# INTRODUCTION

n Bioinformatics, sequence alignment is a prominent method of arranging the sequences of DNA, RNA or protein to identify regions of similarity. Similarity may be functional, structural or evolutionary relationships between the sequences. Aligned sequences of nucleotide, amino acid residues are represented in a row form of a matrix. Identical or similar characters are aligned in successive columns by inserting gaps between the residues. There is a storm of revolution in the areas of Genomics and Bioinformatics in recent years. Bioinformatics is widely used for computational usage and processing of molecular and genetic data. The biologists considered Bioinformatics for the use of computational methods and tools to handle large amounts of data and make the data more understandable and useful. On the other hand, others view Bioinformatics as an area of developing algorithms and tools and to use mathematical and computational approaches to address theoretical and experimental questions in biology. As genomic data is rapidly exposed to increasing research, knowledge based expert system is becoming indispensable for the emerging studies in Bioinformatics. Hence validation and analysis of mass experimental and predicted data to identify relevant biological patterns and to extract the hidden knowledge are becoming important.

In recent years, semantic web based methods are introduced and are designed in such a way that meaning is added to the raw data by using formal descriptions of concepts, terms and relationships encoded within the data. To analyze and understand the data, today's information rich environment developed and designed a number of software tools. These tools provide powerful computational platforms for performing Insilco experiments (8). As there is much complexity and diversity in the analysis of tools, the need is for an intelligent computer system for automated processing. Present researches in Bioinformatics need the use of integrated expert systems to extract more efficient knowledge. In the biological process proteins undergo some interactions. These protein-protein interactions are mediated molecular mechanisms. During this interaction, a small set of residues play a critical role. These residues are called hot spots. The ability to identify the hot spots from sequence accurately and efficiently as expert system that enables and analysis of protein-protein interaction hot spots. This analysis may benefit function prediction and drug development. At present there is a strong need for methods to obtain an accurate description of protein interfaces. Many scientists try to extract protein interaction information from protein data bank.

Alignment Methods Used: In general the hot spots are identified as active sites in protein structures as binding is done using structures. The researcher tried to find the hotspots in protein sequence rather than structure. In this process, taking into consideration the evolutionary history, the families of sequences are aligned using multiple sequence alignment.

In the process of alignment two methods are used Standard method using dynamic programming and A proposed alternative-MSAPSO (Multiple Sequence alignment using Particle Swarm Optimization) method in which alignment is performed using PSO technique. A comparison of these two methods also made. If the sequences are very short or similar they can be aligned by hand. But lengthy and highly variable numerous sequences cannot be aligned manually. To produce high quality sequence alignments, construction of algorithms and application of human knowledge are necessary. Computational approaches to sequence alignments are of two types-Global alignments and local alignments. Global alignment is the alignment to span the entire length of sequences whereas local alignments identify regions of similarity within the long sequences. Then mature mRNA is used as a template for protein synthesis, which is known as translation onto a ribosome. Then read three nucleotides at a time by matching each codon to its base pairing anticodon to form transfer RNA (tRNA). Then tRNA recognizes the amino acid corresponding to the codon. The sequence thus obtained is protein sequence.

The amino acids in a protein sequence are shown in the following table.

The overall structure and function of a protein is determined by the amino sequence. Most proteins fold into 3-dimensional structures and its shape is known as its native state. There are four levels in a protein structure.

?  
G GLY Glycine W TRP Tryptopham A ALA Alanine Y TYR Threonine V VAL Valine N ASN Asparagine L LEU Leucine Q GLN Glutamine I ILE Lsoleucnie D ASP Asparatic Acid F PHE Phenylalanine E GLU Glutamic Acid P PRO Proline K LYS Lysine S SER Serine R ARG Arginine T THR Threonine H HIS Histidine C CYS Cyctenie M MET Methinine
? Enzymes: Enzyme is one of the functions of the protein which carries out most of the reactions involved in metabolic activities. Enzymes are proteins that increase the rate of chemical reaction. Adding or participation of the substance called catalyst does the change in the rate of chemical reaction. Catalysts that speed the reaction are called positive catalysts. Substances that interact with catalysts to slow the reaction are called inhibitors (or negative catalysts). Substances that increase the activity of catalysts are called promoters, and substances that deactivate catalysts are called catalytic poisons.

helix, beta sheet and turns.

