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How to use

Getting started

Clone this repository on your local machine by running:

git clone git@github.com:Bitbol-Lab/Iterative_masking.git

and move inside the root folder. One can the use directly the functions from the cloned repository (in the folder Iterative_masking) or install it with an editable install running:

pip install -e .

We recommend creating and activating a dedicated conda or virtualenv Python virtual environment.

Requirements

In order to use the functions, the following python packages are required:

  • numpy
  • scipy
  • numba
  • fastcore
  • biopython
  • esm==0.4.0
  • pytorch

It is also required to use a GPU (with cuda).

IM_MSA_Transformer: Class with different functions used to generate new MSAs with the iterative masking procedure

gen_MSAs: example function (with parser) that can be used to generate and save new sequences directly from the terminal.

Some examples to generate new MSAs from your jupyter notebook

filename = "PF00072.fasta"
filepath = "examples"
pmask = 0.1
iterations = 20

print('Tokenize')
IM_class = IM_MSA_Transformer(p_mask=pmask, filename=[filename], num=[-1], filepath=filepath)
tokenized_msa = IM_class.msa_batch_tokens
# Dictionary that maps amino acids to their token
idx_list = IM_class.idx_list
# Dictionary that maps tokens to their amino acid
aa_list = {v: k for k,v in idx_list.items()}
# Transform the tokenized MSA back into a string of amino acids
strings_msa = IM_class.untokenize_msa(tokenized_msa[:,:100,:])

It’s possible to decide which tokens to mask by modifying the parameter p_mask

import torch

# Mask all the tokens from 10 to 30 at each iteration (probability of 1) and keep the rest of the tokens unmasked
p_mask = torch.zeros(tokenized_msa.shape[-1])
p_mask[10:30] = 1.
IM_class.p_mask = p_mask
# Mask the tokens from 10 to 30 with a probability of 0.1 and keep the rest of the tokens unmasked
p_mask = torch.zeros(tokenized_msa.shape[-1])
p_mask[10:30] = 0.1
IM_class.p_mask = p_mask
# Mask all the tokens uniformly at random with a probability of 0.1
IM_class.p_mask = 0.1

Generate full MSA (mask all sequences and iterate)

  • If use_pdf=True, generate tokens by sampling from the logits at temperature T.
  • If save_all=True, then the first dimension of generated_tokens is the number of iterations.
  • If rand_perm=True, then the sequence order is shuffled at every iteration (and shuffled back at the end).
msa_tokens = tokenized_msa[:,:200]
# If use_pdf=True, generate tokens by sampling from the logits at temperature T
# If save_all=True, then the first dimension of generated_tokens is the number of iterations
# If rand_perm=True, then the sequence order is shuffled at every iteration (and shuffled back at the end)
generated_tokens = IM_class.generate_all_msa(msa_tokens, iterations, use_pdf=False, T=1, save_all=True, rand_perm=True)
generated_tokens = IM_class.print_tokens(generated_tokens)
print("Shape of the tokenized generated sequences: ", generated_tokens.shape)

Generate MSA with fixed context (mask all but context)

  • If use_pdf=True, generate tokens by sampling from the logits at temperature T.
  • If save_all=True, then the first dimension of generated_tokens is the number of iterations.
  • If rand_perm=True, then the sequence order is shuffled at every iteration (and shuffled back at the end).
  • If use_rnd_ctx=False, then the context is all_context and it’s the same at each iteration.
ancestor = tokenized_msa[:,:10]
all_context = tokenized_msa[:,10:210]

generated_tokens = IM_class.generate_with_context_msa(ancestor, iterations, use_pdf=False, T=1, all_context=all_context,
                                                      use_rnd_ctx=False, save_all=True, rand_perm=True)
generated_tokens = IM_class.print_tokens(generated_tokens)
print("Shape of the tokenized generated sequences: ", generated_tokens.shape)

Generate MSA with variable context (mask all but context)

  • If use_pdf=True, generate tokens by sampling from the logits at temperature T.
  • If save_all=True, then the first dimension of generated_tokens is the number of iterations.
  • If rand_perm=True, then the sequence order is shuffled at every iteration (and shuffled back at the end).
  • If use_rnd_ctx=True, then the context is a different sub-MSA sampled at each iteration from the full MSA (the entire MSA is given as first entry of all_context, the depth of the sub-MSA is given by the second entry of all_context).
ancestor = tokenized_msa[:,:10]
all_context = (tokenized_msa[:,10:], 200)

generated_tokens = IM_class.generate_with_context_msa(ancestor, iterations, use_pdf=False, T=1, all_context=all_context,
                                                      use_rnd_ctx=True, use_two_msas=False, mode="same", save_all=True, rand_perm=True)
generated_tokens = IM_class.print_tokens(generated_tokens)
# If save_all=True, then the first dimension of generated_tokens is the number of iterations
print("Shape of the tokenized generated sequences: ", generated_tokens.shape)

If you want to sample sequences from two different MSAs separately you can use the following parameters: - use_rnd_ctx=True, same as before - use_two_msas=True, if you want to sample from two different MSAs given as a tuple in the first entry of all_context (the second entry of all_context is the depth of each sub-MSA). - mode, is the sampling mode, if mode=“same” then the same number of sequences is sampled from each MSA, if mode=“ratio” then it samples a number of sequences from each MSA proportional to the current iteration, starts with all sequences from the first MSA and no sequences from the second MSA, ends with no sequences from the first MSA and all sequences from the second MSA. - warm_up, used only if mode=“ratio”, is the number of iterations before starting to sample from the second MSA while cool_down is the number of iterations (before the end) when the sampling from the first MSA is stopped.

ancestor = tokenized_msa[:,:10]
all_context = ((tokenized_msa[:,10:1000], tokenized_msa[:,1000:]), 200)

generated_tokens = IM_class.generate_with_context_msa(ancestor, iterations, use_pdf=False, T=1, all_context=all_context,
                                                      use_rnd_ctx=True, use_two_msas=True, mode="same", warm_up=0, cool_down=0, save_all=True, rand_perm=True)
generated_tokens = IM_class.print_tokens(generated_tokens)
# If save_all=True, then the first dimension of generated_tokens is the number of iterations
print("Shape of the tokenized generated sequences: ", generated_tokens.shape)

Example on how to use gen_MSAs to replicate the results of the paper

gen_MSAs(filepath="examples",
         filename=["PF00072.fasta"],
         new_dir="results",
         pdf=False,
         T=1,
         sample_all=False,
         Iters=200,
         pmask=0.1,
         num=[600],
         depth=1,
         generate=False,
         print_all=False,
         range_vals=False,
         phylo_w=False)