functional annotation of proteins with known structure by structure and sequence similarity,

DIMACS 2005-06-13 ILYA SHINDYALOV, UCSD

Functional Annotation of Proteins with Known Structure

by Structure and Sequence Similarity, DNA-protein Interaction Patterns

and GO Framework

Ilya Shindyalov, UCSD/SDSCPhD, Group Leader, Protein Science Research

DIMACS 2005-06-13


Protein

Essential Dataflow in Protein Science

Sequence Structure FunctionData:

Methods:Sequence similarity:

(i) BLAST,

(ii) fold recognition,

(iii) homology modeling

…

Results:

Structure similarity:

(i) DALI,

(ii) VAST,

(iii) CE

…


COVERAGE RATIO FOR FUNCTIONAL ANNOTATION

Disease Biological Cell Molecular Process Component Function

PDB STRUCTURES 0.758 0.396 0.371 0.335

SG TARGETS 0.355 0.315 0.452 0.259

PDB+SG 0.822 0.528 0.593 0.477

HOMOLOGY MODELS 0.984 0.792 0.839 0.821

Do we know the function, if we know the structure?


The Subjects of my Talk

3 Approaches of Using Structure Similarity to Infer Protein Function:

#1: Assigning function from known to unknown – CASE STUDY – Prediction of calcium binding in Acetylcholine Esterase – Projection on SNP responsible for Autism.

#2: Classification of DNA-binding protein domains involving (in addition to structure similarity) – DNA-protein interaction patterns and sequence similarity.

#3: Extending GO annotation using structure similarity – how reliable it can be?

#4 [BONUS]: Why ontology is so important for humans?


CEProtein structure comparison by Combinatorial Extension of the optimal path (Shindyalov and Bourne, 1998).

http://cl.sdsc.edu


CEStep 1. Heuristic search for initial path.

Distance between two fragments

Protein A

Protein B

AFP = Aligned Fragment Pair

Protein A

Protein B

AFP1 AFP2

Protein A Protein A

Pro

tein

B

Pro

t ein

B

Alignment Path


CEStep 2. Iterative dynamic programming on starting

superposition from step 1.


CE vs. other Algorithms ???Novotny, M., Madsen, D., and Kleywegt, G.J. 2004. Evaluation of protein fold comparison

servers. Proteins 54: 260-270.


2ACE vs. 1TN4: RMSD = 4.6Å Z-score = 4.6 LALI = 86 LGAP = 8 Seq. Identity = 3.5%

Acetylcholinestarase vs. Troponin C


• PDB - Protein Data Bank of February 13, 2002 with 17,304 entries was used as the source of original structural data.

- The DNA fragment size is at least 5 bp long.

- At least 5 different protein residues are involved in the interaction with DNA.

- The contact distance cutoff between interacting atoms was < 5Å.

- We did not take into account the different types of DNA (A, B, Z) because of the insufficient level of this annotation in the PDB

• PDP – Protein Domain Parser (Alexandrov, Shindyalov, Bioinformatics, submitted)

• CE – Protein structure alignment by Combinatorial Extension (Shindyalov, Bourne, 1998)

• SCOP - Structure Classification of Proteins (Murzin et al., 1995)

Data and algorithms used:


PDB

Selection of DNA-binding protein chains by analyzing DNA-protein contacts

Parsing of DNA-binding protein chains into domains using PDP

Selection of DNA-binding protein domains by analyzing DNA-protein contacts

All-against-all structural alignment of DNA-binding protein domains using CE

Selection of representative (non-redundant) set of DNA-binding protein domains

Calculating classification of DNA-binding protein domains

Building representative set of domains:


• Rmsd, root mean squared deviation between two aligned and compared protein domains > 2.0 Å;

• Z-score, statistical score obtained from CE is < 4.5;

• Rnar, ratio of the number of aligned residues to the smallest domain length < 90%;

Note: sequence identity in the alignment < 90%;

Parameters measuring structural similarity:


* *** ** ** **** A YKLAAVGTE--FCCILLNIVKLPDGT | | || | || B ASQL—AVREERAFA---GGKAPDQQD ** * * ** ****

(1)Parameters measuring structural similarity: Rmsd, Z-score, Rnar;

(2) Parameter measuring the match between DNA-protein contact patterns, Rmat;

A and B - DNA-protein domain complexes;

Rmat = min{RmatA, RmatB}

RmatX - ratio of the number of matched residues to the total number of residues involved in contacts with DNA in the DNA-protein complex X.


