Reconstruction a protein 3D structure using its contact map is not less than revolutionizes molecular biology.Recently, there are many research efforts that provide guidelines for

protein contact map prediction; these efforts used machine learning approaches such as neural network and distance geometric.One of the approaches to help biologists is applying a software technique. As the consequence there are many categories of tools that have been developed to incorporate this technique.This paper analyses several

predicting protein 3D structure tools. These tools are built to help to understand and predict a protein 3D structure.The paper briefly discusses the advantages of these tools; it also, gives the disadvantages of the existing tools, and, finally, talks about the proposed reconstructing a protein

3D tool

Al-Fawareh, Hamed J.

Alomoush, Nawal T.

Odeh, Jehad Q.

UUM Repository

Reconstruction a Protein 3D Structure From It Contact Map: Tools ComparisonHamed J. Al-Fawareha, Nawal T.Alomoushb,  Jehad Q. OdehbaFaculty of Science and ITZarqa Private University, ZarqaJordanTel: 05-3821110E-Mail: fawareh@zpu,edu.jobFaculty of  Information Technology Al al-Bayt UniversityJordanTel: 02-6297000E-Mail:jehad@aabu.edu.joABSTRACTReconstruction a protein 3D structure using its contact map is not less than revolutionizes molecular biology. Recently,  there are many research efforts that provide guidelines for  protein contact map prediction; these efforts used machine learning approaches such as neural network and distance geometric.  One  of  the  approaches  to  help  biologists  is  applying a software technique.  As the consequence there  are many categories of tools that have been developed to incorporate  this  technique.   This  paper  analyses  several  predicting protein 3D structure tools. These tools are built  to help to understand and predict a protein 3D structure.  The paper briefly discusses the advantages of these tools; it  also,  gives  the  disadvantages  of  the  existing  tools,  and, finally, talks about the proposed reconstructing a protein  3D tool.Keyword: Distance  Geometry,  Embedded  Algorithm,  Protein  structure.1.0 INTRODUCTIONBioinformatics  have  been  applied  in  many  difficult, complex  applications,  and  in  different  environments (Plero,  2001).  Traditional  experimental  techniques  for deriving  macromolecular  structure  data  are   X-ray crystallography,  Nuclear  Magnetic  Resonance  (NMR) spectroscopy  and  electron  microscopy,  this  method  give data  as  a  set  of  Cartesian  coordinates  representing  the position  of  the  atoms  in  these  structure  (Philip  2003, Wikipedia  2008),,   But  these  methods  remain  slow  and laborious and don’t scale up to current sequencing speeds. Furthermore, using experiments to determine how protein function  is  daunting  task,  so  that  predicting  the  3D structure of protein from liner sequence of amino acids is an interesting topic for computer scientists a lot, because it is fundamental  open problem in computational  molecular biology.Each protein may contain thousands of atomic in different shapes,  a  fact  which  makes  it  helpful  to  automatically predict  a protein through software  tools.  These  tools  for replacing a tradition experiment  technique.  This problem becomes even more complicated when the developer uses a complicated  protein.   Contact  map help developers  by giving  them information  about  the  protein  system.  This information  includes,  distance  map,  which  is  created  by contact map, where a distance matrix computed to produce the Boolean values by used a pre assigned threshold value T. Distance map D is a N×N matrix where N is the number of  residues  in a protein  and  Dij  is  the  distance  between coordinate of the α carbon in two residues i and j which measured  in  Angstroms  Å.  Two  residues  i  and  j  in  a protein  are  come  in  contact  with  each  other  if  the  3D distance Dij is less than or equal to some threshold value (Vassura 2008).For the past few years, several tools have been developed in  order  to  help  predicting  a  protein  3D  structure  to understand  protein  functionality.  In  this  paper  we  will highlight  several  tools.  Developers  build  these  tools  for predicting the protein structure, and, each tool manipulates the protein under the protein structure activity. This paper will, also, give a brief discussion about the advantages of the tools; in addition, it talks about the disadvantage of the exiting  tools,  and  proposes  a  new prediction  tool  called “reconstructing  a  protein  from  its  contact  map  using matlap”.2.0 PROTEIN  STRUCTURE  AND  ITS CONTACT MAPMost  of  essential  structure  and  functions  of  cells  is refereed to Proteins. Proteins plays  a vital role in keeping the body working properly. For example, they are used to support the skeleton, control  sense, move muscles, digest food, and defend against infections and process emotions. There  are  more  than  100,000  proteins  that  come  in  all shapes  and  sizes;  however,  they  are  all  made  up of  the same  set  of  20  amino  acids  order  in  different  way,  its primary sequence. The structure of a protein is determined by the folding of this primary sequence (Mireille 2006).Any consideration of protein function must be grounded in an  understanding  of  protein  structure.  