Flavin adenine dinucleotide (FAD) and its derivatives play a crucial role in
biological processes. They are major organic cofactors and electron carriers
in both enzymatic activities and biochemical pathways. We have analysed
the relationships between sequence and structure of FAD-containing proteins
using a machine learning approach. Decision trees were generated using the
C4.5 algorithm as a means of automatically generating rules from biological
databases (TOPS, CATH and PDB). These rules were then used as
background knowledge for an ILP system to characterise the four different
classes of FAD-family folds classified in Dym and Eisenberg (2001). These
FAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR),
p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO). Each FADfamily
was characterised by a set of rules. The “knowledge patterns”
generated from this approach are a set of rules containing conserved sequence
motifs, secondary structure sequence elements and folding information.
Every rule was then verified using statistical evaluation on the measured
significance of each rule. We show that this machine learning approach is
capable of learning and discovering interesting patterns from large biological
databases and can generate “knowledge patterns” that characterise the FADcontaining
proteins, and at the same time classify these proteins into four
different families

Gilbert, D

Tan, A C

Tuson, A

English

Brunel University Research Archive

1Characterisation of FAD-family Folds using a MachineLearning ApproachAik Choon TAN, David GILBERT and Andrew TUSONBioinformatics Research GroupDepartment of ComputingSchool of InformaticsCity UniversityNorthampton SquareEC1V 0HBLondon, U.K.Email: {a.c.tan, drg, andrewt}@soi.city.ac.ukAbstractFlavin adenine dinucleotide (FAD) and its derivatives play a crucial role inbiological processes.  They are major organic cofactors and electron carriersin both enzymatic activities and biochemical pathways.  We have analysedthe relationships between sequence and structure of FAD-containing proteinsusing a machine learning approach.  Decision trees were generated using theC4.5 algorithm as a means of automatically generating rules from biologicaldatabases (TOPS, CATH and PDB).  These rules were then used asbackground knowledge for an ILP system to characterise the four differentclasses of FAD-family folds classified in Dym and Eisenberg (2001).  TheseFAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR),p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO).  Each FAD-family was characterised by a set of rules. The “knowledge patterns”generated from this approach are a set of rules containing conserved sequencemotifs, secondary structure sequence elements and folding information.Every rule was then verified using statistical evaluation on the measuredsignificance of each rule.  We show that this machine learning approach iscapable of learning and discovering interesting patterns from large biologicaldatabases and can generate “knowledge patterns” that characterise the FAD-containing proteins, and at the same time classify these proteins into fourdifferent families.Keywords: flavin adenine dinucleotide (FAD); protein structure-sequence-function; machine learning; decision tree; inductive logic programming;knowledge discovery in biological databases.21. IntroductionIt is generally believed that the three-dimensional structure of a protein isdetermined by its amino acid sequence.  On the other hand, similar proteinfolds can have very different sequences (Doolittle, 1986).  Although somesimilar protein structures may have low sequence similarity, the consensussequence pattern exhibited in the particular protein family usually plays animportant role in protein function or structure properties.  In the post-genomeera, one of the ‘holy-grails’ for the bioinformatics community is to predict thestructure and function of a protein from its amino acid sequence.  Currentfactual biological databases are overwhelmed by experimental data, this hasmotivated the first step to understanding the complex relation betweenprotein sequence, structure and function.In this paper, we characterise FAD-binding proteins into four differentfamilies using a machine learning approach. We have adapted the machinelearning algorithm C4.5 (Quinlan, 1993) which outputs a decision tree that isequivalent to a set of symbolic rules.  We induced the decision trees in aparallel fashion to output a decision forest which contains rules from differentbiological databases.  