Package: berteome
berteomefromberteomeimportprot_bertSome weights of the model checkpoint at Rostlab/prot_bert were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).fromberteomeimportesmfromberteomeimportberteomeDF---------------------------------------------------------------------------

ImportError                               Traceback (most recent call last)

/var/folders/x1/098shbqn0rs8vd5ybvyb0k9h0000gn/T/ipykernel_70195/4107489332.py in <module>
----> 1 from berteome import berteomeDF


ImportError: cannot import name 'berteomeDF' from 'berteome' (/Users/jeffcole/miniconda3/envs/berteome/lib/python3.7/site-packages/berteome-0.1.0-py3.7.egg/berteome/__init__.py)prot_bert.bertPredictionDF("MENDEL")<style scoped>
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}</style>wtwtIndexACDEFGHI...MNPQRSTVWY0M10.0366850.0115010.0482290.1188680.0240640.0391900.0126170.066477...0.0765800.0726370.0247140.0386600.0430910.0702570.0565260.0499110.0077790.0216921E20.0457120.0156590.0419130.0748160.0371460.0443170.0182600.073063...0.0435720.0626550.0252720.0369050.0555320.0644120.0499450.0567790.0126890.0298872N30.0435580.0096840.1625660.1843360.0337770.0446540.0123530.052622...0.0414780.0419840.0199890.0255110.0294280.0480980.0302990.0547340.0074280.0249203D40.0420790.0132430.0497440.0861890.0397330.0559070.0168600.073291...0.0400780.0608170.0320220.0396860.0462240.0623190.0448980.0589330.0108750.0265944E50.0466380.0187690.0798160.0869080.0506340.0504620.0223950.074495...0.0289600.0622290.0238770.0305320.0404860.0651900.0449340.0680320.0121550.0380315L60.0356950.0086150.0609280.1425760.0195810.0462870.0130430.060374...0.0374240.0901770.0193580.0327330.0438230.0458630.0432240.0451210.0098000.0212416 rows × 22 columnsesm.esmPredictionDF("MENDEL")<style scoped>
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}</style>wtwtIndexACDEFGHI...MNPQRSTVWY0M10.0339520.0070390.0547370.0633650.0185900.0292670.0102160.028865...0.4560820.0291660.0233440.0190430.0236030.0301700.0233520.0339820.0047910.0130761E20.0589990.0210310.0546220.0720570.0371860.0530900.0258480.065879...0.0243290.0597100.0357280.0403690.0523000.0736180.0573190.0629750.0140290.0303392N30.0556260.0140600.0778870.1221530.0347270.0588220.0191710.061401...0.0313180.0441720.0296680.0343350.0523720.0573940.0474320.0694320.0132820.0258593D40.0446100.0176270.0362980.0710250.0316290.0497820.0235180.061833...0.0585150.0437620.0372270.0541640.0551200.0600910.0529120.0701250.0175670.0272114E50.0459660.0279270.0474310.0575740.0524500.0545450.0306700.071293...0.0254170.0511060.0334500.0389400.0551940.0764230.0500600.0635500.0176630.0380915L60.0482260.0162310.0602840.1003910.0315340.0525840.0230150.065281...0.0293540.0638400.0255460.0393130.0629370.0641470.0537900.0601770.0137140.0281476 rows × 22 columnsThis file will become your README and also the index of your documentation.Installpip install berteomeHow to useBerteome makes use of the masked language model of BERT to determine predictions for all residues in a protein sequence.For instance, it uses theprot_bertmodel from the Rostlab to generate predictions for a given residue in a sequence as follows by putting a[MASK]token in a spaced out seqeunce.single residue predictionsberteome.unmasker('D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T')[{'sequence': 'D L I P T S S K L V V L D T S L Q V K K A F F A L V T',
  'score': 0.11088439077138901,
  'token': 5,
  'token_str': 'L'},
 {'sequence': 'D L I P T S S K L V V S D T S L Q V K K A F F A L V T',
