Predictors (of secondary structure) based on Machine Learning tools

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1 Predictors (of secondary structure) based on Machine Learning tools

2 Predictors of secondary structure 1 Generation methods: propensity of each residue to be in a given conformation Chou-Fasman 2 Generation methods: context dependence is introduced GOR Neural Networks can be adopted

3 Tools out of machine learning approaches Neural Networks can learn the mapping from sequence to secondary structure Training Data Base Subset Prediction New sequence TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN General rules EEEE..HHHHHHHHHHHH...HHHHHHHH.EEEE Known mapping Prediction

4 Neural network for secondary structure prediction Outputs encode for the structure of the central residue of the input window a b C Output Hidden neurons :4-15 Input Tipical input window: residues M P I L K QK P I H Y H P N H G E A K G How would you encode the input? (numerical values are needed)

5 Neural network for secondary structure prediction Output a b C Input 2- valued vectors for each position prevent from introducing spurious similarities among residues M P I L K QK P I H Y H P N H G E A K G A C D E F G H I 1 K 1 L M N 1 P 1 1 Q R S T V W Y 1

6 A C D E F G H I K L M N P Q R S T V W Y. D (L) R (E) Q (E) G (E) F (E) V (E) P (E) A (H) A (H) Y (H) V (E) K (E) K (E) H E L The actual number of input neurons is 2 x window length

7 Training and testing of a predictor on 822 proteins from the PDB The cross validation procedure Protein set Training set 1 Testing set 1

8 Efficiency of the Neural Network-Based Predictors on 822 Proteins (in Testing Phase) INPUT Single Sequence Accuracy (%) 66.3 Sens[H].69 Sens[E].61 Sens[C].66 PPV[H].7 PPV[E].54 PPV[C].71 MCC[H].54 MCC[E].44 MCC[C].45 On the same set GOR performs at 64% Accuracy Why do NNs (slightly) outperform GOR, despite both uses the same input information?

9 GOR Simplification (1) : only local sequences (window size = 17) are considered I ( S ; R) I( S ; R,, R,, R ) j i j 8 j j 8 Simplification (2) : each residue position is statistically independent 8 NNs I( S ; R,, R,, R ) I( S ; R ) i j 8 j j 8 j j m m 8 The hidden layer performs a NON LINEAR mapping of the input in a new representation Non linearity can better model correlations among the positions in the input window =

10 Neural network for secondary structure prediction Outputs encode for the structure of the central residue of the input window a b C Output Hidden neurons :4-15 Input Tipical input window: residues M P I L K QK P I H Y H P N H G E A K G Can we add more information in the input encoding?

11 Third generation methods: evolutionary information 1 Y K D Y H S - D K K K G E L Y R D Y Q T - D Q K K G D L Y R D Y Q S - D H K K G E L Y R D Y V S - D H K K G E L Y R D Y Q F - D Q K K G S L Y K D Y N T - H Q K K N E S Y R D Y Q T - D H K K A D L G Y G F G - - L I K N T E T T K 9 T K G Y G F G L I K N T E T T K 1 T K G Y G F G L I K N T E T T K Position A 1 C D E 7 F 1 33 G H K I 3 L 3 M 6 N P Q 4 3 R 5 S T V 1 W 1 Y 7 9

12 One problem: Output a b C Each prediction considers a neighborhood of the sequence. Input Output M P I L K Q K P I H Y H P N H G E A K G M P I L K Q K P I H Y H P N H G E A K G M P I L K Q K P I H Y H P N H G E A K G M P I L K Q K P I H Y H P N H G E A K G M P I L K Q K P I H Y H P N H G E A K G C b a a b However the prediction on each window is independent of the predictions in neighbouring windows Predictions are uncorrelated and, sometimes inconsistent. How can we add correlations among neighbouring predictions?