These rules were then converted into backgroundknowledge for the second learning system CProgol4.4 (Muggleton, 2001),which derived a rule-set output which was more accurate and comprehensiblethan the conventional combined data single tree approach.2. FAD-(Flavin Adenine Dinucleotide) binding protein familiesIn this study, we focused on flavin adenine dinucleotide (FAD) bindingproteins. This is because FAD, and other cofactors such as nicotinamideadenine dinucleotide (NADH) and adeninosine triphosphate (ATP), appear inmany biological processes and represent the major fuel molecules in the cell.The main function of flavin-binding proteins is to carry out redox reactions inthe cell.  The unique structure of flavin enables it to take up one or twoelectrons from the substrate.  The intermediate radical state of flavin(FADH!) is able to react with the most powerful oxidising agent in biologicalsystems: molecular oxygen.  This has made flavin different from the othercoenzymes (NADH and ATP) and thus it plays an important role in cellmetabolism.Using the Combinatorial Extension (CE) program, Dym and Eisenberg(2001) have classified the FAD-binding proteins into four different structuralfamilies.  They characterised each family using several conserved sequenceand structure motifs, which involves in the cofactor binding site.  The fourmajor structural families are:(a) Glutathione reductase (GR) which adopts a Rossmann fold withxhxhGxGxxGxxxhxxh(x)8hxhE(D) as the most conserved sequencemotif.  All the family members share the same 3-D structure.3(b) Ferredoxin reductase (FR), having a cylindrical "-domain as the centralstructure with RxYS(T) as the most conserved sequence motif.(c) p-cresol methylhydroxylase (PCMH) consisting of two #+" sub-domainsand the most conserved sequence motif being P(x)6G(A)xN.(d) Pyruvate oxidase (PO) with a structure consisting of five parallel "-strands interspersed by #-helices that lie on both sides of the "-sheet withKxLxxLxxxL(x)6S(T)(x)6GxV as the most conserved sequence motif.3. Machine learning approachIn most application domains we are more concerned with the predictiveaccuracy than the explanatory power of the learning output.  This is not thecase in bioinformatics because we believe that the comprehension of a rule(pattern) is as important as the accuracy of the learning system.  Biology is an“understanding” orientated subject, and because biological databases areaccumulating vast amount of data, human experts find it harder to identify therelationship between properties of the data (e.g. protein structure-sequence-function). Thus, we agree with Muggleton et. al. (1998) when comparing theperformance of learning system in a  bioinformatics context:“If the predictive accuracies of two hypotheses are statisticallyequivalent then the hypothesis with better explanatory power will bepreferred. Otherwise the one with higher accuracy will be preferred.(Muggleton et. al. 1998)”.The first machine learning algorithm we chose to adapt for this work wasC4.5 release 8 (Quinlan, 1993), the successor of the ID3 learning algorithm(Quinlan, 1986).  The decision tree algorithm is well known for itsrobustness, and learning efficiency with learning time complexity ofO(nlog2n) where n is the number of attributes. The output of the algorithm isa decision tree, which can be converted into a set of symbolic rules(IF…THEN…).  The symbolic rules can be directly interpreted andcompared with existing biological knowledge.  Thus, decision trees have highexpressive power in their patterns (rules).The second learning system applied in this study was CProgol 4.4(Muggleton, 2001), which is an inductive learning programming (ILP)program.  ILP algorithms take examples E of a concept (e.g. FAD-bindingprotein family), together with background knowledge B (e.g. relation betweena consensus sequence motif and structure) and construct a hypothesis Hwhich explains E in terms of B.  The hypothesis H can be translated into an(IF…THEN…) rule-set that can characterise E with relation to B.  Thus, webelieve that our final rule-set can have a high comprehensibility and accuracyin characterising protein (e.g. the FAD-binding families).44. MethodologyThe approach applied here is to derive n decision trees from n data sets.  Eachdecision tree was induced from an individual data set.  These decision treesindividually represent different information (e.g. sequence, structure andfunction) for the FAD-binding protein families in m data sets.  