Mapping Monomeric Threading to Protein–Protein Structure Prediction

Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

doi:10.1021/ci300579r

Cited by 70 publications

(74 citation statements)

References 39 publications

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“…45 Briefly, SPRING is a template-based algorithm for protein–protein interaction prediction. SPRING first builds 3-D structures of protein isoforms followed by predicting their interaction potential.…”

Section: Methodsmentioning

confidence: 99%

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

Menon

Govindarajoo

et al. 2015

J. Proteome Res.

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Alternative splicing allows a single gene to produce multiple transcript-level splice isoforms from which the translated proteins may show differences in their expression and function. Identifying the major functional or canonical isoform is important for understanding gene and protein functions. Identification and characterization of splice isoforms is a stated goal of the HUPO Human Proteome Project and of neXtProt. Multiple efforts have catalogued splice isoforms as “dominant”, “principal”, or “major” isoforms based on expression or evolutionary traits. In contrast, we recently proposed highest connected isoforms (HCIs) as a new class of canonical isoforms that have the strongest interactions in a functional network and revealed their significantly higher (differential) transcript-level expression compared to nonhighest connected isoforms (NCIs) regardless of tissues/cell lines in the mouse. HCIs and their expression behavior in the human remain unexplored. Here we identified HCIs for 6157 multi-isoform genes using a human isoform network that we constructed by integrating a large compendium of heterogeneous genomic data. We present examples for pairs of transcript isoforms of ABCC3, RBM34, ERBB2, and ANXA7. We found that functional networks of isoforms of the same gene can show large differences. Interestingly, differential expression between HCIs and NCIs was also observed in the human on an independent set of 940 RNA-seq samples across multiple tissues, including heart, kidney, and liver. Using proteomic data from normal human retina and placenta, we showed that HCIs are a promising indicator of expressed protein isoforms exemplified by NUDFB6 and M6PR. Furthermore, we found that a significant percentage (20%, p = 0.0003) of human and mouse HCIs are homologues, suggesting their conservation between species. Our identified HCIs expand the repertoire of canonical isoforms and are expected to facilitate studying main protein products, understanding gene regulation, and possibly evolution. The network is available through our web server as a rich resource for investigating isoform functional relationships (http://guanlab.ccmb.med.umich.edu/hisonet). All MS/MS data were available at ProteomeXchange Web site (http://www.proteomexchange.org) through their identifiers (retina: PXD001242, placenta: PXD000754).

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Section: Methodsmentioning

confidence: 99%

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

Menon

Govindarajoo

et al. 2015

J. Proteome Res.

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“…Because the approach relies more on the global interface structure comparison rather than the subtle atomic-details of the complex structures, the BindProfX prediction has the potential to be used for the cases with low-resolution complex structure built by protein docking [5,23] and multi-chain threading approaches [6,24]. On a test set of 104 mutations with only unbound monomer structures available [21], BindProfX uses the complex structure docked from structural overlay of monomers onto the homologous complexes and achieved a correlation 0.454 to the experiment that is 3.8 times higher than that calculated from the physics-based potentials.…”

Section: Resultsmentioning

confidence: 99%

BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts

Xiong

Zhang

Zheng

et al. 2017

Journal of Molecular Biology

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126

134

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Understanding how gene-level mutations affect the binding affinity of protein–protein interactions is a key issue of protein engineering. Due to the complexity of the problem, using physical force field to predict the mutation-induced binding free-energy change remains challenging. In this work, we present a renewed approach to calculate the impact of gene mutations on the binding affinity through the structure-based profiling of protein–protein interfaces, where the binding free-energy change (ΔΔG) is counted as the logarithm of relative probability of mutant amino acids over wild-type ones in the interface alignment matrix; three pseudo-counts are introduced to alleviate the limit of the current interface library. Compared with a previous profile score that was based on the log-odds likelihood calculation, the correlation between predicted and experimental ΔΔG of single-site mutations is increased in this approach from 0.33 to 0.68. The structure-based profile score is found complementary to the physical potentials, where a linear combination of the profile score with the FoldX potential could increase the ΔΔG correlation from 0.46 to 0.74. It is also shown that the profile score is robust for counting the coupling effect of multiple individual mutations. For the mutations involving more than two mutation sites where the correlation between FoldX and experimental data vanishes, the profile-based calculation retains a strong correlation with the experimental measurements.

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“…In sequence-based approaches if two query proteins are homologous to two other proteins that form a complex of known structure, the query proteins are first superimposed on their respective homologs in the complex. The likelihood of the query proteins forming a complex can be assessed using scoring schemes based on different factors such as overall sequence similarity, sequence similarity limited to the predicted interface [14] or sequence and structural similarity combined with biophysical properties of the predicted interface [15,16,17]. Interfacial residues in the query proteins are defined as those that align to interfacial residues in their respective templates.…”

Section: Exploiting Global Structural Similaritymentioning

confidence: 99%

Template-based prediction of protein function

Petrey

Chen

Deng

et al. 2015

Current Opinion in Structural Biology

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We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that can’t be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.

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Mapping Monomeric Threading to Protein–Protein Structure Prediction

Cited by 70 publications

References 39 publications

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts

Template-based prediction of protein function

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