import os, sys import shutil import glob import torch import numpy as np import copy from itertools import groupby from operator import itemgetter import json import re import random import matplotlib.pyplot as plt import pandas as pd from tqdm import tqdm import random import Bio from icecream import ic DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') conversion = 'ARNDCQEGHILKMFPSTWYVX-' ### IF ADDING NEW POTENTIAL MAKE SURE TO ADD TO BOTTOM DICTIONARY ### # TEMPLATE CLASS class Potential: def get_gradients(seq): ''' EVERY POTENTIAL CLASS MUST RETURN GRADIENTS ''' sys.exit('ERROR POTENTIAL HAS NOT BEEN IMPLEMENTED') class AACompositionalBias(Potential): """ T = number of timesteps to set up diffuser with schedule = type of noise schedule to use linear, cosine, gaussian noise = type of ditribution to sample from; DEFAULT - normal_gaussian """ def __init__(self, args, features, potential_scale, DEVICE): self.L = features['L'] self.DEVICE = DEVICE self.frac_seq_to_weight = args['frac_seq_to_weight'] self.add_weight_every_n = args['add_weight_every_n'] self.aa_weights_json = args['aa_weights_json'] self.one_weight_per_position = args['one_weight_per_position'] self.aa_weight = args['aa_weight'] self.aa_spec = args['aa_spec'] self.aa_composition = args['aa_composition'] self.potential_scale = potential_scale self.aa_weights_to_add = [0 for l in range(21)] self.aa_max_potential = None if self.aa_weights_json != None: with open(self.aa_weights_json, 'r') as f: aa_weights = json.load(f) else: aa_weights = {} for k,v in aa_weights.items(): aa_weights_to_add[conversion.index(k)] = v aa_weights_to_add = [0 for l in range(21)] self.aa_weights_to_add = torch.tensor(aa_weights_to_add)[None].repeat(self.L,1).to(self.DEVICE, non_blocking=True) # BLOCK TO FIND OUT HOW YOU ARE LOOKING TO PROVIDE AA COMPOSITIONAL BIAS if self.add_weight_every_n > 1 or self.frac_seq_to_weight > 0: assert (self.add_weight_every_n > 1) ^ (self.frac_seq_to_weight > 0), 'use either --add_weight_every_n or --frac_seq_to_weight but not both' weight_mask = torch.zeros_like(self.aa_weights_to_add) if add_weight_every_n > 1: idxs_to_unmask = torch.arange(0,self.L,self.add_weight_every_n) else: indexs = np.arange(0,self.L).tolist() idxs_to_unmask = random.sample(indexs,int(self.frac_seq_to_weight*self.L)) idxs_to_unmask.sort() weight_mask[idxs_to_unmask,:] = 1 self.aa_weights_to_add *= weight_mask if one_weight_per_position: for p in range(self.aa_weights_to_add.shape[0]): where_ones = torch.where(self.aa_weights_to_add[p,:] > 0)[0].tolist() if len(where_ones) > 0: w_sample = random.sample(where_ones,1)[0] self.aa_weights_to_add[p,:w_sample] = 0 self.aa_weights_to_add[p,w_sample+1:] = 0 elif self.aa_spec != None: assert self.aa_weight != None, 'please specify --aa_weight' # Use specified repeat structure to bias sequence repeat_len = len(self.aa_spec) weight_split = [float(x) for x in self.aa_weight.split(',')] aa_idxs = [] for k,c in enumerate(self.aa_spec): if c != 'X': assert c in conversion, f'the letter you have chosen is not an amino acid: {c}' aa_idxs.append((k,conversion.index(c))) if len(self.aa_weight) > 1: assert len(aa_idxs) == len(weight_split), f'need to give same number of weights as AAs in weight spec' self.aa_weights_to_add = torch.zeros(self.L,21) for p,w in zip(aa_idxs,weight_split): x,a = p self.aa_weights_to_add[x,a] = w self.aa_weights_to_add = self.aa_weights_to_add[:repeat_len,:].repeat(self.L//repeat_len+1,1)[:self.L].to(self.DEVICE, non_blocking=True) elif self.aa_composition != None: self.aa_comp = [(x[0],float(x[1:])) for x in self.aa_composition.split(',')] self.aa_max_potential = 0 #just a place holder so not None assert sum([f for aa,f in self.aa_comp]) <= 1, f'total sequence fraction specified in aa_composition is > 1' else: sys.exit(f'You are missing an argument to use the aa_bias potential') def get_gradients(self, seq): ''' seq = L,21 return gradients to update the sequence with for the next pass ''' if self.aa_max_potential != None: soft_seq = torch.softmax(seq, dim=1) print('ADDING SOFTMAXED SEQUENCE POTENTIAL') aa_weights_to_add_list = [] for aa,f in self.aa_comp: aa_weights_to_add_copy = self.aa_weights_to_add.clone() soft_seq_tmp = soft_seq.clone().detach().requires_grad_(True) aa_idx = conversion.index(aa) # get top-k probability of logit to add to where_add = torch.topk(soft_seq_tmp[:,aa_idx], int(f*self.L))[1] # set up aa_potenital aa_potential = torch.zeros(21) aa_potential[conversion.index(aa)] = 1.0 aa_potential = aa_potential.repeat(self.L,1).to(self.DEVICE, non_blocking=True) # apply "loss" aa_comp_loss = torch.sum(torch.sum((aa_potential - soft_seq_tmp)**2, dim=1)**0.5) # get gradients aa_comp_loss.backward() update_grads = soft_seq_tmp.grad for k in range(self.L): if k in where_add: aa_weights_to_add_copy[k,:] = -update_grads[k,:]*self.potential_scale else: aa_weights_to_add_copy[k,:] = update_grads[k,:]*self.potential_scale aa_weights_to_add_list.append(aa_weights_to_add_copy) aa_weights_to_add_array = torch.stack((aa_weights_to_add_list)) self.aa_weights_to_add = torch.mean(aa_weights_to_add_array.float(), 0) return self.aa_weights_to_add class HydrophobicBias(Potential): """ Calculate loss with respect to soft_seq of the sequence hydropathy index (Kyte and Doolittle, 1986). T = number of timesteps to set up diffuser with schedule = type of noise schedule to use linear, cosine, gaussian noise = type of ditribution to sample from; DEFAULT - normal_gaussian """ def __init__(self, args, features, potential_scale, DEVICE): self.target_score = args['hydrophobic_score'] self.potential_scale = potential_scale self.loss_type = args['hydrophobic_loss_type'] print(f'USING {self.loss_type} LOSS TYPE...') # ----------------------------------------------------------------------- # ---------------------GRAVY index data structures----------------------- # ----------------------------------------------------------------------- # AA conversion self.alpha_1 = list("ARNDCQEGHILKMFPSTWYVX") # Dictionary to convert amino acids to their hyropathy index self.gravy_dict = {'C': 2.5, 'D': -3.5, 'S': -0.8, 'Q': -3.5, 'K': -3.9, 'I': 4.5, 'P': -1.6, 'T': -0.7, 'F': 2.8, 'N': -3.5, 'G': -0.4, 'H': -3.2, 'L': 3.8, 'R': -4.5, 'W': -0.9, 'A': 1.8, 'V':4.2, 'E': -3.5, 'Y': -1.3, 'M': 1.9, 'X': 0, '-': 0} self.gravy_list = [self.gravy_dict[a] for a in self.alpha_1] # ----------------------------------------------------------------------- # ----------------------------------------------------------------------- print(f'GUIDING SEQUENCES TO HAVE TARGET GRAVY SCORE OF: {self.target_score}') return None def get_gradients(self, seq): """ Calculate gradients with respect to GRAVY index of input seq. Uses a MSE loss. Arguments --------- seq : tensor L X 21 logits after saving seq_out from xt Returns ------- gradients : list of tensors gradients of soft_seq with respect to loss on partial_charge """ # Get GRAVY matrix based on length of seq gravy_matrix = torch.tensor(self.gravy_list)[None].repeat(seq.shape[0],1).to(DEVICE) # Get softmax of seq soft_seq = torch.softmax(seq,dim=-1).requires_grad_(requires_grad=True).to(DEVICE) # Calculate simple MSE loss on gravy_score if self.loss_type == 'simple': gravy_score = torch.mean(torch.sum(soft_seq*gravy_matrix,dim=-1), dim=0) loss = ((gravy_score - self.target_score)**2)**0.5 #print(f'LOSS: {loss}') # Take backward step loss.backward() # Get gradients from soft_seq self.gradients = soft_seq.grad # plt.imshow(self.gradients.cpu().detach().numpy()) # plt.colorbar() # plt.title('gradients') # Calculate MSE loss on gravy_score elif self.loss_type == 'complex': loss = torch.mean((torch.sum(soft_seq*gravy_matrix, dim = -1) - self.target_score)**2) #print(f'LOSS: {loss}') # Take backward step loss.backward() # Get gradients from soft_seq self.gradients = soft_seq.grad # plt.imshow(self.gradients.cpu().detach().numpy()) # plt.colorbar() # plt.title('gradients') return -self.gradients*self.potential_scale class ChargeBias(Potential): """ Calculate losses and get gradients with respect to soft_seq for the sequence charge at a given pH. T = number of timesteps to set up diffuser with schedule = type of noise schedule to use linear, cosine, gaussian noise = type of ditribution to sample from; DEFAULT - normal_gaussian """ def __init__(self, args, features, potential_scale, DEVICE): self.target_charge = args['target_charge'] self.pH = args['target_pH'] self.loss_type = args['charge_loss_type'] self.potential_scale = potential_scale self.L = features['L'] self.DEVICE = DEVICE # ----------------------------------------------------------------------- # ------------------------pI data structures----------------------------- # ----------------------------------------------------------------------- # pKa lists to account for every residue. pos_pKs_list = [[0.0, 12.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.98, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]] neg_pKs_list = [[0.0, 0.0, 0.0, 4.05, 9.0, 0.0, 4.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0]] cterm_pKs_list = [[0.0, 0.0, 0.0, 4.55, 0.0, 0.0, 4.75, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]] nterm_pKs_list = [[7.59, 0.0, 0.0, 0.0, 0.0, 0.0, 7.7, 0.0, 0.0, 0.0, 0.0, 0.0, 7.0, 0.0, 8.36, 6.93, 6.82, 0.0, 0.0, 7.44, 0.0]] # Convert pKa lists to tensors self.cterm_pKs = torch.tensor(cterm_pKs_list) self.nterm_pKs = torch.tensor(nterm_pKs_list) self.pos_pKs = torch.tensor(pos_pKs_list) self.neg_pKs = torch.tensor(neg_pKs_list) # Repeat charged pKs L - 2 times to populate in all non-terminal residue indices pos_pKs_repeat = self.pos_pKs.repeat(self.L - 2, 1) neg_pKs_repeat = self.neg_pKs.repeat(self.L - 2, 1) # Concatenate all pKs tensors with N-term and C-term pKas to get full L X 21 charge matrix self.pos_pKs_matrix = torch.cat((torch.zeros_like(self.nterm_pKs), pos_pKs_repeat, self.nterm_pKs)).to(DEVICE) self.neg_pKs_matrix = torch.cat((self.cterm_pKs, neg_pKs_repeat, torch.zeros_like(self.cterm_pKs))).to(DEVICE) # Get indices of positive, neutral, and negative residues self.cterm_charged_idx = torch.nonzero(self.cterm_pKs) self.cterm_neutral_idx = torch.nonzero(self.cterm_pKs == 0) self.nterm_charged_idx = torch.nonzero(self.nterm_pKs) self.nterm_neutral_idx = torch.nonzero(self.nterm_pKs == 0) self.pos_pKs_idx = torch.tensor([[1, 8, 11]]) self.neg_pKs_idx = torch.tensor([[3, 4, 6, 18]]) self.neutral_pKs_idx = torch.tensor([[0, 2, 5, 7, 9, 10, 12, 13, 14, 15, 16, 17, 19, 20]]) # ----------------------------------------------------------------------- # ----------------------------------------------------------------------- print(f'OPTIMIZING SEQUENCE TO HAVE CHARGE = {self.target_charge}\nAT pH = {self.pH}' ) def sum_tensor_indices(self, indices, tensor): total = 0 for idx in indices: i, j = idx[0], idx[1] total += tensor[i][j] return total def sum_tensor_indices_2(self, indices, tensor): # Create a tensor with the appropriate