contij

distijij SSS

Realignment using scoring function taking into account structural similarity between two protein domains and protein-

DNA contact pattern

otherwise ,

if ,

2

211

C

CdCdCS ijijdistij

Structure similarity term:

Bj

Ai

contij KKCS 3

Protein-DNA contact pattern term:

where

otherwise ,0

DNAth contact wiin involved is residueprotein if ,1XmK

m – denotes protein residue, X – protein-DNA complex; C3 is a scaling constant;


• If Rmsd > 5.0 Å or Rnar < 70% or Z-score < 3.5, then domains are not considered as similar;

• If Rmsd 3.0 Å and Rnar 80%, then domains are considered as similar;

• If Rmat Rmatthreshold and either: 3.0 Å < Rmsd 5.0 Å and Rnar 70% or 70% Rnar < 80% and Rmsd 5.0 Å, then domains are considered similar;


Comparison of the classification for all 338 DNA-binding domain representatives with SCOP at various threshold parameters


Final classification of DNA-binding protein domains (fragment):


Rmsd

Rnar

53

70

80

Similar if Rmat<80

Not similar

Not similar

Similar


http://spdc.sdsc.edu

SPDC – Structural Protein Domain Сlassification


Why do we need the ontology?

(what’s published in scientific journals is de facto not reaching the community).

•Lack of adequate means for information storage and exchange between:

- scientists, - computers, - scientists and computers

•Qualitative data explosion (new experimental methods and new kinds of data appear, e.g. micro-arrays, interfering-RNA).

• Quantitative data explosion (e.g. exponential growth of sequence data - doubling every 7 month)


GO can serve as a language which can be easily read by both humans and computers. By using GO we ultimately learn to talk in one universal language.

The goal of this work is to further realize the potential of GO.


What is GO?

• Controlled dictionaries for:

- Molecular Function

- Biological Process

- Cellular Component

• Acyclic graph

• “is-a”, “part-of” (“has-a”) relationships

CAR

BMW Wheel

“is-a” “part-of”“has-a”


The GO Annotation (GOA) resources providing annotation of gene products with GO terms

The IEA code, Inferred from Electronic Annotation, this means no human involvement in the assignment


Extending GO annotation of PDB chains using structural and sequence similarity

34,698 protein chains were taken from the PDB of February, 2003 with the exception of theoretical models, short chains (less than 30 Cα atoms), and chains which don’t form domains (no domains detected by PDP algorithm).

GO annotation has been assigned for 25,835 PDB protein chains by EBI from 34,698.

Rseq, sequence identity calculated for the structurally aligned residues.

Rnar, ratio of the number of aligned residues to the length of the shortest polypeptide, it measures overlap between aligned polypeptides.

Z-score, statistically founded score, it characterizes significance of the alignment.

Rmsd, root mean squared deviation between two structurally aligned polypeptides, it characterizes distances between C , C and mainchain O atoms of aligned residues.


For two polypeptides A and B with all calculated parameter values (Rmsd, Z-score, Rnar, Rseq) and given threshold values (Rmsdthreshold, Z-scorethreshold, Rnarthreshold, Rseqthreshold) we define:

SSCAB=(Rmsd<Rmsdthreshold ) (Z-score>Z-scorethreshold)

(Rnar>Rnarthreshold) (Rseq>Rseqthreshold)

- denotes logical AND. SSCAB can only be ascribed two values: true or false. If SSCAB is true, then A and B are similar. If SSCAB is false, then A and B are not similar.

The chains were clustered such that for every two chains in each cluster the above condition (in red) holds true.


Specificity Criteria:For the clusters where GO terms were available for at least two chains we define:

“positive cluster” - where all chains have the same GO terms;

“negative cluster” - where chains have different GO terms (more specific definitions for three criteria will be given further);

TP (true positives) - a number of chains with GO terms in the positive clusters;

FP (false positives) - a number of chains with GO terms in the positive clusters;

ppv (positive predictive value) or specificity is the following ratio - TP/(TP+FP)


Further detailing of specificity (Specificity-4) should involve the semantic distance (e.g. Lord et al, 2003) between terms in judging cluster to be “positive”.

Specificity Criteria (cont.):

Specificity-3 (less rigorous than specificity-2) - “positive” cluster must have a common set of terms {t1,..tL} for all N chains within the cluster:

{t1,..tN} {ti1,..tik(i)}, i=1,…N; {t1,..tN}.

