Predicting structural disruption caused by crossover: a machine learning approach

Denis C. Bauer

Talk CIBCB 2005

Outline

• Introduction in Protein Design

• Theory of SCHEMA

• Our Approach

• Results

• Summary

Protein• Biological Functions

– Proteins are fundamental components of all living cells

• Messenger Function (e.g. Hormones)• Catalystic Function (e.g. Enzymes)• Regulatoy Function (e.g. Antibodies)

• Protein Design for Industry and Medicine – Better adjusted– New function

Introduction

Protein Structure• Primary Structure

• Secondary Structure

• Tertiary Structure

• Quaternary Structure

Pictures from: Principles of BIOCHEMISTRY, Horton, Moran, Ochs, Rawn, Scrimgeours

Introduction

– Huge sequence space

– Not every possible sequence is stable

Protein Design

• Creating new amino acid sequences

20100

possible Amino Acid sequences

Solution: using sequences which already exist

Introduction

Gly Ala– Glu ThrPro Val Gly Asp– – –Glu ThrPro– –– – – – Gly Ala– Glu Pro– ––

KEMHQPLTFGELENLPLLNTDKPVQALM

Benefit of Recombination

Problem: how to identify recombination sites ?

Introduction

KIPDELGLIFKFEAPGRVTRVLSSQ…MH KL NE K AP

TIKELPQPPTFGELKKLPLLNTDKPVQALML KP GK

G

MKIADELGEIFKFEAPGRVTRYLSSQ…AP EL YAMKIPDELGLIFKFEAPGRVTRALSSQ…MKIPDELGLIFKFEAPGRVTRALSSQ…

KEMHQPLTFGELENLPLLNTDKPVQAL KEMHQPLTFGELENLPLLNTDKPVQAL

Better resistant to heat

Higher performance

Higher performance

Better resistant to heat

Mayfly

Lives where its hot

SCHEMA

• Research group of Prof. Francis Arnold

• Idea: Positions where the least interaction are disrupted

SCHEMA

SCHEMA profile

Limitations

• 3D Structure necessary– Problem: hard to derive for some proteins

• time consuming• expensive

Solution: Disengaging from 3D structure

SCHEMA

Our approach

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0.71A31A

residues

SC

HE

MA

sco

re

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0.71A31A

residues

SC

HE

MA

sco

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Alternative to SCHEMA3D Structure Information Schema Alg Schema Score

PredictingSequence

Benefit: All Proteins can be processed

Our Approach

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residues

1A31A

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0.71A31A

SC

HE

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residues

Model

Predicting Schema-Profile

Bidirectional RecurrentNetwork

Predicted Schema Score

Sequence

Support Vector Regression

PredictiveModel

Feed Forward NeuralNetwork

*

* Bodén, M., Yuan, Z. and Bailey, T. L. Prediction of protein continuum secondary structure with probabilistic models. submitted

Our Approach

Results

Method r devA

FFNN 0.86 0.57

BRNN 0.88 0.52

SVR eps 0.82 0.63

SVR nu 0.83 0.62

Table 1 Results for all approaches. r = correlation coefficient (ideally 1), devA = Root Mean Square Error (RMSE) normalized by the standard deviation (ideally 0).

Results

Results

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0.81A4U-A

Sco

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0.81BKP-B

Sco

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0.81AJZ_

Sequence position

Sco

re

Results

Results

Results

Refinements

Contact Numbers

Predicting Model

Predicted Schema Score

ML model

predicted

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0.71A31A

SC

HE

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sco

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Input features

Solvent AccessibilityScore

CC

0.88

0.88

0.6Ensemble

ML model

ML model

ML model

0.88

Results

However…

• Only a limited number of connections are considered• Broken connections are reconnected after recombination

Summary

• Design proteins with recombination rather than from scratch– Identifiy recombination site – Idea: finding the sites where the least interactions are disrupted

(SCHEMA)

• Predicting SCHEMA-score to overcome the limitation• SCHEMA too limited to be the only means for

recombination site prediction• Future work

– All interactions– Actual recombination process

Acknowledgments

• Supervisors Dr. Mikael Bodén and Dr. Ricarda Thier• Dr. Zheng Yuan • Prof. Francis Arnold’s research group

Thank youRef:C. A. Voigt, C. Martinez, Z.-G. Wang, S. L. Mayo, and F. H. Arnold, Protein building blocks preserved by recombination, Nat Struct Biol, vol. 9, no. 7, pp. 553-558, Jul 2002.

Meyer MM, Silberg JJ, Voigt CA, Endelman JB, Mayo SL, Wang ZG, Arnold FH. Library analysis of SCHEMA-guided protein recombination.Protein Sci. 2003 Aug;12(8):1686-93.

Bodén, M., Yuan, Z. and Bailey, T. L. Prediction of protein continuum secondary structure with probabilistic models. submitted.

PDB 1zg4

Recombination Site Identification

• Recombination vs Mutagenesis or Design from scratch

– Higher fraction of functional proteins– Higher diversity higher chance to find

a better hybrid

• Requirement– Identify recombination site – Identify which segments are useful– Identify beneficial segment combinations

• Existing methods– SCHEMA (Hybrid evaluation : avoid breaking connections)– FamClash (Hybrid evaluation : avoid changing properties of

residue pairs)– STAR (Site suggestion according to strucural compactness)

• Known methods too limited to be a good means for recombination site prediction

http://www.che.caltech.edu/groups/fha/

Possible approaches

• Identify a new measure for evaluating hybrids (derived from datasets of biologically produced hybrids)

• Include more information in the decision process– Sequence/Structure (SCHEMA)– Chemical features (FamClash)– Predicting important residues for structure and/or function– Predicting enzyme function from protein sequence– Substitution tolerance– Hydrophobic patterning– Surface clefts or binding sites– Solvent accessibility – Domains/motifs of parents