  'score': 0.0840253233909607,
  'token': 10,
  'token_str': 'S'},
 {'sequence': 'D L I P T S S K L V V V D T S L Q V K K A F F A L V T',
  'score': 0.07328338176012039,
  'token': 8,
  'token_str': 'V'},
 {'sequence': 'D L I P T S S K L V V K D T S L Q V K K A F F A L V T',
  'score': 0.06921844929456711,
  'token': 12,
  'token_str': 'K'},
 {'sequence': 'D L I P T S S K L V V I D T S L Q V K K A F F A L V T',
  'score': 0.06382393091917038,
  'token': 11,
  'token_str': 'I'}]This gives the top five best predictions for the residues in this sequence, with the following fields:sequencethe spaced out full sequence including the predicted residuescorehow likely this residue is to be behind the masktokenthe number associated with the residuetoken_strthe actual residueSomething to note is that only one mask can be predicted at a time. So it is not possible to predict more than one mask.Berteome provides two helper functions to help the user generate single residue predictions with ease. To generate predictions the sequences must be space delimeted and have the[MASK]token. To expedite this berteome providesspacifySeq()which space delimits the sequence. To make the sequenceMENDELspace delimited, do the following.mendel_w_spaces=berteome.spacifySeq("MENDEL")mendel_w_spaces'M E N D E L'Then, the user can put a mask in which ever residue they choose usingmaskifySeq(), so to put a mask in the previous sequence on the third residue, do the following.mendel_mask_3=berteome.maskifySeq(mendel_w_spaces,3)mendel_mask_3'M E N [MASK] E L'Now a prediction for the masked residue can be acieved by providing it tounmasker()mendel_mask_3_predictions=berteome.unmasker(mendel_mask_3)mendel_mask_3_predictions[{'sequence': 'M E N L E L',
  'score': 0.10907968133687973,
  'token': 5,
  'token_str': 'L'},
 {'sequence': 'M E N K E L',
  'score': 0.09135881811380386,
  'token': 12,
  'token_str': 'K'},
 {'sequence': 'M E N E E L',
  'score': 0.08618851006031036,
  'token': 9,
  'token_str': 'E'},
 {'sequence': 'M E N I E L',
  'score': 0.07329064607620239,
  'token': 11,
  'token_str': 'I'},
 {'sequence': 'M E N S E L',
  'score': 0.06231885030865669,
  'token': 10,
  'token_str': 'S'}]multiple residue predictionsBerteome also makes it possible to generate all possible predictions for all residues in the sequence usingallResiduePredictions()mendel_all_predictions=berteome.allResiduePredictions("MENDEL")This provides the raw output, so berteome also has a function to make it a more parseable panda dataframe usingresiduePredictionScore()mendel_all_predictionDF=berteome.residuePredictionScore(mendel_all_predictions,"MENDEL")mendel_all_predictionDF<style scoped>
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}</style>wtwtIndexwtScoreACDEFGH...MNPQRSTVWY0M10.0765800.0366850.0115010.0482290.1188680.0240640.0391900.012617...0.0765800.0726370.0247140.0386600.0430910.0702570.0565260.0499110.0077790.0216921E20.0748160.0457120.0156590.0419130.0748160.0371460.0443170.018260...0.0435720.0626550.0252720.0369050.0555320.0644120.0499450.0567790.0126890.0298872N30.0419840.0435580.0096840.1625660.1843360.0337770.0446540.012353...0.0414780.0419840.0199890.0255110.0294280.0480980.0302990.0547340.0074280.0249203D40.0497440.0420790.0132430.0497440.0861890.0397330.0559070.016860...0.0400780.0608170.0320220.0396860.0462240.0623190.0448980.0589330.0108750.0265944E50.0869080.0466380.0187690.0798160.0869080.0506340.0504620.022395...0.0289600.0622290.0238770.0305320.0404860.0651900.0449340.0680320.0121550.0380315L60.0567660.0356950.0086150.0609280.1425760.0195810.0462870.013043...0.0374240.0901770.0193580.0327330.0438230.0458630.0432240.0451210.0098000.0212416 rows × 23 columnsFor each residue (wt), the score of the actual residue is provided as well as the score for all 20 amino acids. If needed, the raw output looks like this.mendel_all_predictions[[{'sequence': 'E E N D E L',