A  fundamental principle  in  all  protein  science  is  that  protein  structure leads to protein function, and protein functions are divers, so  its  no  surprise  that  protein  structures  are  also  divers (Jorge 1999).Contact  map is a great  interest  for its application in fold recognition and 3D structure determination. A contact map is  representation  tool  of  the  protein  3D  structure. Traditionally,  contact  map  is  creates  from distance  map where a distance matrix computed to produce the Boolean values  by  used  a  pre  assigned  threshold  value.  Contact map  C for  a  protein  sequence  with  N residues  is  N×N asymmetric  Boolean  matrix  whose  element  Cij=1  if residues  i  and  j  are  contact  and  Cij=0  otherwise  (John 1999).  The  contact  map  provides  useful  information, contacts  represent  certain  secondary  structure  and  it captures  non local  interaction  giving clues to its  tertiary structure (Jorge 1999).3.0  TOOLS  FOR  RECONSTRUCTING  A PROTEIN  3D  STRUCTURE  FROM CONTACT MAP Vassura et al. (Vassura 2008) produce a software tool for reconstructing  a  protein  3D structure  form contact  map. The tool based on distance geometry which, finds a set of three dimensional  coordinates consistent with some given contact  map of  threshold  t.  The  contact  map of  a given protein is a binary matrix CM such that CM[i,j] = 1 iff the Euclidean distance between residues i and j is less than or equal  to  a  pre-assigned  threshold  t.  The  tools  divide system  into  two  phases,  the  first  phase;  to  generate  a random  initial  set  of  3D  coordinates.  This  phase  used metric matrix embedding algorithm to obtain good starting coordinates,  before  that  they  splitting  the  initial  contact map in sub matrices. The sub matrices are then separately used to create sets of coordinate then merged it to give an initial solution. The merging procedure used rotation and translation to decrease number of error. While the second phase refines the set of coordinates by applying correction and  perturbation  procedure.  The  refinement  applies  until the set of coordinates is consistent with the given contact map or until a control parameter ε becomes 0. The control parameter  ε  has  initial  a  positive  value  and  it  is decremented every some amount of refinement steps. If it reaches the 0 value before a consistent set of coordinates is found,  then  a  new  random  initial  set  of  coordinates  is generated; ε is initialized again to a strictly positive value and the refinement procedure re-starts from the beginning. This  phase  applies  iteratively  two  local  techniques  to obtain a new set of coordinates more consistent  with the given  CM in this  step  correction  procedure  doesn't  add new errors to the coordinates set but eventually reduces the possibility  to move some coordinate  not  yet well  placed residue.The  tool  shows  that  contact  maps  computed  using threshold  values  greater  than  those  commonly  used  for distances  allow  better  3D  structure  recovery  than  those computed  at  lower  thresholds  (7-9  Å).  Repeated application  of  their  method  show  that  the  contact  map thresholds  rang  from  10  to  18  Angstrom  allow  to reconstruct 3D models that are very similar  to the protein native structure. The disadvantage of this method apply on just  a  set  of  protein  and  ignore  others  protein  in  PDB which may be its more important.Jing hu et al. (Jing 2002)  present techniques describe how data  mining  can be used to  extract  valuable  information from contact  map and focus on discover an extensive set of non local dense patterns and compile a library of such non local  interaction,  and cluster  patterns  based on their similarities and evaluate the quality.This tool used contact map to discover 3D structure by test each  two  amino  acid  to  determine  3D  distance  by coordinate  of  α  carbon  atom.  A pairs  of  amino  acid  in contact  if  distance  less  than  threshold  value  =7  Å.  The method used in this tool is divided into four stages:• mining dense patterns• pruning mined patterns• clustering the dense patterns• Integration of these patterns with biological data.In the first stage they scan the DB of CM with 2D slide window.  The tool  used different  window size to capture denser  contact  close  to  diagonal.  The  second  stage extracted  and  isolated  the  pattern  less  dense  and  less distance  from  the  diagonal  by  weighted  the  minimum density and verifying window size. Also this stage pruned redundant  pattern  by  using  slide  window  to  capture  all possible area in a matrix.In  clustering  stage,  the  pattern  generated  into  groups  of similar  interaction  by  used  agglomerative  clustering method.  