dimensions j = indices.clone().detach().long().to(self.DEVICE) # Select the values using advanced indexing and sum along dim=-1 row_sums = tensor[:, j].sum(dim=-1) # Reshape the result to an L x 1 tensor return row_sums.reshape(-1, 1).clone().detach() def make_table(self, L): """ Make table of all (positive, neutral, negative) charges -> (i, j, k) such that: i + j + k = L (1 * i) + (0 * j) + (-1 * k) = target_charge Arguments: L: int - length of sequence, defined as seq.shape[0] target_charge : float - Target charge for the sequence to be guided towards Returns: table: N x 3 tensor - All combinations of i, j, k such that the above conditions are satisfied """ table = [] for i in range(L): for j in range(L): for k in range(L): # Check that number of residues = L and that sum of charge (i - k) = target_charge # and that there are no 0 entries, as having no pos, no neg, or no neutral is not realistic if i+j+k == L and i-k == self.target_charge and i != 0 and j != 0 and k != 0: table.append([i,j,k]) return torch.tensor(np.array(table)) def classify_resis(self, seq): """ Classify each position in seq as either positive, neutral, or negative. Classification = max( [sum(positive residue logits), sum(neutral residue logits), sum(negative residue logits)] ) Arguments: seq: L x 21 tensor - sequence logits from the model Returns: charges: tensor - 1 x 3 tensor counting total # of each charge type in the input sequence - charges[0] = # positive residues - charges[1] = # neutral residues - charges[2] = # negative residues charge_classification: tensor - L x 1 tensor of each position's classification. 1 is positive, 0 is neutral, -1 is negative """ L = seq.shape[0] # Get softmax of seq soft_seq = torch.softmax(seq.clone(),dim=-1).requires_grad_(requires_grad=True).to(self.DEVICE) # Sum the softmax of all the positive and negative charges along dim = -1 (21 amino acids): # Sum across c-term pKs sum_cterm_charged = self.sum_tensor_indices(self.cterm_charged_idx, soft_seq).item() # print(f'SUM OF CTERM CHARGED RESIS: {sum_cterm_charged}') # print(type(sum_cterm_charged.item())) sum_cterm_neutral = self.sum_tensor_indices(self.cterm_neutral_idx, soft_seq).item() # print(f'SUM OF CTERM NEUTRAL RESIS: {sum_cterm_neutral}') # Classify c-term as negative or neutral cterm_max = max(sum_cterm_charged, sum_cterm_neutral) # print(f'CTERM MAX: {cterm_max}') if cterm_max == sum_cterm_charged: cterm_class = torch.tensor([[-1]]).to(self.DEVICE) else: cterm_class = torch.tensor([[0]]).to(self.DEVICE) # Prep cterm dataframe cterm_df = torch.tensor([[0, sum_cterm_neutral, sum_cterm_charged, cterm_max, cterm_class]]).to(self.DEVICE) # Sum across positive, neutral, and negative pKs sum_pos = self.sum_tensor_indices_2(self.pos_pKs_idx, soft_seq[1:L-1, ...]).to(self.DEVICE) # print(f'SUM POS: {sum_pos}') sum_neg = self.sum_tensor_indices_2(self.neg_pKs_idx, soft_seq[1:L-1, ...]).to(self.DEVICE) # print(f'SUM NEG: {sum_neg}') sum_neutral = self.sum_tensor_indices_2(self.neutral_pKs_idx, soft_seq[1:L-1, ...]).to(self.DEVICE) # print(f'SUM NEUTRAL: {sum_neutral}') # Classify non-terminal residues along dim = -1 middle_max, _ = torch.max(torch.stack((sum_pos, sum_neg, sum_neutral), dim=-1), dim=-1) middle_max = middle_max.to(self.DEVICE) # create an L x 1 tensor to store the result middle_class = torch.zeros((L - 2, 1), dtype=torch.long).to(self.DEVICE) # set the values of the result tensor based on which tensor had the maximum value middle_class[sum_neg == middle_max] = -1 middle_class[sum_neutral == middle_max] = 0 middle_class[sum_pos == middle_max] = 1 # Prepare df of all middle residue classifications and