After growingthe m decision trees individually, they must be combined to produce somefinal rules that characterise the protein families.  In our approach, we usedC4.5 to grow the m decision trees, and C4.5 rules to extract the rules from them trees. These rules were used as the additional background knowledge forthe ILP system when learning on the training examples. ILP outputs a finalrule-set (hypothesis) that has high comprehensibility and accuracy incharacterising the protein examples. The rule set will contain relationshipsbetween sequence, structure and function.The data-set that we used in this study consisted of 42 fad-bindingproteins which have 3 different attribute properties (m=3 in this study).  Theproperties are sequence motifs, structure motifs and enzymatic functionalclasses.   For each family, we divided the training set into positive andnegative examples then learned a rule from that group (e.g. for GR family,GR will be the TPs; FR, PCMH and PO are the TNs).Our research methodology can be summarised as the following steps:Step 1: Select the target data sets (sequence, structures, function).Step 2: Clean up the target data sets.Step 3: Divide-and-conquer search strategy.Step3a: Divide: For every data set, grow an individual decision tree.Step3b: Extract: For every decision tree, derive the rules from the trees.Step3c: Merge: Use the rules induced from the various data sets as thebackground knowledge for the ILP system.Step3d:Conquer: Apply ILP to “combine” the rules.Step4: Evaluate the goodness of the rules.Step5: Repeat step 3 and 4 to obtain the “best” rule-set that characterises theFAD-binding families.The goodness of the rules can be evaluated using the confusionmatrix in table 1. We evaluate the statistical significant of the rule-set bymeasuring their sensitivity (Sn)1, specificity (Sp)2, coefficient correlation(cc)3, positive predictive value (PPV)4.                                                          1 FNTPTPSn$% 10, && Sn2 FPTNTNSp$% 10, && Sp3 ))()()(( TNFNFPTPFPTNFNTPFNFPTNTPcc$$$$'('% 11, &&( cc5Table 1: Confusion matrix showing true positives, true negatives, falsepositives and false negatives covered by the rules.Positive Examples Negative ExamplesCovered True Positive (TP) False Positive (FP)Not covered False Negative (FN) True Negative (TN)5. Results and DiscussionsWe performed two different experiments. The first experiment used ourcombination method, while the second experiment was conventional datacombination, where different data sets were combined into one large tableand a single tree induced from the data. The results from these twoexperiments are shown in table 2 (our combination approach) and table 3(data combination single tree approach).  The rule-set of our approach areshown in figure 1 at the protein topological level with the colouringsecondary structure elements represents the sequence-structure relationship.From table 2 and table 3 we can see that our approach has moreaccuracy over the GR and FR family and the rule set produced was morecomprehensible.  This is because our rule-set included structural informationthat helps to distinguish the noises in the training set. Therefore, our approachincreases Sp, cc and PPV of the rules. Furthermore, our rules also indicate thelocation of the sequence motif in the protein structure, which we believed it ismore meaningful for the biologists in understanding the protein sequence-structure relationship.  For example, in the case of class GR in our methodproduces a rule set that can be translated into natural language as follows:If the protein has a sequence motif GxG(x)2G(x)16-19[DE] in "1-#1-"2 of the 3-layer "-"-# sandwich structure  and carried out oxidoreductases reactionThen it  is GR family.The data combination approach rule only identified the sequence motiffor this family.  The rule from the data combination approach can betranslated into:If the protein has a sequence motif GxG(x)2G(x)16-19[DE] Then it is class GR.Thus the rule induced from the second experiment is less informativecompared to our approach.  This is because in data combination, the learningalgorithm only finds the shortest rule that discriminates between two classesand ignores other ‘important’ features in the data.  For this example, thesequence are more conserved in the family members because these motifsinvolved in the cofactor binding site.  Although the conventional method does                                                                                                                                         4 FPTPTPPPV$%6successfully classify the proteins using the most discriminative patterns, itignores other (structure and function) information in the input data-set.  