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--- |
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license: mit |
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--- |
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# ESM-2 Finetuned for PPI Prediction |
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This is a finetuned version of `facebook/esm2_t33_650M_UR50D` using the masked language modeling objective. |
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The model was finetuned for four epochs on concatenated pairs of interacting proteins, clustered using persistent homology |
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landscapes as explained in [this post](https://huggingface.co/blog/AmelieSchreiber/faster-pha). The dataset consists of 10,000 |
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protein pairs, which can be [found here](https://huggingface.co/datasets/AmelieSchreiber/pha_clustered_protein_complexes). |
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This is a very new method for clustering protein-protein complexes. |
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Using the MLM loss to predict pairs of interacting proteins was inspired by [this paper](https://arxiv.org/abs/2308.07136). However, |
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the authors do not finetune the models for this task. Thus we reasoned that improved performance on this method could be achieved |
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by finetuning the model on pairs of interacting proteins. |
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## Using the Model |
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To use the model, we follow [this blog post](https://huggingface.co/blog/AmelieSchreiber/protein-binding-partners-with-esm2). |
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Below we see how to use the model for ranking potential binders for a target protein of interest. The lower the MLM loss average, |
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the more likely the two proteins are to interact with one another. |
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```python |
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import numpy as np |
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from transformers import AutoTokenizer, EsmForMaskedLM |
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import torch |
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# Load the base model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") |
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model = EsmForMaskedLM.from_pretrained("AmelieSchreiber/esm_mlmppi_ph50_v3") |
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# Ensure the model is in evaluation mode |
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model.eval() |
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# Define the protein of interest and its potential binders |
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protein_of_interest = "MLTEVMEVWHGLVIAVVSLFLQACFLTAINYLLSRHMAHKSEQILKAASLQVPRPSPGHHHPPAVKEMKETQTERDIPMSDSLYRHDSDTPSDSLDSSCSSPPACQATEDVDYTQVVFSDPGELKNDSPLDYENIKEITDYVNVNPERHKPSFWYFVNPALSEPAEYDQVAM" |
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potential_binders = [ |
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# Known to interact |
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"MASPGSGFWSFGSEDGSGDSENPGTARAWCQVAQKFTGGIGNKLCALLYGDAEKPAESGGSQPPRAAARKAACACDQKPCSCSKVDVNYAFLHATDLLPACDGERPTLAFLQDVMNILLQYVVKSFDRSTKVIDFHYPNELLQEYNWELADQPQNLEEILMHCQTTLKYAIKTGHPRYFNQLSTGLDMVGLAADWLTSTANTNMFTYEIAPVFVLLEYVTLKKMREIIGWPGGSGDGIFSPGGAISNMYAMMIARFKMFPEVKEKGMAALPRLIAFTSEHSHFSLKKGAAALGIGTDSVILIKCDERGKMIPSDLERRILEAKQKGFVPFLVSATAGTTVYGAFDPLLAVADICKKYKIWMHVDAAWGGGLLMSRKHKWKLSGVERANSVTWNPHKMMGVPLQCSALLVREEGLMQNCNQMHASYLFQQDKHYDLSYDTGDKALQCGRHVDVFKLWLMWRAKGTTGFEAHVDKCLELAEYLYNIIKNREGYEMVFDGKPQHTNVCFWYIPPSLRTLEDNEERMSRLSKVAPVIKARMMEYGTTMVSYQPLGDKVNFFRMVISNPAATHQDIDFLIEEIERLGQDL", |
