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Improved visual rendering large scoring matrices via Gallery
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import torch
import transformers
from transformers import PreTrainedTokenizerFast
import tranception
import datasets
from tranception import config, model_pytorch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import gradio as gr
tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]"
)
#######################################################################################################################################
############################################### HELPER FUNCTIONS ####################################################################
#######################################################################################################################################
import torch
import transformers
from transformers import PreTrainedTokenizerFast
import tranception
import datasets
from tranception import config, model_pytorch
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import gradio as gr
AA_vocab = "ACDEFGHIKLMNPQRSTVWY"
tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]"
)
def create_all_single_mutants(sequence,AA_vocab=AA_vocab,mutation_range_start=None,mutation_range_end=None):
all_single_mutants={}
sequence_list=list(sequence)
if mutation_range_start is None: mutation_range_start=1
if mutation_range_end is None: mutation_range_end=len(sequence)
for position,current_AA in enumerate(sequence[mutation_range_start-1:mutation_range_end]):
for mutated_AA in AA_vocab:
if current_AA!=mutated_AA:
mutated_sequence = sequence_list.copy()
mutated_sequence[position] = mutated_AA
all_single_mutants[current_AA+str(position+1)+mutated_AA]="".join(mutated_sequence)
all_single_mutants = pd.DataFrame.from_dict(all_single_mutants,columns=['mutated_sequence'],orient='index')
all_single_mutants.reset_index(inplace=True)
all_single_mutants.columns = ['mutant','mutated_sequence']
return all_single_mutants
def create_scoring_matrix_visual(scores,sequence,image_index=0,mutation_range_start=None,mutation_range_end=None,AA_vocab=AA_vocab,annotate=True,fontsize=20):
filtered_scores=scores.copy()
filtered_scores=filtered_scores[filtered_scores.position.isin(range(mutation_range_start,mutation_range_end+1))]
piv=filtered_scores.pivot(index='position',columns='target_AA',values='avg_score').round(4)
mutation_range_len = mutation_range_end - mutation_range_start + 1
fig, ax = plt.subplots(figsize=(50,mutation_range_len))
scores_dict = {}
valid_mutant_set=set(filtered_scores.mutant)
ax.tick_params(bottom=True, top=True, left=True, right=True)
ax.tick_params(labelbottom=True, labeltop=True, labelleft=True, labelright=True)
if annotate:
for position in range(mutation_range_start,mutation_range_end+1):
for target_AA in list(AA_vocab):
mutant = sequence[position-1]+str(position)+target_AA
if mutant in valid_mutant_set:
scores_dict[mutant]= float(filtered_scores.loc[filtered_scores.mutant==mutant,'avg_score'])
else:
scores_dict[mutant]=0.0
labels = (np.asarray(["{} \n {:.4f}".format(symb,value) for symb, value in scores_dict.items() ])).reshape(mutation_range_len,len(AA_vocab))
heat = sns.heatmap(piv,annot=labels,fmt="",cmap='RdYlGn',linewidths=0.30,ax=ax,vmin=np.percentile(scores.avg_score,2),vmax=np.percentile(scores.avg_score,98),\
cbar_kws={'label': 'Log likelihood ratio (mutant / starting sequence)'},annot_kws={"size": fontsize})
else:
heat = sns.heatmap(piv,cmap='RdYlGn',linewidths=0.30,ax=ax,vmin=np.percentile(scores.avg_score,2),vmax=np.percentile(scores.avg_score,98),\
cbar_kws={'label': 'Log likelihood ratio (mutant / starting sequence)'},annot_kws={"size": fontsize})
heat.figure.axes[-1].yaxis.label.set_size(fontsize=int(fontsize*1.5))
heat.figure.axes[-1].yaxis.set_ticklabels(heat.figure.axes[-1].yaxis.get_ticklabels(), fontsize=fontsize)
heat.set_title("Higher predicted scores (green) imply higher protein fitness",fontsize=fontsize*2, pad=40)
heat.set_ylabel("Sequence position", fontsize = fontsize*2)
heat.set_xlabel("Amino Acid mutation", fontsize = fontsize*2)
yticklabels = [str(pos)+' ('+sequence[pos-1]+')' for pos in range(mutation_range_start,mutation_range_end+1)]
heat.set_yticklabels(yticklabels)
heat.set_xticklabels(heat.get_xmajorticklabels(), fontsize = fontsize)
heat.set_yticklabels(heat.get_ymajorticklabels(), fontsize = fontsize, rotation=0)
plt.tight_layout()
image_path = 'fitness_scoring_substitution_matrix_{}.png'.format(image_index)
plt.savefig(image_path,dpi=100)
plt.show()
return image_path
def suggest_mutations(scores):
intro_message = "The following mutations may be sensible options to improve fitness: \n\n"
#Best mutants
top_mutants=list(scores.sort_values(by=['avg_score'],ascending=False).head(5).mutant)
top_mutants_fitness=list(scores.sort_values(by=['avg_score'],ascending=False).head(5).avg_score)
