Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest

Hou, Qingzhen; Geest, Paul P.F.G. De; Vranken, Wim; Heringa, Jaap; Feenstra, K. Anton

doi:10.1093/bioinformatics/btx005

Cited by 74 publications

(140 citation statements)

References 46 publications

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“…By implementing these features, we trained our sequence-based protein interface predictors using both homomeric and heteromeric protein interaction datasets. Predictions were significantly more accurate than other predictors on the same test-sets (Hou et al, 2017).…”

Section: Introductionmentioning

confidence: 84%

“…The webserver provides a 'remastered' version of our previous approach (Hou et al, 2017) which improves the speed of the process by deriving sequence conservation profiles for the homologues by remastering the blast profile of the input sequence (Simossis and Heringa, 2004). The procedure is described in more detail in Supplementary Section S2, see also Supplementary Figure S2.…”

Section: The Serendip Webservermentioning

confidence: 99%

“…Based on the features generated with the new approach, we retrain our predictors using the same training and testing protocols as previous research (Hou et al, 2017) to obtain the RF classifiers. More detail is provided in the Supplementary Material.…”

Section: The Serendip Webservermentioning

confidence: 99%

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SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

et al. 2019

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Motivation Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein–protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein–protein interactions. Here, we present a webserver that implements this method efficiently. Results With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. Availability and implementation Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 84%

Section: The Serendip Webservermentioning

confidence: 99%

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SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

et al. 2019

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“…It can be found that the values of six measures achieved in these three independent benchmark datasets are lower than that in the dataset used in this work, but it is reasonable because the IHT-XGB model is built on this original dataset. Table 4 also shows the prediction performance of five models, i.e., SSWRF, LORIS, PSIVER, SCRIBER, and DELPHI, in identification of protein-protein interaction sites [31][32][33][34][35][36][37]. The highest results in each performance measures for different models are highlighted as bold type.…”

Section: Prediction Performance In Independent Benchmark Datasetsmentioning

confidence: 99%

“…Theoretically, the strength of a protein-protein interaction can be characterized by a dissociation constant K D = k d /k a , where k d is the dissociation rate constant and k a is the association rate constant. Many commonly used techniques provide measurements of K D , which can be calculated by the concentration of free proteins, but most of them do not offer the real-time measurements of k d and k a [34].…”

Section: Limits Of Prediction Validationmentioning

confidence: 99%

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Deng

Zhang

Wang

et al. 2020

IJMS

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The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.

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Machine Learning Advances in Predicting Peptide/Protein‐Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Zheng

et al. 2023

Advanced Biology

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Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high‐throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide–protein interactions (PepPIs) and protein–protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

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Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 74 publications

References 46 publications

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Machine Learning Advances in Predicting Peptide/Protein‐Protein Interactions Based on Sequence Information for Lead Peptides Discovery

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