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"MAAGVAGWGVEAEEFEDAPDVEPLEPTLSNIIEQRSLKWIFVGGKGGVGKTTCSCSLAVQLSKGRESVLIISTDPAHNISDAFDQKFSKVPTKVKGYDNLFAMEIDPSLGVAELPDEFFEEDNMLSMGKKMMQEAMSAFPGIDEAMSYAEVMRLVKGMNFSVVVFDTAPTGHTLRLLNFPTIVERGLGRLMQIKNQISPFISQMCNMLGLGDMNADQLASKLEETLPVIRSVSEQFKDPEQTTFICVCIAEFLSLYETERLIQELAKCKIDTHNIIVNQLVFPDPEKPCKMCEARHKIQAKYLDQMEDLYEDFHIVKLPLLPHEVRGADKVNTFSALLLEPYKPPSAQ", |
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"EKTGLSIRGAQEEDPPDPQLMRLDNMLLAEGVSGPEKGGGSAAAAAAAAASGGSSDNSIEHSDYRAKLTQIRQIYHTELEKYEQACNEFTTHVMNLLREQSRTRPISPKEIERMVGIIHRKFSSIQMQLKQSTCEAVMILRSRFLDARRKRRNFSKQATEILNEYFYSHLSNPYPSEEAKEELAKKCSITVSQSLVKDPKERGSKGSDIQPTSVVSNWFGNKRIRYKKNIGKFQEEANLYAAKTAVTAAHAVAAAVQNNQTNSPTTPNSGSSGSFNLPNSGDMFMNMQSLNGDSYQGSQVGANVQSQVDTLRHVINQTGGYSDGLGGNSLYSPHNLNANGGWQDATTPSSVTSPTEGPGSVHSDTSN", |
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# Not known to interact |
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"MRQRLLPSVTSLLLVALLFPGSSQARHVNHSATEALGELRERAPGQGTNGFQLLRHAVKRDLLPPRTPPYQVHISHREARGPSFRICVDFLGPRWARGCSTGN", |
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"MSGIALSRLAQERKAWRKDHPFGFVAVPTKNPDGTMNLMNWECAIPGKKGTPWEGGLFKLRMLFKDDYPSSPPKCKFEPPLFHPNVYPSGTVCLSILEEDKDWRPAITIKQILLGIQELLNEPNIQDPAQAEAYTIYCQNRVEYEKRVRAQAKKFAPS" |
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] # Add potential binding sequences here |
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def compute_mlm_loss(protein, binder, iterations=5): |
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total_loss = 0.0 |
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for _ in range(iterations): |
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# Concatenate protein sequences with a separator |
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concatenated_sequence = protein + binder |
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# Mask a subset of amino acids in the concatenated sequence (excluding the separator) |
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tokens = list(concatenated_sequence) |
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mask_rate = 0.35 # For instance, masking 35% of the sequence |
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num_mask = int(len(tokens) * mask_rate) |
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# Exclude the separator from potential mask indices |
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available_indices = [i for i, token in enumerate(tokens) if token != ":"] |
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probs = torch.ones(len(available_indices)) |
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mask_indices = torch.multinomial(probs, num_mask, replacement=False) |
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for idx in mask_indices: |
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tokens[available_indices[idx]] = tokenizer.mask_token |
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masked_sequence = "".join(tokens) |
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inputs = tokenizer(masked_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length', add_special_tokens=False) |
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# Compute the MLM loss |
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with torch.no_grad(): |
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outputs = model(**inputs, labels=inputs["input_ids"]) |
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loss = outputs.loss |
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total_loss += loss.item() |
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# Return the average loss |
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return total_loss / iterations |
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# Compute MLM loss for each potential binder |
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mlm_losses = {} |
