Top PDF Identification of Fucose-α(1-2)-Galactose Binding Proteins in the Mammalian Brain

Identification of Fucose-α(1-2)-Galactose Binding Proteins in the Mammalian Brain

Identification of Fucose-α(1-2)-Galactose Binding Proteins in the Mammalian Brain

The plasma membrane is organized into various subdomains of clustered macromolecules. These clusters are organized by specialized scaffolding proteins including caveolins, which stabilize lipid rafts, and galectins, which crosslink multiple glycoproteins to form lattice microdomains. The crosslinking of these cell-surface glycoconjugates facilitates protein-protein interactions within various subdomains, triggering a cascade of transmembrane signaling events. 46,61 Indeed, galectins are implicated in different steps of tumorigenic processes, including tumour cell transformation, cell-cycle regulation and apoptosis. 62 For example, galectin-1 and galectin-3 are involved in tumorigenesis through their interactions with oncogenic Ras, which promotes Ras-mediated signal transduction involving the phosphatidylinositol-3-kinase (PI3K) and extracellular signal-regulated kinase (ERK) 1/2 pathways. 63,64 Similarly, the activation of several C-type lectin receptors (CLRs) family leads to a cascade of signaling events mediated by the action of protein kinases and phosphatases at their cytoplasmic domains. 65 Dectin-1, a member of the CLR family, is activated when it recognize the β-1,3-glucan epitope during fungal infection, resulting in the phosphorylation of its cytoplasmic domain and subsequent NF-κB pathway activation. 66 While mammalian lectin function is generally ascribed to cellular recognition, its involvement in cellular signaling is undoubtedly becoming more evident.
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Molecular characterisation of steroids in the mammalian brain

Molecular characterisation of steroids in the mammalian brain

Steroids are usually derivatised to protect thermally labile compounds at the elevated temperatures in GC and may also avoid tailing of polar compounds or increase the response o f a compound in the detection. Methods most commonly used for derivatisation of compounds for GC are silylation, alkylation, oxime formation, and acylation. The most versatile silylation reagent for steroids is trimethylsilylimidazole (TMSI). TMSI reacts with hydroxyl groups to form ethers and is usually used in combination with methoxyamine hydrochloride (MO), which reacts with keto-functions to give oximes [35]. TMSI on its own can also be used to react with keto-functions, forming enols. MO derivatisation has the disadvantage o f formation o f syn/anti - isomers that can separate in GC. Hexamethyldisilazane (HMDS) and trimethylchlorosilane (TMCS) are milder silyl donors and are only used for few steroids. Higher alkyl- dimethylsilyl (e.g. ré?r/-butyldimethylsilyl (TBDMS)-donors) derivatising reagents have been investigated for use in steroid analysis. They react with hydroxyl groups to give TBDMS ethers, ketone functions react after énolisation [154,155], These TBDMS- derivatives are less susceptible to fragmentation in MS and give few highly abundant ions. This makes them useful for quantitation in high sensitivity analyses. However there is a loss in structural information and identification is less specific. Sensitivities achieved were reported in the range of 1 (testosterone) and 5 pg (progesterone) in analyses of TBDMS-derivatives with GC-EIMS [5].
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Understanding the Chemical Basis of Neuronal Development and Communication: I  The Role of Fucose α(1 2) Galactose Carbohydrates in Neuronal Growth  II  Structure Function Analysis of Chondroitin Sulfate in the
Brain

Understanding the Chemical Basis of Neuronal Development and Communication: I The Role of Fucose α(1 2) Galactose Carbohydrates in Neuronal Growth II Structure Function Analysis of Chondroitin Sulfate in the Brain

