CATH functional families predict protein functional sites

Functional Sites ◽

Protein Protein Interaction ◽

Functional Characterisation ◽

Evolutionary Features ◽

Functional Families

AbstractMotivationIdentification of functional sites in proteins is essential for functional characterisation, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams).ResultsFunSite’s prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed all publicly-available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite’s performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyse which structural and evolutionary features are most predictive for functional sites.AvailabilityThe datasets and prediction models are available on [email protected] informationSupplementary data are available at Bioinformatics online.

CATH functional families predict functional sites in proteins

Bioinformatics ◽

10.1093/bioinformatics/btaa937 ◽

2020 ◽

Author(s):

Sayoni Das ◽

Harry M Scholes ◽

Neeladri Sen ◽

Christine Orengo

Keyword(s):

Functional Characterization ◽

Functional Site ◽

Training Data ◽

Conserved Residues ◽

Functional Sites ◽

Protein Protein Interaction ◽

Evolutionary Features ◽

Functional Families

Abstract Motivation Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein–protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). Results FunSite’s prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite’s performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites. Availabilityand implementation https://github.com/UCL/cath-funsite-predictor. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

Bioinformatics ◽

10.1093/bioinformatics/btaa701 ◽

2020 ◽

Cited By ~ 1

Author(s):

Amelia Villegas-Morcillo ◽

Stavros Makrodimitris ◽

Roeland C H J van Ham ◽

Angel M Gomez ◽

Victoria Sanchez ◽

...

Keyword(s):

Protein Function ◽

Prediction Models ◽

Protein Function Prediction ◽

3D Structure ◽

Function Prediction ◽

Feature Representation ◽

Training Data ◽

Molecular Function ◽

Structure Information

Abstract Motivation Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. Results We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining. Availability and implementation Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Supplementary information Supplementary data are available at Bioinformatics online.

Evidence for differences in DNA methylation between Germans and Japanese

International Journal of Legal Medicine ◽

10.1007/s00414-021-02736-3 ◽

2021 ◽

Author(s):

J. Becker ◽

P. Böhme ◽

A. Reckert ◽

S. B. Eickhoff ◽

B. E. Koop ◽

...

Keyword(s):

Dna Methylation ◽

Age Estimation ◽

Prediction Models ◽

Total Sample ◽

Training Data ◽

Research Strategies ◽

Different Populations ◽

The Impact ◽

Age Prediction

AbstractAs a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included “robust CpGs” prevented relevant effects of differences in DNAm at two CpGs.Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for “robust” CpG markers—which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

PeerJ ◽

10.7717/peerj.5518 ◽

2018 ◽

Vol 6 ◽

pp. e5518 ◽

Cited By ~ 78

Author(s):

Tomislav Hengl ◽

Madlene Nussbaum ◽

Marvin N. Wright ◽

Gerard B.M. Heuvelink ◽

Benedikt Gräler

Keyword(s):

Random Forest ◽

Data Quality ◽

Cross Validation ◽

Prediction Models ◽

Proximity Effects ◽

Training Data ◽

Calibration Data ◽

Temporal Prediction ◽

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as “knowledge engines” in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality—especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques

Bioinformatics ◽

10.1093/bioinformatics/btaa712 ◽

2020 ◽

Author(s):

Joshua M Toth ◽

Paul J DePietro ◽

Juergen Haas ◽

William A McLaughlin

Keyword(s):

Protein Structure ◽

Structure Prediction ◽

Functional Site ◽

Cumulative Probability ◽

Reference Structure ◽

Model Quality ◽

Difference Scores ◽

Prediction Techniques

Abstract Motivation Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. Results Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. Availabilityand implementation Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. Supplementary information Supplementary data are available at Bioinformatics online.

Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

10.7287/peerj.preprints.26693v3 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tomislav Hengl ◽

Madlene Nussbaum ◽

Marvin N Wright ◽

Gerard B.M. Heuvelink ◽

Benedikt Gräler

Keyword(s):

Random Forest ◽

Data Quality ◽

Cross Validation ◽

Prediction Models ◽

Proximity Effects ◽

Training Data ◽

Calibration Data ◽

Temporal Prediction ◽

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using 5 – fold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as "knowledge engines" in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates, sensitivity of predictions to input data quality and extrapolation problems. The key to the success of the RFsp framework might be the training data quality — especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with fewer number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions

Bioinformatics ◽

10.1093/bioinformatics/btaa618 ◽

2020 ◽

Vol 36 (20) ◽

pp. 4977-4983 ◽

Cited By ~ 1

Author(s):

Jing-Bo Zhou ◽

Yao Xiong ◽

Ke An ◽

Zhi-Qiang Ye ◽

Yun-Dong Wu

Keyword(s):

Prediction Models ◽

Disease Association ◽

Training Data ◽

Single Nucleotide Variants ◽

Sequence Alignments ◽

Single Nucleotide ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Disordered Regions

Abstract Motivation Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. Results We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. Availability and implementation The software is freely available at http://www.wdspdb.com/IDRMutPred. Supplementary information Supplementary data are available at Bioinformatics online.

Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

10.7287/peerj.preprints.26693v2 ◽

2018 ◽

Author(s):

Tomislav Hengl ◽

Madlene Nussbaum ◽

Marvin N Wright ◽

Gerard B.M. Heuvelink ◽

Benedikt Gräler

Keyword(s):

Random Forest ◽

Data Quality ◽

Cross Validation ◽

Prediction Models ◽

Proximity Effects ◽

Training Data ◽

Calibration Data ◽

Temporal Prediction ◽

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using 5 – fold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as 'knowledge engines' in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates, sensitivity of predictions to input data quality and extrapolation problems. The key to the success of the RFsp framework might be the training data quality — especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with fewer number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

TrainSel: An R Package for Selection of Training Populations

Frontiers in Genetics ◽

10.3389/fgene.2021.655287 ◽

2021 ◽

Vol 12 ◽

Author(s):

Deniz Akdemir ◽

Simon Rio ◽

Julio Isidro y Sánchez

Keyword(s):

Supervised Learning ◽

Prediction Models ◽

R Package ◽

Training Data ◽

Major Barrier ◽

Predictive Learning ◽

Learning Tasks ◽

Training Examples ◽

Selection Of

A major barrier to the wider use of supervised learning in emerging applications, such as genomic selection, is the lack of sufficient and representative labeled data to train prediction models. The amount and quality of labeled training data in many applications is usually limited and therefore careful selection of the training examples to be labeled can be useful for improving the accuracies in predictive learning tasks. In this paper, we present an R package, TrainSel, which provides flexible, efficient, and easy-to-use tools that can be used for the selection of training populations (STP). We illustrate its use, performance, and potentials in four different supervised learning applications within and outside of the plant breeding area.

Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

10.7287/peerj.preprints.26693 ◽

2018 ◽

Author(s):

Tomislav Hengl ◽

Madlene Nussbaum ◽

Marvin N Wright ◽

Gerard B.M. Heuvelink ◽

Benedikt Gräler

Keyword(s):

Random Forest ◽

Data Quality ◽

Cross Validation ◽

Prediction Models ◽

Proximity Effects ◽

Training Data ◽

Calibration Data ◽

Temporal Prediction ◽

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using 5 – fold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as "knowledge engines" in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates, sensitivity of predictions to input data quality and extrapolation problems. The key to the success of the RFsp framework might be the training data quality — especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with fewer number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.