The Domain Structure of Proteins: Prediction and Organization. Golan Yona Dept. of Computer Science Cornell University (joint work with Niranjan Nagarajan)

The Domain Structure of Proteins: Prediction and

Organization.

Golan YonaDept. of Computer Science

Cornell University(joint work with Niranjan Nagarajan)

Golan Yona, Cornell University


PDB: 1a8y 367aa long MKIIRIETSRIAVPLTKPFKTALRTVYTAESVIVRITYDSGAVGWGEAPPTLVITGDSM…………


The domain structure of a protein

A domain is considered the fundamental unit of protein structure, folding, function, evolution and design.

Compact Stable Folds independently? Has a specific function


A protein is a combination of domains

Protein1

Protein2

Protein3


Any signals that might indicate domain boundaries?

A very weak signal if any in the sequence

Usually domain delineation is done based on structure

Best methods available – manual! But structural information is sparse..


Definitions and assumptions

Domain: continuous sequence that corresponds to an elemental building block of protein folds.

A subsequence that is likely to be stable as an independent folding unit.

Was formed as an independent unit, and later was combined with others – more complex functions.

There are traces of the autonomous units..


First step..

Gather data – database search Histogram of matches is informative but

noisy

Mutations, insertions, deletions, conflicting evidence

sequence


Previous methods

Methods based on the use of similarity searches and knowledge of sequence termini to delineate domain boundaries using heuristics/rules (MKDOM, Domainer, DIVCLUS, DOMO).

Methods that rely on expert knowledge of protein families to construct models like HMMs to identify other members of the family (Pfam, TigrFam, SMART).

Methods that try to infer domain boundaries by using sequence information to predict tertiary structure first (SnapDragon. Rigden’s covariance analysis)

Methods that use multiple alignments to predict domain boundaries (PASS, Domination).

Others..(e.g. CSA and DGS = guess based on size)


How do you evaluate the different methods?

No universal measures A variety of qualitative and quantitative

evaluation criteria, external resources and manual analysis are used to verify domain boundaries


Method outline

Source/test data – SCOP Processed data - alignments Learning system:

– Domain-information-content scores– NN– Probabilistic model

Evaluation

“A Multi-Expert System for the Automatic Detection of Protein Domains from Sequence Information” Niranjan Nagaragan and Golan Yona, in the proceedings of RECOMB2003


OverviewSeed Sequence

Multiple Alignment

blast search

Neural Network

Correlation

Entropy

Sequence Participation

Contact Profile

Secondary Structure

Physio-Chemical Properties

Final Predictions

DNA DATA

Intron Boundaries


The source/test data set

PDB structures with their partitions into domains as defined in SCOP:– 1ctf: domain1 1-76 domain2 77-123

Remove sequences shorter than 40 aa and almost identical entries


Alignments

Search each query against a database of ~1 million non-redundant sequences

Remove fragments first Two phase alignment procedure

– First phase: blast– Second phase: multiple iteration psi-blast

Select one representative from each group of similar proteins

Remove proteins that are less than 90% covered (missing information)

Number of domains ranging from 1-7 Final set: 605 multi-domain proteins and 576 single

domain proteins (1/4)


The domain-information-content of an alignment column

Measures that (are believed) to reflect structural properties of proteins

A total of 20 measures– Conservation measures – Consistency and correlation measures – Measures of structural flexibility – Residue type based measures – Predicted secondary structure information– Intron-exon data


Conservation measures

Entropy: some positions are more conserved than others

Class entropy: some positions have preference towards a class of amino-acids (similar physio-chemical properties)

Evolutionary pressure (span): sum of pairwise similarities

Motivation: consider the mutual similarity of amino acids


Consistency and correlation measures

All domain appearances should maintain its integrity Consistency: difference in sequence counts

Asymmetric correlation: consistency of individual sequences.

Symmetric correlation: reinforcement by missing sequences

Measures are averaged over a window


Consistency and correlation measures – cont.

Sequence termination: strong but elusive– Fragments– Premature halt in alignment– Loosely aligned

Product of left and right termination scores: given c sequences that terminate at a position, with evalues e1,e2,e3,…ec



Measures of structural flexibility

Indel entropy: variability indicates structural flexibility (likely to occur near domain boundaries)

Correlated mutations: indicative of contacts Contact profiles


Contact profile


Residue type based measures

hydrophobic vs. hydrophilic cystines and prolines Classes of amino acids

Predicted secondary structures Helices and strands are rigid Loops are more abundant near domain

boundaries


Intron-exon data

Exon boundaries are expected to coincide with domain boundaries

1

1 2

3

2

1 3

2

Protein1

Protein2

Protein3


Score refinement and normalization

Smoothing using a window w (optimized)

Unification to a single scale – zscore over all positions


Maximizing the information content of scores

Opt for the most distinct distributions of domain positions vs. boundary positions