To confirm that probe 2 is binding selectively through the Fucα (1-2)Gal disaccharide, we performed competition experiments with several sugars. Several alternative sugars were synthesized by Dr. Kalovidouris that vary in the configuration of the fucose saccharide in the model Fucα (1-2)Gal disaccharide (Figure 4.4A). Dissociated forebrain cells were first incubated with Fucα(1-2)Gal-OEt disaccharide, L - FucαOEt, or D -FucαOEt (each at 150 mM) for 2 h and then with probe 2 (0.3 mM) for an additional 2 h. Cells were irradiated and lysed as described above and the proteins were resolved and detected as above by Western blotting (Figure 4.4B). The proteins captured by probe 2 (lane 1) are no longer detected upon treatment with the Fucα (1- 2)Gal-OEt competitor (lane 2). Treatment with L -FucαOEt or D -FucαOEt reduced the concentrations of proteins captured, however the reduction was incomplete (lanes 3 and 4). These studies demonstrate that the lectins are recognizing the probe specifically via the sugar moiety. Moreover, comparison of the L -Fucα OEt or D -Fuc α OEt
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Identification and Characterization of the Plasticity-Relevant Fucose-α(1-2) Galactose Glycoproteome from Mouse Brain

Identification and Characterization of the Plasticity-Relevant Fucose-α(1-2) Galactose Glycoproteome from Mouse Brain

We envisioned exploiting a series of deoxy sugar analogues to map the structure of the oligosaccharide present on synapsin. Our approach stems from the observation that 2-dGal can inhibit protein fucosylation by competing with D -galactose for incorporation into the oligosaccharide chain. 44, 45 Upon cellular uptake, 2-dGal is converted via the Leloir pathway to the 1-phosphate analogue, which is subsequently converted to the activated uridyl diphosphate (UDP) sugar. 46 Based upon these observations, we postulated that 3-dGal, 4-dGal and 6-dGal might also be converted into UDP sugars in sufficient quantity to compete with D-galactose. We have previously demonstrated in Chapter 2 that 2-dGal is an efficient substrate for metabolism through the Leloir pathway. While the conversion efficiencies of other deoxy sugars via this pathway have not been systematically examined in mammalian cells, in vitro studies using purified enzymes have suggested that substitution at the various positions should be tolerated, with the C4 position least favored. 47-49 Subsequent incorporation of the deoxy analogues into glycoproteins would inhibit formation of the corresponding fucose- galactose disaccharide and permit mapping of the precise linkage (Figure A1.1).
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Identification of a ubiquitous family of membrane proteins and their expression in mouse brain

Identification of a ubiquitous family of membrane proteins and their expression in mouse brain

A rat cDNA encoding TPO1 that exhibits 37 % identity with TMS-1 and 40 % identity with TMS-2 has been cloned, and its expression has been analyzed (Krueger et al., 1997). Membrane proteins encoded by the same genes in different rodents usually exhibit over 80 % identity in their amino acid sequences (Liu et al., 1992a,b). Indeed, the amino acid sequence of human MUSTETU is 96 % identical to mouse TMS-2, leaving little doubt that they are the same gene product. Human Diff33 shares 78 % identity with mouse TMS-1, suggesting that they are the same gene product that has undergone more rapid divergence than TMS-2 and MUSTETU. Because of the relatively low levels of identity of the amino acid sequences, we suggest that the TPO1 protein belongs to the family but is not the TMS-1 or the TMS-2 homologue; it may represent a third gene product that has not yet been identified in mouse libraries. The rat cDNA was detected by northern analysis in the lung, liver and brain and was expressed at relatively high levels in cultured oligodendrocytes (Krueger et al., 1997). Expression of genes in cultured cells frequently does not reflect their expression in situ. The very high levels of expression of TMS-1 in liver tumours support this notion. A search in the EST databank revealed several ESTs in libraries obtained from different tumours as well as other rapidly growing tissues such as human placenta and mouse testicular tumours (Lebel and Mes-Masson, 1994). Several genes are induced in these tissues, and the presence of mRNA encoding TMS family members gives no clue to their function. However, the primary cells utilized for detecting the expression of TPO1 may exhibit better correlation with the in situ situation. We intend to generate multiple gene knockouts in yeast cells and to use these mutants to determine the function of Tms1p. We hope that this will also shed light on the function of the other family members in mammalian brain and peripheral tissues.
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Identification of Carbohydrate-Binding Domains in the Attachment Proteins of Type 1 and Type 3 Reoviruses