To  find  non  local  interaction  it  calculated  a distance  between each  pair  of  pattern  and  between each pair  of  cluster,  before  they  start  clustering.  This  stage determined threshold for cluster.  Then compare all pair of cluster and mark the closest. If the distance between two clusters is less than threshold t merged them into a single cluster.  Finally,  return  to  the  first  stage  to  continue  the clustering.  If  the  distance  between  the  closest  pair  is greater than certain threshold, the clustering stops.Their experiments used non redundant set of 2702 proteins from  PDB,  binary  contact  maps  were  generated  using several  contact  thresholds.  They  discovered  9929  dense patterns  in sliding window.  The tool  results  showed that they can give 35% accuracy and 37% coverage for protein structure. The results are encouraging, but it's still far from providing  sufficient  accuracy  for  reliable  3D  structure prediction.  Pollastri and Baldi, (Pollastri 2002) used a Neural Network to predict  protein contact  map and find its  3D structure. The  tool  focus  in  grained  contact  map  prediction.  The approach  concentrates  on find a 3D structure  from liner sequences of protein. The major task in this approach is to propose  and  verify  precise  and  robust  adaptation  rule  to predict contact map.The approach taken was to extract  data from PDB. Then choose  the proteins  have  a single  chain with number  of amino acid less than 50, because of the difficulty in Neural Network to training with long chain of protein.This  tool  used  distance  formula  to  compute  distance matrix and normalize the distance matrix by convert all the distance into (0, 1). In addition, it used a set of threshold value  to  extract  a  pair  node  is  in  contact.  We  can summarize  the  approach  described  in  this  tool  by  four different neural networks to get contact map as follows:• Back propagation neural network • Learning vector quantization neural network• Radial basis function neural network• Reinforcement network•The tool used 20 amino acids as inputs and output scheme. It proposed an easy input encoding scheme which used 5 bit  to  encode  each  amino  acid and  used fixed length of protein.  The  approaches  keep  global  information  to  get better prediction.  The disadvantages of this approach  are time  expensive  and  limitation  on  the  length  of  protein sequences. The advantages of this approach it has higher resolution than just one contact map.Jorge and zhijun, (Jorge1999), developed a tool based on Gaussian  smoothing  to  develop  an  efficient  and  reliable code  to  solve  the  distance  geometry  problem in protein structure. The algorithm in this tool work with the sparse set of distance constraints while other algorithm work for distance  geometry which tend to work with dense  set of constraints.The problem in this approach is the distance geometry for determination of protein structures. The distance geometry is  specified  by  a  subset  of  all  atom  pairs.  The  distance between  i,j  atoms  in  a  subset  determine  the  lower  and upper  bounds  to  find a set  of  positions  of  the  specified atoms. This problem is formulated in terms of finding the global minimum of the function. The  approach  in  this  tool  used  Gaussian  smoothing  to transform  function  F into  smoother  function  with  fewer minimizers.  The  optimization  algorithm  applied  to  the transformed  function  and  continuation  techniques.  The optimizations  are  used  to  trace  the  minimizers  of  the smooth  function  back  to  the  original  function.  The advantage  of  this  approach  is  work  per  iteration  and proportional  to  a  subset  for  sparse  distance.  The computational experiments show that the tool provides an efficient approach to the solution of the distance geometry problem and show an interesting issue is the dependence of the structures on the distance data.   3.1 Proposed Reconstructing ToolReconstructing a protein from its contact map using matlap is proposed tool to assists in enhancing constituents and predicts a protein 3D structure. Further more, its, provides information that helps users to correct faults in the protein 3D structure by shifting and rotation. Thus, it helps to make a protein 3D structure is less fault. This section briefly describe the proposed tool finds a set of three dimensional coordinates consistent with some given contact map of threshold.The proposed reconstructing tool contains three modules as shown in Figure1. The EXTRACTOR MODULE reads the protein from the PDB, and constructs a protein contact map table. The SCANNER MODULE accepts the contact map CM as an input, and produces a new contact map NCM. Scanning the contact map for a protein is much more reliable to predict the more important areas of the contact map which we call it NCM.  