Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Minimum epistasis interpolation for sequence-function relationships.

ABSTRACT: Massively parallel phenotyping assays have provided unprecedented insight into how multiple mutations combine to determine biological function. While such assays can measure phenotypes for thousands to millions of genotypes in a single experiment, in practice these measurements are not exhaustive, so that there is a need for techniques to impute values for genotypes whose phenotypes have not been directly assayed. Here, we present an imputation method based on inferring the least epistatic possible sequence-function relationship compatible with the data. In particular, we infer the reconstruction where mutational effects change as little as possible across adjacent genetic backgrounds. The resulting models can capture complex higher-order genetic interactions near the data, but approach additivity where data is sparse or absent. We apply the method to high-throughput transcription factor binding assays and use it to explore a fitness landscape for protein G.

SUBMITTER: Zhou J

PROVIDER: S-EPMC7156698 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Minimum epistasis interpolation for sequence-function relationships.

Zhou Juannan J McCandlish David M DM

Nature communications 20200414 1

Massively parallel phenotyping assays have provided unprecedented insight into how multiple mutations combine to determine biological function. While such assays can measure phenotypes for thousands to millions of genotypes in a single experiment, in practice these measurements are not exhaustive, so that there is a need for techniques to impute values for genotypes whose phenotypes have not been directly assayed. Here, we present an imputation method based on inferring the least epistatic possi ...[more]

PMID: 32286265

Similar Datasets

Intrinsically disordered domains: Sequence ? disorder ? function relationships.

Project description:Disordered domains are long regions of intrinsic disorder that ideally have conserved sequences, conserved disorder, and conserved functions. These domains were first noticed in protein-protein interactions that are distinct from the interactions between two structured domains and the interactions between structured domains and linear motifs or molecular recognition features (MoRFs). So far, disordered domains have not been systematically characterized. Here, we present a bioinformatics investigation of the sequence-disorder-function relationships for a set of probable disordered domains (PDDs) identified from the Pfam database. All the Pfam seed proteins from those domains with at least one PDD sequence were collected. Most often, if a set contains one PDD sequence, then all members of the set are PDDs or nearly so. However, many seed sets have sequence collections that exhibit diverse proportions of predicted disorder and structure, thus giving the completely unexpected result that conserved sequences can vary substantially in predicted disorder and structure. In addition to the induction of structure by binding to protein partners, disordered domains are also induced to form structure by disulfide bond formation, by ion binding, and by complex formation with RNA or DNA. The two new findings, (a) that conserved sequences can vary substantially in their predicted disorder content and (b) that homologues from a single domain can evolve from structure to disorder (or vice versa), enrich our understanding of the sequence ? disorder ensemble ? function paradigm.

| S-EPMC6699093 | biostudies-literature

Sequence-function relationships in folding upon binding.

Project description:Folding coupled to binding is ubiquitous in biology. Nevertheless, the relationship of sequence to function for protein segments that undergo coupled binding and folding remains to be determined. Specifically, it is not known if the well-established rules that govern protein folding and stability are relevant to ligand-linked folding transitions. Upon small ligand biotinoyl-5'-AMP (bio-5'-AMP) binding the Escherichia coli protein BirA undergoes a disorder-to-order transition that results in formation of a network of packed hydrophobic side chains. Ligand binding is also allosterically coupled to protein association, with bio-5'-AMP binding enhancing the dimerization free energy by -4.0 kcal/mol. Previous studies indicated that single alanine replacements in a three residue hydrophobic cluster that contributes to the larger network disrupt cluster formation, ligand binding, and allosteric activation of protein association. In this work, combined equilibrium and kinetic measurements of BirA variants with alanine substitutions in the entire hydrophobic network reveal large functional perturbations resulting from any single substitution and highly non-additive effects of multiple substitutions. These substitutions also disrupt ligand-linked folding. The combined results suggest that, analogous to protein folding, functional disorder-to-order linked to binding requires optimal packing of the relevant hydrophobic side chains that contribute to the transition. The potential for many combinations of residues to satisfy this requirement implies that, although functionally important, segments of homologous proteins that undergo folding linked to binding can exhibit sequence divergence.

| S-EPMC4315658 | biostudies-literature

The simplicity of protein sequence-function relationships.

Project description:How complicated is the genetic architecture of proteins - the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a protein's function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence - causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions - or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a protein's entire genotype-phenotype map. This method yields a maximally efficient explanation of a protein's genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.

| S-EPMC10508729 | biostudies-literature

High-resolution mapping of protein sequence-function relationships.

Project description:We present a large-scale approach to investigate the functional consequences of sequence variation in a protein. The approach entails the display of hundreds of thousands of protein variants, moderate selection for activity and high-throughput DNA sequencing to quantify the performance of each variant. Using this strategy, we tracked the performance of >600,000 variants of a human WW domain after three and six rounds of selection by phage display for binding to its peptide ligand. Binding properties of these variants defined a high-resolution map of mutational preference across the WW domain; each position had unique features that could not be captured by a few representative mutations. Our approach could be applied to many in vitro or in vivo protein assays, providing a general means for understanding how protein function relates to sequence.

| S-EPMC2938879 | biostudies-literature

Structure-Function Relationships in Perimetric Glaucoma: Comparison of Minimum-Rim Width and Retinal Nerve Fiber Layer Parameters.

Project description:To test the hypotheses that: (1) structure-function (SF) relationships between visual fields (VF) and Bruch's membrane opening-based minimum rim width (BMO-MRW) measurements are superior to those for peripapillary retinal nerve fiber layer (pRNFL) in perimetric glaucoma; (2) BMO-MRW measurements may extend the utility of structural measurement across the range of glaucoma severity; and (3) to estimate the influence of Bruch's membrane opening (BMO) size on BMO-MRW measurements.One hundred eight perimetric glaucoma eyes (68 patients) with good quality spectral-domain optical coherence tomography images of the optic disc and pRNFL, and reliable VF within 6 months were recruited. Relationship of global and sectoral BMO-MRW and pRNFL thickness with corresponding VF parameters and the influence of normalizing BMO-MRW (on BMO circumference, nBMO-MRW) on SF relationships were investigated. Broken-stick models were used to compare the point at which pRNFL and BMO-MRW parameters reached their measurement floor.The median (interquartile range) of VF mean deviation was -5.9 (-12.6 to -3.6) dB. Spearman correlation coefficients between pRNFL, BMO-MRW, and nBMO-MRW measures and corresponding VF cluster average deviations ranged between 0.55 to 0.80, 0.35 to 0.66, and 0.38 to 0.65, respectively. Bruch's membrane opening-MRW parameters demonstrated weaker SF relationships compared with pRNFL globally and in temporal, temporal-superior, and nasal-inferior sectors (P < 0.03). Normalization of BMO-MRW did not significantly influence SF relationships.Structure-function relationships were somewhat weaker with BMO-MRW parameters compared with pRNFL in eyes with perimetric glaucoma. Bruch's membrane opening-MRW normalization did not significantly change SF relationships in this group of eyes with mild departures from average BMO size.

| S-EPMC5596793 | biostudies-literature

Peptide barcoding for one-pot evaluation of sequence-function relationships of free nanobodies

Project description:Optimization of protein binders relies on laborious screening processes. Particularly slow is investigation of sequence-function relationships of protein binders in which diverse amino-acid-substituted mutants are constructed, purified, and evaluated individually. Here, we developed peptide barcoding 2.0, a high-throughput approach for accurate investigation of sequence-function relationships of hundreds of protein binders at once. Our approach is based on combining generation of a mutagenized protein binder library fused with unique peptide barcodes, formation of binder–antigen complexes at different ratios, fine fractionation of binder–antigen complexes by size exclusion chromatography, and highly specific quantification of peptide barcodes by mass spectrometry. Applying peptide barcoding 2.0 for evaluation of anti-green fluorescent protein nanobody as a model, we succeeded in identifying various mutant Nbs with decreased affinities at once. Moreover, we successfully discriminated subtle changes in KD at the order of nM to sub-nM. These results showed that peptide barcoding 2.0 is a powerful tool for engineering of protein binders, enabling reliable one-pot evaluation of sequence-function relationships.

2021-05-30 | PXD026359 | JPOST Repository

Quantitative sequence-function relationships in proteins based on gene ontology.

Project description:BACKGROUND: The relationship between divergence of amino-acid sequence and divergence of function among homologous proteins is complex. The assumption that homologs share function--the basis of transfer of annotations in databases--must therefore be regarded with caution. Here, we present a quantitative study of sequence and function divergence, based on the Gene Ontology classification of function. We determined the relationship between sequence divergence and function divergence in 6828 protein families from the PFAM database. Within families there is a broad range of sequence similarity from very closely related proteins--for instance, orthologs in different mammals--to very distantly-related proteins at the limit of reliable recognition of homology. RESULTS: We correlated the divergence in sequences determined from pairwise alignments, and the divergence in function determined by path lengths in the Gene Ontology graph, taking into account the fact that many proteins have multiple functions. Our results show that, among homologous proteins, the proportion of divergent functions decreases dramatically above a threshold of sequence similarity at about 50% residue identity. For proteins with more than 50% residue identity, transfer of annotation between homologs will lead to an erroneous attribution with a totally dissimilar function in fewer than 6% of cases. This means that for very similar proteins (about 50 % identical residues) the chance of completely incorrect annotation is low; however, because of the phenomenon of recruitment, it is still non-zero. CONCLUSION: Our results describe general features of the evolution of protein function, and serve as a guide to the reliability of annotation transfer, based on the closeness of the relationship between a new protein and its nearest annotated relative.

| S-EPMC1976327 | biostudies-literature

Network Analysis of Sequence-Function Relationships and Exploration of Sequence Space of TEM ?-Lactamases.

Project description:The Lactamase Engineering Database (www.LacED.uni-stuttgart.de) was developed to facilitate the classification and analysis of TEM ?-lactamases. The current version contains 474 TEM variants. Two hundred fifty-nine variants form a large scale-free network of highly connected point mutants. The network was divided into three subnetworks which were enriched by single phenotypes: one network with predominantly 2be and two networks with 2br phenotypes. Fifteen positions were found to be highly variable, contributing to the majority of the observed variants. Since it is expected that a considerable fraction of the theoretical sequence space is functional, the currently sequenced 474 variants represent only the tip of the iceberg of functional TEM ?-lactamase variants which form a huge natural reservoir of highly interconnected variants. Almost 50% of the variants are part of a quartet. Thus, two single mutations that result in functional enzymes can be combined into a functional protein. Most of these quartets consist of the same phenotype, or the mutations are additive with respect to the phenotype. By predicting quartets from triplets, 3,916 unknown variants were constructed. Eighty-seven variants complement multiple quartets and therefore have a high probability of being functional. The construction of a TEM ?-lactamase network and subsequent analyses by clustering and quartet prediction are valuable tools to gain new insights into the viable sequence space of TEM ?-lactamases and to predict their phenotype. The highly connected sequence space of TEM ?-lactamases is ideally suited to network analysis and demonstrates the strengths of network analysis over tree reconstruction methods.

| S-EPMC4862526 | biostudies-literature

Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications.

Project description:Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences--the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The 'environmental packages' apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

| S-EPMC3367316 | biostudies-literature

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases.

Project description:Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able to discriminate GH7 CBHs and EGs with up to 99% accuracy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our machine learning results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function.

| S-EPMC8329511 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data