Theresults will be less useful for knowledge acquisition purposes, becausebiologists tend to prefer more comprehensive rules that can help them toexplain the complex relationship between protein sequence-structure-functionproperties.The other advantage of our method compared to data combination using asingle-tree is that our approach reduces the learning time complexity andmemory space requirements of the algorithm.  Most of the learningalgorithms receive the input file as a flat file format, thus if the input data-setis very large, the memory space will be taken up by the input files.  Thus, thecomputational demands will increase. At the same time, the learning time forthe algorithm will decrease if the input n is a large value.  In our approach,we divide the various data sources into sub-tables (n/m), the learning timecomplexity is O(mlog2(n/m)) + ILP learning time, and thus retain the fastefficient learning time of the learning algorithms (decision trees).Table 2: Rule-set generated from the combination of decision trees and ILP.Rule Sn Sp cc PPVGR Class(‘GR’,A):-protein(A,B,C,D),Sequence(B,GxG(x)2G(x)16-19[DE]),Structure(C,bbasandwich),Has_seq(strand1_helix1_strand2,B,C),Function(D,oxidoreductases).1.0 1.0 1.0 1.0FR Class(‘FR’,A):-protein(A,B,C,D),Sequence(B,RxY[ST]),Structure(C,betabarrel),Has_seq(strand4,B,C),Function(D,oxidoreductases).1.0 1.0 1.0 1.0PCMH Class(‘PCMH’,A):-protein(A,B,C,D),Sequence(B,[AP](x)6-8[AG]xN),Structure(C,abasandwich),Has_seq(helix1_strand2,B,C),Function(D,oxidoreductases).1.0 1.0 1.0 1.0PO Class(‘PO’,A):-protein(A,B,C,D),Sequence(B,KxL(x)2(x)3L),Structure(C,abasandwich),has_seq(helix1_strand2,B,C),Function(D,oxidoreductases).1.0 1.0 1.0 1.0Table 3: Rule-set generated from single decision trees derived from datacombination.Rule Sn Sp cc PPVGR GxG(x)2G(x)16-19[DE]=yes ) class GR 1.0 .97 .91 .86FR RxY[ST] = yes ) class FR 1.0 .91 .91 .91PCMH [AP](x)6-8[AG]xN = yes ) class PCMH 1.0 1.0 1.0 1.0PO K(x)7I(x)2D(x)10D = yes ) class PO 1.0 1.0 1.0 1.06. ConclusionsWhen trying to learn from large and diverse data sets (e.g. biologicaldatabases) it is important to produce a rule-set that encapsulates all theinformation from different sources.  In this work we investigated an approachwhere decision trees were derived individually from various data sets and therules from the decision trees were given to the ILP as background knowledge.The ILP system induces rules by combining the additional background7knowledge and the training examples to produce a “knowledge” rule-set.  Wehave shown that the rules produced from our approach are more informativethan the conventional method.  Our current work is to apply this approach tolarger data sets in order to learn significant relationships between proteinsequence-structure-function.Figure 1: The TOPS (Westhead et. al., 1998) cartoons showing four FAD-binding families. The coloured SSEs are the sequence-structure relationshipin the protein.7. AcknowledgementsWe would like to thank Eduardo Alonso, Olivier Sand, Gilleain Torrance, Ali Al-Shahib and Mallika Veeramalai for discussion.  AC Tan’s scholarships was fundedby the Department of Computing, City University.8. ReferencesDoolittle, R.F. (1986). Of URFs and ORFs: A primer on how to analyse derivedamino acid sequences.  University Science Books, Mill Valley: CA.Dym, O. and Eisenberg, D. (2001). Sequence-structure analysis of FAD-containingproteins. Protein Science, 10: 1712-1728.Mitchell, T. (1997). Machine Learning. McGraw-Hill.Muggleton, S., Srinivasan, A., King, R.D. and Sternberg, M.J.E. (1998). Biochemicalknowledge discovery using inductive logic programming.  In H. Motoda (Ed.) Proc.Of the First Conference on Discovery Science, Berlin, Springer-Verlag.Muggleton, S. (2001). CProgol4.4: a tutorial introduction. In Inductive LogicProgramming and Knowledge Discovery in Databases. Springer-Verlag, To appear.Quinlan, J.R. (1986). Induction on decision trees. Machine Learning, 1: 81-106.Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann,San Mateo: C.A.Westhead, D. R., Slidel, T.W.F., Flores, T. P. J. and Thornton, J. M. (1999). Proteinstructural topology: automated analysis, diagrammatic representation and databasesearching, Protein Science, 8: 897-904.

Characterisation of FAD-family folds using a machine learning approach

Abstract

Similar works

Full text

Available Versions

Brunel University Research Archive