Homology modeling of an RNP domain from a human RNA-binding protein: Homology-constrained energy optimization provides a criterion for distinguishing potential sequence alignments

Sahasrabudhe, Parag V.; Tejero, Roberto; Kitao, Saori; Furuichi, Yasuhiro; Montelione, Gaetano T.

doi:10.1002/(sici)1097-0134(19981201)33:4<558::aid-prot8>3.0.co;2-z

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…A very large number of homology models have been built over the years. Targets have included antibodies[119] and many proteins involved in human biology and medicine[120121]. …”

Section: Applications Of Homology Modelingmentioning

confidence: 99%

Homology modeling a fast tool for drug discovery: Current perspectives

Vk¹,

Ukawala²,

Ghate³

et al. 2012

Indian J Pharm Sci

225

116

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Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

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“…A very large number of homology models have been built over the years. Targets have included antibodies[119] and many proteins involved in human biology and medicine[120121]. …”

Section: Applications Of Homology Modelingmentioning

confidence: 99%

Homology modeling a fast tool for drug discovery: Current perspectives

Vk¹,

Ukawala²,

Ghate³

et al. 2012

Indian J Pharm Sci

225

116

View full text Add to dashboard Cite

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“…A useful method must be both robust to input alignment errors and have reliable metrics for assessing the accuracy of the resulting models, in particular indicating when a structure calculation is likely to have significant errors. Energy functions have been used to a limited degree for distinguishing correct from incorrect sequence alignments in homology modeling calculations (31), while in molecular replacement methods for X-ray crystallography, the fit to the diffraction data distinguishes correct from incorrect homologous structure information (32). Here we show that the combination of rapidly obtained backbone chemical shift data together with the Rosetta energy functions provides a robust and reliable method for distinguishing correct from incorrect homology information and for generating accurate homologybased models of protein structures.…”

mentioning

confidence: 99%

Accurate protein structure modeling using sparse NMR data and homologous structure information

Thompson

Sgourakis

Liu

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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While information from homologous structures plays a central role in X-ray structure determination by molecular replacement, such information is rarely used in NMR structure determination because it can be incorrect, both locally and globally, when evolutionary relationships are inferred incorrectly or there has been considerable evolutionary structural divergence. Here we describe a method that allows robust modeling of protein structures of up to 225 residues by combining 1 H N , 13 C, and 15 N backbone and 13 Cβ chemical shift data, distance restraints derived from homologous structures, and a physically realistic all-atom energy function. Accurate models are distinguished from inaccurate models generated using incorrect sequence alignments by requiring that (i) the all-atom energies of models generated using the restraints are lower than models generated in unrestrained calculations and (ii) the low-energy structures converge to within 2.0 Å backbone rmsd over 75% of the protein. Benchmark calculations on known structures and blind targets show that the method can accurately model protein structures, even with very remote homology information, to a backbone rmsd of 1.2-1.9 Å relative to the conventional determined NMR ensembles and of 0.9-1.6 Å relative to X-ray structures for well-defined regions of the protein structures. This approach facilitates the accurate modeling of protein structures using backbone chemical shift data without need for side-chain resonance assignments and extensive analysis of NOESY cross-peak assignments.biochemistry | biophysics | computational biology | nuclear magnetic resonance | structural genomics I n recent years, the application of multidimensional data collection techniques in isotopically enriched proteins (1) as well as development of selective labeling schemes in perdeuterated samples (2-5) and other methodological improvements (6, 7) have allowed the application of NMR methods to larger proteins (8-10). Conventional NMR structure determination relies primarily on the availability of distance restraints from NOESY experiments, which requires time-consuming experiments, including extensive analysis of side-chain resonance assignments and laborious assignments of most of the observed NOESY crosspeak resonances. While automated assignment methods (11-13) have greatly stream-lined the process (14), the assignment of NOE cross-peaks in spectra of larger proteins presents a significant challenge due to increased spectral overlap, line broadening, and low signal-to-noise ratios, rendering existing automated assignment methods ineffective in the absence of a preliminary structural model. Accurate structures can be generated for small proteins (up to 100-120 residues) using chemical shift information to guide structure prediction calculations (15, 16), but additional NMR data, including backbone NOEs and residual dipolar coupling (RDC) data, are required to obtained converged structures for larger proteins (17) and protein oligomers (18). Such data are often hard to collect and a...

