Emerging methods in protein co-evolution

By David de Juan, Florencio Pazos, Alfonso Valencia. 2013

597 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, methodology): “Such co-evolutionary information 54 detected in MSAs has been utilized to assist the protein structure prediction in AF2.”
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “Biologically, the functional regions of proteins tend to be more conserved and closely associated with binding [15, 16], allowing the model to learn critical information and generalize better to unseen proteins.”
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, background): “two residues can be modeled using pair-wise statistics such as covariance [9, 10].”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII (2019, background): “For a quarter of a century,(8) attempts have been made to predict three-dimensional contacts between residues in proteins, based on correlations in amino-acid substitutions found in protein family protein sequence alignments.(9) For many years, the precision of these methods as measured in CASP was ”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “Two residues, both being in contact to a third residue, will coevolve even if they are not in direct contact.”
End-to-end differentiable learning of protein structure (2018, background): “With a large and diverse set of homologous sequences—typically tens to hundreds of thousands—co-evolution methods can accurately predict contact maps (Juan et al., 2013).”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “The more general problem of predicting if two proteins interact with each other has been studied extensively using a wide variety of approaches ( de Juan et al., 2013 ; Hosur et al., 2012; Zhang et al., 2012 ; Shoemaker and Panchenko, 2007 , Valencia and Pazos, 2002 , Ochoa and Pazos, 2010 ).”
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, background): “For many years, accurate contact prediction was hampered by the effects of the relatedness of homologous sequences and the inability to extract correct residue pairings from vastly intertwined networks of contacts (de Juan et al., 2013).”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIV (2021, background): “Apparently, this was a consequence of the introduction of improved classical statistical methods.(34) That level of accuracy was”
CASP10 results compared to those of previous CASP experiments (2013, methodology): “19 So far, these methods have not had an impact in CASP, but we look forward to the next experiment.”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, background): “Contact: j3xu@ttic.edu Supplementary information: Supplementary data are available at Bioinformatics online.”