   'score': 0.11886773258447647,
   'token': 9,
   'token_str': 'E'},
  {'sequence': 'K E N D E L',
   'score': 0.10773412883281708,
   'token': 12,
   'token_str': 'K'},
  {'sequence': 'M E N D E L',
   'score': 0.07657960802316666,
   'token': 21,
   'token_str': 'M'},
  {'sequence': 'N E N D E L',
   'score': 0.07263746112585068,
   'token': 17,
   'token_str': 'N'},
  {'sequence': 'L E N D E L',
   'score': 0.07247161120176315,
   'token': 5,
   'token_str': 'L'},
  {'sequence': 'S E N D E L',
   'score': 0.07025714218616486,
   'token': 10,
   'token_str': 'S'},
  {'sequence': 'I E N D E L',
   'score': 0.06647692620754242,
   'token': 11,
   'token_str': 'I'},
  {'sequence': 'T E N D E L',
   'score': 0.05652552843093872,
   'token': 15,
   'token_str': 'T'},
  {'sequence': 'V E N D E L',
   'score': 0.04991144686937332,
   'token': 8,
   'token_str': 'V'},
  {'sequence': 'D E N D E L',
   'score': 0.04822919890284538,
   'token': 14,
   'token_str': 'D'},
  {'sequence': 'R E N D E L',
   'score': 0.04309074580669403,
   'token': 13,
   'token_str': 'R'},
  {'sequence': 'G E N D E L',
   'score': 0.03918968141078949,
   'token': 7,
   'token_str': 'G'},
  {'sequence': 'Q E N D E L',
   'score': 0.03865953907370567,
   'token': 18,
   'token_str': 'Q'},
  {'sequence': 'A E N D E L',
   'score': 0.03668533265590668,
   'token': 6,
   'token_str': 'A'},
  {'sequence': 'P E N D E L',
   'score': 0.024714037775993347,
   'token': 16,
   'token_str': 'P'},
  {'sequence': 'F E N D E L',
   'score': 0.024063965305685997,
   'token': 19,
   'token_str': 'F'},
  {'sequence': 'Y E N D E L',
   'score': 0.0216918233782053,
   'token': 20,
   'token_str': 'Y'},
  {'sequence': 'H E N D E L',
   'score': 0.012617157772183418,
   'token': 22,
   'token_str': 'H'},
  {'sequence': 'C E N D E L',
   'score': 0.01150050014257431,
   'token': 23,
   'token_str': 'C'},
  {'sequence': 'W E N D E L',
   'score': 0.007778722792863846,
   'token': 24,
   'token_str': 'W'},
  {'sequence': 'X E N D E L',
   'score': 0.0003176695026922971,
   'token': 25,
   'token_str': 'X'},
  {'sequence': 'U E N D E L',
   'score': 2.722620462414227e-10,
   'token': 26,
   'token_str': 'U'},
  {'sequence': 'B E N D E L',
   'score': 2.2993504322776914e-10,
   'token': 27,
   'token_str': 'B'},
  {'sequence': 'E N D E L',
   'score': 2.0518205190445116e-10,
   'token': 3,
   'token_str': '[ S E P ]'},
  {'sequence': 'E N D E L',
   'score': 1.9894096092709646e-10,
   'token': 0,
   'token_str': '[ P A D ]'},
  {'sequence': 'E N D E L',
   'score': 1.9621000657554788e-10,
   'token': 2,
   'token_str': '[ C L S ]'},
  {'sequence': 'Z E N D E L',
   'score': 1.7657629525213991e-10,
   'token': 28,
   'token_str': 'Z'},
  {'sequence': 'O E N D E L',
   'score': 1.6853365925051378e-10,
   'token': 29,
   'token_str': 'O'},
  {'sequence': 'E N D E L',
   'score': 1.441355229614416e-10,
   'token': 1,
   'token_str': '[ U N K ]'},
  {'sequence': 'E N D E L',
   'score': 3.96246785383525e-11,
   'token': 4,
   'token_str': '[ M A S K ]'}],
 [{'sequence': 'M L N D E L',
   'score': 0.10648136585950851,
