While recent studies have referred to plant immunity activators, it is difficult to find a compound to use for the immunity activation of plants. In this study, we seek to determine compounds that enable plant immunity activity using ILP. With the proposed method, it is possible to predict compounds that induce plant immunity activity, based on the structural features of the compounds. The predicted structure rule also includes structures of known plant immunity activators. However, further investigation is needed regarding the relationship between plant immunity and structure rules.
Virtual screening is an important approach in the drug discovery process.
Especially, machine learning has recently received broad attention. This paper picks up two
method, Support Vector Machine (SVM) [
On the other hand, decreased production of agricultural crops due to pathogenic
bacteria and pests is a serious problem that has not yet been solved. To address this
problem, grower have made a deal with fungicides and pesticides, however, it is
difficult to act selectively on the target (e.g., pests and pathogens). There is a
possibility that the cause of health damage in humans and destruction of biota. In
addition, long-term use of the same drug may cause the emergence of resistant bacteria;
thus, the effect of the drug gradually decreases. In recent years, plant immunity
activators have attracted attention, based on the idea of increasing the immunity of
the plant rather than directly killing pathogens and pests. However, only three
types of plant immunity activator are currently marketed in Japan (Fig. 1). In
addition, the mechanism of plant-immunity activation is still largely unknown [
In this study, we predict compounds that induce plant-immunity activation using
ILP to study compound structures. ILP can be used to determine relationship
patterns between data; therefore, it is suitable to represent the structure of compounds.
Additionally, we obtained the structure of the predicted compound as a rule, which
is one of the excellent points of ILP. A recent study that was conducted to predict
the structure of compounds using ILP exhibited high performance [
Plant immunity is a defense system to protect plants from various enemies. A
plant-immunity activator is a drug that activates plant immunity. The Kuchitu
group constructed a screening system to find a candidate using the amount of ROS
(reactive oxygen species) generation as an index [
In the present study, the datasets are experiment data about the plant immunity activator in Arabidopsis thaliana, compiled by the Kuchitu group. This dataset includes 10000 compounds. Positive examples are 271 high-ROS compounds, and negative examples are the other 9729 compounds. However, negative examples were reduced to 813 compounds by random sampling for two reasons. First, imbalanced data deteriorates learning accuracy. Second, if there are many compounds, calculation takes a long time. Therefore, 1084 compounds were used in this study. 4. METHOD
This chapter describes our method. We had two approaches. Fig. 2 shows the overview of our method.
With the ILP approach, structural features and some numerical features of the
compound were used as background knowledge. In this study, we used GKS [
Types of atoms present in the compound ・bond (compound_name, atom_id, atom_id, bondtype)
Bonding state between atoms and bond type in the compound ・Num_AromaticRings (compound_name,Num_AromaticRing)
The number of aromatic rings in the compound ・Num_Rings (compound_name, Num_Ring)
The number of rings in the compound ・LogP98 (compound_name, value)
Lipid solubility of the compound ・LogD (compound_name, value)
Indication of a change in lipid solubility by a change in Ph value ・ring (compound_name,ring_id,atom_id,ringsize,ringtype)
Type of ring structure that is composed of each atom. It can represent the connection of the ring structure and other structures by using this predicate. By selecting several predicates as background knowledge, we can obtain the structure of the compound as a learning result (Table 1). Background knowledge is a set of atomic formulas of each predicate. Atom and bond are always necessary. The reason why selecting LogP98 and LogD is result of importance calculation using the average Gini coefficient.
Predicate atom,bond atom,bond,Num_AromaticRings atom,bond,Num_AromaticRings,Num_rings atom,bond,ALogP98 atom,bond,Num_AromaticRings,Num_rings,ALogP98,LogD atom,bond,Num_AromaticRings,Num_rings,LogD atom,bond,LogD,ring atom,bond,ring Mode declaration as input is shown in Fig. 3. A rule selected if it was covered more than 10 positive examples and less than 10 negative examples.
@dock,+molecular @atom,+molecular,+atomid,#atomtype @atom,+molecular,-atomid,#atomtype @bond,+molecular,+atomid,+atomid,#bondtype @bond,+molecular,-atomid,+atomid,#bondtype @bond,+molecular,+atomid,-atomid,#bondtype @bond,+molecular,-atomid,-atomid,#bondtype @Num_Rings,+molecular,#Num_Ring @Num_AromaticRings,+molecular,#Num_AromaticRing @LogD,+molecular,#value @ALogP98,+molecular,#value @ring,+molecular,+ringid,+atomid,#ringsize,#ringtype @ring,+molecular,-ringid,+atomid,#ringsize,#ringtype @ring,+molecular,+ringid,-atomid,#ringsize,#ringtype @ring,+molecular,-ringid,-atomid,#ringsize,#ringtype
Fig. 3. Mode declaration
We also tried SVM for comparison with ILP, using 77 features for learning (Table 2). Detail information is shown in Appendix A. Cost parameters and gamma parameters were determined using a grid search for 20 split from 0.0001 to 10,000. The kernel used RBF.
Ten-fold cross-validation was used in both approaches. True Positive (tp) , False Negative (fn) , True Negative (tn) , False Positive (fp) , Accuracy , Precision , Recall and F value were used for Evaluation. Especially, this paper focuses on tp and F value. 5.
Although SVM F values slightly exceeded those of ILP, ILP tp values greatly exceeded those of SVM. For virtual screening, it is very important to reduce the positive example of misclassification. Results of this study indicate that structural features of the compounds are useful in predicting immunity activation.
Using the ring structure as background knowledge yielded better results than not using ring structure. Therefore, the ring structure is considered an important factor in plant immunity activation.
When analyzing rules using ILP, comparison of known plant immunity activators indicated that Rule 2 was true for all three compounds. For rule showing a structure that is different from the known plant immunity activator, there is a need for further investigation.
In this study, it was possible to predict the partial structure that exists in all compounds of known plant-immunity activators. In addition, the rule that is unknown the relationship between immunity activity has been predicted. In order to improve prediction accuracy, it is essential to improve background knowledge in the future.
Table 6 shows feature list in SVM approach. Feature name depends on Discovery Studio.
Category Feature name C HBA_Count
HBD_Count NPlusO_Count Num_AromaticBonds Num_AromaticRings Num_AtomClasses Num_Atoms Num_Bonds Num_BridgeBonds Num_BridgeHeadAtoms Num_ChainAssemblies Num_Chains Num_ExplicitAtoms Num_ExplicitBonds Num_ExplicitHydrogens Num_H_Acceptors Num_H_Acceptors_Lipinski Num_H_Donors Num_H_Donors_Lipinski Num_Hydrogens Num_NegativeAtoms Num_PositiveAtoms Num_RingAssemblies Num_RingBonds Num_Rings Num_Rings3 Num_Rings5 Num_Rings6 Num_Rings7 Num_Rings8 Num_RotatableBonds Num_SpiroAtoms Num_StereoAtoms Num_StereoBonds Num_TerminalRotomers Num_TrueStereoAtoms Num_UnknownPseudoStereoAtoms Num_UnknownTrueStereoAtoms Organic_Count
Positive Negative 20 10 7 6 2