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ThesisItem Open Access Subunit-subunit interactions in the heterotetrameric structure of rice (Oryza sativa) ADP-Glucose Pyrophosphorylase(CCSHAU, 2012) Dawar, Chhavi; Jain, SunitaADP-glucose pyrophosphorylase, a key allosteric enzyme involved in higher plant starch biosynthesis, is composed of large (LS) and small subunits (SS). Current evidence indicates that the two subunits play distinct roles in enzyme function. The LS is involved in mainly allosteric regulation through its interaction with the catalytic SS. Recently the crystal structure of the SS homotetramer from potato tuber has been solved, but no crystal structure of the native heterotetrameric enzyme is currently available for any species. In this study, homology modeling of the three-dimensional structure of the LS and SS from rice (Oryza sativa) was done using Swiss Model Server to construct the heterotetrameric assembly of the enzyme. The SS from the crystal structure of potato tuber was taken as the template. The models were evaluated using PROCHECK, VERIFY3D and ERRAT from SAVES. Further the two subunits were docked using GRAMM-X rigid docking server first to obtain the stable heterodimer orientation (LS as receptor and SS as ligand) and then the heterotetrameric orientation. The initial heterotetrameric orientation was further refined using RosettaDock Server and idealization of bond geometry and removal of unfavorable non-bonded contacts was performed by energy minimization with force field GROMOS96 from Swiss- Pdb Viewer. A minimized energy state of -69853 KJ/mol for the heterotetramer was obtained. MD simulation of the representative heterodimer and heterotetramer was performed using NAMD on an 8-node Linux cluster operating at 0.1 TFLOPS. The studies indicated that the tail-to-tail interaction of the LS and SS was more stable with -437086 kcal/mol energy as compared to the head-to-head orientation (-396097 kcal/mol) also the heterotetramer energy was minimized to -767011 kcal/mol. Following this the subunit-subunit interaction studies were carried out on the heterotetramer using NACCESS and Dimplot programs. NACCESS indicated interface residues; 57 residues for SS out of which 30 were hydrophobic and 27 were hydrophilic and 63 residues (31 hydrophobic and 32 hydrophilic residues) for LS. Dimplot plotted the hydrophobic interactions and hydrogen bonds between different chains of the heterotetramer. Lastly superimposition of the deduced heterotetramer on the template homotetramer (1YP2) was done and found to be quite similar as evident by 0.822Å RMSD.ThesisItem Open Access Multiple Sequence Alignment: Comparison and Evaluation(2012) Ritu; Jain, SunitaThe present investigation was undertaken to compare and evaluate eleven tools of Multiple Sequence Alignment, namely Bali-phy, ClustalW, DCA, Dialign, Kalign, MAFFT, Multalin, MUSCLE, PRALINE, PROBCONS and T-COFFEE and to present a choice to the users of MSA regarding the optimal program. Nineteen sequences, each of cytochrome C and hexokinase were aligned by these tools using their default parameters. On comparing the various features of MSA tools, it was found that linux was the most favoured operating system and Fasta was the most favoured input as well as output format. PRALINE was the only web-based and Bali-phy was the only stand-alone tool, while the rest of the tools were both stand-alone as well as web-based. Various MSA programs were based on different alignment algorithms and scoring matrices. Alignments produced were used to generate phylogenetic trees. Comparison of these phylogenetic trees with the reference phylogenetic tree i.e. NCBI Taxonomy Common Tree, showed that while the tree obtained from the T-COFFEE alignment tool was nearest to the NCBI Taxonomy Common Tree, phylogenetic trees produced by none of the MSA tools were consistent. Multalin tool was found to give the highest diversity of position scores on Scorecons server for both cytochrome C and hexokinase sequences; while TCOFFEE, MUSCLE, Kalign and MAFFT programs gave low scores. The benchmark database, BAliBASE was used in the current study for cytochrome C sequences. It was found that MSA programs Praline, MAFFT, MUSCLE and PROBCONS gave high SP scores