13 The Network Architecture for Secondary Structure Prediction The First Network (Sequence to Structure) H E C SeqNo No V L I M F W Y G A P S T C H R K Q E N D

The Network Architecture for Secondary Structure Prediction The Second Network (Structure to Structure) H E C CCHHEHHHHCHHCCEECCEEEEHHHCC SeqNo No V L I M F W Y G A P S T C H R K Q E N D 1 1 8 2

14 The Network Architecture for Secondary Structure Prediction The Second Network (Structure to Structure) H E C CCHHEHHHHCHHCCEECCEEEEHHHCC SeqNo No V L I M F W Y G A P S T C H R K Q E N D

15 Efficiency of the Neural Network-Based Predictors on 822 Proteins (in Testing Phase) INPUT Single Sequence Accuracy (%) 66.3 Sens[H].69 Sens[E].61 Sens[C].66 PPV[H].7 PPV[E].54 PPV[C].71 MCC[H].54 MCC[E].44 MCC[C].45 INPUT Multiple Sequence (PSI_BLAST) Accuracy (%) 73.4 Sens[H].75 Sens[E].7 Sens[C].73 PPV[H].8 PPV[E].63 PPV[C].75 MCC[H].67 MCC[E].56 MCC[C].53

16 Secondary Structure Prediction From sequence TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN To secondary structure EEEE..HHHHHHHHHHHH...HHHHHHHH.EEEE... And to the reliability of the prediction Usually computed as INT(1*difference of the two highest output value)

17 Chamaleon sequences QEALEIA GIKSKQEALEIAARRN Translation Initiation Factor 3 Bacillus stearothermophilus FNPQTQEALEIAPSVGV Transcription Factor 1 Bacteriophage Spo1 1WTUA 1TIF

18 We extract: from a set of 822 non-homologous proteins (174,192 residues) 2,452 5-mer chameleons 17 6-mer chameleons 16 7-mer chameleons 1 8-mer chameleon 2,576 couples The total number of residues in chameleons is 26,44 out of 755 protein chains (~15%)

19 Prediction of the Secondary Structure of Chameleon sequences with Neural Networks QEALEIA HHHHHHH a b C QEALEIA CCCCCCC a b C NGDQLGIKSKQEALEIAARRNLDLVLVAP ARKGFNPQTQEALEIAPSVGVSVKPG

20 The Prediction of Chameleons with Neural Networks Method Performance on the Protein data set Performance on Chameleon sequences NN with MSA Input 73.4 % 75.1 % NN with SS Input 66.3 % 58.9 % GOR IV 64.4% 55.2 % Jacoboni I, Martelli PL, Fariselli P, Compiani M, Casadio R. Predictions of protein segments with the same aminoacid sequence and different secondary structure: a benchmark for predictive methods. Proteins. 2

21 Other neural network-based predictors Secondary structure Topology of transmebrane proteins Cysteine bonding state Contact maps of proteins Interaction sites on protein surface.

22 Prediction of membrane protein topology

23 SEVERAL TYPES OF INTERACTION BETWEEN PROTEINS AND MEMBRANES Anchors Integral membrane proteins Peripheral membrane protein Geoffrey M. Cooper The Cell

24 FUNCTIONS OF MEMBRANE PROTEINS Transport Intercellular joining Enzymatic activity Intercellular recognition Signal transduction Attachment to cytoskeleton and extracellular matrix

25 Porin (Rhodobacter capsulatus) Bacteriorhodopsin (Halobacterium salinarum) INTEGRAL MEMBRANE PROTEINS b-barrel a-helices

26 TOPOLOGY OF INTEGRAL MEMBRANE PROTEINS Topography position of Trans Membrane Segments along the sequence Out + Bilayer N In Topology C position of N and C termini with respect to the bilayer +

27 Searching for the most suitable features

28 Starting points 1) Different portions of the protein have different amino-acid composition Frequency (%) Loops Transmembrane alpha-helices Transmembrane beta strands A I L V G M P F W Y N C Q S T H R K E D apolar Aminoacids polar charged

29 Starting points 1) Different portions of the protein have different amino-acid composition Von Heijne s positive inside rule

30 Starting points 1) Different portions of the protein have different amino-acid composition 2) Transmembrane segments and loops are organised following a rigid grammar Outer Side Transmembrane Inner Side

