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Sanjaka, Malinda, M.S., Department of Computer Science, College of Science and Mathematics, North Dakota State University, April 2013. Protein Functional Site Prediction Using the Shortest-Path Graph Kernel Method. Major Professor: Dr. Changhui Yan.

Over the past decade Structural Genomics projects have accumulated structural data for over 75,000 proteins, but the function of most of them are unknown or uncertain due to limitation of laboratory approaches for discovering the functionality of proteins. Computational methods play key roles to minimize this gap. Graphs are often used to describe and analyze the geometry and physicochemical composition of bimolecular structures such as, chemical compounds and protein active sites (phosphorylation and enzyme catalytic sites). A key problem in graph-based structure analysis is to deﬁne a measure of similarity that enables a meaningful comparison of such structures. In this regard, kernel functions have attracted a lot of attention, especially since they allow for the application of a rich repertoire of methods from the ﬁeld of kernel-based machine learning. In this study, we developed an innovative graph method to represent protein surface based on how amino acid residues contact with each other. Then, we implemented a shortest-path graph kernel function to calculate similarities between the graphs. We implemented three variants of the nearest-neighbor method to predict functional sites on protein using the similarity measure given by the shortest-path graph kernel. The prediction methods were evaluated on two datasets using the leave-one-out approach. The best method achieved accuracy as high as 78%. We sorted all examples in the order of decreasing prediction scores. The results revealed that the positive examples (functional sites) were associated with high prediction scores and the functional sites were enriched in the region of top 10 percentile. This project showed that the proposed method were able to capture the similarity between protein functional sites and would provide a useful tool for functional site prediction.

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- 1. PROTEIN FUNCTIONAL SITEPREDICTION USING THE SHORTEST-PATH GRAPH KERNEL METHODPresented by :: Malinda SanjakaMajor Advisor:: Dr. Changhui YanGraduate Committee Members::Dr. Juan (Jen) LiDr. Jun KongDr. Nan YuDate:: 04/22/20131
- 2. OutlineProblem StatementIntroductionMaterials and MethodsResults and DiscussionConclusionFuture Work2
- 3. Problem Statement Problem : Prediction of functional sites on proteinstructures What are the functional sites The functional sites are the small portion of a protein where substratemolecules bind and undergo a chemical reaction. Example:3Phosphorylation SiteProtein 3D Structure
- 4. Problem Statement(2)Importance of Functional Sites Prediction To understand protein functionalities To structure based drug design To design new protein4
- 5. OutlineProblem StatementIntroductionMaterials and MethodsResults and DiscussionConclusionFuture Work5
- 6. Introduction20 Amino AcidProtein6
- 7. Introduction(2)Protein Functional SitesD. Catalytic active site atlas Catalytic active site atlas Phosphorylation Site DNA binding Site Zinc-binding site7Addition of a phosphate to an amino acid The functional sites are the small portion of a protein where substrate molecules bindand undergo a chemical reaction.
