Template For Homology Modeling Research Articles

Homology modeling, agonist binding site identification, and docking in octopamine receptor of Periplaneta americana

AY333178 (from Periplaneta americana, 628 AAs) was selected as a target octopamine receptor (OAR) class OAR2 for this study using Discovery Studio (DS Modeling1.1/1.2, Accelrys Inc.). Blast similarity search was performed and identified that AY333178 contains N-terminal domain of GPCR. Based upon Blast and Pfam results, Rhodopsin 1U19 (protein data bank) was considered as an ideal homologue and used as a template for homology modeling due to its higher X-ray resolution at 2.2 Å. Sequence alignment between AY333178 and 1U19 was done using Align123 followed by a manual modification. The final alignment was carefully evaluated and evidenced to be matching the conserved residue data for class A GPCR fairly well. The 3D model of AY333178 was generated with MODELER, and further refined using CHARMm. Superimposition of the model was done over the template 1U19. Two fairly consistent profiles were observed demonstrating AY333178 model was reasonable and could be employed for the further docking study. Agonist docking into OAR2 model was done using LigandFit. The superimposition of two top poses of representative agonists was performed with a soft surface generated. Those models are considered to be used in designing new leads for hopefully more active compounds. Further research on the comparison of models for the agonists may elucidate the mechanisms of OAR2–ligand interactions.

The solution structure of ribosomal protein S17E from Methanobacterium thermoautotrophicum: a structural homolog of the FF domain.

The ribosomal protein S17E from the archaeon Methanobacterium thermoautotrophicum is a component of the 30S ribosomal subunit. S17E is a 62-residue protein conserved in archaea and eukaryotes and has no counterparts in bacteria. Mammalian S17E is a phosphoprotein component of eukaryotic ribosomes. Archaeal S17E proteins range from 59 to 79 amino acids, and are about half the length of the eukaryotic homologs which have an additional C-terminal region. Here we report the three-dimensional solution structure of S17E. S17E folds into a small three-helix bundle strikingly similar to the FF domain of human HYPA/FBP11, a novel phosphopeptide-binding fold. S17E bears a conserved positively charged surface acting as a robust scaffold for molecular recognition. The structure of M. thermoautotrophicum S17E provides a template for homology modeling of eukaryotic S17E proteins in the family.

A novel approach to structural alignment using realistic structural and environmental information

In the era of structural genomics, it is necessary to generate accurate structural alignments in order to build good templates for homology modeling. Although a great number of structural alignment algorithms have been developed, most of them ignore intermolecular interactions during the alignment procedure. Therefore, structures in different oligomeric states are barely distinguishable, and it is very challenging to find correct alignment in coil regions. Here we present a novel approach to structural alignment using a clique finding algorithm and environmental information (SAUCE). In this approach, we build the alignment based on not only structural coordinate information but also realistic environmental information extracted from biological unit files provided by the Protein Data Bank (PDB). At first, we eliminate all environmentally unfavorable pairings of residues. Then we identify alignments in core regions via a maximal clique finding algorithm. Two extreme value distribution (EVD) form statistics have been developed to evaluate core region alignments. With an optional extension step, global alignment can be derived based on environment-based dynamic programming linking. We show that our method is able to differentiate three-dimensional structures in different oligomeric states, and is able to find flexible alignments between multidomain structures without predetermined hinge regions. The overall performance is also evaluated on a large scale by comparisons to current structural classification databases as well as to other alignment methods.

Molecular modeling of the GABAC receptor ligand-binding domain

We have constructed a molecular model of the ligand-binding domain of the GABA(C) receptor, which is a member of the Cys-loop ligand-gated ion channel family. The extracellular domains of these receptors share similar sequence homology (20%) with Limnaea acetylcholine-binding protein for which an X-ray crystal structure is available. We used this structure as a template for homology modeling of the GABA(C) receptor extracellular domain using FUGUE and MODELLER software. FlexX was then used to dock GABA into the receptor ligand-binding site, resulting in three alternative energetically favorable orientations. Residues located no more than 5 A from the docked GABA were identified for each model; of these, three were found to be common to all models with 14 others present only in certain models. Using data from experimental studies, we propose that the most likely orientation of GABA is with its amine close to Y198, and its carboxylate close to R104. These studies have therefore provided a model of the ligand-binding domain, which will be useful for both GABA(C) and GABA(A) receptor studies, and have also yielded an experimentally testable hypothesis of the location of GABA in the binding pocket. [Figure: see text].

Prediction of homology model quality with multivariate regression.

A new method has been developed for prediction of homology model quality directly from the sequence alignment, using multivariate regression. Hence, the expected quality of future homology models can be estimated using only information about the primary structure. This method has been applied to protein kinases and can easily be extended to other protein families. Homology model quality for a reference set of homology models was verified by comparison to experimental structures, by calculation of root-mean-square deviations (RMSDs) and comparison of interresidue contact areas. The homology model quality measures were then used as dependent variables in a Partial Least Squares (PLS) regression, using a matrix of alignment score profiles found from the Point Accepted Mutation (PAM) 250 similarity matrix as independent variables. This resulted in a regression model that can be used to predict the accuracy of future homology models from the sequence alignment. Using this method, one can identify the target-template combinations that are most likely to give homology models of sufficient quality. Hence, this method can be used to effectively choose the optimal templates to use for the homology modeling. The method's ability to guide the choice of homology modeling templates was verified by comparison of success rates to those obtained using BLAST scores and target-template sequence identities, respectively. The results indicate that the method presented here performs best in choosing the optimal homology modeling templates. Using this method, the optimal template was chosen in 86% of the cases, as compared to 62% using BLAST scores, and 57% using sequence identities. The method presented here can also be used to identify regions of the protein structure that are difficult to model, as well as alignment errors. Hence, this method is a useful tool for ensuring that the best possible homology model is generated.