31 Starting points 1) Different portions of the protein have different amino-acid composition 2) Transmembrane segments and loops are organised following a rigid grammar 1 Frequency (%) Length of transmembrane alpha-helices (residues)

32 Starting points 1) Different portions of the protein have different amino-acid composition 2) Transmembrane segments and loops are organised following a rigid grammar 2 Frequency (%) Length of transmembrane beta strands (residues)

33 Starting points 1) Different portions of the protein have different amino-acid composition 2) Transmembrane segments and loops are organised following a rigid grammar 3) Evolutionary information can increase the prediction performances

34 Differences in composition: propensity scales

35 First generation methods: Single residue statistics Propensity scales For each residue The association between each residue and the different features is statistically evaluated Physical and chemical features of residues A propensity value for any structure can be associated to any residue HOW?

36 Transmembrane alpha-helices: Kyte-Doolittle scale It is computed taking into consideration the octanol-water partition coefficient, combined with the propensity of the residues to be found in known transmembrane helices Ala: 1.8 Arg: -4.5 Asn: -3.5 Asp: -3.5 Cys: 2.5 Gln: -3.5 Glu: -3.5 Gly: -.4 His: -3.2 Ile: 4.5 Leu: 3.8 Lys: -3.9 Met: 1.9 Phe: 2.8 Pro: -1.6 Ser: -.8 Thr: -.7 Trp: -.9 Tyr: -1.3 Val: 4.2

37 More than 2 different hydrophobicity scales in ProtScale, and many many others

38 The Kyte-Doolittle scale The scale is based on an amalgam of experimental observations derived from the literature... In the case of membrane-bound proteins, the portions of their sequences that are located within the lipid bilayer are also clearly delineated by large uninterrupted areas on the hydrophobic side of the midpoint line. As such, the membrane-spanning segments of these proteins can be identified by this procedure. Kyte R, Doolittle RF, JMB 157:15-132, 1982 Ala: 1.8 Arg: -4.5 Asn: -3.5 Asp: -3.5 Cys: 2.5 Gln: -3.5 Glu: -3.5 Gly: -.4 His: -3.2 Ile: 4.5 Leu: 3.8 Lys: -3.9 Met: 1.9 Phe: 2.8 Pro: -1.6 Ser: -.8 Thr: -.7 Trp: -.9 Tyr: -1.3 Val: 4.2

39 4 SecY Translocon Methanococcus jannaschii PDB: 1RHZ:A KD Average

40 4 SecY Translocon Methanococcus jannaschii PDB: 1RHZ:A KD Average ObservedTM helices

41 An hydropathy scale based on thermodynamic principles The Wimley-White scale White SH, Wimley WC, Annu Rev Biophys Biomol Struct 28: (1999)

42 An hydropathy scale based on thermodynamic principles The Wimley-White scales

43 SecY Translocon, Methanococcus jannaschii PDB: 1RHZ:A

44 The Wimley-White scale correlates with free energies of insertion as measured in vivo AAAAAAAAAXAAAAAAAAA Hessa T et al., Nature 433: (25)

45 Length constraints: filtering procedures

46 Algorithms specifically designed to optimise the number and length of segments in a given protein sequence to be compatible with the membrane spanning regions. MaxSubSeq, given a general propensity plot, finds the maximum-scoring subsequences with: constrained segment length 15-4 for all-a membrane proteins 6-25 for b-barrel membrane proteins constrained segment number even number of b-strands in b-barrel membrane proteins Fariselli et al. (23) Bioinformatics 19:5-55

47 SecY Translocon Methanococcus jannaschii PDB: 1RHZ:A

48 SecY Translocon Methanococcus jannaschii PDB: 1RHZ:A

49 MEMSAT 1

50 A more sofisticated model of membrane protein Five structural states are defined: Helix inner end(h i ) Inner loop (L i ) Helix middle (H m ) Outer loop (L o ) Helix outer end (H o ) Jones DT et al, Biochemistry 33: (1994)