Identification of Carbohydrate-Binding Domains in the Attachment Proteins of Type 1 and Type 3 Reoviruses

tein. The wt T3D ␴1 protein was cleaved by trypsin, as was each ␴1 construct containing the T(iv) region of T3D ␴1 (Fig. 3B and 4). Trypsin treatment of this group of expressed ␴1 proteins resulted in the generation of stable cleavage products of approximately 25 kDa, which is characteristic of trypsin- treated wt T3D ␴1 (8, 14, 15, 24, 30, 52). Thus, the pattern of susceptibility of chimeric ␴1 proteins to cleavage by trypsin confirms the location of a protease-sensitive region in T3D ␴1, T(iv), and is consistent with native folding of these molecules. Identification of a domain in ␴ 1 important for multimer stability. As an additional test of protein folding, expressed ␴1 proteins were examined for the capacity to maintain oligo- meric structure during SDS-PAGE. In previous studies, it was shown that virion-associated (3, 51) and expressed (3, 24, 40) T3D ␴1 protein migrates as an oligomer in SDS-polyacryl- amide gels after solubilization in protein sample buffer under specific conditions of temperature and pH. When virions of T1L and T3D were disrupted at 60°C in pH 8.3 sample buffer, the ␴1 protein of T1L migrated as a monomer, whereas T3D ␴1 migrated as an oligomer (Fig. 5A). Incubation of virions at 100°C in pH 6.8 sample buffer resulted in the appearance of ␴1 monomers only. This pattern was replicated by wt T1L and T3D ␴1 proteins expressed in insect cells (Fig. 5B and C). Chimeric and truncated ␴1 proteins containing T1L T(i) and T(ii) sequences migrated as monomers under these conditions, and ␴1 proteins with T(i) and T(ii) sequences derived from T3D migrated as oligomers. Thus, results obtained using chi- meric and truncated ␴1 proteins show that sequences consti- tuting morphologic regions T(i) and T(ii), which are predicted to form almost exclusively ␣-helical coiled coil, determine the difference in stability of T1L and T3D ␴1 oligomers in SDS- polyacrylamide gels. Additionally, these results indicate that native tertiary and quaternary structures are maintained in the amino-terminal aspect of the tail domain of expressed ␴1 pro- teins used for our studies.
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Identification of a DNA-binding domain in the amino terminus of adeno-associated virus Rep proteins.

Identification of a DNA-binding domain in the amino terminus of adeno-associated virus Rep proteins.

2P-labeled AAV terminal repeat hairpin DNA 20,000 cpm per sample was incubated with no extract or with nuclear extracts from 293 cells that were transfected with pARtat plus either contr[r]

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Prediction of DNA-Binding Proteins and their Binding Sites

Prediction of DNA-Binding Proteins and their Binding Sites

With the exponential growth of proteomic data and the enormous complexities involved in their modeling, bioinformatics becomes essential for the management and mining of biological data in modern biology, medicine and drug discovery. The development of computational tools to solve the problem requires the expertise from many fields of computer science, like i) Data science for mining, collection and preparation of data, ii) Scientific Computing to extract useful knowledge from large sets of data and mathematically quantify the knowledge as characteristics features, iii) Machine Learning to develop novel algorithms to model the data using features, and iv) Statistical and Probabilistic Analysis to empirically evaluate the model by comparative analysis and visualize the outputs. These methods have been utilized throughout the course of the thesis to develop novel tools for the prediction of DNA-binding proteins and their binding sites using sequence information only.
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Isolation of the galactose binding lectin that mediates the in vitro adherence of Entamoeba histolytica

Isolation of the galactose binding lectin that mediates the in vitro adherence of Entamoeba histolytica

