scholarly journals Insights into Hox Protein Function from a Large Scale Combinatorial Analysis of Protein Domains

PLoS Genetics ◽  
2011 ◽  
Vol 7 (10) ◽  
pp. e1002302 ◽  
Author(s):  
Samir Merabet ◽  
Isma Litim-Mecheri ◽  
Daniel Karlsson ◽  
Richa Dixit ◽  
Mehdi Saadaoui ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Domains ◽  
Combinatorial Analysis ◽  
Hox Protein

scholarly journals The Generic Facet of Hox Protein Function

2019 ◽  
Vol 35 (4) ◽  
pp. 316
Author(s):  
Andrew J. Saurin ◽  
Marie Claire Delfini ◽  
Corinne Maurel-Zaffran ◽  
Yacine Graba
Keyword(s):  
Protein Function ◽  
Hox Protein

scholarly journals The HOX Homeodomain Proteins Block CBP Histone Acetyltransferase Activity

2001 ◽  
Vol 21 (21) ◽  
pp. 7509-7522 ◽  
Author(s):  
Wei-fang Shen ◽  
Keerthi Krishnan ◽  
H. J. Lawrence ◽  
Corey Largman
Keyword(s):  
Dna Binding ◽  
Protein Function ◽  
Histone Acetyltransferase ◽  
Gene Activation ◽  
Homeodomain Proteins ◽  
Hox Proteins ◽  
Reporter Assays ◽  
Hox Protein

ABSTRACT Despite the identification of PBC proteins as cofactors that provide DNA affinity and binding specificity for the HOX homeodomain proteins, HOX proteins do not demonstrate robust activity in transient-transcription assays and few authentic downstream targets have been identified for these putative transcription factors. During a search for additional cofactors, we established that each of the 14 HOX proteins tested, from 11 separate paralog groups, binds to CBP or p300. All six isolated homeodomain fragments tested bind to CBP, suggesting that the homeodomain is a common site of interaction. Surprisingly, CBP-p300 does not form DNA binding complexes with the HOX proteins but instead prevents their binding to DNA. The HOX proteins are not substrates for CBP histone acetyltransferase (HAT) but instead inhibit the activity of CBP in both in vitro and in vivo systems. These mutually inhibitory interactions are reflected by the inability of CBP to potentiate the low levels of gene activation induced by HOX proteins in a range of reporter assays. We propose two models for HOX protein function: (i) HOX proteins may function without CBP HAT to regulate transcription as cooperative DNA binding molecules with PBX, MEIS, or other cofactors, and (ii) the HOX proteins may inhibit CBP HAT activity and thus function as repressors of gene transcription.

scholarly journals deepNF: Deep network fusion for protein function prediction

2017 ◽  
Author(s):  
Vladimir Gligorijević ◽  
Meet Barot ◽  
Richard Bonneau
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Predictive Performance ◽  
Substantial Improvement ◽  
Function Prediction ◽  
Interaction Networks ◽  
Highly Nonlinear ◽  
High Level ◽  
String Networks

AbstractThe prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.AvailabilitydeepNF is freely available at: https://github.com/VGligorijevic/deepNF

scholarly journals Simplified geometric representations of protein structures identify complementary interaction interfaces

2019 ◽  
Author(s):  
Caitlyn L. McCafferty ◽  
Edward M. Marcotte ◽  
David W. Taylor
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Function ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Protein Complexes ◽  
Protein Structures ◽  
Molecular Properties ◽  
3D Structures ◽  
Geometric Representations

ABSTRACTProtein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a cuboid transformation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and mutation rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 85 overall known protein complexes, we observed F1 scores (a weighted combination of precision and recall) of 19-34% at correctly identifying protein interaction surfaces, comparable to more computationally intensive 3D docking methods in the annual Critical Assessment of PRedicted Interactions. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å RMSD. Thus, a cuboid transformation of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

scholarly journals iProteinDB: an integrative database of Drosophila post-translational modifications

2018 ◽  
Author(s):  
Yanhui Hu ◽  
Richelle Sopko ◽  
Verena Chung ◽  
Romain A. Studer ◽  
Sean D. Landry ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Model Organisms ◽  
General Strategy ◽  
Post Translational Modification ◽  
Post Translational Modifications ◽  
Functional Sites ◽  
Evolutionarily Conserved ◽  
And Function

AbstractPost-translational modification (PTM) serves as a regulatory mechanism for protein function, influencing stability, protein interactions, activity and localization, and is critical in many signaling pathways. The best characterized PTM is phosphorylation, whereby a phosphate is added to an acceptor residue, commonly serine, threonine and tyrosine. As proteins are often phosphorylated at multiple sites, identifying those sites that are important for function is a challenging problem. Considering that many phosphorylation sites may be non-functional, prioritizing evolutionarily conserved phosphosites provides a general strategy to identify the putative functional sites with regards to regulation and function. To facilitate the identification of conserved phosphosites, we generated a large-scale phosphoproteomics dataset from Drosophila embryos collected from six closely-related species. We built iProteinDB (https://www.flyrnai.org/tools/iproteindb/), a resource integrating these data with other high-throughput PTM datasets, including vertebrates, and manually curated information for Drosophila. At iProteinDB, scientists can view the PTM landscape for any Drosophila protein and identify predicted functional phosphosites based on a comparative analysis of data from closely-related Drosophila species. Further, iProteinDB enables comparison of PTM data from Drosophila to that of orthologous proteins from other model organisms, including human, mouse, rat, Xenopus laevis, Danio rerio, and Caenorhabditis elegans.

scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Data ◽  
The Cancer Genome Atlas ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

scholarly journals A large-scale evaluation of computational protein function prediction

2013 ◽  
Vol 10 (3) ◽  
pp. 221-227 ◽  
Author(s):  
Predrag Radivojac ◽  
Wyatt T Clark ◽  
Tal Ronnen Oron ◽  
Alexandra M Schnoes ◽  
Tobias Wittkop ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Scale Evaluation

Classification of sequence signatures: a guide to Hox protein function

BioEssays ◽  
2009 ◽  
Vol 31 (5) ◽  
pp. 500-511 ◽  
Author(s):  
Samir Merabet ◽  
Bruno Hudry ◽  
Mehdi Saadaoui ◽  
Yacine Graba
Keyword(s):  
Protein Function ◽  
Hox Protein ◽  
Sequence Signatures

scholarly journals Algorithms for protein interaction networks

2005 ◽  
Vol 33 (3) ◽  
pp. 530-534 ◽  
Author(s):  
M. Lappe ◽  
L. Holm
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Sequence Similarity ◽  
Functional Characterization ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Interaction Patterns ◽  
Main Challenge

The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.

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