Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Predicting evolutionary outcomes through the probability of accessing sequence variants.

ABSTRACT: Natural selection can only operate on available genetic variation. Thus, determining the probability of accessing different sequence variants from a starting sequence can help predict evolutionary trajectories and outcomes. We define the concept of "variant accessibility" as the probability that a set of genotypes encoding a particular protein function will arise through mutations before subject to natural selection. This probability is shaped by the mutational biases of nucleotides and the structure of the genetic code. Using the influenza A virus as a model, we discuss how a more accessible but less fit variant can emerge as an adaptation rather than a more fit variant. We describe a genotype-accessibility landscape, complementary to the genotype-fitness landscape, that informs the likelihood of a starting sequence reaching different parts of genotype space. The proposed framework lays the foundation for predicting the emergence of adaptive genotypes in evolving systems such as viruses and tumors.

SUBMITTER: Gunnarsson PA

PROVIDER: S-EPMC10381947 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Predicting evolutionary outcomes through the probability of accessing sequence variants.

Gunnarsson P Alexander PA Babu M Madan MM

Science advances 20230728 30

Natural selection can only operate on available genetic variation. Thus, determining the probability of accessing different sequence variants from a starting sequence can help predict evolutionary trajectories and outcomes. We define the concept of "variant accessibility" as the probability that a set of genotypes encoding a particular protein function will arise through mutations before subject to natural selection. This probability is shaped by the mutational biases of nucleotides and the stru ...[more]

PMID: 37506212

Similar Datasets

SPPS: a sequence-based method for predicting probability of protein-protein interaction partners.

Project description:BackgroundThe molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.ResultsWe have developed a web-based tool "Sequence-based Protein Partners Search" (SPPS) to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query). Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.ConclusionsSPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at http://mdl.shsmu.edu.cn/SPPS/.

| S-EPMC3266917 | biostudies-literature

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment.

Project description:BackgroundProtein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.FindingsMost previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, Drosophila, Escherichia coli, and Caenorhabditis elegans. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for Drosophila, 92.73% for E. coli, and 97.51% for C. elegans. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of >/=0.8, indicating that this tool could predict novel PPIs with high confidence.ConclusionsBased on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html.

| S-EPMC2883990 | biostudies-literature

Revealing evolutionary constraints on proteins through sequence analysis.

Project description:Statistical analysis of alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.

| S-EPMC6502352 | biostudies-literature

An ancient competition for the conserved branchpoint sequence influences physiological and evolutionary outcomes in splicing

Project description:Branchpoint (bp) recognition is an early event that is required for spliceosome recruitment to a pre-mRNA substrate. The bp sequence can range from nearly invariant in yeast (UACUAAC) to quite degenerate in higher eukaryotes (YUNAY in humans), which correlates with the frequency of alternative splicing observed in the latter. The closely related KH-type RNA binding proteins (RBPs) SF1 and Quaking (QKI) can bind to a similar bp-like sequence, but the extent to which competition for the bp sequence between these two proteins might influence gene expression regulatory mechanisms is unknown. Here we find genomewide evidence for competition between these two RBPs for a subset of alternatively spliced exons that have ACUAAY-like bp sequences. Using a combination of molecular genetic and biochemical techniques with a model intron containing two bp-like sequences, we find that either can be used as a bp but high affinity binding and strong splicing repression by QKI requires both sites. This directly prevents SF1 binding and the subsequent recruitment of spliceosome-associated factors. Finally the ectopic expression of QKI in S. cerevisiae (which lacks QKI) is lethal, due at least in part to widespread splicing repression. In conclusion, QKI can function as a splicing repressor by directly competing with SF1/BBP for a subset of bp sequences that closely mirror its high affinity binding site. This suggests that QKI and degenerate bp sequences may have co-evolved which allowed for expansion of the alternative splicing repertoire in metazoans, plants, and animals.

2024-11-22 | GSE273838 | GEO

Accessing Nystatin through Mariculture.

Project description:Understanding our oceans and their marine ecosystems has enabled the development of sustainable systems for mariculture. While the bulk of studies to date have focused on the production of food, its remarkable expanse has inspired the translation of other markets towards aquatic environments. This manuscript outlines an approach to pharmaceutical mariculture, by demonstrating a benchmark for future prototyping. Here, design, field evaluation and natural product chemistry are united to successfully produce nystatin at sea. This study begins by evaluating new designs for culture flasks, illustrating a next step towards developing self-contained bioreactors for culturing in marine environments. Through pilot studies, an underwater system was developed to cost effectively produce cultures that yielded 200 mg of nystatin per deployment. Overall, this study demonstrates the potential for the practical culturing of microbes in a marine environment and provides an important next step for the fledgling field of molecular mariculture.

| S-EPMC8708966 | biostudies-literature

Predicting Pathology of Missense Mutations through Protein-Specific Evolutionary Pattern.

Project description:Missense mutations, which are single base pair genetic alternation resulting in a different amino acid, are among the most common occurring variants in exon regions of the human genome and may lead to diseases. Thus to assess the effects of missense mutations, it is essential to investigate the evolutionary history of the protein under selection pressures. In this study, we employ a continuous-time Markov model to investigate the evolutionary patterns in protein sequences and a Bayesian Markov chain Monte Carlo method to estimate the substitution rates for protein of interest, from which we obtain scoring matrices. Specifically, we examined the evolutionary patterns of protein sequences containing missense mutations using a species tree to define the phylogeny of the protein of interest. We thoroughly studied the evolutionary pattern of human muscle glycogen phosphorylase containing 127 known missense mutations, and identified characteristic evolutionary patterns in 63 proteins with 2,238 missense mutations, including both deleterious and neutral effects. Our results show that the estimated protein-specific evolutionary pattern-based scoring matrices (PSM) lead to higher sensitivity in detecting the pathological effects of missense mutations, compared to the general evolutionary pattern-based scoring matrix of Blosum62 (BL62) matrix. By incorporating PSM, the performance of a recently released structure-based model SPRI for evaluating missense mutations is further improved.

| S-EPMC10984725 | biostudies-literature

Predicting base editing outcomes using position-specific sequence determinants.

Project description:CRISPR/Cas base editors promise nucleotide-level control over DNA sequences, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at ∼14 000 target sequences and find that base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, acting to widen or narrow the effective editing window. The impact of features on editing rate depends on the position, with sequence bias efficacy mainly influencing bases away from the center of the window. We use these observations to train a machine learning model to predict editing activity per position, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of disease mutation correcting guides, and find that most of them suffer from more unwanted editing than pure outcomes. This work unravels the position-specificity of base editing biases and allows more efficient planning of editing campaigns in experimental and therapeutic contexts.

| S-EPMC8989541 | biostudies-literature

Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

Project description:Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

| S-EPMC2777313 | biostudies-literature

Predicting effects of noncoding variants with deep learning-based sequence model.

Project description:Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.

| S-EPMC4768299 | biostudies-literature

Predicting protein-protein interactions through sequence-based deep learning.

Project description:MotivationHigh-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data.ResultsWe present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine-receptor binding affinities.Availability and implementationPredicting protein-protein interactions through sequence-based deep learning): https://github.com/hashemifar/DPPI/.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6129267 | 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