top_mutants_recos = [top_mutant+" ("+str(round(top_mutant_fitness,4))+")" for (top_mutant,top_mutant_fitness) in zip(top_mutants,top_mutants_fitness)]
mutant_recos = "The single mutants with highest predicted fitness are (positive scores indicate fitness increase Vs starting sequence, negative scores indicate fitness decrease):\n {} \n\n".format(", ".join(top_mutants_recos))
#Best positions
positive_scores = scores[scores.avg_score > 0]
positive_scores_position_avg = positive_scores.groupby(['position']).mean()
top_positions=list(positive_scores_position_avg.sort_values(by=['avg_score'],ascending=False).head(5).index.astype(str))
position_recos = "The positions with the highest average fitness increase are (only positions with at least one fitness increase are considered):\n {}".format(", ".join(top_positions))
return intro_message+mutant_recos+position_recos
def check_valid_mutant(sequence,mutant,AA_vocab=AA_vocab):
valid = True
try:
from_AA, position, to_AA = mutant[0], int(mutant[1:-1]), mutant[-1]
except:
valid = False
if sequence[position-1]!=from_AA: valid=False
if position<1 or position>len(sequence): valid=False
if to_AA not in AA_vocab: valid=False
return valid
def get_mutated_protein(sequence,mutant):
assert check_valid_mutant(sequence,mutant), "The mutant is not valid"
mutated_sequence = list(sequence)
mutated_sequence[int(mutant[1:-1])-1]=mutant[-1]
return ''.join(mutated_sequence)
def score_and_create_matrix_all_singles(sequence,mutation_range_start=None,mutation_range_end=None,model_type="Small",scoring_mirror=False,batch_size_inference=20,max_number_positions_per_heatmap=50,num_workers=0,AA_vocab=AA_vocab):
if mutation_range_start is None: mutation_range_start=1
if mutation_range_end is None: mutation_range_end=len(sequence)
assert len(sequence) > 0, "no sequence entered"
assert mutation_range_start <= mutation_range_end, "mutation range is invalid"
if model_type=="Small":
model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path="PascalNotin/Tranception_Small")
elif model_type=="Medium":
model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path="PascalNotin/Tranception_Medium")
elif model_type=="Large":
model = tranception.model_pytorch.TranceptionLMHeadModel.from_pretrained(pretrained_model_name_or_path="PascalNotin/Tranception_Large")
if torch.cuda.is_available():
model.cuda()
print("Inference will take place on GPU")
else:
print("Inference will take place on CPU")
model.config.tokenizer = tokenizer
all_single_mutants = create_all_single_mutants(sequence,AA_vocab,mutation_range_start,mutation_range_end)
scores = model.score_mutants(DMS_data=all_single_mutants,
target_seq=sequence,
scoring_mirror=scoring_mirror,
batch_size_inference=batch_size_inference,
num_workers=num_workers,
indel_mode=False
)
scores = pd.merge(scores,all_single_mutants,on="mutated_sequence",how="left")
scores["position"]=scores["mutant"].map(lambda x: int(x[1:-1]))
scores["target_AA"] = scores["mutant"].map(lambda x: x[-1])
score_heatmaps = []
mutation_range = mutation_range_end - mutation_range_start + 1
number_heatmaps = int((mutation_range - 1) / max_number_positions_per_heatmap) + 1
image_index = 0
window_start = mutation_range_start
window_end = min(mutation_range_end,mutation_range_start+max_number_positions_per_heatmap-1)
for image_index in range(number_heatmaps):
score_heatmaps.append(create_scoring_matrix_visual(scores,sequence,image_index,window_start,window_end,AA_vocab))
window_start += max_number_positions_per_heatmap
window_end = min(mutation_range_end,window_start+max_number_positions_per_heatmap-1)
print(score_heatmaps)
return score_heatmaps, suggest_mutations(scores)
def extract_sequence(example):
label, taxon, sequence = example
return sequence
def clear_inputs(protein_sequence_input,mutation_range_start,mutation_range_end):
protein_sequence_input = ""
mutation_range_start = None
mutation_range_end = None
return protein_sequence_input,mutation_range_start,mutation_range_end
#######################################################################################################################################
############################################### GRADIO INTERFACE ####################################################################
#######################################################################################################################################
tranception_design = gr.Blocks()
with tranception_design:
gr.Markdown("# Interactive in silico directed evolution with Tranception")
gr.Markdown(" Perform in silico directed evolution with Tranception to iteratively improve the fitness of a protein of interest, one mutation at a time. At each step, the Tranception model computes the log likelihood ratios of all possible single amino acid substitution Vs the starting sequence, and outputs a fitness heatmap and recommandations to guide the selection of the mutation to apply.")
