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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 #################################################################### | |
| ####################################################################################################################################### | |
| AA_vocab = "ACDEFGHIKLMNPQRSTVWY" | |
| 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,AA_vocab=AA_vocab,mutation_range_start=None,mutation_range_end=None,annotate=True,fontsize=20): | |
| piv=scores.pivot(index='position',columns='target_AA',values='avg_score').round(4) | |
| fig, ax = plt.subplots(figsize=(50,len(sequence)*0.6)) | |
| scores_dict = {} | |
| valid_mutant_set=set(scores.mutant) | |
| if mutation_range_start is None: mutation_range_start=1 | |
| if mutation_range_end is None: mutation_range_end=len(sequence) | |
| 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(scores.loc[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_end-mutation_range_start+1,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.figure.axes[-1].yaxis.set_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() | |
| plt.savefig('fitness_scoring_substitution_matrix.png') | |
| plt.show() | |
| return 'fitness_scoring_substitution_matrix.png' | |
| 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)) | |
| print(top_positions) | |
| 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,num_workers=0,AA_vocab=AA_vocab): | |
| 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") | |
| 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_heatmap = create_scoring_matrix_visual(scores,sequence,AA_vocab,mutation_range_start,mutation_range_end) | |
| return [score_heatmap],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("# In silico directed evolution for protein redesign 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 substitutions 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(): | |
| 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") | |
| #output_plot = gr.Plot(label="Fitness predictions for all single amino acid substitutions in mutation range") | |
| #output_image = gr.Image(label="Fitness predictions for all single amino acid substitutions in mutation range",type="filepath") | |
| output_image = gr.Gallery(label="Fitness predictions (inference may take a few seconds for short proteins & mutation ranges to several minutes for longer ones)",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], | |
| 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) |