? Active Sites in Proteins: An Active site is a part of an enzyme where substrates bind and undergo a chemical reaction. The substrate which is a molecule binds with the enzyme active site and then an enzymesubstrate complex is formed. It is then transformed into one or more products, which are released from the active site. The active site is now free to accept another substrate molecule. In the case of more than one substrate, these may bind in a particular order to the active site, before reacting together to produce products. A product is something "manufactured" by an enzyme from its substrate. For example the products of Lactase are Galactose and Glucose, which are produced from the substrate Lactose. Two models-the lock and key model and induced fit model are the two models proposed to describe how the enzymes work. In the lock and key model the active site perfectly fits for a specific substrate. If once the substrate binds to the enzyme no further modification is necessary. On the other hand in the induced fit model, an active site is more flexible and the presence of certain residues (amino acids) of the active site the enzyme is encouraged to locate the correct substrate. Once the substrate is gone conformational changes may occur. Hot spots are a set of residues recognized or bound in the process of interacting with other proteins. These are the residues in the active site.


# II.


# RESULTS & DISCUSSION

Insulin is one of the important protein sequences which cause diabetes. So we tried to identify the hotspots in this protein sequence using the following methodology.

? 


# CONCLUSION

Hot spots are of residues comprising only a small fraction of interfaces of the binding energy. We present a new and efficient method to determine computational hot spots based on pair wiser technique using potentials and solvent accessibility of interface residues. The conservation does not have significant effect in hot spot prediction as a single feature. Residue occlusions from solvent and pair wise potentials are found to be the main discriminative features in hot spot prediction. The predicted hotspots are observed to match with the experimental hot spots with an accuracy of 70%. The solvent is a necessary factor to define a hot spot, but not sufficient itself. This is also compared our methods and other hot spot prediction methods. Our method outperforms them with its high performance expert system.
1![Particle Swarm Optimization: Particle Swarm Optimization (PSO) is based on stochastic optimization technique. It is one of the machine learning algorithms. It has been considered to be an effective optimization tool in many areas. The interesting point in PSO is that each particle with potential solution searches through the problem by updating itself with its own memory and also the social information gathers from other particles. Multiple Sequence Alignment: When three or more biological sequences namely protein, DNA or RNA are generally aligned, it is called multiple sequence alignment. As it is difficult and also time consuming to align by hand, computational algorithms are used to analyze and produce such biological sequences. Most multiple sequence alignment programs use heuristic methods as the order of the sequences to align plays a vital role. Development of MSA algorithm is now an active are of research. MSA alignments are an essential tool for protein structure and function prediction, phylogeny inference and other common tasks in sequence analysis. 