   'token': 5,
   'token_str': 'L'},
  {'sequence': 'M K N D E L',
   'score': 0.10479484498500824,
   'token': 12,
   'token_str': 'K'},
  {'sequence': 'M E N D E L',
   'score': 0.07481565326452255,
   'token': 9,
   'token_str': 'E'},
  {'sequence': 'M I N D E L',
   'score': 0.07306281477212906,
   'token': 11,
   'token_str': 'I'},
  {'sequence': 'M S N D E L',
   'score': 0.06441245973110199,
   'token': 10,
   'token_str': 'S'},
  {'sequence': 'M N N D E L',
   'score': 0.06265531480312347,
   'token': 17,
   'token_str': 'N'},
  {'sequence': 'M V N D E L',
   'score': 0.05677882954478264,
   'token': 8,
   'token_str': 'V'},
  {'sequence': 'M R N D E L',
   'score': 0.0555323101580143,
   'token': 13,
   'token_str': 'R'},
  {'sequence': 'M T N D E L',
   'score': 0.049945324659347534,
   'token': 15,
   'token_str': 'T'},
  {'sequence': 'M A N D E L',
   'score': 0.045712005347013474,
   'token': 6,
   'token_str': 'A'},
  {'sequence': 'M G N D E L',
   'score': 0.04431714862585068,
   'token': 7,
   'token_str': 'G'},
  {'sequence': 'M M N D E L',
   'score': 0.04357245936989784,
   'token': 21,
   'token_str': 'M'},
  {'sequence': 'M D N D E L',
   'score': 0.041912999004125595,
   'token': 14,
   'token_str': 'D'},
  {'sequence': 'M F N D E L',
   'score': 0.03714616224169731,
   'token': 19,
   'token_str': 'F'},
  {'sequence': 'M Q N D E L',
   'score': 0.036904506385326385,
   'token': 18,
   'token_str': 'Q'},
  {'sequence': 'M Y N D E L',
   'score': 0.029887322336435318,
   'token': 20,
   'token_str': 'Y'},
  {'sequence': 'M P N D E L',
   'score': 0.025271963328123093,
   'token': 16,
   'token_str': 'P'},
  {'sequence': 'M H N D E L',
   'score': 0.018260307610034943,
   'token': 22,
   'token_str': 'H'},
  {'sequence': 'M C N D E L',
   'score': 0.015658656135201454,
   'token': 23,
   'token_str': 'C'},
  {'sequence': 'M W N D E L',
   'score': 0.012689119204878807,
   'token': 24,
   'token_str': 'W'},
  {'sequence': 'M X N D E L',
   'score': 0.00018849420303013176,
   'token': 25,
   'token_str': 'X'},
  {'sequence': 'M U N D E L',
   'score': 2.818865696418982e-10,
   'token': 26,
   'token_str': 'U'},
  {'sequence': 'M N D E L',
   'score': 2.734108772717292e-10,
   'token': 2,
   'token_str': '[ C L S ]'},
  {'sequence': 'M O N D E L',
   'score': 2.455574632520552e-10,
   'token': 29,
   'token_str': 'O'},
  {'sequence': 'M B N D E L',
   'score': 2.428661161069101e-10,
   'token': 27,
   'token_str': 'B'},
  {'sequence': 'M N D E L',
   'score': 2.427744116850761e-10,
   'token': 0,
   'token_str': '[ P A D ]'},
  {'sequence': 'M Z N D E L',
   'score': 2.347084193665694e-10,
   'token': 28,
   'token_str': 'Z'},
  {'sequence': 'M N D E L',
   'score': 2.0238072329092915e-10,
   'token': 1,
   'token_str': '[ U N K ]'},
  {'sequence': 'M N D E L',
   'score': 1.2065035082109432e-10,
   'token': 3,
   'token_str': '[ S E P ]'},
  {'sequence': 'M N D E L',
   'score': 3.644244281342246e-11,
   'token': 4,
   'token_str': '[ M A S K ]'}],
 [{'sequence': 'M E E D E L',
   'score': 0.18433618545532227,
   'token': 9,
   'token_str': 'E'},
  {'sequence': 'M E D D E L',
   'score': 0.16256563365459442,
   'token': 14,
   'token_str': 'D'},
  {'sequence': 'M E L D E L',
   'score': 0.09741301834583282,
   'token': 5,
   'token_str': 'L'},
  {'sequence': 'M E V D E L',
   'score': 0.0547335110604763,
   'token': 8,
   'token_str': 'V'},
  {'sequence': 'M E I D E L',
   'score': 0.05262178182601929,
   'token': 11,
