forward-backward algorithm for secondary structure prediction

Question

I want to use HMM (forward backward model) for protein secondary structure prediction.

Basically, a three-state model is used: States = {H=alpha helix, B=beta sheet, C=coil}

and each state has a emission probability pmf of 1-by-20 (for the 20 amino acids).

After using a "training set" of sequences on the forward backward model, the expectation maximization converges for an optimal transitions matrix (3-by-3 between the three states), and emission probability pmf for each state.

Does anyone know of a dataset (preferably very small) of sequences for which the "correct" values of the transition matrix and emission probabilities are determined. I would like to use that dataset in Excel to apply the forward backward algorithm and build my confidence to determine whether or not I can get the same result.

And then move on to something less primitive than Excel :o)

Pierre, thanks for the advice. I will do that in the meantime too. — Stick Figure
– Stick Figure, Commented May 2, 2013 at 7:24

Ben D. · Accepted Answer · 2015-04-08 21:01:47Z

0

The best way to do this is probably to produce your own simulated data from distributions you decide. Then you run your program to see if the parameter estimations converge towards your known parameters.

In your case, this will involve writing a Markov chain that changes from state to state with some known and arbitrary probability (for instance, P(Helix to Chain)=0.001) and then emits an amino acid with probability (for instance, P(methionine)=0.11). For each step, print out the state and emission. You can then watch your posterior probabilities approach the state for each site.

You can make these as arbitrary as you want, because when you run your HMM you should converge on the proper distributions.

Good luck!

answered Apr 8, 2015 at 21:01

Ben D.

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