Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Sequence, Structure, and Functional Space of Drosophila De Novo Proteins.

ABSTRACT: During de novo emergence, new protein coding genes emerge from previously nongenic sequences. The de novo proteins they encode are dissimilar in composition and predicted biochemical properties to conserved proteins. However, functional de novo proteins indeed exist. Both identification of functional de novo proteins and their structural characterization are experimentally laborious. To identify functional and structured de novo proteins in silico, we applied recently developed machine learning based tools and found that most de novo proteins are indeed different from conserved proteins both in their structure and sequence. However, some de novo proteins are predicted to adopt known protein folds, participate in cellular reactions, and to form biomolecular condensates. Apart from broadening our understanding of de novo protein evolution, our study also provides a large set of testable hypotheses for focused experimental studies on structure and function of de novo proteins in Drosophila.

SUBMITTER: Middendorf L

PROVIDER: S-EPMC11363682 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Sequence, Structure, and Functional Space of Drosophila De Novo Proteins.

Middendorf Lasse L Ravi Iyengar Bharat B Eicholt Lars A LA

Genome biology and evolution 20240801 8

During de novo emergence, new protein coding genes emerge from previously nongenic sequences. The de novo proteins they encode are dissimilar in composition and predicted biochemical properties to conserved proteins. However, functional de novo proteins indeed exist. Both identification of functional de novo proteins and their structural characterization are experimentally laborious. To identify functional and structured de novo proteins in silico, we applied recently developed machine learning ...[more]

PMID: 39212966

Similar Datasets

De novo structure prediction of globular proteins aided by sequence variation-derived contacts.

Project description:The advent of high accuracy residue-residue intra-protein contact prediction methods enabled a significant boost in the quality of de novo structure predictions. Here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm--FRAGFOLD, with PSICOV, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. Using a comprehensive set of 150 diverse globular target proteins, up to 266 amino acids in length, we are able to address the effectiveness and some limitations of such approaches to globular proteins in practice. Overall we find that using fragment assembly with both statistical potentials and predicted contacts is significantly better than either statistical potentials or contacts alone. Results show up to nearly 80% of correct predictions (TM-score ≥0.5) within analysed dataset and a mean TM-score of 0.54. Unsuccessful modelling cases emerged either from conformational sampling problems, or insufficient contact prediction accuracy. Nevertheless, a strong dependency of the quality of final models on the fraction of satisfied predicted long-range contacts was observed. This not only highlights the importance of these contacts on determining the protein fold, but also (combined with other ensemble-derived qualities) provides a powerful guide as to the choice of correct models and the global quality of the selected model. A proposed quality assessment scoring function achieves 0.93 precision and 0.77 recall for the discrimination of correct folds on our dataset of decoys. These findings suggest the approach is well-suited for blind predictions on a variety of globular proteins of unknown 3D structure, provided that enough homologous sequences are available to construct a large and accurate multiple sequence alignment for the initial contact prediction step.

| S-EPMC3956894 | biostudies-literature

De novo recruitment of Polycomb-group proteins in Drosophila embryos.

Project description:Polycomb-group (PcG)-mediated transcriptional repression of target genes can be delineated into two phases. First, following initial repression of target genes by gene-specific transcription factors, PcG proteins recognize the repressed state and assume control of the genes' repression. Second, once the silenced state is established, PcG proteins may maintain repression through an indefinite number of cell cycles. Little is understood about how PcG proteins initially recognize the repressed state of target genes and the steps leading to de novo establishment of PcG-mediated repression. We describe a genetic system in which a Drosophila PcG target gene, giant (gt), is ubiquitously repressed during early embryogenesis by a maternally expressed transcription factor, and show the temporal recruitment of components of three PcG protein complexes: PhoRC, PRC1 and PRC2. We show that de novo PcG recruitment follows a temporal hierarchy in which PhoRC stably localizes at the target gene at least 1 h before stable recruitment of PRC2 and concurrent trimethylation of histone H3 at lysine 27 (H3K27me3). The presence of PRC2 and increased levels of H3K27me3 are found to precede stable binding by PRC1.

| S-EPMC6288389 | biostudies-literature

De novo structure generation using chemical shifts for proteins with high-sequence identity but different folds.

Project description:Proteins with high-sequence identity but very different folds present a special challenge to sequence-based protein structure prediction methods. In particular, a 56-residue three-helical bundle protein (GA(95)) and an alpha/beta-fold protein (GB(95)), which share 95% sequence identity, were targets in the CASP-8 structure prediction contest. With only 12 out of 300 submitted server-CASP8 models for GA(95) exhibiting the correct fold, this protein proved particularly challenging despite its small size. Here, we demonstrate that the information contained in NMR chemical shifts can readily be exploited by the CS-Rosetta structure prediction program and yields adequate convergence, even when input chemical shifts are limited to just amide (1)H(N) and (15)N or (1)H(N) and (1)H(alpha) values.

| S-EPMC2865713 | biostudies-literature

Structural and functional characterization of a putative de novo gene in Drosophila.

Project description:Comparative genomic studies have repeatedly shown that new protein-coding genes can emerge de novo from noncoding DNA. Still unknown is how and when the structures of encoded de novo proteins emerge and evolve. Combining biochemical, genetic and evolutionary analyses, we elucidate the function and structure of goddard, a gene which appears to have evolved de novo at least 50 million years ago within the Drosophila genus. Previous studies found that goddard is required for male fertility. Here, we show that Goddard protein localizes to elongating sperm axonemes and that in its absence, elongated spermatids fail to undergo individualization. Combining modelling, NMR and circular dichroism (CD) data, we show that Goddard protein contains a large central α-helix, but is otherwise partially disordered. We find similar results for Goddard's orthologs from divergent fly species and their reconstructed ancestral sequences. Accordingly, Goddard's structure appears to have been maintained with only minor changes over millions of years.

| S-EPMC7954818 | biostudies-literature

Experimental characterization of de novo proteins and their unevolved random-sequence counterparts.