Affected by the parameters (w smoothing factor) and x (boundary window size)

Use the Jensen-Shannon divergence measure


Examples


Even measures with identical distributions may be informative in a mutli-variate model

To simplify model only the top 12 are selected


The learning system

A neural network is trained to model effectively the complex decision boundary surface

Predicts correctly 94% of domain positions and 88% of the transitions in the test set

Also tried mapping from multiple positions (local input neighborhood) to single/multiple output


OverviewSeed Sequence

Multiple Alignment

blast search

Neural Network

Correlation

Entropy

Sequence Participation

Contact Profile

Secondary Structure

Physio-Chemical Properties

Final Predictions

DNA DATA

Intron Boundaries


Hypothesis evaluation Simple model: refine predictions

– Significant fraction of the positions in a window centered at x should be predicted as transitions

– Order transitions by their quality (depth of the minima) and reject all transitions that are within 30 residues from already predicted transitions


The domain generator model

Multiple hypotheses – find the “best one”

Assume a model: random generator that moves repeatedly between a domain state and a linker state and emits one domain or transition at a time according to different source probability distributions.

Total probability is the product


Formally..S = D1 D2 Dn We are given a sequence S (multiple

alignment) of length L and a possible partition into n domains D=D1,D2,..Dn of lengths l1,l2,..,ln (NN output)

Find the partition that will maximize the posterior probability P(D/S)

Maximize the product of the likelihood and the prior


Calculating the prior P(D)

For an arbitrary protein of length L what is the probability to observe D

Approximate using a simplified model: given the length of the protein, the generator selects the number of domains first and then selects the length of one domain at a time, considering the domains that were already generated.


The prior probabilities

Approximate P0(li/L) by P0(li) normalized to the relevant range.

P0(li/L) is derived based on experimental data


The prior probabilities (cont.)

Calculate Prob(n/L) = Prob(n,L)/P(L)

1

2


The likelihood

Use probabilities of observed scores considering the two different sources

The model D partitions the sequence S into n domains and n-1 transitions: D1,T1,D2,T2,…,Tn-

1,Dn that correspond to the subsequences s1,t1,s2,t2,..,tn-1,sn

Assume domains are independent of each other (additional test can be used)


…likelihood

Each term P(si/Di) and P(tj/Tj) is a product over the probabilities of the individual positions, each one is estimated by the joint probability distribution of the 12 features

How to estimate this probability? (independence assumption does not hold)



Likelihood of individual position

Given k random variables X1,X2,..,Xk their joint prob. Distribution

Use first order dependencies For each pair, calculate the distance between

the joint prob. Distribution and the product of the marginal distributions


Sort all pairs based on their dependency, and pick the most dependent one (denoted by Y1, Y2) and start the expansion

Select the next one based on the strongest dependency with variables that are already in the expansion


Denote by Z=PILLAR(Y) the random variable that Y is most dependent on

Of all possible dependencies involving Y3 pick P(Y3/Z) and add it to the expansion

Proceed until you exhaust all variables Maximize support, minimize error The expansion is different for domain and

transition regions


Finally..

Enumerate all possible hypotheses, calculate the posterior probability for each one, and output the one that maximizes the prob.


Summary of results

Distance accuracy: average distance of the predicted transitions from their associated SCOP transition points.

Distance sensitivity: average distance of SCOP transitions from their associated predicted transition points.

Selectivity: percentage of correct predictions (within 10 residues from SCOP transitions)

Coverage: percentage of correctly identified SCOP transitions (within 10 residues from predicted transitions)


Examples

PDB ID: 2gep Domain Definition:

8-72, 73-272, 273-352, 353-497 Predicted Domains:

1-75, 76-270, 271-352, 353-497 PFam Definition:

1-67, 273-345, 356-425


Examples

PDB ID: 1b6s chain D Domain Definition: 1-78, 79-276, 277-355 Predicted Domains: 1-73, 74-271, 272-355 PFam Definition: 30-167


Examples

PDB ID: 1acc Domain Definition:

14-735 Predicted Domains:

1-158, 159-583, 584-735 PFam Definition:

103-544


Conclusions

A method for predicting the domain structure of a protein from sequence information alone

Protein/DNA data, multiple features, optimization based on information theory principles, learning system and final prediction using the domain-generator model (with confidence values).

Exhaustive hypothesis evaluation Fully automatic and fast Perform very well even compared to the best manual and

semi-manual methods out there (also on CATH data) Dare to say …can be used to verify domain assignments

based on structural data Improvements: other learning systems, more features


Acknowledgments

Niranjan Nagarajan

SCOP CATH PSI-BLAST Pfam InterPro

NSF

Documents

The Domain Structure of Proteins: Prediction and Organization. Golan Yona Dept. of Computer Science Cornell University (joint work with Niranjan Nagarajan)