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for binder in potential_binders: |
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loss = compute_mlm_loss(protein_of_interest, binder) |
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mlm_losses[binder] = loss |
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# Rank binders based on MLM loss |
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ranked_binders = sorted(mlm_losses, key=mlm_losses.get) |
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print("Ranking of Potential Binders:") |
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for idx, binder in enumerate(ranked_binders, 1): |
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print(f"{idx}. {binder} - MLM Loss: {mlm_losses[binder]}") |
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``` |
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## PPI Networks |
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To construct a protein-protein interaction network, try running the following code. Try adjusting the length of the connector |
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(0-25 for example). Try also adjusting the number of iterations and the masking percentage. |
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```python |
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import networkx as nx |
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import numpy as np |
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import torch |
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from transformers import AutoTokenizer, AutoModelForMaskedLM, EsmForMaskedLM |
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import plotly.graph_objects as go |
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from ipywidgets import interact |
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from ipywidgets import widgets |
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# Check if CUDA is available and set the default device accordingly |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load the pretrained (or fine-tuned) ESM-2 model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") |
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model = EsmForMaskedLM.from_pretrained("AmelieSchreiber/esm_mlmppi_ph50_v3") |
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# Send the model to the device (GPU or CPU) |
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model.to(device) |
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# Ensure the model is in evaluation mode |
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model.eval() |
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# Define Protein Sequences (Replace with your list) |
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all_proteins = [ |
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"MFLSILVALCLWLHLALGVRGAPCEAVRIPMCRHMPWNITRMPNHLHHSTQENAILAIEQYEELVDVNCSAVLRFFLCAMYAPICTLEFLHDPIKPCKSVCQRARDDCEPLMKMYNHSWPESLACDELPVYDRGVCISPEAIVTDLPEDVKWIDITPDMMVQERPLDVDCKRLSPDRCKCKKVKPTLATYLSKNYSYVIHAKIKAVQRSGCNEVTTVVDVKEIFKSSSPIPRTQVPLITNSSCQCPHILPHQDVLIMCYEWRSRMMLLENCLVEKWRDQLSKRSIQWEERLQEQRRTVQDKKKTAGRTSRSNPPKPKGKPPAPKPASPKKNIKTRSAQKRTNPKRV", |
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"MDAVEPGGRGWASMLACRLWKAISRALFAEFLATGLYVFFGVGSVMRWPTALPSVLQIAITFNLVTAMAVQVTWKASGAHANPAVTLAFLVGSHISLPRAVAYVAAQLVGATVGAALLYGVMPGDIRETLGINVVRNSVSTGQAVAVELLLTLQLVLCVFASTDSRQTSGSPATMIGISVALGHLIGIHFTGCSMNPARSFGPAIIIGKFTVHWVFWVGPLMGALLASLIYNFVLFPDTKTLAQRLAILTGTVEVGTGAGAGAEPLKKESQPGSGAVEMESV", |
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"MKFLLDILLLLPLLIVCSLESFVKLFIPKRRKSVTGEIVLITGAGHGIGRLTAYEFAKLKSKLVLWDINKHGLEETAAKCKGLGAKVHTFVVDCSNREDIYSSAKKVKAEIGDVSILVNNAGVVYTSDLFATQDPQIEKTFEVNVLAHFWTTKAFLPAMTKNNHGHIVTVASAAGHVSVPFLLAYCSSKFAAVGFHKTLTDELAALQITGVKTTCLCPNFVNTGFIKNPSTSLGPTLEPEEVVNRLMHGILTEQKMIFIPSSIAFLTTLERILPERFLAVLKRKISVKFDAVIGYKMKAQ", |