2. Pair wise Sequence Alignment: If two sequences are arranged for an alignment it is known as pair wise sequence alignment. The degree of relationship between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between sequences. The standard algorithm to align a pair of sequences is Needleman Wunch algorithm. This algorithm uses dynamic programming. In this study an algorithm PSAPSO (Pair wise Sequence alignment using Particle Swarm Optimization) is proposed and is also compared with the standard algorithm to know the accuracy of the results. A gene encoded in the genetic code defines the amino acid sequence in a protein. An amino acid residue is the combination of three nucleotides. Each three-nucleotide set is a codon. The set of codons forms a genetic code. For example AUG stands for methonine M. In this AUG is a codon, M is an amino acid and the residues A, U, G are nucleotides. Genes encoded in DNA are first transcribed into pre-messenger RNA (mRNA) known as primary transcript. Then pre-mRNA process to mature mRNA using various forms of modifications of posttranscriptional modifications.](image-2.png "1 .")
![Primary Structure: Primary structure is nothing but an amino acid sequence. Secondary Structure: Secondary structures are regularly repeating local structures and are stabilized by hydrogen bonds. As they are local in nature different secondary structures can be present in the same protein molecule. Example alpha ? Tertiary Structure: Tertiary structure is the special relationship of the secondary structures to one another and is generally stabilized by the formation of the hydrophobic core, a non-local interaction. Salt bridges, hydrogen bonds; disulphide bonds and even post-transnational modifications also stabilize it. It mainly controls the basic function of the protein. ? Quaternary Structure: This structure is formed by several protein molecules i.e. poly peptide chains and it functions as a single protein complex.](image-3.png "")
![Journal of Computer Science and Technology Volume XII Issue I Version I 50 January 2012 © 2012 Global Journals Inc. (US)](image-4.png "Global")
1One LetterThree LetterFull NameOne LetterThree LetterFull Name
1ai0J1302554131ai0K12190110141ai0L1302554151aiyA12190110161aiyB1302553III.171aiyC12190110181aiyD1302554191aiyE12190110201aiyF1302554211aiyG12190110221aiyH1302554231aiyI12190110241aiyJ1302554251aiyK12190110? Then identify the protein-protein interactions for each of these protein structures shown in theSNOSNO PDB Code PDB CodeChain ChainFirst PDB residue following table. Last PDB residue ChainFirst P01308 (INS_Human ) residue 13 1aiy Last (INS_Human ) P01308 residue CD11a7fA12114901aiy110EF121a7f 1a7fB A1 B2915251aiy53FH231ai0 1ai0A A1 B2116901aiy110GH341ai0 1ai0B B1 D3017251aiy53IJ451ai0 1ai0C C1 D2118901aiy110JL561ai0 1ai0D E1 F3019251aiy54KL671ai0 1ai0E F1 H2120901b9e110AB781ai0 1ai0F G1 H3021251b9e54BD891ai0 1ai0G I1 J2122901b9e110CD9101ai0 1ai0H J1 L3023251guj54AB10111ai0 1ai0I K1 L2124901guj110BD11121ai0 1aiyJ A1 B3025251guj54CD121aiyBD
			January 2012© 2012 Global Journals Inc. (US)
			© 2012 Global Journals Inc. (US) Global Journal of Computer Science and Technology Volume XII Issue I Version I
		
		
			

				


* 
	
		Computational Analysis of Protein Hotspots
		
			ShaomengChao-Yie Yng
		
		
			Wang
		
	
	
		ACS Medicinal Chemistry Letters
		
			1
			3
			
			2010
		
	




* 
	
		Druggability induces for protein targets derived from NMR-based Screening Data
		
			PHajduk
		
	
	
		J Med. Chem
		
			48
			
			2005
		
	




* 
	
		The nature and significance of protein folding
		
			CMDobson
		
	
	
		Mechanisms of Protein Folding 2nd ed. Ed. RH Pain. Frontiers in Molecular Biology series
				New York, NY
		
			OxfordUniversity Press
			2000
		
	




* 
	
		Expert System An Introduction
		
			HintzeMiller
		
		
			B
		
	
	
		PC AI where Intelligent technology meets the real world
		
			2
			3
			26
			1988
		
	




* 
	
		Expert Sstems and Artificial Intelligence
		
			RobertSEngelmopre
		
		
			EdwardFeigenbaum
		
		
			1993
			WTEC Hyper Librarian
		
	




* 
	
		Knowledge Based Expert Systems in Bioinformatics
		
			MohamedRadhoueneAniba
		
		
			JulieDThompson
		
		
			2010
			Petric? Vizureanu
			
		
	
	published in Expert Systems




* 
	
		Bioinformaticstrying to swim in a seaof data
		
			DSRoos
		
	
	
		Science
		
			291
			
			2001
		
	
	Computational biology