Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Novel machine learning approaches revolutionize protein knowledge.

ABSTRACT: Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.

SUBMITTER: Bordin N

PROVIDER: S-EPMC10570143 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Novel machine learning approaches revolutionize protein knowledge.

Bordin Nicola N Dallago Christian C Heinzinger Michael M Kim Stephanie S Littmann Maria M Rauer Clemens C Steinegger Martin M Rost Burkhard B Orengo Christine C

Trends in biochemical sciences 20221209 4

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of expe ...[more]

PMID: 36504138

Similar Datasets

How Machine Learning Will Revolutionize Electrochemical Sciences.

Project description:Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

| S-EPMC8042659 | biostudies-literature

Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches.

Project description:Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable performance can be achieved with both approaches, preliminary results show that deep learning is the more promising approach, eliminating the need for laborious feature extraction and offering a six times lower event detection delay and twelve times lower execution time. However, we achieved the best results by combining deep learning with the knowledge-based and classical machine learning approaches. At the end of this manuscript, we propose general guidelines for efficient system design combining knowledge-based, classical machine learning, and deep learning approaches.

| S-EPMC8618866 | biostudies-literature

Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches.

Project description:The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate.

| S-EPMC10839993 | biostudies-literature

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Project description:Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

| S-EPMC8477400 | biostudies-literature

Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches

Project description:We conducted a two-vector CRISPR/Cas13d proliferation screening experiment. The screening library contains 10,830 sgRNAs targeting 192 protein-coding genes and 234 lncRNAs, and the screening experiment was performed using a melanoma cell line A375. It provides a unique dataset to model Cas13d sgRNA efficiency and specificity. We designed a deep learning model, named DeepCas13, to predict the sgRNA on-target activity with high accuracy from sgRNA sequences and RNA secondary structures.

2023-01-16 | GSE183256 | GEO

Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.

Project description:Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

| S-EPMC6222875 | biostudies-literature

Machine learning and deep learning approaches in IoT.

Project description:The internet is a booming sector for exchanging information because of all the gadgets in today's world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privacy are the IoMT (Internet of Medical Things) and the IoV (Internet of Vehicles). IoMT and IoV devices monitor real-time healthcare and traffic trends to protect an individual's life. With the proliferation of these devices comes a rise in security assaults and threats, necessitating the deployment of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers about the domain were searched, and the top 50 papers were selected. In addition, research objectives are specified concerning the problem, which leads to research questions. After evaluating the associated research, data is retrieved from digital archives. Furthermore, based on the findings of this SLR, a taxonomy of IoT subdomains has been given. This article also identifies the difficult areas and suggests ideas for further research in the IoT.

| S-EPMC10280223 | biostudies-literature

Machine-Learning- and Knowledge-Based Scoring Functions Incorporating Ligand and Protein Fingerprints.

Project description:We propose a novel machine-learning-based scoring function for drug discovery that incorporates ligand and protein structural information into a knowledge-based PMF score. Molecular docking, a simulation method for structure-based drug design (SBDD), is expected to reduce the enormous costs associated with conventional experimental methods in terms of rational drug discovery. Molecular docking has two main purposes: to predict ligand-binding structures for target proteins and to predict protein-ligand binding affinity. Currently available programs of molecular docking offer an accurate prediction of ligand binding structures for many systems. However, the accurate prediction of binding affinity remains challenging. In this study, we developed a new scoring function that incorporates fingerprints representing ligand and protein structures as descriptors in the PMF score. Here, regression analysis of the scoring function was performed using the following machine learning techniques: least absolute shrinkage and selection operator (LASSO) and light gradient boosting machine (LightGBM). The results on a test data set showed that the binding affinity delivered by the newly developed scoring function has a Pearson correlation coefficient of 0.79 with the experimental value, which surpasses that of the conventional scoring functions. Further analysis provided a chemical understanding of the descriptors that contributed significantly to the improvement in prediction accuracy. Our approach and findings are useful for rational drug discovery.

| S-EPMC9178954 | biostudies-literature

Machine learning approaches demonstrate that protein structures carry information about their genetic coding.

Project description:Synonymous codons translate into the same amino acid. Although the identity of synonymous codons is often considered inconsequential to the final protein structure, there is mounting evidence for an association between the two. Our study examined this association using regression and classification models, finding that codon sequences predict protein backbone dihedral angles with a lower error than amino acid sequences, and that models trained with true dihedral angles have better classification of synonymous codons given structural information than models trained with random dihedral angles. Using this classification approach, we investigated local codon-codon dependencies and tested whether synonymous codon identity can be predicted more accurately from codon context than amino acid context alone, and most specifically which codon context position carries the most predictive power.

| S-EPMC9767929 | biostudies-literature

Using integrative bioinformatics approaches and machine‑learning strategies to Identify potential signatures for atrial fibrillation

Project description:Atrial fibrillation (AF) is the most common tachyarrhythmia and seriously affects human health. Key targets of AF bioinformatics analysis can help to better understand the pathogenesis of AF and develop therapeutic targets. The left atrial appendage tissue of 20 patients with AF and 10 patients with sinus rhythm were collected for sequencing, and the expression data of the atrial tissue were obtained. Based on this, 2578 differentially expressed genes were obtained through differential analysis. Different express genes (DEGs) were functionally enriched on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), mainly focusing on neuroactive ligand-receptor interactions, neuronal cell body pathways, regulation of neurogenesis, and neuronal death，regulation of neuronal death, etc. Secondly, 14 significant module genes were obtained by analyzing the weighted gene co-expression network of DEGs. Next, LASSO and SVM analyzes were performed on the differential genes, and the results were in good agreement with the calibration curve of the nomogram model for predicting AF constructed by the weighted gene co-expression network key genes. The significant module genes obtained by the area under the ROC curve (AUC) analysis were analyzed. Through crossover, two key disease characteristic genes related to AF, HOXA2 and RND2, were screened out. RND2 was selected for further research, and qPCR verified the expression of RND2 in sinus rhythm patients and AF patients. Patients with sinus rhythm were significantly higher than those in AF patients. Our study shows that RND2 can be used as a new target for the diagnosis and treatment of AF.

2025-01-29 | GSE282504 | GEO

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