Comments (11)
Hi,
Here is the code for the profile_with_prior
feature, I hope that helps:
def profile_with_prior(amino_acid_probs, prior_amino_probs,
s_ij, nc, beta=10.0):
"""Compute priors which takes into account priors and Blosum matrix.
Args:
amino_acid_probs: the observed amino acid probabilities in a column of MSA.
prior_amino_probs: the prior amino acid probabilities for that column.
s_ij: the substitution matrix going from amino acid i to j.
nc: the mean number of different amino acid types.
beta: weight of the prior, default set to 10.
Returns:
A profile of length 21.
"""
f_j_over_g_j = np.divide(amino_acid_probs, prior_amino_probs)
g_i = np.matmul(s_ij, f_j_over_g_j)
alpha = nc - 1.0
profiles = (alpha * amino_acid_probs + beta * g_i) / (alpha + beta)
return profiles
from deepmind-research.
Hi,
Here is the code for the
profile_with_prior
feature, I hope that helps:def profile_with_prior(amino_acid_probs, prior_amino_probs, s_ij, nc, beta=10.0): """Compute priors which takes into account priors and Blosum matrix. Args: amino_acid_probs: the observed amino acid probabilities in a column of MSA. prior_amino_probs: the prior amino acid probabilities for that column. s_ij: the substitution matrix going from amino acid i to j. nc: the mean number of different amino acid types. beta: weight of the prior, default set to 10. Returns: A profile of length 21. """ f_j_over_g_j = np.divide(amino_acid_probs, prior_amino_probs) g_i = np.matmul(s_ij, f_j_over_g_j) alpha = nc - 1.0 profiles = (alpha * amino_acid_probs + beta * g_i) / (alpha + beta) return profiles
Hi,
Thank you, it's helpful.
But, It's for profile_with_prior_without_gaps with 21 number in the feature.
With gaps, a profile of length is 22.
Additionally, I wonder how to calculate s_ij for with gaps.
Please provide more information about the formula....
Thanks
from deepmind-research.
Hi @newtonjoo,
sorry for the late reply.
But, It's for profile_with_prior_without_gaps with 21 number in the feature.
The code above should also work for profile with gaps.
Additionally, I wonder how to calculate s_ij for with gaps.
# 0.3176 from www.ncbi.nlm.nih.gov/pmc/articles/PMC146917/pdf/253389.pdf
s_ij = np.matmul(prior, prior.T) * np.exp(0.3176 * blosum)
s_ij_without_gaps = (np.matmul(prior_without_gaps, prior_without_gaps.T)
* np.exp(0.3176 * blosum[:-1, :-1]))
from deepmind-research.
Hi @Augustin-Zidek ,
I wonder how do you calculate nc (the mean number of different amino acid types) parameter that you are sending to the function.
from deepmind-research.
Hi @alyosama,
You calculate nc
as:
def mean_unique_aa_types(sequence_matrix):
"""Compute the average unique amino acids in a column for the MSA."""
num_res = sequence_matrix.shape[1]
unique_aa = np.zeros(num_res)
for i in range(num_res):
unique_aa[i] = len(np.unique(sequence_matrix[:, i]))
return np.mean(unique_aa)
nc = mean_unique_aa_types(seq_matrix)
where seq_matrix
is the MSA encoded as a matrix where each amino acid has been replaced with a unique integer.
from deepmind-research.
Hi @Augustin-Zidek Thanks for your fast response,
Also, I was wondering about prior
calculations as well. Are you taking the frequencies of amino acids in the whole MSA as prior ?
from deepmind-research.
Hi @alyosama, the prior
is a static matrix (i.e. it does not depend on the current MSA) we calculated once over a set of high quality proteins.
from deepmind-research.
Hi @alyosama, the
prior
is a static matrix (i.e. it does not depend on the current MSA) we calculated once over a set of high quality proteins.
Thank you, please provide the static matrix. It will be helpful.
from deepmind-research.
Hi John,
here is pseudo-code to regenerate the prior
matrix from a set of proteins with their HHBlits MSAs:
prior = np.zeros((512, 22)) # 20 amino acids + unknown (X) + gap (-)
for protein in proteins:
msa = protein.msa # Shape: (number_of_alignments, sequence_length)
# Iterate over all sequences in the MSA.
for seq in msa:
for i, aa in enumerate(seq):
prior[i, aa] += 1
from deepmind-research.
Hi John,
here is pseudo-code to regenerate the
prior
matrix from a set of proteins with their HHBlits MSAs:prior = np.zeros((512, 22)) # 20 amino acids + unknown (X) + gap (-) for protein in proteins: msa = protein.msa # Shape: (number_of_alignments, sequence_length) # Iterate over all sequences in the MSA. for seq in msa: for i, aa in enumerate(seq): prior[i, aa] += 1
Thanks for your providing.
prior matrix shape is (512, 22), does it mean you consider sequences less than 512 residues?
from deepmind-research.
Does it mean you consider sequences less than 512 residues?
Correct, but that is only when we constructed the prior matrix (which we did once).
from deepmind-research.
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