Project description:De novo gene emergence provides a route for new proteins to be formed from previously non-coding DNA. Proteins born in this way are considered random sequences and typically assumed to lack defined structure. While it remains unclear how likely a de novo protein is to assume a soluble and stable tertiary structure, intersecting evidence from random sequence and de novo-designed proteins suggests that native-like biophysical properties are abundant in sequence space. Taking putative de novo proteins identified in human and fly, we experimentally characterize a library of these sequences to assess their solubility and structure propensity. We compare this library to a set of synthetic random proteins with no evolutionary history. Bioinformatic prediction suggests that de novo proteins may have remarkably similar distributions of biophysical properties to unevolved random sequences of a given length and amino acid composition. However, upon expression in vitro, de novo proteins exhibit moderately higher solubility which is further induced by the DnaK chaperone system. We suggest that while synthetic random sequences are a useful proxy for de novo proteins in terms of structure propensity, de novo proteins may be better integrated in the cellular system than random expectation, given their higher solubility.

| S-EPMC10089919 | biostudies-literature

Size and structure of the sequence space of repeat proteins.

Project description:The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family-the total number of sequences in that family-can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of ∼30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of fustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family, and suggests new strategies for protein design.

| S-EPMC6733475 | biostudies-literature

Multiobjective heuristic algorithm for <i>de novo</i> protein design in a quantified continuous sequence space.

Project description:Protein design usually involves sequence search process and evaluation criteria. Commonly used methods primarily implement the Monte Carlo or simulated annealing algorithm with a single-energy function to obtain ideal solutions, which is often highly time-consuming and limited by the accuracy of the energy function. In this report, we introduce a multiobjective algorithm named Hydra for protein design, which employs two different energy functions to optimize solutions simultaneously and makes use of the latent quantitative relationship between different amino acid types to facilitate the search process. The framework uses two kinds of prior information to transform the original disordered discrete sequence space into a relatively ordered space, and decoy sequences are searched in this ordered space through a multiobjective swarm intelligence algorithm. This algorithm features high accuracy and a high-speed search process. Our method was tested on 40 targets covering different fold classes, which were computationally verified to be well folded, and it experimentally solved the 1UBQ fold by NMR in excellent agreement with the native structure with a backbone RMSD deviation of 1.074 Å. The Hydra software package can be downloaded from: http://www.csbio.sjtu.edu.cn/bioinf/HYDRA/ for academic use.

| S-EPMC8114120 | biostudies-literature

Project description:Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.

| S-EPMC10138783 | biostudies-literature

SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data.

Project description:Gene expression regulation is highly dependent on binding of RNA-binding proteins (RBPs) to their RNA targets. Growing evidence supports the notion that both RNA primary sequence and its local secondary structure play a role in specific Protein-RNA recognition and binding. Despite the great advance in high-throughput experimental methods for identifying sequence targets of RBPs, predicting the specific sequence and structure binding preferences of RBPs remains a major challenge. We present a novel webserver, SMARTIV, designed for discovering and visualizing combined RNA sequence and structure motifs from high-throughput RNA-binding data, generated from in-vivo experiments. The uniqueness of SMARTIV is that it predicts motifs from enriched k-mers that combine information from ranked RNA sequences and their predicted secondary structure, obtained using various folding methods. Consequently, SMARTIV generates Position Weight Matrices (PWMs) in a combined sequence and structure alphabet with assigned P-values. SMARTIV concisely represents the sequence and structure motif content as a single graphical logo, which is informative and easy for visual perception. SMARTIV was examined extensively on a variety of high-throughput binding experiments for RBPs from different families, generated from different technologies, showing consistent and accurate results. Finally, SMARTIV is a user-friendly webserver, highly efficient in run-time and freely accessible via http://smartiv.technion.ac.il/.

| S-EPMC6030986 | biostudies-literature

Overlapping genes produce proteins with unusual sequence properties and offer insight into de novo protein creation.

Project description:It is widely assumed that new proteins are created by duplication, fusion, or fission of existing coding sequences. Another mechanism of protein birth is provided by overlapping genes. They are created de novo by mutations within a coding sequence that lead to the expression of a novel protein in another reading frame, a process called "overprinting." To investigate this mechanism, we have analyzed the sequences of the protein products of manually curated overlapping genes from 43 genera of unspliced RNA viruses infecting eukaryotes. Overlapping proteins have a sequence composition globally biased toward disorder-promoting amino acids and are predicted to contain significantly more structural disorder than nonoverlapping proteins. By analyzing the phylogenetic distribution of overlapping proteins, we were able to confirm that 17 of these had been created de novo and to study them individually. Most proteins created de novo are orphans (i.e., restricted to one species or genus). Almost all are accessory proteins that play a role in viral pathogenicity or spread, rather than proteins central to viral replication or structure. Most proteins created de novo are predicted to be fully disordered and have a highly unusual sequence composition. This suggests that some viral overlapping reading frames encoding hypothetical proteins with highly biased composition, often discarded as noncoding, might in fact encode proteins. Some proteins created de novo are predicted to be ordered, however, and whenever a three-dimensional structure of such a protein has been solved, it corresponds to a fold previously unobserved, suggesting that the study of these proteins could enhance our knowledge of protein space.

| S-EPMC2753099 | 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