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