   'token_str': 'I'},
  {'sequence': 'M E S D E L',
   'score': 0.04809831455349922,
   'token': 10,
   'token_str': 'S'},
  {'sequence': 'M E G D E L',
   'score': 0.044654298573732376,
   'token': 7,
   'token_str': 'G'},
  {'sequence': 'M E A D E L',
   'score': 0.04355783760547638,
   'token': 6,
   'token_str': 'A'},
  {'sequence': 'M E N D E L',
   'score': 0.04198392480611801,
   'token': 17,
   'token_str': 'N'},
  {'sequence': 'M E M D E L',
   'score': 0.041477616876363754,
   'token': 21,
   'token_str': 'M'},
  {'sequence': 'M E K D E L',
   'score': 0.03501797094941139,
   'token': 12,
   'token_str': 'K'},
  {'sequence': 'M E F D E L',
   'score': 0.03377687186002731,
   'token': 19,
   'token_str': 'F'},
  {'sequence': 'M E T D E L',
   'score': 0.030298525467514992,
   'token': 15,
   'token_str': 'T'},
  {'sequence': 'M E R D E L',
   'score': 0.029428450390696526,
   'token': 13,
   'token_str': 'R'},
  {'sequence': 'M E Q D E L',
   'score': 0.025511162355542183,
   'token': 18,
   'token_str': 'Q'},
  {'sequence': 'M E Y D E L',
   'score': 0.024920152500271797,
   'token': 20,
   'token_str': 'Y'},
  {'sequence': 'M E P D E L',
   'score': 0.019988832995295525,
   'token': 16,
   'token_str': 'P'},
  {'sequence': 'M E H D E L',
   'score': 0.012353409081697464,
   'token': 22,
   'token_str': 'H'},
  {'sequence': 'M E C D E L',
   'score': 0.009683980606496334,
   'token': 23,
   'token_str': 'C'},
  {'sequence': 'M E W D E L',
   'score': 0.0074284737929701805,
   'token': 24,
   'token_str': 'W'},
  {'sequence': 'M E X D E L',
   'score': 0.00015013833763077855,
   'token': 25,
   'token_str': 'X'},
  {'sequence': 'M E D E L',
   'score': 1.6619389198169188e-10,
   'token': 3,
   'token_str': '[ S E P ]'},
  {'sequence': 'M E B D E L',
   'score': 1.3698038536791302e-10,
   'token': 27,
   'token_str': 'B'},
  {'sequence': 'M E Z D E L',
   'score': 1.168956598185389e-10,
   'token': 28,
   'token_str': 'Z'},
  {'sequence': 'M E D E L',
   'score': 1.0862983429449358e-10,
   'token': 1,
   'token_str': '[ U N K ]'},
  {'sequence': 'M E D E L',
   'score': 1.0585952253672204e-10,
   'token': 0,
   'token_str': '[ P A D ]'},
  {'sequence': 'M E D E L',
   'score': 1.0177149400991681e-10,
   'token': 2,
   'token_str': '[ C L S ]'},
  {'sequence': 'M E O D E L',
   'score': 9.655934385399689e-11,
   'token': 29,
   'token_str': 'O'},
  {'sequence': 'M E U D E L',
   'score': 9.435673770097353e-11,
   'token': 26,
   'token_str': 'U'},
  {'sequence': 'M E D E L',
   'score': 2.4889090788349222e-11,
   'token': 4,
   'token_str': '[ M A S K ]'}],
 [{'sequence': 'M E N L E L',
   'score': 0.10907968133687973,
   'token': 5,
   'token_str': 'L'},
  {'sequence': 'M E N K E L',
   'score': 0.09135881811380386,
   'token': 12,
   'token_str': 'K'},
  {'sequence': 'M E N E E L',
   'score': 0.08618851006031036,
   'token': 9,
   'token_str': 'E'},
  {'sequence': 'M E N I E L',
   'score': 0.07329064607620239,
   'token': 11,
   'token_str': 'I'},
  {'sequence': 'M E N S E L',
   'score': 0.06231885030865669,
   'token': 10,
   'token_str': 'S'},
  {'sequence': 'M E N N E L',
   'score': 0.06081739813089371,
   'token': 17,
   'token_str': 'N'},
  {'sequence': 'M E N V E L',
   'score': 0.05893290042877197,
   'token': 8,
   'token_str': 'V'},
  {'sequence': 'M E N G E L',
   'score': 0.055906955152750015,
   'token': 7,
   'token_str': 'G'},
  {'sequence': 'M E N D E L',
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