Proteins Secondary Structure Predictions Structural Bioinformatics Proteins Secondary Structure Predictions

Structure Prediction Motivation Better understand protein function Broaden homology Detect similar function where sequence differs (only ~50% remote homologies can be detected based on sequence) Explain disease Explain the effect of mutations Design drugs

Myoglobin – the first high resolution protein structure Solved in 1958 by Max Perutz John Kendrew of Cambridge University. Won the 1962 and Nobel Prize in Chemistry. “ Perhaps the most remarkable features of the molecule are its complexity and its lack of symmetry. The arrangement seems to be almost totally lacking in the kind of regularities which one instinctively anticipates.”

(some times impossible) MERFGYTRAANCEAP…. 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

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

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

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 H-bonds 3.6 residues 5.6 Å

Beta Strand: Pauling and Corey (1951) Different polypeptide chains run alongside each other and are linked together by hydrogen bonds. Each section is called β -strand, and consists of 5-10 amino acids. β -strand

3.47Å 4.6Å Beta Sheet The strands become adjacent to each other, forming beta-sheet. 3.25Å 4.6Å Antiparallel Parallel

Loops Connect the secondary structure elements. Have various length and shapes. Located at the surface of the folded protein and therefore may have important role in biological recognition processes.

Three dimensional Tertiary Structure Describes the packing of alpha-helices, beta-sheets and random coils with respect to each other on the level of one whole polypeptide chain

Secondary Tertiary RBP Globin

How do the (secondary and tertiary) structures relate to the primary protein sequence??

SEQUENCE STRUCTURE -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.

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

The Globin Family

Different sequences can result in similar structures 1ecd 2hhd

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

The Globin Family

Why is Proline 36 conserved in all the globin family ?

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

How are remote homologs related in terms of their structure? retinol-binding protein odorant-binding apolipoprotein D RBD b-lactoglobulin

PSI-BLAST alignment of RBP and b-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 114 + I A +S+ E G + K V + ++ +PAK +++++ + Sbjct: 60 NCIQANYSLMENGNIEVLNKELSPDGTMNQVKGE--AKQSNVSEPAKLEVQFFPL----- 112 Query: 115 KGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEA 164 +WI+ TDY+ YA+ YSC + ++ R+P LPPE Sbjct: 113 MPPAPYWILATDYENYALVYSCTTFFWL--FHVDFFWILGRNPY-LPPET 159

The Retinol Binding Protein b-lactoglobulin

Structure Prediction: Motivation Hundreds of thousands of gene sequences translated to proteins (genbanbk, SW, PIR) Only about ~50000 solved protein structures Experimental methods are time consuming and not always possible Goal: Predict protein structure based on sequence information

Prediction Approaches Tow stage 1. Primary (sequence) to secondary structure 2. Secondary to tertiary One stage - Primary to tertiary structure

According to the most simplified model: In a first step, the secondary structure is predicted based on the sequence. The secondary structure elements are then arranged to produce the tertiary structure, i.e. the structure of a protein chain. For molecules which are composed of different subunits, the protein chains are arranged to form the quaternary structure.

Secondary Structure Prediction Given a primary sequence ADSGHYRFASGFTYKKMNCTEAA what secondary structure will it adopt ?

Secondary Structure Prediction Methods Chou-Fasman / GOR Method Based on amino acid frequencies Machine learning methods PHDsec and PSIpred HMM (Hidden Markov Model)

Chou and Fasman (1974) Success rate of 50% Name P(a) P(b) P(turn) Alanine 142 83 66 Arginine 98 93 95 Aspartic Acid 101 54 146 Asparagine 67 89 156 Cysteine 70 119 119 Glutamic Acid 151 037 74 Glutamine 111 110 98 Glycine 57 75 156 Histidine 100 87 95 Isoleucine 108 160 47 Leucine 121 130 59 Lysine 114 74 101 Methionine 145 105 60 Phenylalanine 113 138 60 Proline 57 55 152 Serine 77 75 143 Threonine 83 119 96 Tryptophan 108 137 96 Tyrosine 69 147 114 Valine 106 170 50 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%

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 TGTAGPOLKCHIQWMLPLKK

Improvements since 1980’s Adding information from conservation in MSA Smarter algorithms (e.g. Machine learning, HMM). Success -> 75%-80%

Machine learning approach for predicting Secondary Structure (PHD, PSIpred) Query SwissProt Step 1: Generating a multiple sequence alignment Query Subject Subject Subject Subject

Step 2: Additional sequences are added using a profile. We end up with a MSA which represents the protein family. Query seed MSA Query Subject Subject Subject Subject

Step 3: The sequence profile of the protein family is compared (by machine learning methods) to sequences with known secondary structure. Query seed Machine Learning Approach MSA Known structures Query Subject Subject Subject Subject

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

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

The above table enables us to calculate the probability of assigning secondary structure to a protein Example TGQ HHH p = 0.45 x 0.041 x 0.8 x 0.028 x 0.8x 0.0635 = 0.0020995

Secondary structure prediction AGADIR - An algorithm to predict the helical content of peptides APSSP - Advanced Protein Secondary Structure Prediction Server GOR - Garnier et al, 1996 HNN - Hierarchical Neural Network method (Guermeur, 1997) Jpred - A consensus method for protein secondary structure prediction at University of Dundee JUFO - Protein secondary structure prediction from sequence (neural network) nnPredict - University of California at San Francisco (UCSF) PredictProtein - PHDsec, PHDacc, PHDhtm, PHDtopology, PHDthreader, MaxHom, EvalSec from Columbia University Prof - Cascaded Multiple Classifiers for Secondary Structure Prediction PSA - BioMolecular Engineering Research Center (BMERC) / Boston PSIpred - Various protein structure prediction methods at Brunel University SOPMA - Geourjon and Delיage, 1995 SSpro - Secondary structure prediction using bidirectional recurrent neural networks at University of California DLP - Domain linker prediction at RIKEN