corresponding values middle_df = pd.DataFrame((torch.cat((sum_pos, sum_neutral, sum_neg, middle_max, middle_class), dim=-1)).detach().cpu().numpy()) middle_df.rename(columns={0: 'sum_pos', 1: 'sum_neutral', 2: 'sum_neg', 3: 'middle_max', 4: 'middle_classified'}, inplace=True, errors='raise') # Sum across n-term pKs sum_nterm_charged = self.sum_tensor_indices(self.nterm_charged_idx, soft_seq).to(self.DEVICE) # print(f'SUM OF NTERM CHARGED RESIS: {sum_nterm_charged}') sum_nterm_neutral = self.sum_tensor_indices(self.nterm_neutral_idx, soft_seq).to(self.DEVICE) # print(f'SUM OF NTERM NEUTRAL RESIS: {sum_nterm_neutral}') # Classify n-term as negative or neutral nterm_max = max(sum_nterm_charged, sum_nterm_neutral) if nterm_max == sum_nterm_charged: nterm_class = torch.tensor([[-1]]).to(self.DEVICE) else: nterm_class = torch.tensor([[0]]).to(self.DEVICE) nterm_df = torch.tensor([[sum_nterm_charged, sum_nterm_neutral, 0, nterm_max, nterm_class]]).to(self.DEVICE) # Prep data to be concatenated into output df middle_df_2 = (torch.cat((sum_pos, sum_neutral, sum_neg, middle_max, middle_class), dim=-1)).to(self.DEVICE) # Concat cterm, middle, and nterm data into one master df with all summed probs, max, and final classification full_tens_np = torch.cat((cterm_df, middle_df_2, nterm_df), dim = 0).detach().cpu().numpy() classification_df = pd.DataFrame(full_tens_np) classification_df.rename(columns={0: 'sum_pos', 1: 'sum_neutral', 2: 'sum_neg', 3: 'max', 4: 'classification'}, inplace=True, errors='raise') # Count number of positive, neutral, and negative charges that are stored in charge_classification as 1, 0, -1 respectively charge_classification = torch.cat((cterm_class, middle_class, nterm_class), dim = 0).to(self.DEVICE) charges = [torch.sum(charge_classification == 1).item(), torch.sum(charge_classification == 0).item(), torch.sum(charge_classification == -1).item()] # print('*'*100) # print(classification_df) return torch.tensor(charges), classification_df def get_target_charge_ratios(self, table, charges): """ Find closest distance between x, y, z in table and i, j, k in charges Arguments: table: N x 3 tensor of all combinations of positive, neutral, and negative charges that obey the conditions in make_table charges: 1 x 3 tensor - 1 x 3 tensor counting total # of each charge type in the input sequence - charges[0] = # positive residues - charges[1] = # neutral residues - charges[2] = # negative residues Returns: target_charge_tensor: tensor - 1 x 3 tensor of closest row in table that matches charges of input sequence """ # Compute the difference between the charges and each row of the table diff = table - charges # Compute the square of the Euclidean distance between the charges and each row of the table sq_distance = torch.sum(diff ** 2, dim=-1) # Find the index of the row with the smallest distance min_idx = torch.argmin(sq_distance) # Return the smallest distance and the corresponding row of the table target_charge_tensor = torch.sqrt(sq_distance[min_idx]), table[min_idx] #print(f'CLOSEST COMBINATION OF VALID RESIDUES: {target_charge_tensor[1]}') return target_charge_tensor[1] def draft_resis(self, classification_df, target_charge_tensor): """ Based on target_charge_tensor, draft the top (i, j, k) positive, neutral, and negative positions from charge_classification and return the idealized guided_charge_classification. guided_charge_classification will determine whether the gradients should be positive or negative Draft pick algorithm for determining gradient guided_charge_classification: 1) Define how many positive, negative, and neutral charges are needed 2) Current charge being drafted = sign of target charge, otherwise opposite charge 3) From the classification_df of the currently sampled sequence, choose the position with the highest probability of being current_charge 4) Make that residue +1, 0, or -1 in guided_charge_classification to dictate the sign of gradients 5) Keep drafting that residue charge until it is used up, then move to the next type Arguments: classification_df: tensor - L x 1 tensor of each position's classification. 