for both divergent sequences and sub-families, while T-COFFEE gave low score. Inconsistent phylogenetic trees and discrepancy between Scorecons and BAliBASE scores from various MSA tools may be attributed to the use of highly divergent sequences from bacteria to human in the current study.ThesisItem Open Access Prediction of codon bias in rice (Oryza sativa) genes(CCSHAU, 2012) Savita; Jain, SunitaPatterns and degrees of codon usage bias vary not only among different organisms, but also among genes in the same organism. Codon usage bias (CUB) conveys useful information about the selection on synonymous codons induced by gene expression, translation efficiency and about the distinctive evolutionary properties. In the present study, by using 28502 complete coding sequences (CDS) of rice (Oryza sativa) with full length cDNA support, a detailed analysis of the codon bias was performed by employing Automated Codon Usage Analysis (ACUA) tool. 27897 genes demonstrated codon bias as their ENc (effective number of codons) values varied from 20 to less than 61. On basis of GC3 content values, 267 biased genes were taken as high GC 3(GC3>=0.8) whereas 27630 biased genes were termed as low GC3(GC3 <0.8) genes. Quantification of genomic compositional asymmetry and dinucleotide frequency distribution analysis were further performed on selected biased genes to provide possible reasons of differences in nucleotide composition and compositional gradients along the coding regions of all the genes. GC3 gradient analysis results revealed that from the 5' end to 3' end of the open reading frame, high GC3 biased genes had a very slight positive gradient and low GC 3biased genes had strong negative gradient. CG3 skewness analysis showed that C3 preference even though higher for high GC 3genes than low GC 3genes, varied differentially as a function of distance from ATG codon. The analysis for CG3- and GC3- skewness as a function of distance from the startcodon in rice genes clearly depicted the prevailing tendency for GC 3 to decrease toward the 3' end. By dinucleotide frequencies ratio distribution analysis, 45/267 high GC 3 biased genes were detected at CG/GC dinucleotide frequencies ratio peak centered at 1.1, whereas 1729/27630 and 1762/27630 low GC3 genes with CG/GC dinucleotide frequencies ratio peaks centered at 0.5 and 1.1, respectively were observed that clearly depicted that high GC 3 genes had a significantly higher frequency of CG dinucleotides than low GC 3genes. The differential pattern of GC 3bias in rice genes shown in our study corroborate with the previous studies that plant codon usage might be affected by translational selection.ThesisItem Open Access Identification of microRNA in rice (Oryza sativa)(CCSHAU, 2012) Mamta Rani; Sudhir KumarTh e pr e s e n t i n ve s t i g a t i on h a s be e n un d er t a k en t o i d e n t i f y m i c r oRNA i n Or y z a s a t i v a u s i n g c om p u t a t i on a l a p pr oa c h . Th e m i RNA p l a y s i g n i f i c a n t r ol e i n pl a n t bi ol og y a s n e g a t i ve r e g u l a t or of g e n e e x p r e s s i on a n d h a ve b e e n s h own t o r e g u l a t e d i ve r s e fu n c t i on s i n c l u di n g t r a n s c r i pt i on , c a t a l ys i s , t r a n s p or t er a c t i vi t y, s t r e s s r e s p on s e , p r ot e i n d e g r a d a t i on , n ut r i e n t m e t a bol i sm , r oot g r owt h , or g a n d e ve l op m e n t s u c h a s l e a f m or ph og e n e s i s . In p l a n t s , m os t o f t h e mi RNA bi n d t o c od i n g s e q u e n c e or op e n r e a di n g f r a m e of t h e i r m RNA t a r g e t s wi t h p er fe c t or n e a r l y p e r fe c t c om p l em e n t a r i t i e s a n d i n d u c e s t a r g e t m RNA c l e a va g e . I n t h e p r e s e n t s t u d y, 1 2 9 m i RNAs we r e i d e n t i f i e d i n r i c e g e n om e b y i d e n t i f yi n g p a l i n dr om e s e q u e n c e s a n d pr e d i c t i on of s e c on d a r y s t r u c t ur e b y R N A f o l d , t h en f i l t e r ou t t h e s e q u e n c e s wh os e MF E s e c on d a r y s t r u c t ur e wa s l e s s t h a n - 4 0 k c a l a n d f i n a l l y a l l r e p or t e d m i RNAs we r e ve r i f i e d a g a i n s t kn own s e q u e n c e s o f m i RNAs i n m i RBa s e d a t a ba s e . T