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Protein structure prediction. June 27, 2003 Learning objectives-Understand the basis of secondary structure prediction programs. Become familiar with the databases that hold secondary structure information. Understand neural networks and how they help to predict secondary structure.

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Protein structure prediction
Protein structure prediction

  • June 27, 2003

  • Learning objectives-Understand the basis of secondary structure prediction programs. Become familiar with the databases that hold secondary structure information. Understand neural networks and how they help to predict secondary structure.

  • Workshop-Analysis of p53 with PSIPRED and BLIMPS.

What is secondary structure
What is secondary structure?

  • Two major types:

    • Alpha Helical Regions

    • Beta Sheet Regions

  • Other classification schemes:

    • Turns

    • Transmembrane regions

    • Internal regions

    • External regions

    • Antigenic regions

  • Some prediction methods
    Some Prediction Methods

    • ab initio methods

      • Based on physical properties of aa’s and bonding patterns

    • Statistics of amino acid distributions in known structures

      • Chou-Fasman

    • Position of amino acid and distribution

      • Garnier, Osguthorpe-Robeson (GOR)

    • Neural networks

    Chou fasman rules mathews van holde ahern
    Chou-Fasman Rules (Mathews, Van Holde, Ahern)

    Amino Acid -Helix -Sheet Turn

    Ala 1.29 0.90 0.78

    Cys 1.11 0.74 0.80

    Leu 1.30 1.02 0.59

    Met 1.47 0.97 0.39

    Glu 1.44 0.75 1.00

    Gln 1.27 0.80 0.97

    His 1.22 1.08 0.69

    Lys 1.23 0.77 0.96

    Val 0.91 1.49 0.47

    Ile 0.97 1.45 0.51

    Phe 1.07 1.32 0.58

    Tyr 0.72 1.25 1.05

    Trp 0.99 1.14 0.75

    Thr 0.82 1.21 1.03

    Gly 0.56 0.92 1.64

    Ser 0.82 0.95 1.33

    Asp 1.04 0.72 1.41

    Asn 0.90 0.76 1.23

    Pro 0.52 0.64 1.91

    Arg 0.96 0.99 0.88







    Chou fasman

    • First widely used procedure

    • If propensity in a window of six residues (for a helix) is above a certain threshold the helix is chosen as secondary structure.

    • If propensity in a window of five residues (for a beta strand) is above a certain threshold then beta strand is chosen.

    • The segment is extended until the average propensity in a 4 residue window falls below a value.

    • Output-helix, strand or turn.

    Gor garnier osguthorpe robeson
    GOR (Garnier, Osguthorpe-Robeson)

    Position-dependent propensities for helix, sheet or turn is calculated for each amino acid. For each position j in the sequence, eight residues on either side of aaj is considered. It uses a PSSM

    A helix propensity table contains info. about propensity for certain residues at 17 positions when the conformation of residue j is helical. The helix propensity tables have 20 x 17 entries.

    The predicted state of aaj is calculated as the sum of the position-dependent propensities of all residues around aaj.

    Psi blast pred ict secondary structure psipred
    Psi-BLAST Predict Secondary Structure (PSIPRED)

    • Three stages:

      • 1) Generation of sequence profile

      • 2) Prediction of initial secondary structure

      • 3) Filtering of predicted structure


    • Uses multiple aligned sequences for prediction.

    • Uses training set of folds with known structure.

    • Uses a two-stage neural network to predict structure based on position specific scoring matrices generated by PSI-BLAST (Jones, 1999)

      • First network converts a window of 15 aa’s into a raw score of h,e (sheet), c (coil) or terminus

      • Second network filters the first output. For example, an output of hhhhehhhh might be converted to hhhhhhhhh.

    • Can obtain a Q3 value of 70-78% (may be the highest achievable)

    Neural networks

    • Computer neural networks are based on simulation of adaptive

    • learning in networks of real neurons.

    • Neurons connect to each other via synaptic junctions which are either

    • stimulatory or inhibitory.