with gr.Tabs():
with gr.TabItem("Input"):
with gr.Row():
protein_sequence_input = gr.Textbox(lines=1,
label="Protein sequence",
placeholder = "Input the sequence of amino acids representing the starting protein of interest or select one from the list of examples below. You may enter the full sequence or just a subdomain (providing full context typically leads to better results, but is slower at inference)"
)
with gr.Row():
mutation_range_start = gr.Number(label="Start of mutation window (first position indexed at 1)", value=1, precision=0)
mutation_range_end = gr.Number(label="End of mutation window (leave empty for full lenth)", value=10, precision=0)
with gr.TabItem("Parameters"):
with gr.Row():
model_size_selection = gr.Radio(label="Tranception model size (larger models are more accurate but are slower at inference)",
choices=["Small","Medium","Large"],
value="Small")
with gr.Row():
scoring_mirror = gr.Checkbox(label="Score protein from both directions (leads to more robust fitness predictions, but doubles inference time)")
with gr.Row():
batch_size_inference = gr.Number(label="Model batch size at inference time",value = 100, precision=0)
with gr.Row():
gr.Markdown("Note: the current version does not leverage retrieval of homologs at inference time to increase fitness prediction performance.")
with gr.Row():
clear_button = gr.Button(value="Clear",variant="secondary")
run_button = gr.Button(value="Predict fitness",variant="primary")
protein_ID = gr.Textbox(label="Uniprot ID", visible=False)
taxon = gr.Textbox(label="Taxon", visible=False)
examples = gr.Examples(
inputs=[protein_ID, taxon, protein_sequence_input],
outputs=[protein_sequence_input],
fn=extract_sequence,
examples=[
['ADRB2_HUMAN' ,'Human', 'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL'],
['IF1_ECOLI' ,'Prokaryote', 'MAKEDNIEMQGTVLETLPNTMFRVELENGHVVTAHISGKMRKNYIRILTGDKVTVELTPYDLSKGRIVFRSR'],
['P53_HUMAN' ,'Human', 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD'],
['BLAT_ECOLX' ,'Prokaryote', 'MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW'],
['BRCA1_HUMAN' ,'Human', 'MDLSALRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLKLLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLEYANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQETSLSVQLSNLGTVRTLRTKQRIQPQKTSVYIELGSDSSEDTVNKATYCSVGDQELLQITPQGTRDEISLDSAKKAACEFSETDVTNTEHHQPSNNDLNTTEKRAAERHPEKYQGSSVSNLHVEPCGTNTHASSLQHENSSLLLTKDRMNVEKAEFCNKSKQPGLARSQHNRWAGSKETCNDRRTPSTEKKVDLNADPLCERKEWNKQKLPCSENPRDTEDVPWITLNSSIQKVNEWFSRSDELLGSDDSHDGESESNAKVADVLDVLNEVDEYSGSSEKIDLLASDPHEALICKSERVHSKSVESNIEDKIFGKTYRKKASLPNLSHVTENLIIGAFVTEPQIIQERPLTNKLKRKRRPTSGLHPEDFIKKADLAVQKTPEMINQGTNQTEQNGQVMNITNSGHENKTKGDSIQNEKNPNPIESLEKESAFKTKAEPISSSISNMELELNIHNSKAPKKNRLRRKSSTRHIHALELVVSRNLSPPNCTELQIDSCSSSEEIKKKKYNQMPVRHSRNLQLMEGKEPATGAKKSNKPNEQTSKRHDSDTFPELKLTNAPGSFTKCSNTSELKEFVNPSLPREEKEEKLETVKVSNNAEDPKDLMLSGERVLQTERSVESSSISLVPGTDYGTQESISLLEVSTLGKAKTEPNKCVSQCAAFENPKGLIHGCSKDNRNDTEGFKYPLGHEVNHSRETSIEMEESELDAQYLQNTFKVSKRQSFAPFSNPGNAEEECATFSAHSGSLKKQSPKVTFECEQKEENQGKNESNIKPVQTVNITAGFPVVGQKDKPVDNAKCSIKGGSRFCLSSQFRGNETGLITPNKHGLLQNPYRIPPLFPIKSFVKTKCKKNLLEENFEEHSMSPEREMGNENIPSTVSTISRNNIRENVFKEASSSNINEVGSSTNEVGSSINEIGSSDENIQAELGRNRGPKLNAMLRLGVLQPEVYKQSLPGSNCKHPEIKKQEYEEVVQTVNTDFSPYLISDNLEQPMGSSHASQVCSETPDDLLDDGEIKEDTSFAENDIKESSAVFSKSVQKGELSRSPSPFTHTHLAQGYRRGAKKLESSEENLSSEDEELPCFQHLLFGKVNNIPSQSTRHSTVATECLSKNTEENLLSLKNSLNDCSNQVILAKASQEHHLSEETKCSASLFSSQCSELEDLTANTNTQDPFLIGSSKQMRHQSESQGVGLSDKELVSDDEERGTGLEENNQEEQSMDSNLGEAASGCESETSVSEDCSGLSSQSDILTTQQRDTMQHNLIKLQQEMAELEAVLEQHGSQPSNSYPSIISDSSALEDLRNPEQSTSEKAVLTSQKSSEYPISQNPEGLSADKFEVSADSSTSKNKEPGVERSSPSKCPSLDDRWYMHSCSGSLQNRNYPSQEELIKVVDVEEQQLEESGPHDLTETSYLPRQDLEGTPYLESGISLFSDDPESDPSEDRAPESARVGNIPSSTSALKVPQLKVAESAQSPAAAHTTDTAGYNAMEESVSREKPELTASTERVNKRMSMVVSGLTPEEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARESQDRKIFRGLEICCYGPFTNMPTDQLEWMVQLCGASVVKELSSFTLGTGVHPIVVVQPDAWTEDNGFHAIGQMCEAPVVTREWVLDSVALYQCQELDTYLIPQIPHSHY'],
['CALM1_HUMAN' ,'Human', 'MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTIDFPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQVNYEEFVQMMTAK'],
['CCDB_ECOLI' ,'Prokaryote', 'MQFKVYTYKRESRYRLFVDVQSDIIDTPGRRMVIPLASARLLSDKVSRELYPVVHIGDESWRMMTTDMASVPVSVIGEEVADLSHRENDIKNAINLMFWGI'],
['GFP_AEQVI' ,'Other eukaryote', 'MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK'],
['GRB2_HUMAN' ,'Human', 'MEAIAKYDFKATADDELSFKRGDILKVLNEECDQNWYKAELNGKDGFIPKNYIEMKPHPWFFGKIPRAKAEEMLSKQRHDGAFLIRESESAPGDFSLSVKFGNDVQHFKVLRDGAGKYFLWVVKFNSLNELVDYHRSTSVSRNQQIFLRDIEQVPQQPTYVQALFDFDPQEDGELGFRRGDFIHVMDNSDPNWWKGACHGQTGMFPRNYVTPVNRNV'],
['HSP82_YEAST' ,'Eukaryote ', 'MASETFEFQAEITQLMSLIINTVYSNKEIFLRELISNASDALDKIRYKSLSDPKQLETEPDLFIRITPKPEQKVLEIRDSGIGMTKAELINNLGTIAKSGTKAFMEALSAGADVSMIGQFGVGFYSLFLVADRVQVISKSNDDEQYIWESNAGGSFTVTLDEVNERIGRGTILRLFLKDDQLEYLEEKRIKEVIKRHSEFVAYPIQLVVTKEVEKEVPIPEEEKKDEEKKDEEKKDEDDKKPKLEEVDEEEEKKPKTKKVKEEVQEIEELNKTKPLWTRNPSDITQEEYNAFYKSISNDWEDPLYVKHFSVEGQLEFRAILFIPKRAPFDLFESKKKKNNIKLYVRRVFITDEAEDLIPEWLSFVKGVVDSEDLPLNLSREMLQQNKIMKVIRKNIVKKLIEAFNEIAEDSEQFEKFYSAFSKNIKLGVHEDTQNRAALAKLLRYNSTKSVDELTSLTDYVTRMPEHQKNIYYITGESLKAVEKSPFLDALKAKNFEVLFLTDPIDEYAFTQLKEFEGKTLVDITKDFELEETDEEKAEREKEIKEYEPLTKALKEILGDQVEKVVVSYKLLDAPAAIRTGQFGWSANMERIMKAQALRDSSMSSYMSSKKTFEISPKSPIIKELKKRVDEGGAQDKTVKDLTKLLYETALLTSGFSLDEPTSFASRINRLISLGLNIDEDEETETAPEASTAAPVEEVPADTEMEEVD'],
