Protein Structure Prediction on a Lattice Model via Multimodal Optimization Techniques

1
Protein Structure Prediction on a Lattice Model
via Multimodal Optimization Techniques

• Ka-Chun Wong, Kwong-Sak Leung, Man-Hon Wong
• Department of Computer Science Engineering
• The Chinese University of Hong Kong, HKSAR, China
• kcwong, ksleung, mhwong_at_cse.cuhk.edu.hk

2
Outline

• Introduction
• Background
• Objective
• Related Works
• Paper Contributions
• Apply multimodal optimization techniques
• Propose a novel mutation method
• Experiments
• Conclusion

3
Introduction

• Protein is
• a sequence of amino acid residues folded into a
  3D structure
• important for living
• Material transportations across cells
• Catalyzing metabolic reactions
• Body defenses against viruses

4
Introduction

• Protein Function
• Substantially depends on its 3D structure

http//www.pdb.org/pdb/explore/explore.do?structur
eId2X7M
5
Introduction

• Protein Structure Determination
• Wet-lab experiments exist
• X-ray crystallography
• NMR spectroscopy
• But they are
• Labor intensive
• Not scalable
• Expensive

6
Introduction

• Wet lab experiments for Protein Structure
  Determination are
• Costly
• Time-consuming
• Not scalable
• Accurate

• Computational approaches for Protein Structure
  Prediction are
• Less Costly
• Fast
• Scalable
• Less Accurate

Complementary Twins Wet-labs for
fine-tuning Computation for coarse-tuning
7
Introduction

• Protein Structure Prediction (PSP)
• Input An amino acid sequence
• Output The 3D structure of the sequence
• Divided into two classes
• Using / Not using
• similar sequences their structures

Prediction
YDVAEGCKVV
Similar sequences their structures
8
Introduction

• This paper focuses on
• De novo protein structure prediction on the 3D HP
  lattice model using evolutionary algorithms
• De novo means the input of the method only
  contains the sequence to be predicted

N. Krasnogor, W.E. Hart, J. Smith, and D. Pelta.
Protein structure prediction with evolutionary
algorithms. In Eiben Garzon Honovar Jakiela
Banzhaf, Daida and Smith, editors, International
Genetic and Evolutionary Computation Conference
(GECCO99), pages 1569-1601. Morgan Kaufmann, 1999.
9
Background

• 3D HP lattice model
• Assume the main driving forces are the
  interactions among the hydrophobic amino acid
  residues
• All known amino acid residues are experimentally
  classified as either hydrophobic (H) or polar
  (P).

10
Background

• 3D HP lattice model
• An amino acid sequence is represented as a string
  H,P
• The sequence folded into a limited space, a cubic
  lattice

11
Background

• Amino acid residue Bead
• Peptide bond Straight Line

HPHPPHHPHPPHPHHPPHPH H Red color P Blue color
12
Objective

• To find the conformation with the minimal energy.
• Maximize the number of the H-H bonds which are
  formed by two non-sequence-adjacent residues
  (non-local H-H bonds)

13
Objective

• Mathematically, it is to minimize the following
  function

Distance Function
Only non-sequence-adjacent residues are checked
Bond Energy
H. Li, R. Helling, C. Tang, and N. Wingreen.
Emergence of Preferred Structures in a Simple
Model of Protein Folding. Science,
273(5275)666669, 1996.
14
Related Works

• Unger et al. first apply a hybridized genetic
  algorithm to solve the problem 1
• Patton et al. use a standard genetic algorithm 2

1 Unger, R. and Moult, J. 1993. Genetic
Algorithm for 3D Protein Folding Simulations. In
Proceedings of the 5th international Conference
on Genetic Algorithms S. Forrest, Ed. Morgan
Kaufmann Publishers, San Francisco, CA, 581-588.
2 Patton, A. L., Punch, W. F., and Goodman, E.
D. 1995. A Standard GA Approach to Native Protein
Conformation Prediction. In Proceedings of the
6th international Conference on Genetic
Algorithms (July 15 - 19, 1995). L. J. Eshelman,
Ed. Morgan Kaufmann Publishers, San Francisco,
CA, 574-581.
15
Related Works

• Berger et al. prove that the problem is
  NP-complete 1
• Krasnogor et al. publish a work discussing the
  basic algorithmic factors affecting the problem
  2

1 Berger, B. and Leighton, T. 1998. Protein
folding in the hydrophobic-hydrophilic (HP) is
NP-complete. In Proceedings of the Second Annual
international Conference on Computational
Molecular Biology. RECOMB '98. ACM, New York, NY,
30-39. 2 N. Krasnogor, W.E. Hart, J. Smith, and
D. Pelta. Protein structure prediction with
evolutionary algorithms. In Eiben Garzon Honovar
Jakiela Banzhaf, Daida and Smith, editors,
International Genetic and Evolutionary
Computation Conference (GECCO99), pages
1569-1601. Morgan Kaufmann, 1999.
16
Related Works

• Since then, many related algorithms are proposed.
  Some examples
• Multimeme algorithm by Krasnogor et al.
• Guided genetic algorithm by Hoque et al.
• Ant colony algorithm by Shmygelska et al.
• Differential Evolution by Bitello et al.
• Immune Algorithm by Cutello et al.
• EDA by Santana et al.