Entamoeba histolytica adheres to human colonic mucus, colonic epithelial cells, and other target cells via a galactose (Gal) or N-acetyl-D-galactosamine (GalNAc) inhibitable surface lectin. Blockade of this adherence lectin with Gal or GalNAc in vitro prevents amebic killing of target cells. We have identified and purified the adherence lectin by two methods: affinity columns derivatized with galactose monomers or galactose terminal glycoproteins, and affinity columns and immunoblots prepared with monoclonal antibodies that inhibit amebic adherence. By both methods the adherence lectin was identified as a 170-kD secreted and membrane-bound amebic protein. The surface location of the lectin was confirmed by indirect immunofluorescence. Purified lectin competitively inhibited amebic adherence to target cells by binding to receptors on the target Chinese hamster ovary cells in a Gal- inhibitable manner.
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Identification of an intestine-specific promoter and inducible expression of bacterial α-galactosidase in mammalian cells by a lac operon system

Identification of an intestine-specific promoter and inducible expression of bacterial α-galactosidase in mammalian cells by a lac operon system

We also found that the lac operon had a low-level leakiness; the target gene was still expressed at a low level when the repressor bound to the lac operator. This phenomenon was also investigated by Wyborski and DuCoeur, although they had successfully used the lac op- eron to regulate gene expression in vivo [20]. The LacO inserted at both −10 and −35 led to promoter activity being decreased 45-fold in the presence of a repressor [19]. In addition, the specificity of the repressor-operator interaction can be further increased by introducing a small degree of asymmetry in the operator; the symmetry in the operator alters the translational efficiency, but it cannot affect transcription [35]. We observed that α- galactosidase mRNA and protein levels have a similar trend, shown in Figure 4, which suggested that the loca- tion rather than the symmetry of the operator in the HMUC2 promoter should be changed to reduce the low- level leakiness. Re-encoding or mutating some amino acids in the lacI sequence can significantly improve sup- pression capability, as has been successfully shown by Mueller-Hartmann and Mueller-Hill [36]. Thus, the problem of low-level leakiness can be overcome in future work by altering the location of LacO and by introducing appropriate mutations into the LacI sequence.
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Identification of genotype 4 Hepatitis E virus binding proteins on swine liver cells

Identification of genotype 4 Hepatitis E virus binding proteins on swine liver cells

Hepatitis E virus (HEV), a member of the genus Hepe- virus, is a non-enveloped virus with a positive-stranded RNA genome approximately 7.2 kb in length, which consists three open reading frames (ORF1-3)[1]. ORF1 locates at the 5 ’ of genome and encodes non-structural proteins, including the methyltransferase, protease, heli- case and RNA-dependent RNA polymerase (RdRp) [2]. ORF2 maps to the 3 ’ terminus and encodes for a major structural protein, and ORF3 overlaps both and encodes a thus far unknown function [3]. It has been hypothe- sised that zoonosis is involved in the transmission of HEV [4]. HEV isolates were divided into 4 distinct geno- types which were recently proposed to be further classi- fied into 24 subtypes [5]. Genotypes 1 and 2 have been identified exclusively in humans, while genotypes 3 and 4 have been found in humans and several species of ani- mals. Swine stands out as a reservoir for hepatitis E virus (HEV) infection in humans, as suggested by the close genetic relationship of swine and human virus and cross-species infection of HEV [6,7].
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alpha Fucose mediated binding and degradation of tissue type plasminogen activator by HepG2 cells

alpha Fucose mediated binding and degradation of tissue type plasminogen activator by HepG2 cells

Binding and degradation of tissue-type plasminogen activator by the human hepatoma cell line HepG2.. Interactions between tissue-type plasminogen activator and extracellular matrix assoc[r]

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Identification and characterization of specific binding proteins for growth hormone in normal human sera