Specificity-2 (less rigorous than specificity-1) - “positive” cluster must have for every pair of chains (i, j) with different number of GO terms the following: for the chain with a smaller number of terms – all terms must be present amongst the terms for a chain with a larger number of GO terms: {ti1,..tik(i)} {tj1,..tjk(j)}, if k(i) k(j); i{1,…N}, j{1,…N}; {t1,..tN}.

Specificity-1 (the most rigorous) - “positive” cluster must have every pair of chains (i, j) with the same set of GO terms: tin = tjn , n=1,…k(i), k(i)=k(j), for (i, j), i{1,…N}, j{1,…N}.

{ti1,..tik(i)} - is a set of GO terms k(i) for i-th chain.

Each specificity is defined for a clusters with at least two annotated chains.


Clusterization of PDB chains and the accuracy of GO annotation at different threshold values of structural

similarity parameters.

Threshold values Clusters and chains Performance of GO annotation

Rseq

Rnar

Rmsd

Å

Z-score

Clusters and single-

tons

Clusters

Clusters with at least two chains

with GO

Chains in clusters with at least two chains with

GO

FP chains

(specificit

y-1)

Specificity-1,

%

FP chains

specificity-2)

Specificity-2,

%

FP chains

(specificity-3)

Specificity-3,

%

Cove rage,

%

Newly annotated

chains

New chain-GO term

associations

New added chain-GO

term associations

Chains with

added GO terms

0% 90% 2.0 4.5 9940 2919 2435 20799 8255 60.3 4463 78.5 158 99.2 60.9 5397 170372 3531 1953

25% 90% 2.0 4.5 9959 2923 2440 20797 8091 61.1 4410 78.8 155 99.3 60.6 5367 169864 3386 1893

35% 90% 2.0 4.5 10069 2995 2490 20768 7534 63.7 3972 80.9 113 99.5 59.9 5310 167254 3281 1841

50% 90% 2.0 4.5 10368 3180 2643 20719 5618 72.9 2686 87.0 64 99.7 57.4 5089 160523 2937 1376

70% 90% 2.0 4.5 10867 3515 2886 20606 3759 81.8 1137 94.5 42 99.8 52.3 4639 153801 2062 1015

90% 90% 2.0 4.5 11478 3834 3033 20493 1536 92.5 517 97.5 29 99.8 45.2 4002 147162 861 359

0% 70% 5.0 3.8 3401 1962 1687 24805 17730 28.5 15163 38.9 5604 77.4 83.8 7426 266757 2858 1318

25% 70% 5.0 3.8 4261 2533 2142 24610 13683 44.4 9608 61.0 734 97.0 78.5 6956 229366 3961 2153

35% 70% 5.0 3.8 4778 2885 2431 24507 11606 52.6 7299 70.2 328 98.7 75.9 6728 215972 3962 2285

50% 70% 5.0 3.8 5455 3330 2787 24357 8200 66.3 4063 83.3 85 99.7 71.7 6351 197765 4164 1960

70% 70% 5.0 3.8 6199 3819 3152 24196 5042 79.2 1567 93.5 58 99.8 64.9 5749 187239 3187 1440

90% 70% 5.0 3.8 7031 4269 3359 23984 2235 90.7 734 97.0 29 99.9 56.5 5007 178146 1566 588


PDB chains (34,698)

5,007

588

25,247

3,856

newly annotated chains (this work)

chains annotated by EBI with added new GO terms (this work)annotated by EBI

not annotated anywhere

"GO term - chain" associations (335,322)

178,675

1,661

154,986

new associations for newly annotated chains (this work)

new associations added for chains annotated by EBI (this work)

annotated by EBI

New added "GO term - chain" associations for previously annotated chains

(1,661)

Process52%

Cellular component

6%

Function42%

New "GO term - chain" associations (178,675)

Process33%

Cellular component

14%

Function53%

Assignment of GO annotation with structural similarity parameters (Rmsd 5.0Å, Z-score 3.8, Rnar 70%, Rseq 90%).

Red dot denotes newly annotated chains, red arrow denotes new “GO term – chain” associations assigned for newly annotated chains. Purple line denotes new “GO term – chain” associations assigned for chains previously annotated (by EBI). Black arrow denotes existing “GO term – chain” associations assigned by EBI.


The example of “negative” cluster by the definition of specificity-1 and “positive” cluster by the definitions of specificity-2 and specificity-3. Seven GO terms could be assigned to chains 1h9dA, 1h9dC (Rmsd 5.0Å, Z-score 3.8, Rnar 70%, Rseq 90%).

