A Filtering Technique for Fragment Assembly- Based Proteins Loop Modeling with Constraints

Methods to predict the structure of a protein often rely on the knowledge of macro sub-structures and their exact or approximated relative positions in space. The parts connecting these sub-structures are called loops and, in general, they are characterized by a high degree of freedom. The modeling of loops is a critical problem in predicting protein conformations that are biologically realistic. This paper introduces a class of constraints that models a general multi-body system; we present a proof of NP-completeness and provide filtering techniques, inspired by inverse kinematics, that can drastically reduce the search space of potential conformations. The paper shows the application of the constraint in solving the protein loop modeling problem, based on fragments assembly.

[1]  Pietro Cozzini,et al.  A new approach for investigating protein flexibility based on Constraint Logic Programming. The first application in the case of the estrogen receptor. , 2012, European journal of medicinal chemistry.

[2]  Alessandro Dal Palù,et al.  Computing approximate solutions of the protein structure determination problem using global constraints on discrete crystal lattices , 2010, Int. J. Data Min. Bioinform..

[3]  Michael J. Cahill,et al.  On the kinematics of protein folding , 2002, J. Comput. Chem..

[4]  David C. Jones Predicting novel protein folds by using FRAGFOLD , 2001, Proteins.

[5]  R A Friesner,et al.  Prediction of loop geometries using a generalized born model of solvation effects , 1999, Proteins.

[6]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[7]  Holger H. Hoos,et al.  An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem , 2005, BMC Bioinformatics.

[8]  Alessandro Dal Palù,et al.  Exploring Protein Fragment Assembly Using CLP , 2011, IJCAI.

[9]  Richard A Friesner,et al.  Prediction of Protein Loop Conformations using the AGBNP Implicit Solvent Model and Torsion Angle Sampling. , 2008, Journal of chemical theory and computation.

[10]  C. Deane,et al.  CODA: A combined algorithm for predicting the structurally variable regions of protein models , 2001, Protein science : a publication of the Protein Society.

[11]  Cinque S. Soto,et al.  Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Yuxing Liao,et al.  CASP9 assessment of free modeling target predictions , 2011, Proteins.

[13]  Yoonjoo Choi,et al.  FREAD revisited: Accurate loop structure prediction using a database search algorithm , 2010, Proteins.

[14]  Shoji Takada,et al.  SimFold energy function for de novo protein structure prediction: Consensus with Rosetta , 2005, Proteins.

[15]  Rolf Backofen,et al.  A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models , 2006, Constraints.

[16]  Jonathan Casper,et al.  Combining local‐structure, fold‐recognition, and new fold methods for protein structure prediction , 2003, Proteins.

[17]  Chaok Seok,et al.  Protein loop modeling by using fragment assembly and analytical loop closure , 2010, Proteins.

[18]  Adrian A Canutescu,et al.  Cyclic coordinate descent: A robotics algorithm for protein loop closure , 2003, Protein science : a publication of the Protein Society.

[19]  Andrzej Kolinski,et al.  Modeling of loops in proteins: a multi-method approach , 2010, BMC Structural Biology.

[20]  Alessandro Dal Palù,et al.  Constraint Logic Programming approach to protein structure prediction , 2004, BMC Bioinformatics.

[21]  Julian Lee,et al.  PROTEINS: Structure, Function, and Bioinformatics 56:704–714 (2004) Prediction of Protein Tertiary Structure Using PROFESY, a Novel Method Based on Fragment Assembly and , 2022 .

[22]  Pu Liu,et al.  A Self-Organizing Algorithm for Modeling Protein Loops , 2009, PLoS Comput. Biol..

[23]  B. Honig,et al.  A hierarchical approach to all‐atom protein loop prediction , 2004, Proteins.

[24]  Jaime Prilusky,et al.  Assessment of CASP8 structure predictions for template free targets , 2009, Proteins.

[25]  Lisa Yan,et al.  LOOPER: a molecular mechanics-based algorithm for protein loop prediction. , 2008, Protein engineering, design & selection : PEDS.

[26]  Alessandro Dal Palù,et al.  A constraint solver for discrete lattices, its parallelization, and application to protein structure prediction , 2007, Softw. Pract. Exp..

[27]  Pedro Barahona,et al.  Constraint Programming in Structural Bioinformatics , 2007, Constraints.

[28]  Alessandro Dal Palù,et al.  CLP-based protein fragment assembly* , 2010, Theory and Practice of Logic Programming.

[29]  Pascal Van Hentenryck,et al.  On Lattice Protein Structure Prediction Revisited , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[30]  C Kooperberg,et al.  Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. , 1997, Journal of molecular biology.

[31]  Hongyi Zhou,et al.  Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction , 2002, Protein science : a publication of the Protein Society.

[32]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[33]  A. Sali,et al.  Modeling of loops in protein structures , 2000, Protein science : a publication of the Protein Society.

[34]  A. Giuliani,et al.  A computational approach identifies two regions of Hepatitis C Virus E1 protein as interacting domains involved in viral fusion process , 2009, BMC Structural Biology.

[35]  A. Sali,et al.  Statistical potential for assessment and prediction of protein structures , 2006, Protein science : a publication of the Protein Society.

[36]  Prasanna R Kolatkar,et al.  Assessment of CASP7 structure predictions for template free targets , 2007, Proteins.