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PredictProtein - Predicting Protein Structure and Function for 29 Years.

ABSTRACT: Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

SUBMITTER: Bernhofer M

PROVIDER: S-EPMC8265159 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Diminished peak lung function in young adulthood is a risk factor for future chronic obstructive pulmonary disease. The association between lung disease and cardiovascular disease later in life is well documented. Whether peak lung function measured in young adulthood is associated with risk of future cardiovascular events is unknown. Methods and Results CARDIA (The Coronary Artery Risk Development in Young Adults) study is a prospective, multicenter, community-based, longitudinal cohort study including 4761 participants aged 18 to 30 years with lung function testing we investigated the association between lung health in young adulthood and risk of subsequent cardiovascular events. We performed Cox proportional hazards regression to test the association between baseline and years 10 and 20 pulmonary function with incident cardiovascular events. Linear and logistic regression was performed to explore the associations of lung function with development of risk factors for cardiovascular disease as well as carotid intima-media thickness and coronary artery calcified plaque. At baseline, mean age (± SD ) was 24.9±3.6 years. Baseline forced expiratory volume in 1 second (hazard ratio) per -10-unit decrement in percent predicted forced expiratory volume in 1 second (hazard ratio, 1.18; 95% CI, 1.06-1.31 [ P=0.002]) and FVC per -10-unit decrement in percent predicted FVC (hazard ratio, 1.19; 95% CI, 1.06-1.33 [ P=0.003]) were associated with future cardiovascular events independent of traditional cardiovascular risk factors. Baseline lung function was associated with heart failure and cerebrovascular events but not coronary artery disease events. Conclusions Lung function in young adulthood is independently associated with cardiovascular events into middle age. This association appears to be driven by heart failure and cerebrovascular events rather than coronary heart disease. Clinical Trial Registration URL : https://www.clinicaltrials.gov . Unique identifier: NCT 00005130.

Project description:PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training-testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training-testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.

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PredictProtein - Predicting Protein Structure and Function for 29 Years.

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