CSCI4969-6969 Assign2

Protein Secondary Structure Prediction

Due date: Mon 24th Feb, before midnight

In this assignment, we will use the word embeddings for ngrams (from assign1) to predict the secondary structure for each position in a protein sequence.

The (training dataset) comprises 5365 protein sequences of length at most 700, along with the secondary structure (SS) label at each residue position. Each line in the file contains a protein sequence, followed by a space, followed by the label sequence (there is a one-to-one correspondence between an amino acid and the SS label).

Likewise the (test dataset) comprises 514 sequences, along with the true labels, in the format. Obviously, during testing, you cannot look at the labels, but you can use the labels to assess how well your model performs.

The data is from the paper (Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction). The training sequences are from the cullPDB server, and the testing are from the harder cb513 dataset.

The SS labels belong to an alphabet of size 8, denoting 8 different types of secondary structures, namely: 'L', 'B', 'E', 'G', 'I', 'H', 'S', 'T'. There correspond to 3-helix(G), 4-helix (H), 5-helix (I), residue in isolated beta-bridge (B), extended strand in beta-ladder (E), H-bonded turn (T), bend (S), and loop (L).

Your task is the implement approach IV, as described in That is, train the word2vec model with ngram size $n$ and embedding dimensionality $d$. Store the embeddings in a file as done in assign1. Next, define a context size $w$, and for each position $i$ in the input sequence extract the vector embedding for the ngram centered at position $i$. Next extract the embeddings for the $pm w$ ngrams surrounding the center ngram as the context embeddings.

From the center word embeddings, say $v_i$ and the context word embeddings $v_j$ for \(j \in i \pm w\), create two types of representations: 1) add or average them to obtain a $d$ dimensional vector which will be used as input to an MLP that predicts the 8 types of labels. 2) concatenate the vectors to obtain a \(d \times (2w+1)\) dimensional input vector for an MLP.

Test various combinations of ngrams including $n=1,3,5$, and context windows $w=1,2,3,...$. Report the best prediction accuracy on the test set.

If you are feeling ambitious, then also include a comparison with an RNN or LSTM based model.


Submit your code via submitty as a python script or notebook, named or assign2.ipynb.

You should include the various prediction accuracy values on the test data with various values of the parameters, and your best results.

Update: Everyone should also evaluate the classification accuracy on the pretrained 100d embeddings from the ProtVec paper. This will allow you to compare your embeddings vs those from the paper.

You can download the file protVec_100d_3grams.csv from the data site: