, however, imply both the alternatives and hence, carry out their marketing accordingly.

Do You Burn More Calories When You Think Hard

According to Popular Science, your brain requires a tenth of a calorie per minute, just to stay alive. Compare this to the energy used by your muscles. Walking burns about four calories a minute. Kickboxing can burn a whopping ten calories a minute. Reading and pondering this article? That melts a respectable 1.5 calories a minute. Feel the burn (but try the kickboxing if youre trying to lose weight). While 1.5 calories per minute might not seem like very much, its a rather impressive number when you take into account your brain only accounts for about 2% of your mass and that, when you add up these calories over the course of a day, this one organ uses 20% or 300 of the 1300 calories the average person needs per day.​ Where the Calories Go Its not all to your gray matter. Heres how it works: The brain is comprised of neurons, cells that communicate with other neurons and transmit messages to and from body tissues. Neurons produce chemicals called neurotransmitters to relay their signals. To produce neurotransmitters, neurons extract 75% of the sugar glucose (available calories) and 20% of the oxygen from the blood. PET scans have revealed your brain doesnt burn energy uniformly.  The frontal lobe of your brain is where your thinking takes place, so if you are pondering lifes big questions, like what to have for lunch to replace the calories you are burning, that part of your brain will need more glucose. Calories Burned While Thinking Unfortunately, being a mathlete wont get you fit. In part, thats because you still have to work muscles to earn that six-pack, and also because pondering the mysteries of the universe only burns twenty to fifty more calories per day compared with lounging by the pool. Most of the energy used by the brain goes toward keeping you alive. Whether youre thinking or not, your brain still controls breathing, digestion, and other essential activities. Calories and Mental Fatigue Like most biochemical systems, the brains energy expenditure is a complex situation. Students routinely report mental exhaustion following key exams, like the SAT or MCAT. The physical toll of such tests is real, although its likely due to a combination of stress and concentration. Researchers have found the brains of people who think for a living (or for recreation) become more efficient as using energy. We give our brains a workout when we focus on difficult or unfamiliar tasks. Sugar and Mental Performance Scientists have studied the effect of sugar and other carbohydrates on the body and brain. In one study, simply rinsing the mouth with a carbohydrate solution activated parts of the brain that enhance exercise performance. But, does the effect translate into improved mental performance? A review of the effects of carbohydrates and mental performance yields conflicting results. There are evidence carbohydrates (not necessarily sugar) can improve mental function. Several variables affect the outcome, including how well your body regulates blood sugar, age, time of day, the nature of the task, and the type of carbohydrate. If youre facing a tough mental challenge and dont feel up to the task, theres a good chance a quick snack is just what you need.