1 is positive, 0 is neutral, -1 is negative target_charge_tensor: tensor - 1 x 3 tensor of closest row in table that matches charges of input sequence Returns: guided_charge_classification: L x 1 tensor - L x 1 tensor populated with 1 = positive, 0 = neutral, -1 = negative - in get_gradients, multiply the gradients by guided_charge_classification to determine which direction the gradients should guide toward based on the current sequence distribution and the target charge """ charge_dict = {'pos': 0, 'neutral': 0, 'neg': 0} # Define the target number of positive, neutral, and negative charges charge_dict['pos'] = target_charge_tensor[0].detach().clone() charge_dict['neutral'] = target_charge_tensor[1].detach().clone() charge_dict['neg'] = target_charge_tensor[2].detach().clone() # Determine which charge to start drafting if self.target_charge > 0: start_charge = 'pos' elif self.target_charge < 0: start_charge = 'neg' else: start_charge = 'neutral' # Initialize guided_charge_classification guided_charge_classification = torch.zeros((classification_df.shape[0], 1)) # Start drafting draft_charge = start_charge while charge_dict[draft_charge] > 0: # Find the residue with the max probability for the current draft charge max_residue_idx = classification_df.loc[:, ['sum_' + draft_charge]].idxmax()[0] # print(max_residue_idx[0]) # print(type(max_residue_idx)) #print(f'MAX RESIDUE INDEX for {draft_charge}: {max_residue_idx}') # Populate guided_charge_classification with the appropriate charge if draft_charge == 'pos': guided_charge_classification[max_residue_idx] = 1 elif draft_charge == 'neg': guided_charge_classification[max_residue_idx] = -1 else: guided_charge_classification[max_residue_idx] = 0 # Remove selected row from classification_df classification_df = classification_df.drop(max_residue_idx) # print(classification_df) # Update charges dictionary charge_dict[draft_charge] -= 1 #print(f'{charge_dict[draft_charge]} {draft_charge} residues left to draft...') # Switch to the other charged residue if the starting charge has been depleted if charge_dict[draft_charge] == 0: if draft_charge == start_charge: draft_charge = 'neg' if start_charge == 'pos' else 'pos' elif draft_charge == 'neg': draft_charge = 'pos' elif draft_charge == 'pos': draft_charge = 'neg' else: draft_charge = 'neutral' return guided_charge_classification.requires_grad_() def get_gradients(self, seq):#, guided_charge_classification): """ Calculate gradients with respect to SEQUENCE CHARGE at pH. Uses a MSE loss. Arguments --------- seq : tensor L X 21 logits after saving seq_out from xt Returns ------- gradients : list of tensors gradients of soft_seq with respect to loss on partial_charge """ # Get softmax of seq # soft_seq = torch.softmax(seq.clone(),dim=-1).requires_grad_(requires_grad=True).to(DEVICE) soft_seq = torch.softmax(seq,dim=-1).requires_grad_(requires_grad=True).to(DEVICE) # Get partial positive charges only for titratable residues pos_charge = torch.where(self.pos_pKs_matrix != 0, ((1) / (((10) ** ((self.pH) - self.pos_pKs_matrix)) + (1.0))), (0.0)) neg_charge = torch.where(self.neg_pKs_matrix != 0, ((1) / (((10) ** (self.neg_pKs_matrix - (self.pH))) + (1.0))), (0.0)) # partial_charge = torch.sum((soft_seq*(pos_charge - neg_charge)).requires_grad_(requires_grad=True)) if self.loss_type == 'simple': # Calculate net partial charge of soft_seq partial_charge = torch.sum((soft_seq*(pos_charge - neg_charge)).requires_grad_(requires_grad=True)) print(f'CURRENT PARTIAL CHARGE: {partial_charge.item()}') # Calculate MSE loss on partial_charge loss = ((partial_charge - self.target_charge)**2)**0.5 #print(f'LOSS: {loss}') # Take backward step loss.backward() # Get gradients from soft_seq self.gradients = soft_seq.grad # plt.imshow(self.gradients) # plt.colorbar() # plt.title('gradients') elif self.loss_type == 'simple2': # Calculate net partial charge of soft_seq # partial_charge = torch.sum((soft_seq*(pos_charge - neg_charge)).requires_grad_(requires_grad=True)) print(f'CURRENT PARTIAL CHARGE: {partial_charge.item()}') # Calculate MSE loss on partial_charge loss = (((torch.sum((soft_seq*(pos_charge - neg_charge)).requires_grad_(requires_grad=True))) - self.target_charge)**2)**0.5 #print(f'LOSS: {loss}') # Take backward step loss.backward() # Get gradients from soft_seq self.gradients = soft_seq.grad # plt.imshow(self.gradients) # plt.colorbar() # plt.title('gradients') elif self.loss_type == 'complex': # Preprocessing using method functions table = self.make_table(seq.shape[0]) charges, classification_df = self.classify_resis(seq) target_charge_tensor = self.get_target_charge_ratios(table, charges) guided_charge_classification = self.draft_resis(classification_df, target_charge_tensor) # Calculate net partial charge of soft_seq soft_partial_charge = (soft_seq*(pos_charge - neg_charge)) # print(f'SOFT PARTIAL CHARGE SHAPE: {soft_partial_charge.shape}') # Define partial charge as the sum of softmax * partial charge matrix partial_charge = torch.sum(soft_partial_charge, dim=-1).requires_grad_() #print(partial_charge) # partial_charge = torch.sum((soft_seq*(pos_charge - neg_charge)).requires_grad_(requires_grad=True)) print(f'CURRENT PARTIAL CHARGE: {partial_charge.sum().item()}') # print(f'DIFFERENCE BETWEEN TARGET CHARGES AND CURRENT CHARGES: {((guided_charge_classification.to(self.DEVICE) - partial_charge.unsqueeze(1).to(self.DEVICE))**2)**0.5}') # Calculate loss on partial_charge loss = torch.mean(((guided_charge_classification.to(self.DEVICE) - partial_charge.unsqueeze(1).to(self.DEVICE))**2)**0.5) # loss = torch.mean((guided_charge_classification.to(self.DEVICE) - partial_charge.to(self.DEVICE))**2) #print(f'LOSS: {loss}') # Take backward step loss.backward() # Get gradients from soft_seq self.gradients = soft_seq.grad # print(f'GUIDED CHARGE CLASSIFICATION SHAPE: {guided_charge_classification.shape}') # print(f'PARTIAL CHARGE SHAPE: {partial_charge.unsqueeze(1).shape}') # print(partial_charge) # fig, ax = plt.subplots(1,2, dpi=200) # ax[0].imshow((partial_charge.unsqueeze(1)).detach().numpy()) # ax[0].set_title('soft_seq partial charge') # ax[1].imshow(self.gradients)#.detach().numpy()) # ax[1].set_title('gradients') # print(seq) return -self.gradients*self.potential_scale class PSSMbias(Potential): def __init__(self, args, features, potential_scale, DEVICE): self.features = features self.args = args self.potential_scale = potential_scale self.DEVICE = DEVICE self.PSSM = np.loadtxt(args['PSSM'], delimiter=",", dtype=float) self.PSSM = torch.from_numpy(self.PSSM).to(self.DEVICE) def get_gradients(self, seq): print(seq.shape) return self.PSSM*self.potential_scale ### ADD NEW POTENTIALS INTO LIST DOWN BELOW ### POTENTIALS = {'aa_bias':AACompositionalBias, 'charge':ChargeBias, 'hydrophobic':HydrophobicBias, 'PSSM':PSSMbias}