51 Propensity scales are computed for each state Starting from the proteins whose topology is known (either from 3-D coordinates or low-resolution experiments), the propensity parameters Prop(aa,state)=-Log[p(aa,state)/p(aa)p(state)] are computed. Jones DT et al, Biochemistry 33: (1994)

52 Jones DT et al, Biochemistry 33: (1994)

53 Strategies for finding better propensities: 1) Considering the sequence context

54 Neural network for secondary structure prediction Output TM nottm Non linear mapping Input sually: nput residues idden neurons :4-15 M P I L K QK P I H Y H P N H G E A K G A C D E F G H I 1 K 1 L M N 1 P 1 1 Q R S T V W Y 1

55 Strategies for finding better propensities: 2) Introducing evolutionary information

56 MSA Sequence profile Evolutionary information and sequence profiles 1 Y K D Y H S - D K K K G E L Y R D Y Q T - D Q K K G D L Y R D Y Q S - D H K K G E L Y R D Y V S - D H K K G E L Y R D Y Q F - D Q K K G S L Y K D Y N T - H Q K K N E S Y R D Y Q T - D H K K A D L G Y G F G - - L I K N T E T T K 9 T K G Y G F G L I K N T E T T K 1 T K G Y G F G L I K N T E T T K sequence position A 1 C D E 7 F 1 33 G H K I 3 L 3 M 6 N P Q 4 3 R 5 S T V 1 W 1 Y 7 9

57 A C L P R P... t Sequence of characters s t KD (s t ) Sequence of 2-dimensional vectors 9 1 n Ex: Propensity scales weighted on sequence profiles v t S v t (i) KD (i) i=1 2

58 SecY Translocon Methanococcus jannaschii PDB: 1RHZ:A KD+MaxSubSeq: psi-kd+maxsubseq: 72% correct topography 83% correct topography

59 Propensities can be computed with a NN or a SVM

network D iq = Expected Value The Back Propagation Training Algorithm (gradient

60 Artificial Neural Networks Single Layer Perceptron d y 1 Outputs y m a j = S w ji x i i = y j = g (a j ) The Error Function Bias x x 1 Inputs x d Y i (X q ) = Output of the network D iq = Expected Value The Back Propagation Training Algorithm (gradient descent: Rumelhart et al. 1986) Correction to the weights m = learning rate h = momentum term

61 Neural Network for the prediction of TMS in membrane proteins TM nontm A V L I A W M L C.. Local information

62 Dynamic programming filtering procedure TM activation Predicted TM Segment TM activation Sequence Maximum-scoring subsequences with constrained segment length and number

65 Modelling the grammar: Hidden Markov Models

66 A generic model for membrane proteins End Outer Side Transmembrane Inner Side Begin

67 Model of a-helix membrane proteins (HMM1) Outer Side Transmembrane Inner Side

68 Model of a-helix membrane proteins (HMM1) Outer Side Transmembrane Inner Side

69 Hidden Markov Models Generation of the sequence: path through the states. Each state emits a residue of the sequence with a peculiar probability distribution. Given a sequence and a trained model we can compute the probability of the sequence the decoding of the sequence (which state emits a given residue?)

70 Decoding Viterbi decoding: given a sequence, the Viterbi algorithm finds the optimal path through the states A posteriori decoding: given a sequence, for each position i and for each state k, we compute P(state(i) = k sequence) then we sum over all the k corresponding to transmembrane states.

71 Dynamic programming filtering procedure TM probability Predicted TM Segment TM probability Sequence Maximum-scoring subsequences with constrained segment length and number

73 Sequence profile based HMMs

74 Sequence-profile-based HMM A C L P R P E T... t Sequence of characters s t Sequence of 2-dimensional vectors v t v t (n) 1 t, n S k=1 v t (k) = 1 t M 9 1 n Constraints

75 Sequence-profile-based HMM Martelli PL, Fariselli P, Krogh A, Casadio R. A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics. 22;18 Suppl 1:S Probability of emission from state k Sequence of characters s t P(s t k) = e k (s t ) Sequence of M-dimensional vectors v t P(v t k) = 1 Z S M n=1 v t (n) e k (n) constraints P(v t k) d M v t = 1 Z = S A A! M S n=1 e k (n) M If S n=1 e k (n) = 1 Z is independent of the state Algorithms for training and probability computation can be derived

