protein structure prediction

Question 1

motivation for structure prediction

Answer 1

• inform about function
• guide raitonal drug design
• mutagenesis
• solve structures from experimental data
• fundamental understanding of chemistry of protein structure

Question 2

CASP

Answer 2

• critical assessment of protein structure prediction
• blind trial to evaluate different approaches
• sequences sent to predictors prior to revealing experimental coordinates
• manual evaluation every 2 years
• combined with server-only predictions

Question 3

ab initio energy calculations

Answer 3

• original idea to describe interactions between atoms
• search for conformaiton of lowest energy
• energy minimisation methods, followed by molecular dynamics
• from first principles
• energy function needed first

Question 4

energy function

Answer 4

• potential energy of a protein in a particular conformation
• V = bond length + bond angle + bond dihedral rotation + VDW + electrostatic interactions
• molecular dynamics adds water molecules
• energy minimsation adds ad hoc terms for hydrophobicity
  
  • or works in vacuo

Question 5

energy minimisation

Answer 5

• x, y, z obtained for each atom
• calculate energy
• make small positional changes to find path to lowest energy conformation (deltaG is minimal)
• some success with small proteins

Question 6

issues with energy minimisation

Answer 6

• can get stuck in local minimum
  
  • think it is the lowest point but there is a global minimum
    
    • just can’t get there
  • solve with molecular dynamics
    
    • simulate protein as moving object
    • has momentum to overcome energy barriers
• energy ladnscape is difficult to define
  
  • unsure if you are going up or down
  • energy terms are difficult to define - calculation can be wrong

Question 7

secondary structure prediction

Answer 7

• identify local structures
  
  • alpha, beta, coil, sometimes turn
  • 3 or 4 state prediction
• determines local 3D structure to an extent
• doesn’t work with 7 residue sequences
  
  • same 7 residue sequence in different proteins can produce compeltely different structure
• algorithms look at window of ~15
  
  • long range effects involved

Question 8

secondary structure prediction

accuracy measure

Answer 8

• no of residues correctly predicted/no of residues considered
• Q3 = accuracy measure of 3 state prediction
  
  • random result with equal numbers of each state = 33%
  • in a protein dominated by helices (80:20), best random prediction would say all helical = 80%
  • typical mix of 3 states, random result = 40%

Question 9

old single sequence methods

Answer 9

• simpler
• used to derive newer methods
• mainly based on obtaining rules from counting frequencies of residues in known structures
  
  • empirical
• e.g. chou fasman

Question 10

chou-fasman

Answer 10

• numerical residue scores derived from data and ad hoc rules
• based on secondary structure propensity
• score>1 implies residue occurs in helix morefrequently than by chance
• create matrix for alpha and beta propensities of all amino acids
• pro/gly = helix breakers
• some residues are similar
• can be greater than 1 (not probability)

Question 11

helix breakers

Answer 11

• helices need H bonds between NH and CO
• pro:
  
  • side chains bends back to covalently bind NH
    
    • no H bond
• gly:
  
  • small residue
  • makes a cavity
  • packs poorly against the rest of the helix

Question 12

rules of chou-fasman

Answer 12

• helix if:
  
  • run of 4 out of 6 residues favouring a helix
  • average helix propensity > 1 and > average beta strand propensity
• extend helix until pro is found, or run of 4 residues with helix propensity <1

Question 13

stereochemical methods

Answer 13

• recognise patterns of hydrophobic residues that favour secondary structures
• empirical
  
  • enhanced by inspection of structures
• no longer used but pattern concept still important
  
  • difficult to program
• original Q3 ~ 60%
  
  • improved by ML and neural networks

Question 14

stereochemical methods

alpha vs beta

Answer 14

• alpha:
  
  • 3.6 res per turn
  • amphipathic pattern consistent with helix
  • helical wheel plot
    
    • one side hydrophobic, other hydrophilic
• beta:
  
  • can be buried
    
    • sheet with helices either side, run of hydrophobic residues
  • can be surface
    
    • stacked pair of beta sheets (Ig fold)
    • bottom sheet alternates

Question 15

artifical neural networks

Answer 15

• simulates computation of brains
• input signal and set of nodes
  
  • weight nodes so that input signal gives an output signal of alpha/beta
  • input and answer known - only need to find weights
• once weights know, new sequence output can be produced
• improved with MSAs

Question 16

use of MSAs

Answer 16

• average Q3 = 80%
  
  • nearly all alpha identified, most beta
  • short edge beta strands poorly predicted
  • errors in defining precise ends
• programs:
  
  • psipred
  • jnet neural nets

Question 17

homology modelling

Answer 17

• infer structural similarity from sequence homology
  
  • search query sequence against sequence library of known structures
• comparative/template based modelling
• most accurate structure prediction method

Question 18

process of homology modelling

Answer 18

• match query to database sequences
• use best match to predict fold
  
  • >20% identity in psi-blast
  • can use multiple sequences/structures
    
    • helps gap placement
• fit sequence and align main chain
• add and adjust side chains to create predicted 3D structure

Question 19

loop regions in homology modelling

Answer 19

• fix backbone coordinates immediately in structurally conserved regions
• model variable regions in one of 2 ways:
  
  • database search of PDB
    
    • find short region of best guess of loop and transplant
  • energy minimisation to find geometrically viable methods for 5 residue regions
• <6 residues generally well predicted
• long loops poorly predicted

Question 20

homology modelling

side chain packing

Answer 20

• specify dihedral chi angles of side chains
• build up library of allowed angles
  
  • series of rotamers
  • each has position and likelihood
• use algorithm to decide which rotaemrs fit well together
  
  • remove clashes to find best combination

Question 21

homology modelling

refinement

Answer 21

• improve predicted structure with energy minimisation
• remove bumps
• molecular mechanics to calculate energy
• CASP:
  
  • only a few gorups managed to improve models
  • often makes it worse
  • bumps drag things a long way out of position
    
    • better off to leave them sometimes

Question 22

fold recognition

Answer 22

• threading
• enhanced version of remote homolog recognition, beyond PSI-BLAST and template-based modelling
• 3D information of library as well as searching the sequence
  
  • recongising fold as well as sequence
• match sequence vs sequence with HMM as well as structure vs structure relationships
• HHSEARCH
• HHPRED

Question 23

Phyre2

Answer 23

• algorithm for fold recognition
• single domain:
  
  • HHblits gives MSA for query
  • PSIPRED predicts secondary structure
  • HMM for sequence and structure
  • carry over fixed regions from alignment
  • loop modelling
  • add side chains
• multiple domains need to be combined
  
  • add together with ab initio methods
  • final mdoel with linked domains