The evolution of contact prediction: evidence that contact selection in statistical contact prediction is changing

Chonofsky, Mark; Oliveira, Saulo H. P. de; Krawczyk, Konrad; Deane, Charlotte

doi:10.1093/bioinformatics/btz816

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…(iv) McLachlan-based Substitution Correlation (McBASC [57][58][59] ), which is based on coordinated changes within physiochemical classes; and (v) Z-Normalized Mutual Information (ZNMI 52 ), which uses an information theoretic approach. Although several newer co-evolutionary analyses have been developed, the focus of the field has been on improving amino acid contact prediction (reviewed in 60 ). We decided not to use these versions: since many rheostat positions do not contact each other, we reasoned that these algorithms would impose unsuitable constraints.…”

Section: Bioinformatic Score Calculationsmentioning

confidence: 99%

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation

et al. 2021

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When amino acids vary during evolution, the outcome can be functionally neutral or biologically‐important. We previously found that substituting a subset of nonconserved positions, “rheostat” positions, can have surprising effects on protein function. Since changes at rheostat positions can facilitate functional evolution or cause disease, more examples are needed to understand their unique biophysical characteristics. Here, we explored whether “phylogenetic” patterns of change in multiple sequence alignments (such as positions with subfamily specific conservation) predict the locations of functional rheostat positions. To that end, we experimentally tested eight phylogenetic positions in human liver pyruvate kinase (hLPYK), using 10–15 substitutions per position and biochemical assays that yielded five functional parameters. Five positions were strongly rheostatic and three were non‐neutral. To test the corollary that positions with low phylogenetic scores were not rheostat positions, we combined these phylogenetic positions with previously‐identified hLPYK rheostat, “toggle” (most substitution abolished function), and “neutral” (all substitutions were like wild‐type) positions. Despite representing 428 variants, this set of 33 positions was poorly statistically powered. Thus, we turned to the in vivo phenotypic dataset for E. coli lactose repressor protein (LacI), which comprised 12–13 substitutions at 329 positions and could be used to identify rheostat, toggle, and neutral positions. Combined hLPYK and LacI results show that positions with strong phylogenetic patterns of change are more likely to exhibit rheostat substitution outcomes than neutral or toggle outcomes. Furthermore, phylogenetic patterns were more successful at identifying rheostat positions than were co‐evolutionary or eigenvector centrality measures of evolutionary change.

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Section: Bioinformatic Score Calculationsmentioning

confidence: 99%

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation

et al. 2021

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“…Since no method has been shown to find more “important” positions than any other method, we previously used five, mathematically-divergent co-evolution algorithms for our studies (45, 46): (i) Observed Minus Expected Squared (OMES; (114, 115)), which is based on Chi-squared-like goodness of fit); (ii) Explicit Likelihood of Subset Covariation (ELSC; (116)) and (iii) Statistical Coupling Analysis (SCA; (117)), which are based on a subset perturbation approach; (iv) McLachlan-based Substitution Correlation (McBASC; (118-120)), which is based on coordinated changes within physiochemical classes; and (v) Z-Normalized Mutual Information (ZNMI; (113)), which uses an information theoretic approach. Although several newer co-evolutionary analyses have been developed, the focus of the field has been on improving amino acid contact prediction (reviewed in (121)). We decided not to use these versions because, since many rheostat positions do not contact each other, we reasoned that these algorithms would impose unsuitable constraints.…”

Section: Methodsmentioning

confidence: 99%

“Multiplex” rheostat positions cluster around allosterically critical regions of the lactose repressor protein

Bantis

Parente

Fenton

et al. 2020

Preprint

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Amino acid variation at “rheostat” positions provides opportunity to modulate various aspects of protein function – such as binding affinity or allosteric coupling – across a wide range. Previously a subclass of “multiplex” rheostat positions was identified at which substitutions simultaneously modulated more than one functional parameter. Using the Miller laboratory’s dataset of ∼4000 variants of lactose repressor protein (LacI), we compared the structural properties of multiplex rheostat positions with (i) “single” rheostat positions that modulate only one functional parameter, (ii) “toggle” positions that follow textbook substitution rules, and (iii) “neutral” positions that tolerate any substitution without changing function. The combined rheostat classes comprised >40% of LacI positions, more than either toggle or neutral positions. Single rheostat positions were broadly distributed over the structure. Multiplex rheostat positions structurally overlapped with positions involved in allosteric regulation. When their phenotypic outcomes were interpreted within a thermodynamic framework, functional changes at multiplex positions were uncorrelated. This suggests that substitutions lead to complex changes in the underlying molecular biophysics. Bivariable and multivariable analyses of evolutionary signals within multiple sequence alignments could not differentiate single and multiplex rheostat positions. Phylogenetic analyses – such as ConSurf – could distinguish rheostats from toggle and neutral positions. Multivariable analyses could also identify a subset of neutral positions with high probability. Taken together, these results suggest that detailed understanding of the underlying molecular biophysics, likely including protein dynamics, will be required to discriminate single and multiplex rheostat positions from each other and to predict substitution outcomes at these sites.

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“…These interactions are prone to fracture due to external forces (e.g., heat), resulting in protein deactivation. Previous studies [45,46] have suggested that evolutionary couplings can detect side-chain interactions.…”

Section: Prediction Performance Of Physical-chemical Interactionmentioning

confidence: 99%

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

Zhang

Hao

et al. 2022

Tsinghua Sci. Technol.

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Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3D structure. An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3D structure prediction. Recently, with the combination of deep learning and direct coupling techniques, the performance of residue contact prediction has achieved significant progress. However, a considerable number of current Deep-Learning (DL)-based prediction methods are usually time-consuming, mainly because they rely on different categories of data types and third-party programs. In this research, we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence. We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment, followed by training a DL model for residue-residue contact prediction based on the massive statistical information. The proposed method is robust in terms of different test sets, showed high reliability on model confidence score, could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

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The evolution of contact prediction: evidence that contact selection in statistical contact prediction is changing

Cited by 5 publications

References 34 publications

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation

“Multiplex” rheostat positions cluster around allosterically critical regions of the lactose repressor protein

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

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