MODBASE, a database of annotated comparative protein structure models and associated resources.

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).

Subnanometer-resolution electron cryomicroscopy-based domain models for the cytoplasmic region of skeletal muscle RyR channel.

The skeletal muscle Ca(2+) release channel (RyR1), a homotetramer, regulates the release of Ca(2+) from the sarcoplasmic reticulum to initiate muscle contraction. In this work, we have delineated the RyR1 monomer boundaries in a subnanometer-resolution electron cryomicroscopy (cryo-EM) density map. In the cytoplasmic region of each RyR1 monomer, 36 alpha-helices and 7 beta-sheets can be resolved. A beta-sheet was also identified close to the membrane-spanning region that resembles the cytoplasmic pore structures of inward rectifier K(+) channels. Three structural folds, generated for amino acids 12-565 using comparative modeling and cryo-EM density fitting, localize close to regions implicated in communication with the voltage sensor in the transverse tubules. Eleven of the 15 disease-related residues for these domains are mapped to the surface of these models. Four disease-related residues are found in a basin at the interfaces of these regions, creating a pocket in which the immunophilin FKBP12 can fit. Taken together, these results provide a structural context for both channel gating and the consequences of certain malignant hyperthermia and central core disease-associated mutations in RyR1.

Protein structure modeling with MODELLER.

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

MODBASE: a database of annotated comparative protein structure models and associated resources.

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).

A composite score for predicting errors in protein structure models.

Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.

How well can the accuracy of comparative protein structure models be predicted?

Comparative structure models are available for two orders of magnitude more protein sequences than are experimentally determined structures. These models, however, suffer from two limitations that experimentally determined structures do not: They frequently contain significant errors, and their accuracy cannot be readily assessed. We have addressed the latter limitation by developing a protocol optimized specifically for predicting the Calpha root-mean-squared deviation (RMSD) and native overlap (NO3.5A) errors of a model in the absence of its native structure. In contrast to most traditional assessment scores that merely predict one model is more accurate than others, this approach quantifies the error in an absolute sense, thus helping to determine whether or not the model is suitable for intended applications. The assessment relies on a model-specific scoring function constructed by a support vector machine. This regression optimizes the weights of up to nine features, including various sequence similarity measures and statistical potentials, extracted from a tailored training set of models unique to the model being assessed: If possible, we use similarly sized models with the same fold; otherwise, we use similarly sized models with the same secondary structure composition. This protocol predicts the RMSD and NO3.5A errors for a diverse set of 580,317 comparative models of 6174 sequences with correlation coefficients (r) of 0.84 and 0.86, respectively, to the actual errors. This scoring function achieves the best correlation compared to 13 other tested assessment criteria that achieved correlations ranging from 0.35 to 0.71.

Comparative protein structure modeling using MODELLER.

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

Comparative protein structure modeling using Modeller.

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

K317, R319, and E320 within the proximal C-terminus of the bradykinin B2 receptor form a motif important for phospholipase C and phospholipase A2 but not connective tissue growth factor related signaling.

We showed previously that large domain exchanges between the bradykinin B2 (BKB2) and angiotensin II type 1a (AT1a) receptors can result in functional hybrids. However, when we proceeded to exchange the entire bradykinin B2 receptor (BKB2R) C-terminal tail with the AT1aR C-terminus, the hybrid, while continuing to bind BK and be endocytosed as wild type (WT) BKB2R, lost much of its ability to activate phosphatidylinositol (PI) turnover or the release of arachidonic acid (ARA). In this study, we constructed chimeric receptors within the proximal C-terminus between the BKB2R and AT1aR or bradykinin B1 receptor (BKB1R). The mutant and WT receptor cDNAs were stably transfected into Rat-1 cells. Also, point mutations were generated to evaluate the role of the individual residues within this region. These chimeric studies revealed that the proximal portion of the BKB2R C-tail is crucial for G protein-linked BKB2R functions. This region could not be swapped with the AT1aR to obtain a BK activated PI turnover or ARA release. Further studies demonstrated that the distal portion (325-330) of this region is exchangeable; however, the middle portion (317-324) is not. Small motif exchanges within this section identified the KSR and EVY motifs as crucial for G(alphaq), G(alphai) related signaling of the BKB2R. Point mutations then showed that the charged amino acids K317, R319, and E320 are the residues critical for linking to PI turnover and ARA release. However, these proximal chimeras showed normal receptor uptake. Interestingly, while apparently not activating G protein-linked signaling, the proximal tail AT1aR exchange mutant and the entire C-terminus exchange hybrid continued to cause a substantial bradykinin effected increase in connective tissue growth factor (CTGF) mRNA level, as WT BKB2R.