Mutational analysis of flavonol synthase of <i>M. pinnata</i> towards enhancement of binding affinity: a computational approach

Millettia pinnata is an important medicinal plant that has been used as a treatment of various diseases due to presence of wide range of pharmacological properties. The plant contains quercetin, kaempferol, karanjin, pongaglabrone, kanjone, kanugin, gammatin, pongaglabol, and other bioflavonoids. Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, and antidiabetic activities etc. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols, i.e. biosynthesis of kaempferol from dihydrokaempferol. The current work examined the binding affinity-based approach to improve the enzyme catalytic activity using computational methods. Sequential site-directed mutagenesis was used to create four mutants with the goal to increase hydrogen bonds and further improving the ligand (dihydrokaempferol) binding efficiency. Simulations were done to monitor the stability of the mutants followed by molecular docking to confirm interactions with ligand. For structure validation, various dynamic analysis like RMSD, RMSF, ROG, SASA, H-bond, PCA, DCCM, and FEL were performed, which predicts the stability of wild-type (WT) proteins and mutants. The Mutant_2 and Mutant_3 showed maximum H-bonding and better stability than other mutants and WT that proved higher affinity suggesting improved catalysis. Mutant_2 and Mutant_3 exhibited binding affinities of −7.6 and −8.2 kcal/mol, respectively for the ligand. The outcome of present study will provide significant improvement in synthesis of kaempferol and other plant-based flavonoids. Communicated by Ramaswamy H. Sarma</p

Table_1_Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea.docx

IntroductionPlant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.MethodsIn this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.Results and DiscussionA total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.</p

DataSheet_1_Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea.csv

IntroductionPlant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.MethodsIn this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.Results and DiscussionA total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.</p