A new Approach to Structural Prediction of Proteins Heiko Schröder Bertil Schmidt Jiujiang Zhu School of Computer Engineering Nanyang Technological University Singapore

Contents zProtein Structure zProtein Structure Prediction zApproach based on Local Protein Structure zRefinements zConclusions and Future Work

Protein Structure zProteins are large molecules composed of smaller molecules called amino acids zThere are 20 kinds of amino acids found in natural proteins zAll share a common structure R side chain carboxyl groupamine group alpha carbon (with attached hydrogen)

Protein Structure

From Primary to Tertiary Structure zA protein’s 3D shape is determined by its primary amino acid sequence (Anfinsen, 1963) zPredicting tertiary structure from amino acid sequence is an unsolved problem yDifficult to model the energies that stabilize a protein molecule yConformational search space is enormous

Prediction Methods zGiven an amino acid sequence: ysearch a set of known folds by aligning sequence and a template fold representative ypredict the fold that gets the best scoring alignment Target amino acid sequence Template Fold library YLAADTYK Template amino acid sequence FISSETCNMEPSSYVTGLIRKN Target/template Score: 7212

Prediction Methods zThis method is very effective when target and template have >30% sequence identity zApproximately 1/3 of protein sequences can be assigned folds and modeled this way zOur aim is to contribute to determine tertiary structures in case matching sequences cannot be found

Local structure and prediction zWhat is Local structure ? ydescribes environment of an amino acid yan amino acid’s relationship to neighbors zwe use this information to predict structure from primary sequence

Dihedral Angles zThe 6 atoms in each peptide unit lie in the same plane   and  free to rotate  The structure of a protein is almost totally determined, if all angles  and  are known

Idea of our Approach zStiff  free zlocal predictability  database of sub-chain structures zreduction of the number of degrees of freedom by 10, reduces the computation time significantly in combination with a global optimization algorithm (e.g. GA or SA) Side chains Back bone CC  and  C N

Classification of Dihedral Angles Selected PDB structures Dihedral angle extraction Histogram for each amino acids pair stiff multiple flexible

Classification of Dihedral Angles  ALA-ALA Frequency   LEU-ARG Frequency   GLY-ILE Stiff multiple flexible

Classification of Dihedral Angles Selected PDB structures Dihedral angle extraction Histogram for each amino acids pair stiff multiple flexible zStiff angles: determine mean value zMultiple angles: determine sequence of mean values, one for each peak in decreasing order of these peaks zFlexible angles: determine mean value and mark as flexible

Prediction based on Classification zGiven a sequence of amino acids, find the subsequence in which all angles are of type stiff zpredict structure of these subsequences, using the mean values of the corresponding histograms

Prediction based on Classification zPart of a protein predicted with this method (backbone of a helix, original structure on the left, predicted structure on the right) zSuccessfully predicted certain stiff structures of subsequences up to the length of 15

Refinement of the method zFor multiple angles: yconsider sequences of length 3 or 4:  extract sequences (C,A,B,D) and determine the histogram of angles  and  related to the peptide chain between A and B  if histogram for  for amino acids (A,B) is multiple, check if angle for (A,B,C,D) is stiff xwith longer subsequences the occurrences of these sequences drops dramatically

Refinement of the method zFor multiple angles: yif an amino acid sequence has only a small number of multiple edges, it is possible to try all combinations of possible peaks ymany combinations lead to collisions in part of the protein, and thus can be eliminated

Conclusion and Future Work zPresented a method to predict stiff structures of subsequences up to the certain length zPresented a refinement of the method to handle multiple angles zhow to handle flexible angles ? zUsing the local prediction as an input for a global optimization method, e.g. based on Simulated Annealing