76 ENSEMBLE system for predicting the topology of all alpha membrane proteins

77 A new Bologna Predictor trained on high resolved TM a-helices of Membrane Proteins From the PDB: 59 Transmembrane Chains 229 Transmembrane a-helices Bacterial rhodopsin, G-protein coupled receptors, Light harvesting complexes, Photosystems, Potassium channels, Mechanosensitive channel, Chloride channel, Other channels, Glycophorin, ATP Binding Cassette transporters, Ca ATPase, Fumarate reductase, FoATP synthase, Cytochrome C Oxidase, Cytochrome bc1 complexes, Formate dehydrogenase Martelli PL, Fariselli P, Casadio R. An ENSEMBLE machine learning approach for the prediction of all-alpha membrane proteins. Bioinformatics. 23;19 Suppl 1:i25-11.

Model of a-helix membrane proteins (HMM1)... 1... 13... 12.

78 Model of a-helix membrane proteins (HMM1) Outer Side Transmembrane Inner Side

79 Model of a-helix membrane proteins (HMM2) Outer Side Transmembrane Inner Side

80 Neural Network for the prediction of TMS in membrane proteins TM nontm nnttttttttttttttttnnnnnnnttttttttnnnnttt SeqNo No V L I M F W Y G A P S T C H R K Q E N D

81 Sequence Sequence Profiles NN HMM1 HMM2 S Jury MaxSubSeq Topography HMM decoding Topology Prediction

82 ENSEMBLE corrects the errors of the single methods Cytocrhome Bc1 Bos taurus PDB: 1BGY:C ENSEMBLE prediction HMM2 prediction HMM1 prediction NN prediction Observed TMHs Sequence (1bgyC)

83 Performance on the 71 high-resolved proteins: Q topography Q topology NN 58/71 (82%) 44/71 (62%) HMM1 58/71 (82%) 47/71 (66%) HMM2 47/71 (8%) 5/71 (7%) ENSEMBLE 63/59 (89%) 54/71 (76 %)

84 Performances

85 SecY Translocon Methanococcus jannaschii PDB: 1RHZ:A TMHMM KD PSI KD PRODIV HMM PHDhtm MEMSAT ENSEMBLE Observed

86 Calcium ATPase Rabbit PDB: 1EUL TMHMM KD PSI KD PRODIV HMM PHDhtm MEMSAT ENSEMBLE Observed

87 Clc Chloride Channel Salmonella typhimurium PDB: 1KPL TMHMM KD PSI KD PRODIV HMM PHDhtm MEMSAT ENSEMBLE Observed

88 Performance on the 71 high-resolved proteins: Q topography Q topology NN 58/71 (82%) 44/71 (62%) HMM1 58/71 (82%) 47/71 (66%) HMM2 47/71 (8%) 5/71 (7%) ENSEMBLE 63/59 (89%) 54/71 (76 %) Q topography Q topology PHD 5/71 (7%) 35/71 (49%) HMMTOP 53/71 (75%) 46/71 (65%) TMHMM 49/71 (69%) 38/71 (54%) MEMSAT 44/71 (73%) 44/6 (73%) PRODIV-HMM 6/71 (85%) 56/71 (78%) KD 51/71 (72%) -- PSI-KD 59/71 (83%) --

89 Rate of classification errors (%) Discriminating all-a membrane proteins Globular Membrane ,7,72,74,76,78,8,82,84,86,88,9,92,94,96,98 Height of the maximum TM peak

90 Prediction of the cysteine bonding state Tryparedoxin-I from Crithidia fasciculata (1QK8) MSGLDKYLPGIEKLRRGDGEVEVKSLAGKLVFFYFSASWCPPCRGFTPQLIEFYDKFHES KNFEVVFCTWDEEEDGFAGYFAKMPWLAVPFAQSEAVQKLSKHFNVESIPTLIGVDADSG DVVTTRARATLVKDPEGEQFPWKDAP Free cysteines Cys68 Disulphide bonded cysteines Cys4 Cys43