- 8. Introduction(3)Laboratory Methods for Functional Sites Determination X-ray Crystallography Nuclear Magnetic Resonance(NMR) Challenges Time consume High cost Lack of support for some protein Need skilled professional bodies8
- 9. Introduction(4)The Need for Computational MethodsStructural Genomics (SG) projects reveal large number of protein structuresbut least understanding of protein function. Advantages Low cost Less execution time Less environmental impacts Results optimize by repeating Reusable Run as simulation Reduce human mistakes Disadvantage Accuracy is less than laboratory experimental results Computational methods provide helpful guide line for experimental approach9
- 10. Introduction(5)Computational Methods for Functional Sites Prediction Template-based Identify the structure similar template An alignment a target and the template Predict functional groups Micro environment-based Focus on a single residue or position Used structural and physicochemical properties Supervised machine learning approaches Macro environment-based Local structural region is involved Protein to protein interaction Structure-based drug design DNA-binding sites and ligand-binding sites10
- 11. Introduction(6)Overview of Our ApproachWe used graphs to represent each residue with contacting neighbors in aprotein structure.Central Residue(+/Functional)Contacting ResiduesOne Residue isconsist of number ofatoms11Residue(-/Non-Functional) Contacting
- 12. Introduction(7)Overview of Our Approach –PredictionDatabase Knowledge(Experimentally Verified)Positive(Functional/Active)Negative(Non-Functional/Non-Active)Target Graph(Functional or Non-Functional)Similarity PredictionNearest NeighborMethodShortest-Path GraphKernel12
- 13. OutlineProblem StatementIntroductionMaterials and MethodsResults and DiscussionConclusionFuture Work13
- 14. Materials and MethodsDatasets How to get protein structure Download::[http://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/all/] How to get the protein sequence PDB Database ::[ftp://ftp.wwpdb.org/pub/pdb/derived_data/pdb_seqres.txt]. PDB ID and Change ID :: 101m_A FASTA Format:: >101m_Amol:protein length:154 MYOGLOBINMVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRVKHLKTEAEMKASEDLKKH14
- 15. Materials and Methods(2)Catalytic Binding Site (CSA)[http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/CSA/CSA_Show_EC_List.pl] 73 Protein Chains 201 Active Catalytic Sites 20398 Non-Active Residues Balanced Dataset 201 Active Catalytic Sites 201 Non-Active ResiduesPhosphorylation Site Section 3.3.4 of this paper[http://www.informatics.indiana.edu/predrag/publications.htm]. 679 Protein Chains 2062 Active Phosphorylation Site Residues 139795 Non-Active Residues Balanced Dataset 2062 Active Phosphorylation Site Residues 2062 Non-Active Residues15
- 16. Materials and Methods(3)Graph Representation Definition A graph G=<V, E> V vertices (nodes) and E edges (arcs) A path in G is a sequence of vertices<v0, v1, v2, ..., vn> Directed Graph Undirected Graph Adjacency Matrix16Node(Label)Edge(Weight)
- 17. Materials and Methods(4)Graph Representation Contd. Node Edge Weight Labels(PSSM <Biological conservation of amino acid>)(Position-specific scoring matrix) blast-2.2.25+ NR Database Distance ContactingResidue (Node-Labeled(PSSM))Edge(Arch) –weight (1)CalculationDistance (d1)2+ (y1-y2)2+ (z1-z2)2 VDW- radius of each atoms(van der Waals-VDW.radii file)d1 <= (R1+R2+0.5)Protein Sequences17R1 R2d1<x,y,z> PDBResidue1.Atom1 Residue2.Atom1
- 18. Materials and Methods(6)Shortest-path graph Kernel What is a kernel Simply Kernel is a matrix AxA =<v1…..Vn,v1…..Vn> =Matrix elements What is a graph kernel Use graph instead of vectors What is shortest-path graph kernel Compare the each pair of node by usingshortest- path between each nodeV1V1V2V2VnVng1 g2 gng2g1gn18