wo n ew m i RN A ( Os a - MI R8 1 2 m , Os a - MI R8 1 2 o) we r e r e p or t e d on t h i r d a n d s e v e n t h c h r om os om e . Nu m b e r s o f m i RNA r e p or t e d we r e d i f f e r e n t on d i f fe r e n t ch r om o s om e s ; h i gh er m i RNA ( 1 7 ) we r e r e p or t e d on s i xt h c h r om os om e wh i l e on l y t h r e e m i RNA we r e i d e n t i f i e d on 1 2 t h c h r om os om e . Th e m os t a bu n d a n t l y e x p r e s s e d m i RNA fa m i l i e s i n r i c e s e e d l i n g s a r e MI R1 6 9 a n d MI R1 5 6 on c h r om os om e 2 n d a n d 4 t h . S om e m i RNA ( Os a - MI R4 2 6 , Os a - MI R4 1 7 , Os a MI R4 2 0 Os a - MI R4 1 6 ) we r e f ou n d t o be c on s e r v e d be t we e n r i c e a n d A r a bi d o p s i s wh i l e Os a MI R4 4 4 e , Os a - MI R1 4 2 7 , Os a - MI R1 4 3 0 , Os a - MI R1 4 3 7 , Os a - MI R1 6 7 , Os a - MI R1 4 4 0 we r e f ou n d t o be a bs e n t i n A r a b i d o p s i s bu t pr e s e n t i n c l os e l y r e l a t e d m on oc ot s . T h e mi RNA Os a MI R3 9 9 d , Os a - MI R2 1 2 3 a we r e r e p or t e d on f i r s t a n d t h i r d c h r om os om e a n d i n vol v e d i n s t r e s s r e s p on s e s . Ot h e r m i RNA l i k e Os a - MI R8 1 2 e , Os a - MI R2 8 7 3 , Os a - MI R5 4 9 1 , Os a MI R5 5 3 4 b, Os a - MI R8 1 2 g , Os a - MI R8 1 9 k a n d Os a - MI R2 1 0 2 i d e n t i f i e d on f i r s t , t h i r d a n d n i n t h ch r om os om e a n d h a ve r e g u l a t or y r ol e i n p l a n t pos t em br yog e n i c d e ve l op m e n t , p ol l en a n d s e e d d e ve l op m e n t , d i f f e r e n t r ol e s i n s u p er i or a n d i n fe r i or s p i k e l e t s of r i c e . C on s e r ve d m i RNAs - t a r g e t pa i r s we r e f ou n d t o be a s s oc i a t e d wi t h d e ve l op m e n t a l pr oc e s s e s a n d t r a n s c r i p t i on a l r e g u l a t i on , wh i l e n on - c on s e r ve d p a i r s we r e i n vol ve d i n s i gn a l t r a n s d u c t i on a n d r e s p on s e t o s t r e s s pr oc e s s e s . A n on - s i gn i f i c a n t c or r e l a t i on ( 0. 2 7 ) h a s be e n fou n d be t we e n g e n e s a n d m i RNAs p r e d i c t e d i n r i c e g e n om e .ThesisItem Open Access Virtual high-throughput screening of peroxisome proliferator-activated receptor Inhibitors(CCSHAU, 2012) Sharma, Drishta; Sudhir KumarPeroxisome proliferator-activated receptors are ligand activated transcription factors that belong to the nuclear receptor super family and are involved in control of energy, lipid and glucose homeostasis. There are three different isotypes of the receptor namely, PPARα, PPARβ/δ and PPARγ. The moderate (or partial) levels of PPAR-γ activation coordinate an evolutionary beneficial and adaptive response. Current evidence shows that there are natural PPAR activators, which brings this tightly regulated system out of balance, and contributes to the pathogenesis of life-style associated diseases, such as obesity, type2 diabetes, and atherosclerosis. This also indicates that inhibiting PPAR activity, rather than activating, might be the preferred therapeutic strategy to treat the above disorders. In this study, virtual high throughput screening of PPAR inhibitors was performed using UCSF DOCK 6.0. The three sets of databases were prepared namely, NCI database, ZIN C (target-wise) database and ZINC (lead-like) database. PPARγ was selected as the receptor as the three isotypes were 80% similar. The ouput of the first run (NCI database) contained 1516 compounds. The output of second run (ZINC target-wise) contained 20 compounds. The output of third run (ZINC lead-like) contained 103 compounds. Scoring was done for each compound by summing the energies calculated by DOCK. The cluster analysis was done using Ches-Mapper. In the first run, NCI dataset was used, in which 1516 compounds were taken and resulted in the identification of 8 ligands. Out of 20 compounds obtained from virtual screening of ZINC (target-wise), two of them were identified as the ligands. In the third run, ZINC (lead-like) database was used in which 7 ligands were obtained. Lastly, the obtained hits were compared with the known inhibitors and it was found that the groups present in the inhibitors are similar to the groups present in the hits.