91 Perceptron (input: sequence profile) bonded Non bonded NGDQLGIKSKQEALCIAARRNLDLVLVAP

-2-1 1 2 3 4 5 Residue Residue Residue V L I M F W Y G A P S T

92 Position Position Plotting the trained weigths Hinton s plot bonding state non bonding state Residue Residue Residue V L I M F W Y G A P S T C H R K Q E N D & # V L I M F W Y G A P S T C H R K Q E N D & #

93 It is possible to model a sintax (bonded cysteines must be in even number)? Begin 1 2 Free states Bonded states 3 4 End

94 A path 1 3 Begin 2 4 Bonding Residue State State C4 C43 C68 End

95 A path 1 3 Begin 2 4 Bonding Residue State State C4 1 F C43 C68 P(seq) = P(1 Begin) P(C4 1)... End

96 A path Begin 1 3 End 2 4 Bonding Residue State State C4 1 F C43 2 B C68 P(seq) = P(1 Begin) P(C4 1)... P(2 1) P(C43 2)..

97 A path 1 Begin 2 Bonding Residue State State C4 1 F C43 2 B C68 4 B 3 End 4 P(seq) = P(1 Begin) P(C4 1)... P(2 1) P(C43 2).. P(4 2) P(C68 4)..

98 A path 1 Begin 2 Bonding Residue State State C4 1 F C43 2 B C68 4 B 3 End 4 P(seq) = P(1 Begin) P(C4 1)... P(2 1) P(C43 2).. P(4 2) P(C68 4).. P(End 4)

99 4 possible paths Begi n Bonding Residue State State C4 1 F C43 2 B C68 4 B Begi n Bonding Residue State State C4 2 B C43 3 F C68 4 B End End Begi n Bonding Residue State State C4 1 F C43 1 F C68 1 F Begi n Bonding Residue State State C4 2 B C43 4 B C68 1 F End End

100 Hybrid system W 1 W 2 W 3 MYSFPNSFRFGWSQAGFQCEMSTPGSEDPNTDWYKWVHDPENMAAGLCSGDLPENGPGYWGNYKTFHDNAQKMCLKIARLNVEWSRIFPNP... P(B W 1 ), P(F W 1 ) P(B W 2 ), P(F W 2 ) P(B W 3 ), P(F W 3 ) Begi n Free Cys Bonded Cys End Viterbi path Prediction of bonding state of cysteines

101 Residue C4 C43 C68 Prediction for Triparedoxin

102 Prediction for Triparedoxin NN Output NN pred Residue B F C B C B C B

103 Prediction for Triparedoxin Begi n NN Output NN pred HMM HMM pred Residue B F Viterbi path C B 2 B C B 4 B C B 1 F End

104 Performance Neural Network Table I. Performance of the NN predictor (2-fold cross validation) Set Q2 C Q(B) Q(F) P(B) P(F) Q2prot WD RD Table II. Performance of the Hidden NN predictor (2-fold cross validation) Set Q2 C Q(B) Q(F) P(B) P(F) Q2prot WD RD Hybrid system B= cysteine bonding state, F=cysteine free state. WD= whole database (969 proteins, 4136 cysteines) RD= Reduced database, in which the chains containing only one cysteine are removed (782 proteins, 3949 cysteines). Martelli PL, Fariselli P, Malaguti L, Casadio R. -Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks- Protein Eng. 15: (22)

Secondary Structure. Bioch/BIMS 503 Lecture 2. Structure and Function of Proteins. Further Reading. Φ, Ψ angles alone determine protein structure

Secondary Structure. Bioch/BIMS 503 Lecture 2. Structure and Function of Proteins. Further Reading. Φ, Ψ angles alone determine protein structure Bioch/BIMS 503 Lecture 2 Structure and Function of Proteins August 28, 2008 Robert Nakamoto rkn3c@virginia.edu 2-0279 Secondary Structure Φ Ψ angles determine protein structure Φ Ψ angles are restricted