Proteins Secondary Structure Predictions Structural Bioinformatics

2 In there were 89,110 protein structures in the protein structure database. Great increase but still a magnitude lower then the total number of protein sequence databases (close to 1,000,000) Was solved in 1958 by Max Perutz John Kendrew of Cambridge University. (Won the 1962 and Nobel Prize in Chemistry ) The first high resolution structure of a protein-myoglobin

3 Predicting the three dimensional structure from sequence of a protein is very hard (some times impossible) However we can predict with relative high precision the secondary structure MERFGYTRAANCEAP…. What can we do to bridge the gap??

What do we mean by Secondary Structure ? Secondary structure are the building blocks of the protein structure: =

5 What do we mean by Secondary Structure ? Secondary structure is usually divided into three categories: Alpha helix Beta strand (sheet) Anything else – turn/loop

6 The different secondary structures are combined together to form the Tertiary Structure of the Proteins

7 RBP Globin Tertiary Secondary ? ? ?

Secondary Structure Prediction Given a primary sequence ADSGHYRFASGFTYKKMNCTEAA what secondary structure will it adopt (alpha helix, beta strand or random coil) ? 8

9 Secondary Structure Prediction Methods Statistical methods –Based on amino acid frequencies –HMM (Hidden Markov Model) Machine learning methods –SVM, Neural networks

10 Chou and Fasman (1974) Name P(a) P(b) P(turn) Alanine Arginine Aspartic Acid Asparagine Cysteine Glutamic Acid Glutamine Glycine Histidine Isoleucine Leucine Lysine Methionine Phenylalanine Proline Serine Threonine Tryptophan Tyrosine Valine The propensity of an amino acid to be part of a certain secondary structure (e.g. – Proline has a low propensity of being in an alpha helix or beta sheet  breaker) Success rate of 50% Statistical Methods for SS prediction

11 Secondary Structure Method Improvements ‘Sliding window’ approach Most alpha helices are ~12 residues long Most beta strands are ~6 residues long  Look at all windows of size 6/12  Calculate a score for each window. If >threshold  predict this is an alpha helix/beta sheet TGTAGPQLKCHIQWMLPLKK

12 Improvements since 1980’s Adding information from conservation in MSA Smarter algorithms (e.g. Machine learning, HMM).

13 HMM enables us to calculate the probability of assigning a sequence to a secondary structure TGTAGPOLKCHIQWML HHHHHHHLLLLBBBBB p = ? HMM (Hidden Markov Model) approach for predicting Secondary Structure

14 The probability of observing a residue which belongs to an α-helix followed by a residue belonging to a turn = 0.15 The probability of observing Alanine as part of a β- sheet Table built according to large database of known secondary structures α-helix followed by α-helix Beginning with an α- helix

15 Example What is the probability that the sequence TGQ will be in a helical structure?? TGQ HHH p = 0.45 x x 0.8 x x 0.8x = Success of HMM based methods-> 75%-80%

What can we learn from secondary structure predictions??

csc Mad Cow Disease PrP c to PrP sc PRP c PRP sc

18 How do the protein structure relate to the primary protein sequence??

19 -Early experiments have shown that the sequence of the protein is sufficient to determine its structure (Anfisen) - Protein structure is more conserved than protein sequence and more closely related to function. SEQUENCE

20 How (CAN) Different Amino Acid Sequence Determine Similar Protein Structure ?? Lesk and Chothia 1980

21 The Globin Family

22 Different sequences can result in similar structures 1ecd2hhd

23 We can learn about the important features which determine structure and function by comparing the sequences and structures ?

24 The Globin Family

25 Why is Proline 36 conserved in all the globin family ?

26 Where are the gaps?? The gaps in the pairwise alignment are mapped to the loop regions

27 How are remote homologs related in terms of their structure? b-lactoglobulin RBD

28 PSI-BLAST alignment of RBP and  -lactoglobulin: iteration 3 Score = 159 bits (404), Expect = 1e-38 Identities = 41/170 (24%), Positives = 69/170 (40%), Gaps = 19/170 (11%) Query: 3 WVWALLLLAAWAAAERD CRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQ 54 V L+ LA A + S V+ENFD ++ G WY + K Sbjct: 1 MVTMLMFLATLAGLFTTAKGQNFHLGKCPSPPVQENFDVKKYLGRWYEIEKIPASFE-KG 59 Query: 55 DNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQ I A +S+ E G + K V PAK Sbjct: 60 NCIQANYSLMENGNIEVLNKELSPDGTMNQVKGE--AKQSNVSEPAKLEVQFFPL Query: 115 KGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEA 164 +WI+ TDY+ YA+ YSC + ++ R+P LPPE Sbjct: 113 MPPAPYWILATDYENYALVYSCTTFFWL--FHVDFFWILGRNPY-LPPET 159

29 The Retinol Binding Proteinb-lactoglobulin

30 MERFGYTRAANCEAP…. Taken together FUNCTION

Pfam Database that contains a large collection of multiple sequence alignments of protein families (common structures) Very useful for function prediction.

The zinc-finger family (domain) Known family of Transcription Factors ZINC FINGER DOMAIN Protein sequence

Pfam Based on Profile hidden Markov Models (HMMs) which represents the protein family HMM in comparison to PSSM is a model which considers dependencies between the different columns in the matrix (different residues) and is thus much more powerful!!!!

Profile HMM (Hidden Markov Model) can accurately represent a MSA D16D17D18 D19 M16M17M18M19 I16I19I18I17 100% D 0.8 S 0.2 P 0.4 R 0.6 T 1.0 R 0.4 S 0.6 XXXX 50% D R T R D R T S S - - S S P T R D R T R D P T S D - - S D - - R Match delete insert

Extra Slides (for your interest) 35

residues 5.6 Å Alpha Helix : Pauling (1951) A consecutive stretch of 5-40 amino acids (average 10). A right-handed spiral conformation. 3.6 amino acids per turn. Stabilized by Hydrogen bonds

37 Beta Strand : Pauling and Corey (1951) > An extended polypeptide chains is called β –strand (consists of 5-10 amino acids > The chains are connected together by Hydrogen bonds to form b-sheet β -strand β -sheet

38 Loops Connect the secondary structure elements (alpha helix and beta strands). Have various length and shapes.