['IF1_ECOLI' ,'Prokaryote', 'MAKEDNIEMQGTVLETLPNTMFRVELENGHVVTAHISGKMRKNYIRILTGDKVTVELTPYDLSKGRIVFRSR'],
['KCNH2_HUMAN' ,'Human', 'MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS'],
['KKA2_KLEPN' ,'Prokaryote', 'MIEQDGLHAGSPAAWVERLFGYDWAQQTIGCSDAAVFRLSAQGRPVLFVKTDLSGALNELQDEAARLSWLATTGVPCAAVLDVVTEAGRDWLLLGEVPGQDLLSSHLAPAEKVSIMADAMRRLHTLDPATCPFDHQAKHRIERARTRMEAGLVDQDDLDEEHQGLAPAELFARLKARMPDGEDLVVTHGDACLPNIMVENGRFSGFIDCGRLGVADRYQDIALATRDIAEELGGEWADRFLVLYGIAAPDSQRIAFYRLLDEFF'],
['MSH2_HUMAN' ,'Human', 'MAVQPKETLQLESAAEVGFVRFFQGMPEKPTTTVRLFDRGDFYTAHGEDALLAAREVFKTQGVIKYMGPAGAKNLQSVVLSKMNFESFVKDLLLVRQYRVEVYKNRAGNKASKENDWYLAYKASPGNLSQFEDILFGNNDMSASIGVVGVKMSAVDGQRQVGVGYVDSIQRKLGLCEFPDNDQFSNLEALLIQIGPKECVLPGGETAGDMGKLRQIIQRGGILITERKKADFSTKDIYQDLNRLLKGKKGEQMNSAVLPEMENQVAVSSLSAVIKFLELLSDDSNFGQFELTTFDFSQYMKLDIAAVRALNLFQGSVEDTTGSQSLAALLNKCKTPQGQRLVNQWIKQPLMDKNRIEERLNLVEAFVEDAELRQTLQEDLLRRFPDLNRLAKKFQRQAANLQDCYRLYQGINQLPNVIQALEKHEGKHQKLLLAVFVTPLTDLRSDFSKFQEMIETTLDMDQVENHEFLVKPSFDPNLSELREIMNDLEKKMQSTLISAARDLGLDPGKQIKLDSSAQFGYYFRVTCKEEKVLRNNKNFSTVDIQKNGVKFTNSKLTSLNEEYTKNKTEYEEAQDAIVKEIVNISSGYVEPMQTLNDVLAQLDAVVSFAHVSNGAPVPYVRPAILEKGQGRIILKASRHACVEVQDEIAFIPNDVYFEKDKQMFHIITGPNMGGKSTYIRQTGVIVLMAQIGCFVPCESAEVSIVDCILARVGAGDSQLKGVSTFMAEMLETASILRSATKDSLIIIDELGRGTSTYDGFGLAWAISEYIATKIGAFCMFATHFHELTALANQIPTVNNLHVTALTTEETLTMLYQVKKGVCDQSFGIHVAELANFPKHVIECAKQKALELEEFQYIGESQGYDIMEPAAKKCYLEREQGEKIIQEFLSKVKQMPFTEMSEENITIKLKQLKAEVIAKNNSFVNEIISRIKVTT'],
['PABP_YEAST' ,'Other eukaryote', 'MADITDKTAEQLENLNIQDDQKQAATGSESQSVENSSASLYVGDLEPSVSEAHLYDIFSPIGSVSSIRVCRDAITKTSLGYAYVNFNDHEAGRKAIEQLNYTPIKGRLCRIMWSQRDPSLRKKGSGNIFIKNLHPDIDNKALYDTFSVFGDILSSKIATDENGKSKGFGFVHFEEEGAAKEAIDALNGMLLNGQEIYVAPHLSRKERDSQLEETKAHYTNLYVKNINSETTDEQFQELFAKFGPIVSASLEKDADGKLKGFGFVNYEKHEDAVKAVEALNDSELNGEKLYVGRAQKKNERMHVLKKQYEAYRLEKMAKYQGVNLFVKNLDDSVDDEKLEEEFAPYGTITSAKVMRTENGKSKGFGFVCFSTPEEATKAITEKNQQIVAGKPLYVAIAQRKDVRRSQLAQQIQARNQMRYQQATAAAAAAAAGMPGQFMPPMFYGVMPPRGVPFNGPNPQQMNPMGGMPKNGMPPQFRNGPVYGVPPQGGFPRNANDNNQFYQQKQRQALGEQLYKKVSAKTSNEEAAGKITGMILDLPPQEVFPLLESDELFEQHYKEASAAYESFKKEQEQQTEQA'],
['P53_HUMAN' ,'Human', 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPRVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD'],
['PTEN_HUMAN' ,'Human', 'MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV'],