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“…12, 13 We have shown previously that conformational energies (computed with or without electrostatics) resulting from homology modeling calculations can distinguish between different potential protein sequence alignments. 13 Our previous work, 12, 13, 44, 45 which included a simple form of the electrostatic potential, also demonstrated for a handful of experimental and homology‐modeled protein structures that the steric conformational energy alone (excluding electrostatic and solvation energies) is a useful discriminator of correct from incorrect structures. In this work, we focused on using a parameter we call “steric conformational energy per residue,” calculated by dividing the sum of the VdW, bond length, bond angle, and improper angle energy terms reported by XPLOR by the number of residues.…”

Section: Resultsmentioning

confidence: 99%

“…Experimentally determined correct structures exhibited steric conformational energies of −4.7 to −5.1 kcal/mol‐residue using the CHARMM force field. 12, 13…”

Section: Resultsmentioning

confidence: 99%

“…We have previously described a method of homology modeling by satisfaction of spatial restraints. 12, 13 This method was demonstrated to be effective, but it was not integrated into a homology modeling software package. In this article, we describe a fully automated homology modeling method and server, Homology Modeling Automatically (HOMA), critically evaluate its accuracy in modeling proteins with known 3D structures, and compare the accuracy of these models with homology models generated with other commonly used methods.…”

Section: Introductionmentioning

confidence: 99%

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Assessing model accuracy using the homology modeling automatically software

et al. 2007

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Homology modeling is a powerful technique that greatly increases the value of experimental structure determination by using the structural information of one protein to predict the structures of homologous proteins. We have previously described a method of homology modeling by satisfaction of spatial restraints (Li et al., Protein Sci 1997;6:956-970). The Homology Modeling Automatically (HOMA) web site, , is a new tool, using this method to predict 3D structure of a target protein based on the sequence alignment of the target protein to a template protein and the structure coordinates of the template. The user is presented with the resulting models, together with an extensive structure validation report providing critical assessments of the quality of the resulting homology models. The homology modeling method employed by HOMA was assessed and validated using twenty-four groups of homologous proteins. Using HOMA, homology models were generated for 510 proteins, including 264 proteins modeled with correct folds and 246 modeled with incorrect folds. Accuracies of these models were assessed by superimposition on the corresponding experimentally determined structures. A subset of these results was compared with parallel studies of modeling accuracy using several other automated homology modeling approaches. Overall, HOMA provides prediction accuracies similar to other state-of-the-art homology modeling methods. We also provide an evaluation of several structure quality validation tools in assessing the accuracy of homology models generated with HOMA. This study demonstrates that Verify3D (Luthy et al., Nature 1992;356:83-85) and ProsaII (Sippl, Proteins 1993;17:355-362) are most sensitive in distinguishing between homology models with correct or incorrect folds. For homology models that have the correct fold, the steric conformational energy (including primarily the Van der Waals energy), MolProbity clashscore (Word et al., Protein Sci 2000;9:2251-2259), and the PROCHECK G-factors (Laskowski et al., J Biomol NMR 1996;8:477-486) provide sensitive and consistent methods for assessing accuracy and can distinguish between homology models of higher and lower accuracy. As demonstrated in the accompanying paper (Bhattacharya et al., accompanying paper), combinations of these scores for models generated with HOMA provide a basis for distinguishing low from high accuracy models.

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Homology modeling of an RNP domain from a human RNA-binding protein: Homology-constrained energy optimization provides a criterion for distinguishing potential sequence alignments

Cited by 11 publications

References 35 publications

Homology modeling a fast tool for drug discovery: Current perspectives

Homology modeling a fast tool for drug discovery: Current perspectives

Accurate protein structure modeling using sparse NMR data and homologous structure information

Assessing model accuracy using the homology modeling automatically software

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