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Dali: A Protein Structural Comparison Algorithm Using 2D Distance Matrices

This paper explores how structural comparison can provide valuable insights into protein analysis through the use of a 2-D distance matrix to represent 3-D structures. Specific algorithms like Monte Carlo optimization are discussed. Structural comparison is vital as low sequence homology can result in similar structures. The Distance Matrix is examined in detail, showing how equivalent residue pairs are assigned and scored for similarity using Ca-Ca distances. The robustness of Dali in generating accurate alignments is demonstrated through the detection of conserved functional residues and inter-domain motions. Dendrogram analysis showcases the relatedness of proteins. This algorithm has various applications, including predicting 3D structures from amino acid sequences based on residue interactions.

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Dali: A Protein Structural Comparison Algorithm Using 2D Distance Matrices

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  1. Dali: A Protein Structural Comparison Algorithm Using 2D Distance Matrices

  2. Main Points for Discussion • Overview of why structural comparison can be a useful mode of analysis. • Using a 2-D distance matrix to represent a 3-D protein structure. • Specific computer algorithms that have been used to accomplish this analysis, including Monte Carlo optimization. • Further applications of Dali.

  3. Why consider structural comparison? • 1D sequence comparisons has traditionally been (and still is) used to determine degree of relatedness, although a low degree of sequence homology may yield surprisingly similar structures. • 3D structural alignment is aimed at providing more information about the structure-function similarities between proteins with non-detectable evolutionary relationships.

  4. The Distance Matrix and How It’s Read 1 2 3

  5. Assignment of Equivalent Residue Pairs

  6. Additive Similarity Score (general) S = S Sf(i,j) L L • i and j are labeled pairs of equivalent (matched) residues (i.e. i = iA,iB). • f = similarity measure based on Ca-Ca distances dAij and dBij • Largest S corresponds to optimal set of equivalencies. i = 1 j = 1

  7. Rigid Similarity Score fR(i,j) = q R – | dAij – dBij | • dAij and dBij are equivalenced residues • in proteins A and B. • q R = zero level of similarity

  8. Elastic Similarity Score | dAij – dBij | q E - • d*ij = the average of dAij and dBij • q E = tolerance of 20% deviation • w(r) = envelope function = exp(-r2/a2) w(d*ij) fE(i,j) = d*ij q E

  9. Robustness of Dali

  10. Quality of Generated Alignments • Accuracy was verified by examining conserved functional residues in seeemingly divergent structures. • The elasticity score is useful in that it captures relative movements of structural elements (e.g. ATP binding site in hsp70) and leaves only extremely non-homologous loops unaligned.

  11. Quality of Generated Alignments (cont.) • Detection of inter-domain motion brings functionally important residues into focus (e.g. ATP binding site in hsp70). • Manipulation of the elastic similarity score determines the stringency of the alignment.

  12. Dendrogram Examination of Relatedness Using a Dendrogram

  13. Further Applications of Dali • Continuing further in an attempt to map the entire protein space using quantitative comparisons between structures (correspondence analysis on p. 133) • Applications to residue-residue energy interactions to create a more accurate biochemical representation of the protein. Also able to yield more useful information to predict 3D structure from amino acid sequence due to the energies of interacting residues.

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