Identification and characterization of specific binding proteins for growth hormone in normal human sera

completely displaced by excess unlabeled hGH. When serum alone was chromatographed two peaks of specific binding were subsequently detected, the major peak, eluting between 74,000 and 85,000 mol wt corresponded to the 125I-hGH-binding protein complex observed at approximately 120,000 mol wt. Using a mini-gel filtration system for separating bound from free hormone, binding of 125I-hGH by normal human serum was dependent on time (equilibrium was reached in 2 h at 21 degrees C), temperature (21 degrees C greater than 37 degrees C), Ca2+ and serum concentrations. Binding was reversible and highly specific for hGH, not being displayed by GH or prolactins from several species. Scatchard analysis revealed linear plots with an affinity (KA) of 0.32 +/- 0.06 X 10(9) M-1 (n = 7). Human serum with low endogenous hGH levels, when added to rabbit liver membranes, decreased the binding of 125I-hGH in this tissue in a dose-dependent manner. These data indicate that human sera contain a specific, high affinity […]
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Identification of epitopes on respiratory syncytial virus proteins by competitive binding immunoassay

Identification of epitopes on respiratory syncytial virus proteins by competitive binding immunoassay

Epitopes on the same protein can be distinguished from each other by MAbs which give i distinct reaction patterns against different strains of a virus, either naturally occurring or labo[r]

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Identification of high affinity folate binding proteins in human erythrocyte membranes

Identification of high affinity folate binding proteins in human erythrocyte membranes

saturable, and was much greater for pteroylmonoglutamate and 5-methyltetrahydrofolate than 5-formyltetrahydrofolate and amethopterin. On detergent solubilization of membranes, two peaks of specific folate binding with Mr greater than or equal to 200,000 and 160,000 were identified on Sephacryl S-200 gel filtration chromatography in Triton X-100, and this corresponded to two similar peaks of immunoprecipitated material when solubilized iodinated membranes were probed with anti-human placental folate receptor antiserum. Age-dependent separation of erythrocytes by Stractan density gradients revealed a sevenfold greater folate binding capacity in membranes purified from younger compared with aged erythrocytes. Since this difference was not reflected in proportionately higher immunoreactive folate binding protein, (as determined by a specific radioimmunoassay for these proteins), or differences in affinity in younger than aged cells, these findings indicate that erythrocyte folate binding proteins become progressively nonfunctional at the onset of red cell aging.
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Identification of RNA Binding Proteins and RNA Binding Residues Using Effective Machine Learning Techniques

Identification of RNA Binding Proteins and RNA Binding Residues Using Effective Machine Learning Techniques

Today, there has been a lot of development in genomics and hence there is an increased number of proteomic data available in different online databases. Experimental methods alone are time consuming and costly. So, bioinformatics offers a faster, cheaper way to mine, evaluate and interpreted such biological data. Today, bioinformatics has become essential in dealing with biological data because of its efficiency and success in various research works. The development of computational tools for analysis and interpretation of such data through bioinformatics involves few steps: i) Data mining, collection, and preparation of data, ii) Computing to extract useful information or characteristics, can also be thought of as features, from the data, iii) Apply various Machine Learning Algorithms to develop a robust classifier that uses the features extracted in the previous step, and iv) Analyze, compare and evaluate obtained results from the classifiers. These three steps have been utilized in this thesis to develop predictors for annotation of RNA Binding Proteins and RNA Binding residues.
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Membrane proteins specified by herpes simplex viruses. V. Identification of an Fc-binding glycoprotein.

Membrane proteins specified by herpes simplex viruses. V. Identification of an Fc-binding glycoprotein.