This process based on prediction quality more than quantity of contacts. This process helps to predict a protein 3D more reliable. In all previous studies shows that predict 50% of the contact map with 5% errors much reliable than predicting 100% of contact map with 25% errors (Vassura, 2008, Gutpa 2004). The proposed algorithm reprocessing all contact residues and assumes that two atoms i and  j are in contact if and only if they share a high number of neighbors, i.e C(i , j)=1 are in contact and share with K neighbors that are closest to a specific point. The PRODUCER MODULE produces distance matrix procedure, which find a possible set of distance between nodes DЄ R N× N depending on threshold value range from 10 to 18 A˚ consistent. In addition by using some literature survey about the physical conformation of the proteins this module can know the average distance between adjacent alpha carbons D[i,j] which is 3.84 A˚ i.e | i – j |=1. also, the other distance can be obtained from classified protein.To compute a 3D point this module used a consistent distance matrix D with supported by MATLAB tools. These tools are used to compute a set of three dimensional coordinates. These coordinates are the best 3D representation for the distance matrix D. The module predicts the initialize starting coordinates randomly. This module iteratively applies some procedure to the current set of coordinate to extract new contact map and compare two contact map (native CM with predict CM) to find number of differences. The module accept the result with error percentage ε is less than 0.3 otherwise a new random initial set of coordinate is generated and the procedure restart from beginning by using MATLAB tool. Finally, when the consistent set of coordinates is found the module used plot 3D function from MATLAB to predict a protein 3D structure. Also, the module does some translate or rotate prediction 3D structure to obtain the most accurate protein 3D structure.            CM, T             NCM           DЄ RN× N PRODUCER                             C Є RN× N      Initial set         ε ---> percentage of    of coordinate        differences between  CMNoFigure 1: Steps of the New Approach4.0 CONCLUSIONReconstructing a protein structure is one of the approaches that  have  been  used  in  folding  a  protein  3D  structure. Reconstructing  a  protein  3D  structure  uses  distance geometry  and  neural  network  approaches  to  achieve  the predictions activity. Furthermore distance geometry is the process  of  mathematical  properties  that  can  be  derived from distance value between pairs of point.  The distance geometry method is used for extracting information from contact  map  systems  in  order  to  help  prediction  of  a protein 3D structure. In the past few years, several protein prediction tools have been produced. In this paper we have compared  the  existing  predicting  protein  3D  structure tools. In addition, we have given the disadvantage of these tools. Furthermore, we have discussed about the proposed tool,  which  is  called  “reconstructing  a  protein  from  its contact map using matlap”.REFERENCESPlero.,   F,  Osavaldo.  O,  Rtta.C  and  Alfonso.  V, (2001)"Prediction of Contact Maps With Neural Network  and  Correlated  mutation",  biology  department,  Univ. Bologna, Italy, 2001, protein engineering vo.14, no.11, pp.835-843.Vassura.  M,  Margara.L,   and  others,  (2008),  "Fault  Tolerance Reconstruction of 3D Structure from Protein Contact  Maps",  CS  department,  bioinformatics  group, Univ. Bologna, Italy, 2008.Vassura.  M.,  Luciano.  M,  Filippo.  M,  Pietro.  D  and piero.F, (2008), "Reconstruction of 3D Structures From Protein Contact Maps",  CS department,  bioinformatics group, Univ. Bologna, Italy, 2008.Wikipedia,(2008), “The free encyclopedia”, Bioinformatics web, modified on 26 March 2008.Philip  and  Helge.  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B, (2002)  "Mining  Protein  Contact  Map",  BIOKDD02: workshop  on  data  mining  on  bioinformatics,  with SIGKDD02mConference 2002: pp 3-10.n.Gutpa  N,  Managal  N,  and  Biswas  S.  (2004)  “Evolution and similarity Evaluation of protein structure in contact  map space. Proteins: structure, function”, Bioinformatics 2004;95920:196-204. Pollastri  G,  and  Baldi  P.  (2002)  “Prediction  of  contact maps  by  recurrent  neural  networks  architectures  and  hidden context propagation from all cardinal corners”. Bioinformatics, 2002;1:1-19.SCANNER:ScanningCMCompute distance between nodesEXTRACTOR: Extract a proteinFrom PDB (ID)Predict contact map and compare it with nativeAcceptable Map 3D structure by plot 3D in MATLABMATLAB Tool (compute set of coordinate)

Reconstruction a protein 3D structure from it contact map: Tools comparison

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Reconstruction a protein 3D structure from it contact map: Tools comparison

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