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"MAAAVPRRPTQQGTVTFEDVAVNFSQEEWCLLSEAQRCLYRDVMLENLALISSLGCWCGSKDEEAPCKQRISVQRESQSRTPRAGVSPKKAHPCEMCGLILEDVFHFADHQETHHKQKLNRSGACGKNLDDTAYLHQHQKQHIGEKFYRKSVREASFVKKRKLRVSQEPFVFREFGKDVLPSSGLCQEEAAVEKTDSETMHGPPFQEGKTNYSCGKRTKAFSTKHSVIPHQKLFTRDGCYVCSDCGKSFSRYVSFSNHQRDHTAKGPYDCGECGKSYSRKSSLIQHQRVHTGQTAYPCEECGKSFSQKGSLISHQLVHTGEGPYECRECGKSFGQKGNLIQHQQGHTGERAYHCGECGKSFRQKFCFINHQRVHTGERPYKCGECGKSFGQKGNLVHHQRGHTGERPYECKECGKSFRYRSHLTEHQRLHTGERPYNCRECGKLFNRKYHLLVHERVHTGERPYACEVCGKLFGNKHSVTIHQRIHTGERPYECSECGKSFLSSSALHVHKRVHSGQKPYKCSECGKSFSECSSLIKHRRIHTGERPYECTKCGKTFQRSSTLLHHQSSHRRKAL", |
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"MGQPWAAGSTDGAPAQLPLVLTALWAAAVGLELAYVLVLGPGPPPLGPLARALQLALAAFQLLNLLGNVGLFLRSDPSIRGVMLAGRGLGQGWAYCYQCQSQVPPRSGHCSACRVCILRRDHHCRLLGRCVGFGNYRPFLCLLLHAAGVLLHVSVLLGPALSALLRAHTPLHMAALLLLPWLMLLTGRVSLAQFALAFVTDTCVAGALLCGAGLLFHGMLLLRGQTTWEWARGQHSYDLGPCHNLQAALGPRWALVWLWPFLASPLPGDGITFQTTADVGHTAS", |
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"MGLRIHFVVDPHGWCCMGLIVFVWLYNIVLIPKIVLFPHYEEGHIPGILIIIFYGISIFCLVALVRASITDPGRLPENPKIPHGEREFWELCNKCNLMRPKRSHHCSRCGHCVRRMDHHCPWINNCVGEDNHWLFLQLCFYTELLTCYALMFSFCHYYYFLPLKKRNLDLFVFRHELAIMRLAAFMGITMLVGITGLFYTQLIGIITDTTSIEKMSNCCEDISRPRKPWQQTFSEVFGTRWKILWFIPFRQRQPLRVPYHFANHV", |
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"MLLLGAVLLLLALPGHDQETTTQGPGVLLPLPKGACTGWMAGIPGHPGHNGAPGRDGRDGTPGEKGEKGDPGLIGPKGDIGETGVPGAEGPRGFPGIQGRKGEPGEGAYVYRSAFSVGLETYVTIPNMPIRFTKIFYNQQNHYDGSTGKFHCNIPGLYYFAYHITVYMKDVKVSLFKKDKAMLFTYDQYQENNVDQASGSVLLHLEVGDQVWLQVYGEGERNGLYADNDNDSTFTGFLLYHDTN", |
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"MGLLAFLKTQFVLHLLVGFVFVVSGLVINFVQLCTLALWPVSKQLYRRLNCRLAYSLWSQLVMLLEWWSCTECTLFTDQATVERFGKEHAVIILNHNFEIDFLCGWTMCERFGVLGSSKVLAKKELLYVPLIGWTWYFLEIVFCKRKWEEDRDTVVEGLRRLSDYPEYMWFLLYCEGTRFTETKHRVSMEVAAAKGLPVLKYHLLPRTKGFTTAVKCLRGTVAAVYDVTLNFRGNKNPSLLGILYGKKYEADMCVRRFPLEDIPLDEKEAAQWLHKLYQEKDALQEIYNQKGMFPGEQFKPARRPWTLLNFLSWATILLSPLFSFVLGVFASGSPLLILTFLGFVGAASFGVRRLIGVTEIEKGSSYGNQEFKKKE", |
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"MDLAGLLKSQFLCHLVFCYVFIASGLIINTIQLFTLLLWPINKQLFRKINCRLSYCISSQLVMLLEWWSGTECTIFTDPRAYLKYGKENAIVVLNHKFEIDFLCGWSLSERFGLLGGSKVLAKKELAYVPIIGWMWYFTEMVFCSRKWEQDRKTVATSLQHLRDYPEKYFFLIHCEGTRFTEKKHEISMQVARAKGLPRLKHHLLPRTKGFAITVRSLRNVVSAVYDCTLNFRNNENPTLLGVLNGKKYHADLYVRRIPLEDIPEDDDECSAWLHKLYQEKDAFQEEYYRTGTFPETPMVPPRRPWTLVNWLFWASLVLYPFFQFLVSMIRSGSSLTLASFILVFFVASVGVRWMIGVTEIDKGSAYGNSDSKQKLND", |
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"MALLLCFVLLCGVVDFARSLSITTPEEMIEKAKGETAYLPCKFTLSPEDQGPLDIEWLISPADNQKVDQVIILYSGDKIYDDYYPDLKGRVHFTSNDLKSGDASINVTNLQLSDIGTYQCKVKKAPGVANKKIHLVVLVKPSGARCYVDGSEEIGSDFKIKCEPKEGSLPLQYEWQKLSDSQKMPTSWLAEMTSSVISVKNASSEYSGTYSCTVRNRVGSDQCLLRLNVVPPSNKAGLIAGAIIGTLLALALIGLIIFCCRKKRREEKYEKEVHHDIREDVPPPKSRTSTARSYIGSNHSSLGSMSPSNMEGYSKTQYNQVPSEDFERTPQSPTLPPAKVAAPNLSRMGAIPVMIPAQSKDGSIV", |
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"MSYVFVNDSSQTNVPLLQACIDGDFNYSKRLLESGFDPNIRDSRGRTGLHLAAARGNVDICQLLHKFGADLLATDYQGNTALHLCGHVDTIQFLVSNGLKIDICNHQGATPLVLAKRRGVNKDVIRLLESLEEQEVKGFNRGTHSKLETMQTAESESAMESHSLLNPNLQQGEGVLSSFRTTWQEFVEDLGFWRVLLLIFVIALLSLGIAYYVSGVLPFVENQPELVH", |
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"MRVAGAAKLVVAVAVFLLTFYVISQVFEIKMDASLGNLFARSALDTAARSTKPPRYKCGISKACPEKHFAFKMASGAANVVGPKICLEDNVLMSGVKNNVGRGINVALANGKTGEVLDTKYFDMWGGDVAPFIEFLKAIQDGTIVLMGTYDDGATKLNDEARRLIADLGSTSITNLGFRDNWVFCGGKGIKTKSPFEQHIKNNKDTNKYEGWPEVVEMEGCIPQKQD", |
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"MAPAAATGGSTLPSGFSVFTTLPDLLFIFEFIFGGLVWILVASSLVPWPLVQGWVMFVSVFCFVATTTLIILYIIGAHGGETSWVTLDAAYHCTAALFYLSASVLEALATITMQDGFTYRHYHENIAAVVFSYIATLLYVVHAVFSLIRWKSS", |
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"MRLQGAIFVLLPHLGPILVWLFTRDHMSGWCEGPRMLSWCPFYKVLLLVQTAIYSVVGYASYLVWKDLGGGLGWPLALPLGLYAVQLTISWTVLVLFFTVHNPGLALLHLLLLYGLVVSTALIWHPINKLAALLLLPYLAWLTVTSALTYHLWRDSLCPVHQPQPTEKSD", |
