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BINF6201/8201 Patterns in Protein Families 11-16-2009. Problems for pairwise alignment for database searches.

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slide1

BINF6201/8201

Patterns in Protein Families

11-16-2009

slide2

Problems for pairwise alignment for database searches

  • To predict the function of a new sequences, we typically identify the best hit of the sequence in a well-annotated the database such as Swiss-Prot using a pairwise alignment tool such as FASTA or BLAST, and then predict the function of the new sequence according to the annotated function of the best hit sequences.
  • However this approach may cause problems, and result in incorrect functional predictions:
  • Sequence complexity affects the search results;
  • The sites are equally weighted, so the score may not reflect the similarity of the functions;
  • The annotated function of the best hit sequence could be incorrect.
  • Low complexity regions
  • Multi-domain proteins
slide3

Motifs are more powerful for predicting protein functions

  • These problems can be partially solved by using the sequence signatures (motifs) shared by the members of a protein family.

Motif 1

Motif 2

Motif 2

Motif 4

slide4

Motifs in a protein family

Conserved catalytic domain in the caspase-like superfamily

Catalytic motif 1

Catalytic motif 2

slide5

Representation of a motif

TACGAT

TATAAT

TATAAT

GATACT

TATGAT

TATGTT

TATAGT

Consensus sequence

TATAAT

A collection of s70 binding sites in E. coli

Regular expression

[TG]A[TC][GA]XT

Frequency matrix

Position specific scoring matrix (PSSM)

slide6

Searching with regular expression (regex) of motifs

  • The regular express of a motif is constructed based on the multiple alignment of sequences of the motifs

E-x-[ED]-x-K-[IVM](2)-x-[KR]-V-[IV]-x-[QE]-M-C-x(2)-Q-Y

slide7

Search motifs using regexs

  • Examples of regex expression of motifs.
  • Each window of a sequence is compared with the regex, and the exact matches are returned as the hits.
  • More flexible than the search using the consensus sequence, but still lacks enough flexibility.
slide8

Search motifs using regexs

  • To increase the flexibility of regex searches, amino acids with similar physico-chemical properties can be allowed—permissive regexs.

E-x-[ED]-x-K-[IVM](2)-x-[KR]-V-[IV]-x-[QE]-M-C-x(2)-Q-Y

E-x-[EDQN]-x-K-[LIVM](2)-x-[KRH]-LIVM (2)-x-[DNQE]-M-C-x(2)-Q-Y

slide9

Searching with fingerprints

  • Several motifs (fingerprint) can be compiled from the alignment of the members of a protein family, and the frequency matrix can be constructed.

Multiple alignment

Frequency profile

slide10

Searching with fingerprints

  • The frequency matrix or the resulting PSSM can be considered as a specialized scoring matrix of the fingerprint, while PAM and BLOSUM are general purpose scoring matrices.

The frequency matrix after 3 iterative searches of Swiss-Prot database using the matrix of the fingerprint

The frequency matrix after 3 iterative searches of Swiss-Prot database using a PAM matrix.

slide11

Searching with fingerprints

  • Using the fingerprint (8 motifs) of the prion protein family to scan the human and chick prion protein sequences.
  • Human prion has complete match, but chick prion is still qualified as a prion protein.
slide12

Searching with blocks

  • Ungapped longer conserved sequences called blocks can be also constructed for protein families.
  • A frequency matrix can be computed for a block, and each sequence can have a different contribution to the score using different weights.
  • Highly similar sequences in the block are clustered, and are given smaller weight, while relatively distantly related one is given a higher weight.
slide13

Searching with block/profiles

  • The PSSM of a few blocks of protein family can be used for annotating members of the family.
  • In this case, gaps between blocks are considered, and gaps are also allowed in the block to make the search more flexible.
slide14

Analysis of G protein coupled receptors (GPCRs)

  • GPCRs are a very large group of proteins found in species from bacteria to mammals.

Have a very diverse spectrum of biological functions;

In vertebrates, PCRs have under gone lineage specific expansions through gene duplications, e.g., the human genome encodes more than 800 GPCR genes.

50% of marked drugs are targeted to GPCRs;

Generate a revenue of $16 billion.

http://www.aderis.com/img/art_gprotein.gif

  • GPCRs carry out their functions by converting a extracellular signal to an intracellular signal.
  • A GPCR protein binds a signal molecule in the membrane domain or extracellular domain, and binds a specific G-protein via its intracellular domain.
slide15

Classification of GPCRs

  • Phylogenetic analysis suggests that GPCR can be divided into three super-families (classes):
  • Rhodopsin-like
  • Secretin-like
  • Metabotropic glutamate receptor-like

Nature Reviews Drug Discovery1; 599-608 (2002); doi:10.1038/nrd872

slide16

Diversity of the sequences of different GPCR classes

  • The sequence similarity between different GPCR super-families can be very low, e.g., bacterial rhodopsin and bovine rhodopsin share only16% sequence identity.
slide17

The diversity of sequences of different GPCR classes

  • Although all GPCR classes possess a 7 transmembrane domain architecture, their 3-D structures could be quite different.
  • Therefore, it is still contentious whether all the extant GPCR are evolved from the same ancestor, or are arisen independently.

bacteriorhodopsin

bovine rhodopsin

slide18

Sequence similarity of members of the same GPCR class

  • On the other hand, GPCRs in the same super-family may have very similar sequences, because they are arisen through recent linage-specific gene expansions.
  • Because of their very similar sequences, it can be very challenging to predict their functions (orthologous relationship) by pairwise sequence similarity comparison.

Example: two families of GPCRs, rhodopsins and opsins, control the light sensation of animals.

A rhodopsin binds a chromophore (retinal, a vitamin A derivative), and responsible for vision in dim light.

Different opsins bind a different chromophores, and sense Red, Green and Blue lights, respectively.

Rhodopsins and opsins share 40% sequence similarity on average. However, opsins may have 98% sequence similarity.

slide19

Similarity of members of the same GPCR class

  • Pairwise sequence comparisons of rhodopsin and opsins can be misleading.

Green opsins of chick and goldfish are more similar to rhodopins,

Blue and purple opsins in gecko and chameleon are more similar to rhodopins

Canonical green and red opsins

slide20

Similarity of members of the same GPCR class

  • BLAST fails to predict the function of urotensin II receptor. According the BLAST results, we might predict it as a somatostain or a galanin receptor.
slide21

Using motifs, fingerprints and domain signatures can help predict the function of a GPCR

  • Using the InterPro database for functional annotation.

The result of a search of InterPro with the human vasopressin 1A receptor sequence (VIAR_HUMAN)

  • RINTS fingerprints PR00896 and PR00752 give the most specific annotations.
slide22

Using motifs, fingerprints and domain signatures can help predict the function of a GPCR

  • The PRINTS fingerprint of urotensin family can identify the human urotensin receptor as a member, but that of the somatostatin family cannot.
slide23

Analysis of G protein coupled receptors (GPCRs)

  • In generally, signatures shared at different hierarchical levels of the super/family predicts functions with different specificity.

Sub-family signature

Family signature

7 TM

Super-family signature