    • Adaptive learning involves the formation or suppression of the right

    • combinations of stimulatory and inhibitory synapses so that a set

    • of inputs produce an appropriate output.

    Neural networks cont 1
    Neural Networks (cont. 1)

    • The computer version of the neural network involves

    • identification of a set of inputs - amino acids in the

    • sequence, which transmit through a network of

    • connections.

    • At each layer, inputs are numerically

    • weighted and the combined result passed to the next

    • layer.

    • Ultimately a final output, a decision, helix, sheet or

    • coil, is produced.

    Neural networks cont 2
    Neural Networks (cont. 2)

    90% of training set was used (known structures)

    10% was used to evaluate the performance of the neural

    network during the training session.

    Neural networks cont 3
    Neural Networks (cont. 3)

    • During the training phase, selected sets of proteins of known structure are scanned, and if the decisions are incorrect, the input weightings are adjusted by the software to produce the desired result.

    • Training runs are repeated until the success rate is maximized.

    • Careful selection of the training set is an important aspect of this technique. The set must contain as wide a range of different fold types as possible without duplications of structural types that may bias the decisions.

    Neural networks cont 4
    Neural Networks (cont. 4)

    • An additional component of the PSIPRED procedures involves sequence alignment with similar proteins.

    • The rationale is that some amino acids positions in a sequence contribute more to the final structure than others. (This has been demonstrated by systematic mutation experiments in which each consecutive position in a sequence is substituted by a spectrum of amino acids. Some positions are remarkably tolerant of substitution, while others have unique requirements.)

    • To predict secondary structure accurately, one should place little weight on the tolerant positions, which clearly contribute little to the structure, and strongly emphasize the intolerant positions.

    Provides info

    on tolerant or

    intolerant positions

    Row specifies aa position

    15 groups of 21 units

    (1 unit for each aa plus

    one specifying the end)

    Filtering network

    three outputs are helix, strand or coil

    Example of output from psipred
    Example of Output from PSIPRED



    Conf: Confidence (0=low, 9=high)

    Pred: Predicted secondary structure (H=helix, E=strand, C=coil)

    AA: Target sequence

    Conf: 923788850068899998538983213555268822788714786424388875156215



    10 20 30 40 50 60

    3d structure prediction threading
    3D structure prediction-Threading

    Threading, is a mechanism to address the alignment of two sequences that have <30% identity and are typically considered non-homologous. Essentially, one fits—or threads—the unknown sequence onto the known structure and evaluates the resulting structure’s fitness using environment- or knowledge-based potentials.

    Recognizing motifs in proteins
    Recognizing motifs in proteins.

    • PROSITE is a database of protein families and domains.

    • Most proteins can be grouped, on the basis of similarities in their sequences, into a limited number of families.

    • Proteins or protein domains belonging to a particular family generally share functional attributes and are derived from a common ancestor.

    Prosite database
    PROSITE Database

    • Contains 1087 different proteins and more than 1400 different patterns/motifs or signatures.

    • A “signature” of a protein allows one to place a protein within a specific function based on structure and/or function.

    • An example of an entry in PROSITE is:

    How are the profiles constructed in the first place


    This pattern is translated as: Ala, Thr, His, [Asp or Glu], any,

    Val, any, any, any, any, any but Glu or Asp

    How are the profiles constructed in the first place?

    Sequences are aligned manually by

    expert in field. Then a profile is





    Example of a prosite record
    Example of a PROSITE record


    PA C-x-H-x-[LIVMFY]-C-x(2)-C-[LIVMYA]

    Prosite database cont 1
    PROSITE Database Cont. 1

    • Families of proteins have a similar function:

      Enzyme activity

      Post-translational modification

      Domains-Ca2+ binding domain

      DNA/RNA associated protein-Zn Finger

      Transport proteins-Albumin, transferrin

      Structural proteins-Fibronectin, collagen


      Peptide hormones

    Prosite database cont 2
    PROSITE Database Cont. 2

    • FindProfile is a program that searches the Prosite database. It uses dynamic programming to determine optimal alignments. If the alignment produces a high score, then the match is given.

