Detecting the Domain Structure of Proteins from Sequence Information

Niranjan Nagarajan and Golan YonaDepartment of Computer Science

Cornell University

What’s and Why’s Why?

Function Prediction Improved Alignments and more accurate

Evolutionary Studies Protein Design

What? Delineating Sequence Contiguous

Domains Work exclusively on Sequence

Information

Past Work The Pfam Protein Families Database, Bateman

et al (2002) Nucleic Acids Research 30:276-280 ProDom and ProDom-CG: tools for protein

domain analysis and whole genome comparisons, Corpet et al (2000) Nucleic Acids Research 28:267-269

Automated Protein database classification: I. Integration of compositional similarity search, local similarity search and multiple sequence alignment. II. Delineation of domain boundaries from sequence similarities, Jerome et al (1998) Bioinformatics 14:164-187

Overview of the ProcessSeed Sequence

Multiple Alignment

blast search

Neural Network

Correlation

Entropy

Sequence Participation

Contact Profile

Secondary Structure

Physio-Chemical Properites

Final Predictions

Motivation Simple and Extensible Tests an array of novel sources of

information Automated method based on

statistical analysis of the scores Domain transition signals are

learned rather than programmed in

Score Design Efficiently Computable Yields single value per profile column Robustness to Alignment inaccuracies Useful in distinguishing in-domain

from out-domain columns in isolation or in combination with other scores

Correlation Measures the conservation of the

alignment in a regionHigh Correlation Low Correlation

Entropy Estimates the diversity of the amino-

acid distribution for a column

Low Entropy High Entropy

Sequence Participation Identifies and quantifies the

significance of regions where there is a major change in sequence participation

Secondary Structure Uses psipred secondary structure

predictions for the seed sequence

Contact Profile Contacts are predicted based on correlated

mutation values that are significantly larger than random values

Physio-Chemical Properties

We tested properties like Hydrophobicity, Molecular Weight, and Charge and various classifications of the amino acids for their information content

Scores were calculated by: Using the classification to assign values

in the range [0, 1] to every residue Taking the average of the values for a

profile column

Generating the Data Set Seed Sequences: 4810 non-redundant (95% identity)

PDB sequences that are at least 40 amino acids long (PDB data as of may 2002)

Alignments: The seeds were blasted against a composite non-redundant

database with 693,912 non-fragmented entries The resulting hits were compiled in a database The seeds were queried using PSI-BLAST (until convergence)

against these smaller databases to generate the alignment Domain Definitions: Definitions in SCOP 1.57 were used

(seeds with inconsistent definitions or less than 90% coverage were removed)

The final set, after filtering to ensure to ensure a balance in the number of single (576) and multi-domain (605) proteins, contained 1181 seed proteins and their alignments

Massaging and Optimizing the Scores

Scores were smoothed over various smoothing windows to test the importance of evening out local fluctuations

Scores were normalized to ensure that values from different proteins were comparable

The size of the smoothing window was optimized using the Jensen-Shannon Divergence between the distributions for in-domain and out-domain columns

Designing and Training the Neural Network

Matlab’s Neural Network Toolbox was used to design and train networks

Network Properties: Feed-Forward Back Propagation network with

Tangent Sigmoid activation function Current best network takes in 11 inputs and has two

hidden layers with 10 and 5 neurons respectively Neural network trained on a set of 484 proteins with

a validation set of 237 proteins and test set of 460 proteins

Best network has accuracy of 91% for in-domain and 70% for out-domain columns in test set

From Neural Network to Cutpoint Predictions

A column is predicted as a cutpoint if a significant fraction of columns in a window centered at it are predicted as being out-domain

For regions with multiple cutpoints near one another, minimas of the smoothed prediction curve are used to decide the most suitable cutpoint

Comparative Results

Accuracy evaluates predictions with respect to the true definitions

Sensitivity evaluates true definitions with respect to the definitions

Average accuracy in residues Average sensitivity in residues Percentage Accuracy Percentage CoverageOur Method 43 (48) 32 (36) 47 (21) 49 (22)

Pfam 38 (43) 14 (22) 47 (25) 78 (42)ProDom 10 (10) 89 (89) 35 (24) 9 (7)SMART 14 (14) 73 (74) 38 (27) 26 (19)

Tigr 5 (3) 97 (94) 35 (18) 2 (1)

Examples Seed Number: 9847 PDB ID: 1b6s chain D Domain Definition:1-78, 79-276, 277-355 Predicted Cutpoints: 73, 271 PFam Definition: 30-167

More Examples Seed Number: 11791 PDB ID: 1acc Domain Definition: 14-735 Predicted Cutpoints: 158, 583 PFam Definition: 103-544

Highlights  Correctly predicts domain definitions

for 237 (52%) of the proteins in the test set thus comparing favorably with PFam (258 and 56%)

The procedure is simple and fast and comparable in accuracy and coverage to PFam

General purpose method for delineating domain boundaries that relies solely on sequence information