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"MEESVVRPSVFVVDGQTDIPFTRLGRSHRRQSCSVARVGLGLLLLLMGAGLAVQGWFLLQLHWRLGEMVTRLPDGPAGSWEQLIQERRSHEVNPAAHLTGANSSLTGSGGPLLWETQLGLAFLRGLSYHDGALVVTKAGYYYIYSKVQLGGVGCPLGLASTITHGLYKRTPRYPEELELLVSQQSPCGRATSSSRVWWDSSFLGGVVHLEAGEKVVVRVLDERLVRLRDGTRSYFGAFMV" |
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] |
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def compute_average_mlm_loss(protein1, protein2, iterations=10): |
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total_loss = 0.0 |
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connector = "G" * 25 # Connector sequence of G's |
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for _ in range(iterations): |
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concatenated_sequence = protein1 + connector + protein2 |
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inputs = tokenizer(concatenated_sequence, return_tensors="pt", padding=True, truncation=True, max_length=1024) |
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mask_prob = 0.35 |
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mask_indices = torch.rand(inputs["input_ids"].shape, device=device) < mask_prob |
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# Locate the positions of the connector 'G's and set their mask indices to False |
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connector_indices = tokenizer.encode(connector, add_special_tokens=False) |
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connector_length = len(connector_indices) |
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start_connector = len(tokenizer.encode(protein1, add_special_tokens=False)) |
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end_connector = start_connector + connector_length |
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# Avoid masking the connector 'G's |
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mask_indices[0, start_connector:end_connector] = False |
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# Apply the mask to the input IDs |
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inputs["input_ids"][mask_indices] = tokenizer.mask_token_id |
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inputs = {k: v.to(device) for k, v in inputs.items()} # Send inputs to the device |
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with torch.no_grad(): |
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outputs = model(**inputs, labels=inputs["input_ids"]) |
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loss = outputs.loss |
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total_loss += loss.item() |
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return total_loss / iterations |
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# Compute all average losses to determine the maximum threshold for the slider |
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all_losses = [] |
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for i, protein1 in enumerate(all_proteins): |
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for j, protein2 in enumerate(all_proteins[i+1:], start=i+1): |
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avg_loss = compute_average_mlm_loss(protein1, protein2) |
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all_losses.append(avg_loss) |
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# Set the maximum threshold to the maximum loss computed |
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max_threshold = max(all_losses) |
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print(f"Maximum loss (maximum threshold for slider): {max_threshold}") |
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def plot_graph(threshold): |
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G = nx.Graph() |
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# Add all protein nodes to the graph |
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for i, protein in enumerate(all_proteins): |