This autoradiogram of an acrylamide gel slab shows the labeled polypeptides that were bound to and eluted from either IgG-BSA-Sepharose IgG or control BSA-Sepharose C and also the labele[r]

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CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

Due to Ca 2+ dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. For CaM interaction prediction, CaMELS uses protein features coupled with a large-margin classifier and gives significantly improved prediction accuracy in comparison to existing techniques. CaMELS can not only identify whether a protein binds CaM or not, it can also predict CaM-binding residues in those proteins. It models the binding site prediction problem using multiple instance machine learning with a novel optimization algorithm. In comparison to conventional classification techniques, our proposed stochastic sub-gradient solver for multiple instance learning allows more effective training with a data set containing imprecisely annotated CaM-binding sites. We benchmarked the performance of CaMELS using a non-redundant set of binding proteins and binding sites in the CaM target database as well as the A. thaliana proteome. As a case study, we have used CaMELS for predicting the binding sites of Adenylyl cyclase domain from B. pertussis and have found our sequence-only prediction to be in close agreement with the known structure of the protein complex. Our interaction prediction results for the A. Thaliana proteome also show a high degree of overlap of gene ontology enrichment with known CaM targets. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL:
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CD23 is a glycan-binding receptor in some mammalian species

CD23 is a glycan-binding receptor in some mammalian species

glucans contain appended mannose and GlcNAc residues that would be potential ligands (27). However, the fact that bacterial curdlan can be used to mimic the effects of the fungus suggests that glucose residues also can mediate binding to CD23 (14,28). Although the main chain β1-3 linkages would block the 3-OH groups on glucose and prevent binding, glucose residues at the nonreducing termini of chains would be able to interact with CD23. Previous studies have suggested that human CD23 mediates pathogen recognition indirectly by forming complexes with IgE bound to mycobacteria or leishmania parasites (29,30), but the results presented here and in the recent study of binding to fungi are consistent with a direct mechanism for mouse CD23 interacting with sugars on the surfaces of micro-organisms. In cows, there might be a particular need for such direct recognition of micro-organisms. As ruminants, cows may have high levels of non- pathogenic bacteria and fungi in the intestinal tract and appropriate recognition may be necessary to maintain T cell anergy on the intestinal surfaces.
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Ligand binding and homology modelling of insect odorant-binding proteins

Ligand binding and homology modelling of insect odorant-binding proteins

through manipulation of insect pest behaviours (Leite et al., 2009; Lagarde et al., 2011). An OBP-based screening of putative bioactive chemicals with homology modelling can serve as a good complement together with robust biological assays to study ligand–OBP interactions, as well as to research ‘super-ligands’ for insect behavioural manipulation. Leal (2005) proposes a reverse chemical ecology concept, which utilizes a protein-based screening of attractants, pheromones and repellents through their binding affinity to OBPs, as an interesting approach for using these chemicals in pest man- agement. An advantage of this approach is that insect OBPs are structurally different from and have no sequence homology with vertebrate OBPs (also called lipocalins). However, insect OBPs are functionally similar to vertebrate OBPs. Insect OBPs have mainly α-helical domains, whereas vertebrate OBPs have only β-strands and a short α-helix. Nevertheless, both insect and vertebrate OBPs have conserved disulphide bridges and are small soluble proteins. Ligand binding assays indicate that both insect and vertebrate OBPs bind to a wide range of volatile molecules, with dissociation constants of either a micro- or mil- limolar concentration (Tegoni et al., 2000; Pelosi, 2001; Briand et al., 2002; Löbel et al., 2002; Nespoulous et al., 2004; Grolli et al., 2006; Wei et al., 2008; Brimau et al., 2010). However, electrophysiological recordings show evidence of selective binding of BmorPBPs and ApolPBPs to pheromones (Pophof, 2004). Moreover, selective binding of ApolPBP1 at pH 6.5 and pH 4.5 in the nanomolar range was reported by Katre et al. (2009). Although many studies have used homology modelling in structure-based drug discovery, only a few modelling studies have been performed on insects OBPs. As a result of the high similarity of OBPs across lepidopteran species, and a large num- ber of experimentally determined 3D structures (Tegoni et al., 2004; Pelosi et al., 2006; Damberger et al., 2007; Zhou, 2010), these proteins could be used as good targets for homology modelling and molecular dynamic (MD) simulations to obtain the best structural models in terms of energy. Thus, the present review aims to present current knowledge of computer-assisted protein modelling by homology to predict the ligand-binding affinities, focusing on lepidopteran OBPs.
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