- 19. Materials and Methods(7)Shortest-Path Graph Kernel Contd. Original G1 and G2 graphs converted into shortest-path graphs S1 (V1, E1) and S2(V2, E2) The Floyd-Warshall algorithm The kernel function is used to calculate similarity between G1 and G2 bycomparing all pairs of edges between S1 and S2. Calculation11 22),(),( 2121Ee Eeedge eekGGKWhere, kedge ( ) is a kernel function for comparing two edges19e1 e2v1 w1 w2v2
- 20. Materials and Methods(8))2||)()(||exp(),( 22wlabelsvlabelswvknodeWhere, labels (v) returns the vector of attributes associated with node v. Note that Knode() is a Gaussiankernel function. 221was set to 72 by trying different values between 32 and 128 with increments of 2.|))()(|,0max(),( 2121 eweighteweightceekweightWhere, weight (e) returns the weight of edge e. Kweight( ) is a Brownian bridge kernel that assigns thehighest value to the edges that are identical in length. Constant c was set to 2 as in Borgward etal.(2005).Shortest-Path Graph Kernel Contd.Let e1 be the edge between nodes v1 and w1, and e2 be the edge between nodes v2 and w2. Then,),(*),(*),(),( 21212121 wwkeekvvkeek nodeweightnodeedgeWhere, knode( ) is a kernel function for comparing the labels of two nodes, and kweight( ) is akernel function for comparing the weights of two edges. These two functions are defined asin Borgward et al.(2005):20v1<Pssm1>e1=1w2w1 v2 e2=1<Pssm2> <Pssm3><Pssm4>
- 21. Materials and Methods(9)Prediction Methods Nearest Neighbor Algorithm Classify a new example x by finding the trainingexample <Xi-Yj> that is nearest to x according toEuclidean distance: NNM_Max NNM_AVE NNM_TOP10AVEPositive(Functional/Active)Negative(Non-Functional/Non-Active) ?Test SetTrain Set(Experimentally Verified )21Similarity
- 22. Materials and Methods(10) K-fold Cross-Validation Leave-One-Out Cross-ValidationEvolution of Predictors22
- 23. Materials and Methods(11)Measurements for EvaluationTrue Positive/ False PositiveSensitivitySpecificityAccuracy23
- 24. OutlineProblem StatementIntroductionMaterials and MethodsResults and DiscussionConclusionFuture Work24
- 25. Results and DiscussionEnzyme Catalytic SiteEnzyme catalytic siteTP TP % FN FN% FP FP% TN TN% Contact Not Contact Accuracy Sensitivity SpecificityNNM_Max 150 74.5% 51 25.3% 64 31.8% 137 68.1% 5 59 71.3% 74.5% 68.1%NNM_Ave 155 77.1% 46 22.8% 46 22.8% 155 77.1% 5 41 77.1% 77.1% 77.1%NNM_Top10Ave 156 77.6% 45 22.3% 51 25.3% 150 74.6% 5 46 76.1% 77.6% 74.6%Phosphorylation SitePhosphorylationTP TP% FN FN% FP FP% TN TN% Contact Not Contact Accuracy Sensitivity SpecificityNNM_Max 1104 53.5% 958 46.4% 758 36.7% 1304 50.1% 73 685 58.3% 53.5% 50.1%NNM_Ave 1054 51.1% 1008 48.8% 482 23.3% 1580 76.6% 54 428 63.8% 51.1% 76.6%NNM_Top10Ave1085 52.6% 977 47.3% 667 32.3 1395 67.6% 60 607 60.1% 52.6% 67.6%25
- 26. Results and Discussion(2)Percentile Ranking Used full dataset Ordered list Position ranking Majority of functional sitesare less 10% percentile NNM_MAX NNM_AVE NNM_TOP10AVE26
- 27. Percentile Result(CSA) Active(Functional)0204060800.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0Number Active Residues Vs.Percentile[Max]Number ActiveResidues0204060800.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0Number Active Residues Vs. Percentile[Ave]Number ActiveResidues0204060800.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0Number Active Residues Vs. Percentile[Top10 Ave]Number ActiveResiduesResults and Discussion(3)27
- 28. Percentile Result(CSA) Non-Active(Non-Functional)18.51919.52020.5210.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0Number Non-Active Residues Vs.Percentile[Max]Number Non-ActiveResidues18.51919.52020.5210.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0Number Non-Active Residues Vs.Percentile[Ave]Number Non-ActiveResidues18.51919.52020.5210.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.90.9-1.0Number Non-Active Residues Vs.Percentile[Top 10 Ave]Number Non-ActiveResiduesResults and Discussion(4)28
