CATH and SCOP

Topic 8Chapters 17 & 18, Gu and Bourne “ Structural Bioinformatics”

SCOP vs. CATH

The basic classification unit is domain. -- A distinct structural unit and may fold as an independent, compact unit. -- Considered as the basic unit of protein folding, function and evolution. -- Domain partition, either manual or automatic, is not trivial.

CATH is semi-automatic SCOP is mainly manual (human expertise)

Similarity level

high

relationship

clear

lowclass

fold

superfamilyfamily

Protein domains

Pyruvate kinase Elongation faction EF-Tu

SCOP vs. CATH

“CATH – A hierarchic classification of protein structure domains” Oregngo et al., Structure, 5: 1093-1108, 1997.

Protein Structure Classification

Why bother? Provides structural and evolutionary relationship Provides current fold space Assists protein structure prediction (details later)

Two popular protein classification databases: SCOP (Structural Classification Of Proteins ) http://scop.mrc-lmb.cam.ac.uk/scop/ Latest release: v1.75 (June 2009)

110,800 domains Murzin et al. J. Mol. Biol. 247, 536-540, 1995

CATH: Class (C), Architecture (A), Topology (T) and Homologous superfamily (H). http://www.cathdb.info/ Recent release: v3.5 (Sept 2011) 173,536 domains Orengo et al. Structure, 5, 1093-1108, 1997

Hierarchical Structure Classification

“CATH – A hierarchic classification of protein structure domains” Oregngo et al., Structure, 5: 1093-1108, 1997.

Hierarchical Structure Classification

SCOP• Class ()• Fold (TIM beta/alpha-barrel)• Superfamily (Triosephosphate isomerase)• Family (Triosephosphate isomerase)

CATH• Class ()• Architecture (-barrel)• Topology (TIM barrel)• Homologous Superfamily (Aldolase class 1)• Sequence Family (Isomerase)

Hierarchical Structure Classification

SCOP C F S F

CATH C A T H S

Conservation:

2o str

uctu

re c

onte

nt

High

-leve

l str

uctu

re si

mila

rity

Typi

cally

ort

holo

gs

Low

er-le

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truc

ture

sim

ilarit

y

This seems trivial.Why are we wasting valuable class time discussing it?

• While a hierarchical description of protein structure is conceptually straightforward, as you will see, automating it is not. Moreover, the domain boundary problem is actually quite difficult.

• Also, this discussion is nice in the sense that it ties together a lot of different bioinformatics concepts into one unified effort. Some of these concepts are structural; however, many are not.

Flavodoxin (toplogy = Rossman fold)

Domain 1 of -lactamase – Same architecture, but different topology

Topology vs. Architecture

Caution: Due to how secondary structures are interconnected, varying topologies can converge on the same overall architecture.

An even trickier example

“CATH – A hierarchic classification of protein structure domains” Oregngo et al., Structure, 5: 1093-1108, 1997.

The CATH Classification Strategy

(1.) Close relatives are identified via sequence comparisons.

(2.) Sequence profiles and structure comparison protocols are used to detect more distant homologies.

(3.) Structures unclassified at this stage are then examined using both automatic and manual procedures to determine domain boundaries.

(4.) Unclassified domain structures are recomputed using the methods employed in steps 2 and 3.

(5.) Finally, structure(s) remaining unclassified are manually assigned to existing or new architectures within CATH.

The CATH Classification Strategy

“CATH – A hierarchic classification of protein structure domains” Oregngo et al., Structure, 5: 1093-1108, 1997.

Automatic procedure“If a given domain has sufficiently high sequence and structural similarity (ie. 35% sequence identity, SSAP score >= 80) with a domain that has been previously classified in CATH, the classification is automatically inherited from the other domain”.

CATH Classification-Domain Assignment

http://www.cathdb.info/

Since the classification is performed on individual domains, therefore the very first step is to assign domains (find domain boundaries)

Use both automatic and manual techniques

If it has high sequence identity (80%) and structural similarity (SSAP score >= 80) with a protein chain X that has been classified in CATH, use the boundaries of X.