['RL40A_YEAST' ,'Eukaryote ', 'MQIFVKTLTGKTITLEVESSDTIDNVKSKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGGIIEPSLKALASKYNCDKSVCRKCYARLPPRATNCRKRKCGHTNQLRPKKKLK'],
['SCN5A_HUMAN' ,'Human', 'MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV'],
['SUMO1_HUMAN' ,'Human', 'MSDQEAKPSTEDLGDKKEGEYIKLKVIGQDSSEIHFKVKMTTHLKKLKESYCQRQGVPMNSLRFLFEGQRIADNHTPKELGMEEEDVIEVYQEQTGGHSTV']
],
)
gr.Markdown("<br>")
gr.Markdown("# Fitness predictions for all single amino acid substitutions in mutation range")
gr.Markdown("Inference may take a few seconds for short proteins & mutation ranges to several minutes for longer ones")
output_image = gr.Gallery(label="Fitness predictions for all single amino acid substitutions in mutation range",type="filepath") #Using Gallery to be able to scroll large matrix images
output_recommendations = gr.Textbox(label="Mutation recommendations")
clear_button.click(
inputs = [protein_sequence_input,mutation_range_start,mutation_range_end],
outputs = [protein_sequence_input,mutation_range_start,mutation_range_end],
fn=clear_inputs
)
run_button.click(
fn=score_and_create_matrix_all_singles,
inputs=[protein_sequence_input,mutation_range_start,mutation_range_end,model_size_selection,scoring_mirror,batch_size_inference],
outputs=[output_image,output_recommendations],
)
gr.Markdown("# Mutate the starting protein sequence")
with gr.Row():
mutation_triplet = gr.Textbox(lines=1,label="Selected mutation", placeholder = "Input the mutation triplet for the selected mutation (eg., M1A)")
mutate_button = gr.Button(value="Apply mutation to starting protein", variant="primary")
mutated_protein_sequence = gr.Textbox(lines=1,label="Mutated protein sequence")
mutate_button.click(
fn = get_mutated_protein,
inputs = [protein_sequence_input,mutation_triplet],
outputs = mutated_protein_sequence
)
gr.Markdown("<p>You may now use the output mutated sequence above as the starting sequence for another round of in silico directed evolution.</p>")
gr.Markdown("For more information about the Tranception model, please refer to our paper below:")
gr.Markdown("<p><b>Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval</b><br>Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks<sup>*</sup>, Yarin Gal<sup>*</sup><br><sup>* equal senior authorship</sup></p>")
gr.Markdown("Links: <a href='https://proceedings.mlr.press/v162/notin22a.html' target='_blank'>Paper</a> <a href='https://github.com/OATML-Markslab/Tranception' target='_blank'>Code</a> <a href='https://sites.google.com/view/proteingym/substitutions' target='_blank'>ProteinGym</a>")
tranception_design.launch(debug=True)