- 29. OutlineProblem StatementIntroductionMaterials and MethodsResults and DiscussionConclusionFuture Work29
- 30. Conclusions We developed an innovative graph method to represent proteinsurface based on how amino acid residues contact with each other. We implemented a shortest-path graph kernel method and used itto compute the similarity between graphs. We developed three nearest neighbor variants to predict bothdataset based on the similarity matrix that the graph kernel methodproduced. The predictors were able to predict catalytic sites with accuracy upto 77.1%. This work showed that the proposed methods were able to capturethe similarity between enzyme catalytic sites and would provide auseful tool for catalytic site prediction.30
- 31. OutlineProblem StatementIntroductionMaterials and MethodsResults and DiscussionConclusionFuture Work31
- 32. Future WorkAdd more parameters into labels(graphs, nodes)Improve the program as web serviceWorking with other kernel methods suchas, Minimum Spring Tree and etc.Optimize algorithm for large datasets32
- 33. AcknowledgementsI would like to express my deep gratitude to my adviser Dr.Changhui Yan for his continuousencouragements, guidance, and supports to complete thispaper successfully.My sincere thanks also go to my committee members, Dr. Juan(Jen) Li, Dr. Jun Kong, and Dr. Nan Yu for their willingness toserve as committee members.33
- 34. Thank you.?34
- 35. Introduction ….vdw.PDBNRDatabaseBlast35
- 36. Protein…-CUA-AAA-GAA-GGU-GUU-AGC-AAG…-L-K-E-G-V-S-K-D-…DNAprotein sequence36
- 37. Important of Functional SitePredictionUnderstanding Protein FunctionalitiesReveal the Structural ProteinDrug DesignDesign New Protein37
- 38. Rationale for Understanding Protein Structure andFunctionProtein sequence-large numbers ofsequences, includingwhole genomesProtein function- rational drug design and treatment of disease- protein and genetic engineering- build networks to model cellular pathways- study organismal function and evolution?structure determinationstructure predictionhomologyrational mutagenesisbiochemical analysismodel studiesProtein structure- three dimensional- complicated38
- 39. Existing Applications for ProteinActive Sites Prediction39
- 40. Our Approach Shortest-path Distance Theory Graph with Adjacent Matrix and Graph kernel Nearest Neighbor Variant (Max, Ave, Top10 Ave) Leave-one-out Cross-Validation True Positive & False Positive Increment percentile40
- 41. Literature Review Graph Adjacency Matrix Shortest Distance Path Algorithm Cross Validation True Positive vs. False Positive Percentile Ranking 41
- 42. Graph A graph G=<V, E> V vertices (nodes) and E edges (arcs) A path in G is a sequence of vertices <v0, v1, v2, ..., vn> Directed Graph Undirected Graph 42
- 43. Adjacency Matrix A simple graph is a matrix with rows and columnslabeled by graph vertices1 = Adjacent0 = Not Adjacent0s on the diagonal43
- 44. Shortest Distance Path Algorithm Used in communications, transportation, electronics, andbioinformatics problems. The all-pairs shortest-path problem involves finding theshortest path between all pairs of vertices in a graph.A i j=1 if there is an edge (Vi,Vj) ; otherwise, A i j =044
- 45. Percentile Ranking There is no proper definition for percentilecalculation Ordered List Position Ranking Max, Ave, Top1045
- 46. Method And Material Data Gathering Identify the Active Residues Balance Dataset Generating a Map File Generate Set of Graphs Development of Graph Kernel46