Otherwise, apply several domain partition programs (CATHEDRAL, DETECTIVE (Swindells, 1995), PUU (Holm & Sander, 1994), DOMAK (Siddiqui and Barton, 1995).

Consensus assign automatically No consensus assign manually.

Domain Partition Problem

SSAP (Sequential Structure Alignment Program)

Structure Comparison Problem

The CATH Hierarchy and Classification

http://www.cathdb.info/

Class, C-level Based on the secondary structure content of the domain There are four classes:

1. mainly-alpha, 2. mainly-beta,3. alpha-beta, (a combination of / and + in SCOP)4. low secondary structure content

The CATH Hierarchy and Classification

http://www.cathdb.info/

Architecture, A-level This level describes the overall shape of the domain structure as determined by the orientations of the secondary structures but ignores the connectivity between the secondary structures.

It is assigned manually using a simple description of the secondary structure arrangement .

The CATH Hierarchy and Classification

http://www.cathdb.info/

Topology (Fold family), T-level Members in the fold family share the same overall shape and connectivity of the secondary structures in the domain core.

Domains in the same fold group may have different structural decorations to the common core.

The CATH Hierarchy and Classification

Topology (Fold family), T-level. 3.40.30.xx

The CATH Hierarchy and Classification

Homologous Superfamily, H-level Protein domains in each H-group are thought to share a common ancestor and are homologous.

Similarities are identified either by high sequence identity or structure similarity using SSAP. Domains are classified in the same homologous superfamily if they satisfy one of the following criteria:

1. Sequence identity >= 35%, overlap >= 60% of larger structure equivalent to smaller.

2. SSAP score >= 80.0, sequence identity >= 20%, 60% of larger structure equivalent to smaller.

3. SSAP score >= 70.0, 60% of larger structure equivalent to smaller, and domains which have related functions, which is informed by the literature and Pfam protein family database (Bateman et al., 2004).

4. Significant similarity from HMM-sequence searches and HMM-HMM comparisons using SAM (Hughey &Krogh, 1996), HMMER (http://hmmer.wustl.edu) and PRC (http://supfam.org/PRC).

The CATH Hierarchy and Classification

Sequence Family Levels: (S,O,L,I,D) Domains within each H-level are subclustered into sequence families using multi-linkage clustering at the following levels:

***The D-level is assigned as a counter within each S100 family to ensure that each domain in CATH has a unique CATHSOLID classification

http://www.cathdb.info/

Not completely manual: SCOP Workflow

Andreeva, et al, NAR, 2008

SCOP: Structural Classification of Proteins

Family: Clear evolutionarily relationship (1) pairwise residue identities between the proteins are 30% and greater. (2) Proteins with low sequence similarity but very similar functions and structures; for

example, many globins have sequence identities of only 15%.

Superfamily: Probable common evolutionary origin Proteins that have low sequence identities, but whose structural and functional features suggest that a common evolutionary origin is probable are placed together in superfamilies. For example, actin, the ATPase domain of the heat shock protein, and hexakinase together form a superfamily.

Fold: Major structural similarity (1) have same major secondary structures in same arrangement and with the same topological connections.

(2) Proteins placed together in the same fold category may not have a common evolutionary origin: the structural similarities could arise just from the physics and chemistry of proteins favoring certain packing arrangements and chain topologies.

Class: secondary structure content and organization

Murzin et al. J. Mol. Biol. 247, 536-540, 1995

SCOP Parsable Files-very useful!!