17
Paper Contributions

• Observation
• Some diversity preserving techniques are
  incorporated in most algorithms
• Duplicate predator 1
• Aging operator 2
• Additional renormalization of the pheromone 3

1 G. A. Cox, T. V. Mortimer-Jones, R. P.
Taylor, and R. L. Johnston. Development and
optimisation of a novel genetic algorithm for
studying model protein folding. Theoretical
Chemistry Accounts Theory, Computation, and
Modeling, 112(3)163178, 2004. 2 V. Cutello,
G. Nicosia, M. Pavone, and J. Timmis. An immune
algorithm for protein structure prediction on
lattice models. IEEE Transactions on Evolutionary
Computation, 11(1)101117, Feb. 2007. 3 A.
Shmygelska and H. Hoos. An ant colony
optimisation algorithm for the 2d and 3d
hydrophobic polar protein folding problem. BMC
Bioinformatics, 6(1)30, 2005.
18
Paper Contributions

• Observation
• Unger et al. have observed that there can be
  multiple conformations for each energy value 1
• A study also indicates the fitness landscapes of
  the problem are multimodal 2

1 R. Unger and J. Moult. Genetic algorithms for
protein folding simulations. J. Mol. Biol.,
2317581, May 1993. 2 S. D. Flores and J.
Smith. Study of fitness landscapes for the HP
model of protein structure prediction. In
Evolutionary Computation, 2003. CEC 03. pages
23382345, Dec. 2003.
19
Paper Contributions

• In this paper
• Apply multimodal optimization techniques to solve
  the PSP problem
• Fitness Sharing (SharingGA) 1
• Species Conserving (SCGA) 2
• Crowding (CGA) 3

1. Goldberg, D. E. and Richardson, J. 1987. Genetic
   algorithms with sharing for multimodal function
   optimization. In Proceedings of the Second
   international Conference on Genetic Algorithms on
   Genetic Algorithms and their Application, 41-49.
2. Li, J., Balazs, M. E., Parks, G. T., and
   Clarkson, P. J. 2002. A species conserving
   genetic algorithm for multimodal function
   optimization. Evol. Comput. 10, 3 (Sep. 2002),
   207-234.
3. De Jong, K. A. 1975 An Analysis of the Behavior
   of a Class of Genetic Adaptive Systems.. Doctoral
   Thesis. UMI Order Number AAI7609381., University
   of Michigan.

20
Paper Contributions

• In this paper
• Proposes a novel mutation method
• Mixing two types of mutations together
• Sometimes use RM, sometimes use AM

• and apply it in CGA (called CGA-mixed)

RM Mutation in Relative Encoding AM Mutation in
Absolute Encoding
21
Experiments

• Experiments are conducted
• Relative Encoding 1
• Hamming Distance
• 100 Individuals (Overlapping)
• Uniform Deterministic (Parent Selection)
• Truncation (Survival Selection)
• 50 runs
• 105 and 5x106 energy evaluations
• UN 2 as a control algorithm

• N. Krasnogor, W.E. Hart, J. Smith, and D. Pelta.
  Protein structure prediction with evolutionary
  algorithms. In Eiben Garzon Honovar Jakiela
  Banzhaf, Daida and Smith, editors, International
  Genetic and Evolutionary Computation Conference
  (GECCO99), pages 1569-1601. Morgan Kaufmann,
  1999.
• K.A. De Jong, Evolutionary computation a unified
  approach. MIT Press, Cambridge MA, 2006

22
Experiments

• 105 energy evaluations over 50 runs

H(x) The lowest energy over 50 runs means The
lowest energy of a run averaged over 50 runs
23
Experiments

• 5x106 energy evaluations over 50 runs

H(x) The lowest energy over 50 runs means The
lowest energy of a run averaged over 50 runs
24
Experiments

• The experimental results quoted in the following
  literatures are taken and compared under the same
  termination condition
• Santana, R. Larranaga, P. Lozano, J.A. ,
  "Protein Folding in Simplified Models With
  Estimation of Distribution Algorithms,"
  Evolutionary Computation, IEEE Transactions on ,
  vol.12, no.4, pp.418-438, Aug. 2008
• Cutello, V. Nicosia, G. Pavone, M. Timmis, J.
  , "An Immune Algorithm for Protein Structure
  Prediction on Lattice Models," Evolutionary
  Computation, IEEE Transactions on , vol.11, no.1,
  pp.101-117, Feb. 2007

25
Experiments

• 105 energy evaluations over 50 runs

H(x) The lowest energy over 50 runs means The
lowest energy of a run averaged over 50 runs
26
Experiments

• 5 x 106 energy evaluations over 50 runs

H(x) The lowest energy over 50 runs means The
lowest energy of a run averaged over 50 runs
27
Conclusion

• In this paper, we
• Apply multimodal optimization techniques for PSP
• Propose a novel mutation method for PSP
• Some results comparable with the state-of-the-art
  algorithms have been obtained
• The source codes can be downloaded at
  http//pc89075.cse.cuhk.edu.hk8080/myapp/GECCO201
  0-PSP-LatticeModels.zip

28
QA

• The source codes can be downloaded at
  http//pc89075.cse.cuhk.edu.hk8080/myapp/GECCO201
  0-PSP-LatticeModels.zip

29
Paper Contributions

• Proposed mutation method

• and apply it in CGA (called CGA-mixed)