- 47. Data GatheringCatalytic Binding Site (CSA)http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/CSA/CSA_Show_EC_List.pl EC1, EC2…EC6 HTML Regular Expression Finding Large Single Group Selected EC 3.4 73 Protein chains 201 Active Catalytic Site 20398 Non-Active Resides47
- 48. Data Gathering..Phosphorylation Site Section 3.3.4 of This Paper[http://www.informatics.indiana.edu/predrag/publications.htm]. 679 protein chains 2062 Active Phosphorylation Site Residues 139795 Non-Active Resides48
- 49. Identify the Active ResiduesCatalytic Binding Site (CSA) CSA Annotation –Database(CSA_2_2_12.dat)[ http://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/CSA/CSA_Download.pl] 251777 Records List of Active Residue(201)Phosphorylation Site[http://www.informatics.indiana.edu/predrag/publications.htm] List of Active Residue(2062)49
- 50. Balance DatasetComputation TimeLeave-One-Out Cross-ValidationRandom SelectionCatalytic Binding Site (CSA)-Active 201 , Non Active 201Phosphorylation Site-Active 2062, Non Active 206250
- 51. Generating a Map File Map with Protein PDB ID with Protein Sequences Atomic Solvent Accessible Area Calculations (RASA) Position-Specific Scoring Matrix Calculations (PSSM) Active Residues51
- 52. Map with Protein PDB ID with ProteinSequences PDB ID and Change ID101m_A PDB Database[ftp://ftp.wwpdb.org/pub/pdb/derived_data/pdb_seqres.txt].FASTA Format>101m_Amol:protein length:154 MYOGLOBINMVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRVKHLKTEAEMKASEDLKKHGVTVLTALGAILKKKGHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRHPGNFGADAQGAMNKALELFRKDIAAKYKELGYQG52
- 53. Atomic Solvent Accessible AreaCalculations (RASA) Calculate the Solvent Accessible Area (RASA) of eachProtein Naccess V2.11 Program– Linux/Unix systems /Cygwin– [http://www.bioinf.manchester.ac.uk/naccess/]– ./naccess 1a91.pdb & ./naccess 1afo.pdb & ./naccess 1aig.pdb PDB DATA Bank –PDB File– [http://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/all/]ncbi-blast-2.2.24+RASA >053
- 54. Position-Specific Scoring MatrixCalculations (PSSM) Download PDB Files blast-2.2.25+ Program– Microsoft Windows NR Database (non-redundant protein sequence)Process p = new Process();p.StartInfo.UseShellExecute = false;p.StartInfo.RedirectStandardOutput = true;p.StartInfo.FileName = "C:blast-2.2.25+binpsiblast.exe";p.StartInfo.Arguments = string.Format("{0}", "-query " + FileNameIN + " -db C:blast-• 2.2.25+dbnr -num_iterations 2 -out_ascii_pssm " + FileNameOUT);p.Start();• Example: Sample record of .PSSM1 A 5 -2 -2 -2 -1 -1 -2 1 -2 -2 -3 -1 -2 -3 -2 2 -1 -3 -3 -1 77 0 0 0 0 0 0 10 0 0 0 0 0 0 0 13 0 0 0 0 0.59 1.#J54
- 55. Sample Mapping File>1neg_ASeq :KELVLALYDYQEKSPREVTMKKGDILTLLNSTNKDWWKVEVNDRQGFVPAAYVKKLAAAWSHPQFSUR :11101011111111111111111111111111111111011111111011110111111111111Site :00000000000000000000000000000000000000000000000000000000000010000rASA:115.47,81.22,64.82,.00,20.59,.00,41.60,111.13,56.32,14.17,124.18,35.41,127.39,43.03,111.84,160.37,10.00,.71,33.57,1.82,120.20,91.83,15.89,41.40,69.81,.77,20.31,2.22,49.44,65.40,30.56,97.39,80.11,152.72,75.17,80.10,47.20,64.49,.00,57.09,16.33,101.38,111.31,104.16,71.57,2.73,60.84,.00,18.67,8.04,64.07,71.08,.00,125.10,66.68,24.97,32.49,79.86,65.19,179.94,87.62,51.01,109.35,145.21,71.53,entropy:0.80,0.85,0.25,0.92,0.44,1.48,1.02,2.42,1.57,2.01,0.44,0.93,0.49,0.73,0.73,0.83,1.72,1.46,0.59,2.15,0.72,0.98,1.99,1.65,0.60,1.20,0.35,0.94,0.66,0.65,0.51,0.23,1.04,0.45,1.09,4.74,3.91,0.67,1.38,0.61,0.45,0.75,1.43,0.49,0.36,2.32,0.72,1.63,3.17,0.46,1.53,2.78,1.61,0.38,0.45,0.26,0.15,0.51,0.17,0.38,0.47,0.46,0.93,2.04,1.73,pdbindex:6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70, 55
- 56. Generate Set of GraphsShorted Distance Path (Dijkstra Theory)Adjacent Matrix TheoryContacting Neighbor’s ResiduesLabeledWeightedVarious Numbers of Node and EdgeNormalization Graph– Linear Normalization(X1) =(X-Min)/ (Max-Min)56