Common Folds

Immunoglobulin fold • All-β protein sandwich• Consists of 2 layers • ~7 antiparallel β-strands

arranged in two β-sheets• 28 SCOP superfamilies

Tim barrel fold • /β protein fold • Named after glycolytic

enzyme triosephosphate isomerase• Eight α-helices and eight

parallel β-strands• 33 SCOP superfamilies

Rossman fold • /β protein fold • Named after Michael

Rossman• Parallel β-strands

connected by -helices• 12 SCOP families

Common Folds

“CATH – A hierarchic classification of protein structure domains” Oregngo et al., Structure, 5: 1093-1108, 1997.

Immunoglobulin-like beta-sandwich

VL

CLVH

CH1

CH2

CH3

Antibody domains CuZn Superoxide Dismutase

Red = Rossman fold domain within the enzyme alcohol dehydrogenase

Rossman fold

TIM-Barrel fold

TIM-barrel

33 different superfamilies that share the same fold

Aldolase

Enolase

TIM-barrel

SC

OP

CAT

H

“A systematic comparison of protein structure classifications: SCOP, CATH and FSSP” Caroline Hadley and David T Jones, Structure, 7(9): 1099-1112 , 1999

SCOP vs. CATH

The number of domains into which each chain is separated in S C O P and CATH is compared. For the most part, the two classification schemes agree on the number of domains per chain (5681 of 6875 chains is 82% ∼agreement). However, in the case of chains split into two domains in CATH, almost half are considered as only one domain within S C O P.

SCOP vs. CATH

“A systematic comparison of protein structure classifications: SCOP, CATH and FSSP” Caroline Hadley and David T Jones, Structure, 7(9): 1099-1112 , 1999

SCOP: small proteinCATH: mainly

CATH ignores the presence of small β strands in the lysozyme superfamily and considers the protein mainly α

SCOP takes into account the functional and evolutionary importance of these strands, and classifies the lysozymes α/β.

The Russian doll effect

The recurrence of common motifs within many of the superfolds and major architectures gives rise to an overlapof structures in these regions of fold space.

This means that it becomes harder to distinguish between structural families for these architectures and it is perhaps more appropriate to consider a continuum of protein folds.

This is particularly apparent in the layer-based sandwich architectures of the mainly β and α−β classes. For example, within the α−β three-layer doubly wound architectures, it is possible to generate a very large family of structures.

Each new structure added to a family will be related to the last by a simple extension of one or more βαβ motifs and the structures are then embedded within each other in a ‘Russian doll’ like effect.

“CATH – A hierarchic classification of protein structure domains” Oregngo et al., Structure, 5: 1093-1108, 1997.

Meaning, the designations implied below aren’t so definitive

Structural redundancy

(563)(423)

(283)

AN ASIDE: Commonly, SCOP/CATH classifications are used to remove structural redundancy from a dataset. For example, the plots are above are from a paper that my lab published characterizing a catalytic site prediction algorithm.

Other ways of classifying proteins: EC #

The EC (enzyme classification) system creates a controlled vocabulary vis-à-vis enzyme function.

Ribose-phosphate diphosphokinase

G6P Phosphatase vs. Hexokinase

Glucose-6-Phosphate PhosphataseEC 3.1.3.9

EC 2.7.1.1

Other ways of classifying proteins: EC #

From a former QE question

Given the following information about two proteins, Protein A and B, what can you tell about the structural and functional similarity/dissimilarity of the two proteins? Comment on the evolution of the two proteins.

EC Number CATH Classification

Protein A 3.4.21.1 2.40.10.10

Protein B 3.4.21.14 3.40.50.200

From Wikipedia: Gene ontology, or GO, is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. More specifically, the project aims to:

1. Maintain and develop its controlled vocabulary of gene and gene product attributes;

2. Annotate genes and gene products, and assimilate and disseminate annotation data;

3. Provide tools for easy access to all aspects of the data provided by the project, and to enable functional interpretation of experimental data using the GO, for example via enrichment analysis.

GO is part of a larger classification effort, the Open Biomedical Ontologies (OBO).

Other ways of classifying proteins: GO

Other ways of classifying proteins: GO

Other ways of classifying proteins: KEGG

KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies