Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A minimal sequence code for switching protein structure and function.

ABSTRACT: We present here a structural and mechanistic description of how a protein changes its fold and function, mutation by mutation. Our approach was to create 2 proteins that (i) are stably folded into 2 different folds, (ii) have 2 different functions, and (iii) are very similar in sequence. In this simplified sequence space we explore the mutational path from one fold to another. We show that an IgG-binding, 4beta+alpha fold can be transformed into an albumin-binding, 3-alpha fold via a mutational pathway in which neither function nor native structure is completely lost. The stabilities of all mutants along the pathway are evaluated, key high-resolution structures are determined by NMR, and an explanation of the switching mechanism is provided. We show that the conformational switch from 4beta+alpha to 3-alpha structure can occur via a single amino acid substitution. On one side of the switch point, the 4beta+alpha fold is >90% populated (pH 7.2, 20 degrees C). A single mutation switches the conformation to the 3-alpha fold, which is >90% populated (pH 7.2, 20 degrees C). We further show that a bifunctional protein exists at the switch point with affinity for both IgG and albumin.

SUBMITTER: Alexander PA

PROVIDER: S-EPMC2779201 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Guiding discovery of protein sequence-structure-function modeling.

Project description:MotivationProtein engineering techniques are key in designing novel catalysts for a wide range of reactions. Although approaches vary in their exploration of the sequence-structure-function paradigm, they are often hampered by the labor-intensive steps of protein expression and screening. In this work, we describe the development and testing of a high-throughput in silico sequence-structure-function pipeline using AlphaFold2 and fast Fourier transform docking that is benchmarked with enantioselectivity and reactivity predictions for an ancestral sequence library of fungal flavin-dependent monooxygenases.ResultsThe predicted enantioselectivities and reactivities correlate well with previously described screens of an experimentally available subset of these proteins and capture known changes in enantioselectivity across the phylogenetic tree representing ancestorial proteins from this family. With this pipeline established as our functional screen, we apply ensemble decision tree models and explainable AI techniques to build sequence-function models and extract critical residues within the binding site and the second-sphere residues around this site. We demonstrate that the top-identified key residues in the control of enantioselectivity and reactivity correspond to experimentally verified residues. The in silico sequence-to-function pipeline serves as an accelerated framework to inform protein engineering efforts from vast informative sequence landscapes contained in protein families, ancestral resurrects, and directed evolution campaigns.AvailabilityJupyter notebooks detailing the sequence-structure-function pipeline are available at https://github.com/BrooksResearchGroup-UM/seq_struct_func.

| S-EPMC10789314 | biostudies-literature

Sequence-structure-function relationships in the microbial protein universe.

Project description:For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.

| S-EPMC10133388 | biostudies-literature

Code-Switching Does Not Equal Code-Switching. An Event-Related Potentials Study on Switching From L2 German to L1 Russian at Prepositions and Nouns.

Project description:Studies on event-related potentials (ERP) in code-switching (CS) have concentrated on single-word insertions, usually nouns. However, CS ranges from inserting single words into the main language of discourse to alternating languages for larger segments of a discourse, and can occur at various syntactic positions and with various word classes. This ERP study examined native speakers of Russian who had learned German as a second language; they were asked to listen to sentences with CS from their second language, German, to their first language, Russian. CS included either a whole prepositional phrase or only the lexical head noun of a prepositional phrase. CS at nouns resulted in a late positive complex (LPC), whereas CS at prepositions resulted in a broad early negativity, which was followed by an anterior negativity with a posterior positivity. Only in the last time window (800-1000 ms) did CS at prepositions result in a broad positivity similar to CS at nouns. The differences between both types of CS indicate that they relate to different psycholinguistic processes.

| S-EPMC7324795 | biostudies-literature

Does code-switching influence novel word learning?

Project description:Code-switching occurs regularly in the input to bilingual children. Yet, the effect of code-switched input on language development is unclear. To test whether word learning would be affected by code-switching, Spanish-English bilingual children (N = 45, 19 boys, MeanAge = 5.05 years; ethnicity: 37 Hispanic/Latino, six Non-Hispanic/Latino, two unreported) were taught English-like novel words in two conditions. In the English-only condition, definitions for novel words were provided entirely in English. In the code-switch condition, definitions for novel words were provided in English and Spanish, incorporating code-switches. Children required fewer exposures to retain novel words in the code-switch than the English-only condition and this effect was not moderated by children's language ability or exposure to code-switching, suggesting that code-switched input does not pose word-learning risks to bilingual children, including children with lower levels of language ability.

| S-EPMC10163668 | biostudies-literature

COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information.

Project description:The COFACTOR web server is a unified platform for structure-based multiple-level protein function predictions. By structurally threading low-resolution structural models through the BioLiP library, the COFACTOR server infers three categories of protein functions including gene ontology, enzyme commission and ligand-binding sites from various analogous and homologous function templates. Here, we report recent improvements of the COFACTOR server in the development of new pipelines to infer functional insights from sequence profile alignments and protein-protein interaction networks. Large-scale benchmark tests show that the new hybrid COFACTOR approach significantly improves the function annotation accuracy of the former structure-based pipeline and other state-of-the-art functional annotation methods, particularly for targets that have no close homology templates. The updated COFACTOR server and the template libraries are available at http://zhanglab.ccmb.med.umich.edu/COFACTOR/.

| S-EPMC5793808 | biostudies-literature

A minimal avian retroviral packaging sequence has a complex structure.

Project description:We have defined a 160-nucleotide region, Mpsi, from the 5' leader region of the Rous sarcoma virus genome that is sufficient to direct the packaging of a heterologous RNA. Mpsi contains the putative O3 stem structure that has previously been shown, and that has been confirmed in this study, to be important for the efficient packaging of avian leukosis-sarcoma virus RNA. Analyses of several O3 stem mutants revealed that other regions within Mpsi can interfere with the proper folding of altered sequences which are predicted to form a wild-type O3 stem.

| S-EPMC110434 | biostudies-literature

Small-residue packing motifs modulate the structure and function of a minimal de novo membrane protein.

Project description:Alpha-helical integral membrane proteins contain conserved sequence motifs that are known to be important in helix packing. These motifs are a promising starting point for the construction of artificial proteins, but their potential has not yet been fully explored. Here, we study the impact of introducing a common natural helix packing motif to the transmembrane domain of a genetically-encoded and structurally dynamic de novo membrane protein. The resulting construct is an artificial four-helix bundle with lipophilic regions that are defined only by the amino acids L, G, S, A and W. This minimal proto-protein could be recombinantly expressed by diverse prokaryotic and eukaryotic hosts and was found to co-sediment with cellular membranes. The protein could be extracted and purified in surfactant micelles and was monodisperse and stable in vitro, with sufficient structural definition to support the rapid binding of a heme cofactor. The reduction in conformational diversity imposed by this design also enhances the nascent peroxidase activity of the protein-heme complex. Unexpectedly, strains of Escherichia coli expressing this artificial protein specifically accumulated zinc protoporphyrin IX, a rare cofactor that is not used by natural metalloenzymes. Our results demonstrate that simple sequence motifs can rigidify elementary membrane proteins, and that orthogonal artificial membrane proteins can influence the cofactor repertoire of a living cell. These findings have implications for rational protein design and synthetic biology.

| S-EPMC7495484 | biostudies-literature

From sequence to function through structure: Deep learning for protein design.

Project description:The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.

| S-EPMC9755234 | biostudies-literature

Learning the sequence code for mRNA and protein abundance in human immune cells

Project description:mRNA and protein abundance are defined by transcriptional and post-transcriptional regulatory mechanisms. Here, we develop a machine learning pipeline, termed SONAR, to decipher the endogenous sequence code that determines mRNA and protein abundance in human cells. SONAR models predict up to 62% of mRNA and 63% of protein abundance independent of promoter or enhancer information, and reveal a strong—yet dynamic—cell-type specific sequence code. We also find that the effect of sequence features is dependent on their location within the mRNA transcript. Using SONAR, we design synthetic 3’UTRs, with which protein expression levels can be manipulated and tailored to a specific cell-type. Beyond its fundamental findings, our work provides novel means to improve immunotherapies and biotechnology applications.

2023-09-20 | GSE240919 | GEO

CATH: an expanded resource to predict protein function through structure and sequence.

Project description:The latest version of the CATH-Gene3D protein structure classification database has recently been released (version 4.1, http://www.cathdb.info). The resource comprises over 300 000 domain structures and over 53 million protein domains classified into 2737 homologous superfamilies, doubling the number of predicted protein domains in the previous version. The daily-updated CATH-B, which contains our very latest domain assignment data, provides putative classifications for over 100 000 additional protein domains. This article describes developments to the CATH-Gene3D resource over the last two years since the publication in 2015, including: significant increases to our structural and sequence coverage; expansion of the functional families in CATH; building a support vector machine (SVM) to automatically assign domains to superfamilies; improved search facilities to return alignments of query sequences against multiple sequence alignments; the redesign of the web pages and download site.

| S-EPMC5210570 | 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