Prediction to Protein Structure Fall 2005 CSC 487/687 Computing for Bioinformatics

Psi-BLAST Predict Secondary Structure (PSIPRED) Three stages: 1) Generation of sequence profile 2) Prediction of initial secondary structure 3) Filtering of predicted structure

PSIPRED 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 Q 3 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) 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) 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) 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) 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 less weight on the tolerant positions, which clearly contribute little to the structure One must also put more weight on the intolerant positions.

15 groups of 21 units (1 unit for each aa plus one specifying the end) Row specifies aa position three outputs are helix, strand or coil Filtering network Provides info on tolerant or intolerant positions

Example of Output from PSIPRED

Workshop

3D structure data The largest 3D structure database is the Protein Database It contains over 33,000 records Each record contains 3D coordinates for macromolecules 80% of the records were obtained from X-ray diffraction studies, 15% from NMR and the rest from other methods and theoretical calculations

ATOM 1 N ARG A N ATOM 2 CA ARG A C ATOM 3 C ARG A C ATOM 4 O ARG A O ATOM 5 CB ARG A C ATOM 6 CG ARG A C ATOM 7 CD ARG A C ATOM 8 NE ARG A N ATOM 9 CZ ARG A C ATOM 10 NH1 ARG A N ATOM 11 NH2 ARG A N Part of a record from the PDB

Steps to tertiary structure prediction Comparative protein modeling Extrapolates new structure based on related family members Steps 1. Identification of modeling templates 2. Alignment 3. Model building

Identification of modeling templates One chooses a cutoff value from FastA or BLAST search (10 -5 ) Up to ten templates can be used but the one with the highest sequence similarity to the target sequence (lowest E-value) is the reference template C  atoms of the templates are selected for superimposition. This generates a structurally corrected multiple sequence alignment

Alignment “Common core” of target sequence is threaded onto the template structure using only alpha carbons

Framework construction

Building the model Framework construction Average the position of each atom in target, based on the corresponding atoms in template. Portions of the target sequence that do not match the template are constructed from a “spare part” algorithm. Each loop is defined by its length and C  atom coordinates of the four amino acids preceding and following the loop.

Building the model Completing the backbone-a library of PDB entries is consulted to add carbonyl groups and amino groups. The 3-D coordinates come from a separate library of pentapeptide backbone fragments. These backbone fragments are fitted onto the target C alpha carbons. The central tri-peptide is averaged from each backbone atom (N,C,C(O)). Side chains are added from a table of most probable rotamers that depend on backbone conformation. Model refinement-minimization of energy