Determinants of protein function revealed by combinatorial entropy optimization

By B. Reva, Yevgeniy Antipin, Christopher Sander. 2007

309 citations recorded by the source corpus.

We use a new algorithm (combinatorial entropy optimization [CEO]) to identify specificity residues and functional subfamilies in sets of proteins related by evolution. Specificity residues are conserved within a subfamily but differ between subfamilies, and they typically encode functional diversity. We obtain good agreement between predicted specificity residues and experimentally known functional residues in protein interfaces. Such predicted functional determinants are useful for interpreting the functional consequences of mutations in natural evolution and disease.

Subfields: Molecular Biology

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “In this work, we reduce the noise factor by requiring at least 1000 sequences in the protein family alignment, although one may be able to reduce this limit in the future with more refined methods for taking into account the density distribution of family members in protein sequence space, as well a”
Predicting the functional impact of protein mutations: application to cancer genomics (2011, methodology): “To quantify the entropy difference resulting from a mutation that affects conserved residue patterns in protein subfamilies, we (i) determine distinct sequence subfamilies from protein family alignments using Equation (4) (46) and (ii) compute a specificity conservation score in analogy to the famil”
Initial genome sequencing and analysis of multiple myeloma (2011, unknown): “Molecular cloning of complex chromosomal translocation t(8;14;12)(q24.”
Emerging methods in protein co-evolution (2013, methodology): “When compared with other methods 48 , such as rvET 56 , mutational behav‑ iour 47 , combinatorial entropy optimization (CEO; see below) 60 and an automated version of SequenceSpace 48 , S3det provides better information about subfamily clas‑ sification, whereas rvET and S3det are the most effec‑…”
Subtype-specific genomic alterations define new targets for soft tissue sarcoma therapy (2010, unknown): “To predict the likely functional impact of missense mutations, we used the computation method Mutation Assessor (see URLs ), which predicts the effect of a given mutated residue on protein function using evolutionary information from protein-family sequence alignment”
Learning protein constitutive motifs from sequence data (2018, methodology): “Other methods rely on phylogeny [7], entropy (variability in amino-acid content) [8, 9], or hybrid combination of both [10].”
Assessment of computational methods for predicting the effects of missense mutations in human cancers (2013, background): “Interestingly, specificity residues were found to be predominantly located in binding interfaces on the protein surface implicating them in protein interaction [18].”
3D Protein Structure Predicted from Sequence (2011, unknown): “In this work, we reduce the noise factor by requiring at least 1000 sequences in the protein family alignment, although one may be able to reduce this limit in the future with more refined met”
Prediction of Missense Mutation Functionality Depends on both the Algorithm and Sequence Alignment Employed (2011, unknown): “* Xvar (http://xvar.org/): this recently developed Web-based algorithm [Reva et al., 2010] by the same authors of the original algorithm combinatorial entropy optimization [Reva et al., 2007] Sets of missense mutations with known functionality are needed to compare the algorithms.”
The nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma (2011, unknown): “To predict the likely functional impact of missense mutations, we used the computation method Mutation Assessor (see URLs ), which predicts the effect of a given mutated residue on protein function using evolutionary information from protein-family sequence alignments and residue placement in known”
The genetic landscape of mutations in Burkitt lymphoma (2012, methodology): “Predictions of the phenotypic severity of variants not previously annotated were determined using Mutation Assessor 34 (see URLs), Polyphen-2 (ref.”
Evolutionary paths to antibiotic resistance under dynamically sustained drug stress (2011, methodology): “The functional impact of individual mutations was predicted using the online Mutation Assessor tool (see URLs ) available from the Computational Biology Center of the Memorial Sloan-Kettering Cancer Cente”