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G.add_node(f"protein {i+1}") |
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# Loop through all pairs of proteins and calculate average MLM loss |
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loss_idx = 0 # Index to keep track of the position in the all_losses list |
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for i, protein1 in enumerate(all_proteins): |
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for j, protein2 in enumerate(all_proteins[i+1:], start=i+1): |
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avg_loss = all_losses[loss_idx] |
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loss_idx += 1 |
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# Add an edge if the loss is below the threshold |
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if avg_loss < threshold: |
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G.add_edge(f"protein {i+1}", f"protein {j+1}", weight=round(avg_loss, 3)) |
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# 3D Network Plot |
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# Adjust the k parameter to bring nodes closer. This might require some experimentation to find the right value. |
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k_value = 2 # Lower value will bring nodes closer together |
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pos = nx.spring_layout(G, dim=3, seed=42, k=k_value) |
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edge_x = [] |
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edge_y = [] |
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edge_z = [] |
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for edge in G.edges(): |
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x0, y0, z0 = pos[edge[0]] |
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x1, y1, z1 = pos[edge[1]] |
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edge_x.extend([x0, x1, None]) |
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edge_y.extend([y0, y1, None]) |
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edge_z.extend([z0, z1, None]) |
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edge_trace = go.Scatter3d(x=edge_x, y=edge_y, z=edge_z, mode='lines', line=dict(width=0.5, color='grey')) |
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node_x = [] |
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node_y = [] |
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node_z = [] |
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node_text = [] |
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for node in G.nodes(): |
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x, y, z = pos[node] |
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node_x.append(x) |
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node_y.append(y) |
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node_z.append(z) |
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node_text.append(node) |
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node_trace = go.Scatter3d(x=node_x, y=node_y, z=node_z, mode='markers', marker=dict(size=5), hoverinfo='text', hovertext=node_text) |
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layout = go.Layout(title='Protein Interaction Graph', title_x=0.5, scene=dict(xaxis=dict(showbackground=False), yaxis=dict(showbackground=False), zaxis=dict(showbackground=False))) |
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fig = go.Figure(data=[edge_trace, node_trace], layout=layout) |
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fig.show() |
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# Create an interactive slider for the threshold value with a default of 8.50 |
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interact(plot_graph, threshold=widgets.FloatSlider(min=0.0, max=max_threshold, step=0.05, value=8.25)) |
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``` |
