Development and Application of Novel ... - Jacobs University

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PART I: CAPDE FlexPred [84] ElNemo [92] WEBnm@ [93] FlexServ [94] HINGEprot [95] conformation ensemble and transitions using geometrical constrains based prediction of protein conformational flexibility The web server predicts residue flexibility in the protein using SVM approach. The web server predicts large amplitude motions in the protein using NMA. The web server determines and analyzes protein flexibility using coarse-grained modeling approach. The web server detects hinge region [83] coord.html Human PrP http://flexpred.rit.al [105] bany.edu/ HIV-1 protease, E. http://igs- coli server.cnrs- membrane mrs.fr/elnemo/index channel .html protein TolC Calcium http://apps.cbu.uib.n ATPase [93] o/webnma/home http://mmb.pcb.ub.e - s/FlexServ/input.ph p Calmodulin http://www.prc.bou protein, n.edu.tr/appserv/prc 22
PART I: CAPDE in the protein using hemoglobin /hingeprot/ both GNM and ANM. [95] The web server predicts domain Hemoglobin, DynDom3D motions using 70S http://fizz.cmp.uea.a [98] conformational ribosome c.uk/dyndom/3D/ changes in the [98] protein. 1.6. Mutational effects in protein For biotechnological applications, the enhancement of protein thermal stability or tolerance is a common requested task in protein engineering.[106] Highly stable structure correlates with well-packed highly compact structure and has increased tolerance to mutation because mostly the mutations are deleterious i.e. related to instability of protein.[107] Generally the effect of the mutation on protein has been calculated by the free energy differences between two states of protein like thermodynamic stability as change in free energy in folded and unfolded state (ΔΔG). The mutational effect has been predicted by using different machine learning and selection methods (as SVM, Decision Tree (DT) or Random Forest (RE) [108]) for classification or regression of data or by using statistical or empirical methods taking into account the atomic interactions or structural properties like solvent accessibility. Most of the servers based on these approaches use available information of mutational effects (fetched from databases like PMD [48], ProTherm [51]) to predict the effect of new substitutions. Table 1.4 summarizes the available tools to predict mutational effects on protein stability and activity using different methods. I-Mutant2.0 [109] and MUpro [110] are SVM based methods to predict stabilizing or destabilizing amino acid substitutions based on free energy change (ΔΔG). iPTREE-STAB [111] server employ a DT approach to predict the effect of single mutation on protein stability considering physicochemical properties and contact information of the substituted 23
Page 1 and 2: Development and Application of Nove
Page 3 and 4: Funding The work described in this
Page 5 and 6: Abstract In the last decades, enzym
Page 7 and 8: Table of Content Acknowledgement ..
Page 9 and 10: 4.5. References ...................
Page 11 and 12: PART I: CAPDE Chapter 1 Computer-Ai
Page 13 and 14: PART I: CAPDE of protein structure
Page 15 and 16: PART I: CAPDE In this chapter, for
Page 17 and 18: PART I: CAPDE library size and rand
Page 19 and 20: PART I: CAPDE 1.4. Evolutionary con
Page 21 and 22: PART I: CAPDE The web server perfor
Page 23 and 24: PART I: CAPDE based MSA a protein s
Page 25 and 26: PART I: CAPDE name or structure and
Page 27 and 28: PART I: CAPDE for experimental scre
Page 29 and 30: PART I: CAPDE characterizes it bind
Page 31: PART I: CAPDE stability using inter
Page 35 and 36: PART I: CAPDE Table 1.4: Summarizin
Page 37 and 38: PART I: CAPDE in which the volume o
Page 39 and 40: PART I: CAPDE 21. Chica RA, Doucet
Page 41 and 42: PART I: CAPDE 44. Knoll M, Hamm TM,
Page 43 and 44: PART I: CAPDE 69. Vangone A, Spinel
Page 45 and 46: PART I: CAPDE 95. Emekli U, Schneid
Page 47 and 48: PART I: CAPDE 119. Parthiban V, Gro
Page 49 and 50: PART I: MAP 2.0 3D 2.2. Introductio
Page 51 and 52: PART I: MAP 2.0 3D improve the fitn
Page 53 and 54: PART I: MAP 2.0 3D ⎛ 0.0 9.70 19.
Page 55 and 56: PART I: MAP 2.0 3D amino acid (α)
Page 57 and 58: PART I: MAP 2.0 3D of transformatio
Page 59 and 60: PART I: MAP 2.0 3D amino acid oxida
Page 61 and 62: PART I: MAP 2.0 3D aliphatic amino
Page 63 and 64: PART I: MAP 2.0 3D Figure 2.5, char
Page 65 and 66: PART I: MAP 2.0 3D Figure 2.5: Chem
Page 67 and 68: PART I: MAP 2.0 3D 2.4.2. Phytase P
Page 69 and 70: PART I: MAP 2.0 3D Figure 2.6: MAP
Page 71 and 72: PART I: MAP 2.0 3D Figure 2.7: Amin
Page 73 and 74: PART I: MAP 2.0 3D active site resi
Page 75 and 76: PART I: MAP 2.0 3D (δ(282)Q→ch =
Page 77 and 78: PART I: MAP 2.0 3D 11. Turner NJ (2
Page 79 and 80: PART I: MAP 2.0 3D 36. Liao GJ, Lee
Page 81 and 82: PART II: MD Simulation Chapter 3 In
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PART II: MD Simulation Force field
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PART II: MD Simulation Electrostati
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PART II: MD Simulation 3.4. Structu
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PART II: MD Simulation where vi and
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PART II: P450BM-3 Reductase Domain
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PART II: P450BM-3 HEME/FMN Complex
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PART II: P450BM-3 HEME/FMN & CoSep
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In the second part of the thesis, m
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Curriculum vitae Personal Details N
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Statutory Declaration I, RAJNI VERM
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