    • If a “hit” is obtained the program gives an output that shows the region of the query that contains the pattern and a reference to the 3-D structure database if available.

    Other algorithms that search for protein patterns
    Other algorithms that search for protein patterns.

    • BLIMPs-A program that uses a query sequence to search the BLOCKs database. (written by Bill Alford)

    • BLOCKs- database of multiply aligned ungapped segments corresponding to the most highly conserved regions of proteins.

    • The blocks that comprise the BLOCKs Database are made automatically by searching for the most highly conserved regions in groups of proteins documented in the Prosite Database.

    • These blocks are then calibrated against the SWISS-PROT database to determine such a sequence would occur by chance.

    Example of entry in blocks database
    Example of entry in BLOCKS database

    Median of

    standardized scores

    for true positives

    Min and max dist

    to next block

    Family description

    ID p99.1.2414; BLOCK

    AC BP02414A; distance from previous block=(29,215)


    BL LCC; width=27; seqs=8; 99.5%=1080; strength=1292









    Sequence weight (higher number

    is more distant)

    Start position of the sequence segment

    How does blimps search the blocks database
    How does BLIMPS search the BLOCKS database?

    • It transforms each block into a position specific scoring matrix (PSSM).

    • Each PSSM column corresponds to a block position and contains values based on frequency of occurrence at that position.

    • A comparison is made between the query sequence and the BLOCK by sliding the PSSM over the query.

    • For every alignment each sequence position receives a score.

    • This sliding window procedure is repeated for all BLOCKS in the database.

    Example of a pattern search using blimps
    Example of a pattern search using BLIMPS

    Note that any score less than 1000 may be due to chance. The score above 1000 is

    a score that is better than 95.5% of the true negatives.

    Helical wheel
    Helical Wheel

    If you can predict an alpha helix it is sometimes useful

    to be able to tell if the helix is amphipathic. This would indicate

    whether one face of the helix faces the solvent or perhaps another

    protein. They have been particularly useful in predicting a

    “super-secondary” structure known as coiled coils.

    The helical wheel is based on the ideal alpha helix placing an amino acid every 100º around the circumference of the helix cylinder

    Coiled coil predictors
    Coiled-coil predictors

    The alpha-helical coiled-coil structure has a strong signature

    heptad pattern abcdefg where a and d are typically non

    polar (leucine rich) and e and g are often charged. This makes

    scoring from a sequence scale plot relatively easy.

    3d structure data
    3D structure data

    • The largest 3D structure database is the Protein Database

      • It contains over 15,000 records

      • Each record contains 3D coordinates for macromolecules

      • 80% of the records were obtained from X-ray diffraction studies, 16% from NMR and the rest from other methods and theoretical calculations

    Part of a record from the PDB

    ATOM 1 N ARG A 14 22.451 98.825 31.990 1.00 88.84 N

    ATOM 2 CA ARG A 14 21.713 100.102 31.828 1.00 90.39 C

    ATOM 3 C ARG A 14 22.583 101.018 30.979 1.00 89.86 C

    ATOM 4 O ARG A 14 22.105 101.989 30.391 1.00 89.82 O

    ATOM 5 CB ARG A 14 21.424 100.704 33.208 1.00 93.23 C

    ATOM 6 CG ARG A 14 20.465 101.880 33.215 1.00 95.72 C

    ATOM 7 CD ARG A 14 20.008 102.147 34.637 1.00 98.10 C

    ATOM 8 NE ARG A 14 18.999 103.196 34.718 1.00100.30 N

    ATOM 9 CZ ARG A 14 18.344 103.507 35.833 1.00100.29 C

    ATOM 10 NH1 ARG A 14 18.580 102.835 36.952 1.00 99.51 N

    ATOM 11 NH2 ARG A 14 17.441 104.479 35.827 1.00100.79 N