- 57. Calculate Distance between Atomsand Check the Contacting2+ (y1-y2)2+ (z1-z2)2 PDB File VDW(van der Waals-VDW.radii file)D1 <= (R1+R2+0.5)Example of a contact residue2 A _ 3 A! : 1.33441Example of a non-contact residue.4 A _ 2 A : 4.14432 57
- 58. Structure of a Graph58
- 59. Development of Graph KernelOriginal G1 and G2 graph converted intoshortest-path graphs S1 (V1, E1) and S2 (V2, E2)The Floyd-Warshall algorithmThe kernel function is used to calculate thesimilarity between G1 and G2 by comparingall pairs of edges between S1 and S2.59
- 60. The Floyd-Warshall Algorithmfor i = 1 to Nfor j = 1 to Nif there is an edge from i to jdist[0][i][j] = the length of the edge from i to jelse dist[0][i][j] = INFINITYfor k = 1 to Nfor i = 1 to Nfor j = 1 to Ndist[k][i][j] = min(dist[k-1][i][j], dist[k-1][i][k] + dist[k-1][k][j])To find the shortest path between all vertices v V for a weighted graph G = (V; E).D(k)ij=the weight of the shortest path from vertex I to vertex j for which all intermediatevertices are in the set {1,2,……k}60
- 61. ImplementationdoublePssm(intResidueA, intResidueB){inti;double sum=0;for (i=0; i<20; i++){sum+=pow((double)(seq_a_pssm[ResidueA][i]-seq_b_pssm[ResidueB][i]), 2);}sum=((double)sum);return sum;}dis+=Pssm(i, j);attr_dis[i][j]=exp((-1)*parm_gamma*dis);sum=0;for (i=0; i<seq_a_len; i++)for (j=0; j<seq_b_len; j++)for (k=i+1; k<seq_a_len; k++)for (r=j+1; r<seq_b_len; r++){xx1 = seq_a_dist[i][k]-seq_b_dist[j][r];Klen=MaxValue(0, CC-fabs(xx1));product1=attr_dis[i][j]*attr_dis[k][r];product2=attr_dis[k][j]*attr_dis[i][r];value=MaxValue(product1, product2);sum+=value*Klen;}return sum;61
- 62. Compare SimilarityMaxAveTop 10 Ave62
- 63. Result and DiscussionComparison Similarity (TP/FP)– Max– Ave– Top 10 AvePercentile Ranking calculation RASA Value63
- 64. Percentile Result(CSA)64
- 65. rASA Vs. Active Residues65
- 66. 66
- 67. staticIEnumerable<string>SortByLength(IEnumerable<string> e){var sorted = from s in eorderbys.Length descendingselect s;return sorted;}Section 3.467
- 68. Protein Chain (CSA)68
- 69. List ofPhosphorylationSite69
- 70. Catalytic Binding Site (CSA)-Active ResidueBack70
- 71. Phosphorylation Site-Active ResiduesBack71
- 72. van der Waals-VDW.radii fileBackRESIDUE ATOM ALA 5ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0RESIDUE ATOM ARG 11ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.87 0ATOM CD 1.87 0ATOM NE 1.65 1ATOM CZ 1.76 0ATOM NH1 1.65 1ATOM NH2 1.65 1RESIDUE ATOM ASP 8ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.76 0ATOM OD1 1.40 1ATOM OD2 1.40 1RESIDUE ATOM ASN 8ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.76 0ATOM OD1 1.40 1ATOM ND2 1.65 1RESIDUE ATOM CYS 6ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM SG 1.85 0RESIDUE ATOM GLU 9ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.87 0ATOM CD 1.76 0ATOM OE1 1.40 1ATOM OE2 1.40 1RESIDUE ATOM GLN 9ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.87 0ATOM CD 1.76 0ATOM OE1 1.40 1ATOM NE2 1.65 1RESIDUE ATOM GLY 4ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1RESIDUE ATOM HIS 10ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.76 0ATOM ND1 1.65 1ATOM CD2 1.76 0ATOM CE1 1.76 0ATOM NE2 1.65 1RESIDUE ATOM ILE 8ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG1 1.87 0ATOM CG2 1.87 0ATOM CD1 1.87 0RESIDUE ATOM LEU 8ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.87 0ATOM CD1 1.87 0ATOM CD2 1.87 0RESIDUE ATOM LYS 9ATOM N 1.65 1ATOM CA 1.87 0ATOM C 1.76 0ATOM O 1.40 1ATOM CB 1.87 0ATOM CG 1.87 0ATOM CD 1.87 0ATOM CE 1.87 0ATOM NZ 1.50 172
- 73. PDB FILE SAMPLEBack73
- 74. Distance File ExampleBack74

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