1e50A (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1e50C (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1e50E (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1e50G (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1e50Q (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1e50R (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1cmoA (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1co1A (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1ljmA (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1ljmB (7) 3677, 3700, 5524, 5634, 6355, 7275, 8151,1hjbC (4) 3677, 5524, 5634, 6355,1hjbF (4) 3677, 5524, 5634, 6355,1hjcA (4) 3677, 5524, 5634, 6355,1hjcD (4) 3677, 5524, 5634, 6355,1io4C (4) 3677, 5524, 5634, 6355,1eanA (4) 3677, 5524, 5634, 6355,1eaoA (4) 3677, 5524, 5634, 6355,1eaoB (4) 3677, 5524, 5634, 6355,1eaqA (4) 3677, 5524, 5634, 6355,1eaqB (4) 3677, 5524, 5634, 6355,1h9dA no go terms1h9dC no go terms

3677 (F) - DNA binding 3700 (F) - transcription factor activity 5524 (F) - ATP binding 5634 (C) - nucleus 6355 (P) - regulation of transcription, DNA-dependent 7275 (P) - development 8151 (P) - cell growth and/or maintenance

The cluster of the same proteins which is Runt-related transcription factor 1 (synonyms: core-binding factor alfa subunit, acute myeloid leukemia 1 protein etc.).


The example of “positive” cluster by definition of specificity-1.Phospholipase A2.

1cl5A (5) 4623, 5509, 15070, 16042, 16787,1cl5B (5) 4623, 5509, 15070, 16042, 16787,1fb2A (5) 4623, 5509, 15070, 16042, 16787,1fb2B (5) 4623, 5509, 15070, 16042, 16787,1fv0A (5) 4623, 5509, 15070, 16042, 16787,1fv0B (5) 4623, 5509, 15070, 16042, 16787,1jq8A (5) 4623, 5509, 15070, 16042, 16787,1jq8B (5) 4623, 5509, 15070, 16042, 16787,1jq9A (5) 4623, 5509, 15070, 16042, 16787,1jq9B (5) 4623, 5509, 15070, 16042, 16787,1kpmB no go terms

4623 (F) - phospholipase A2 activity 5509 (F) - calcium ion binding 15070 (F) - toxin activity 16042 (P) - lipid catabolism 16787 (F) - hydrolase activity


Only four “negative” clusters have occurred by definition of specificity-3:

An example of missed GO terms for 2mtaC and other chains of Cytochrome c-L (cytochrome c551i)

2mtaC (3) 5489, 6118, 159451mg2D (2) 16021, 16032, 1mg2H (2) 16021, 16032,1mg2L (2) 16021, 16032,1mg2P (2) 16021, 16032,1mg3D (2) 16021, 16032,1mg3H (2) 16021, 16032,1mg3L (2) 16021, 16032,1mg3P (2) 16021, 16032,

5489 (F) - electron transporter activity 6118 (P) - electron transport 15945 (P) - methanol metabolism 16021 (C) - integral to membrane 16032 (P) - viral life cycle


Evolution of complex systems:

Computers: complexity doubles in every 18 month per $$$ (Moore’s Law)

Human Brain: very slow (complexity doubles in ~100,000 years)

System Short Term Storage

Long Term Storage

Speed Cost

PC cluster (256 units)

65GB 5 TB 256 GFLOP $130K

Human Brain (Average)

57 TB 1137 TB 4.4 TFLOP $130K

Complexity = Speed x MemoryComputer = 5TB x 256 GFLOP = 1024 memory FLOPs

Brain = 1137TB x 4.4 TFLOP = 5x1027 memory FLOPs

Brain/Computer=5x103 or 3.7 log units

Moore’s Law: 3.5 years/log unit

Based on (Ramsey, 1997)

Human brain capacity for computers will be reached: 2000+3.7x3.5=2013


The accuracy of predicting the future for the next 2 years equals 10%


Credits:

Julia Ponomarenko (she did #2 and #3)

Phil Bourne (discussions, conceptualizations, logistics)

Lei Xie (PDB statistics)

NIH Grant GM63208

NSF Grants DBI 9808706, DBI 0111710

Gift from Ceres Inc.

functional annotation of proteins with known structure by structure and sequence similarity,

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protein function

sequence similarity

known structure

binding protein domains

ce protein structure

classification of dna

different protein residues

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