Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Narykov, Oleksandr; Johnson, Nia; Korkin, Dmitry

doi:10.1016/j.celrep.2021.110045

Cited by 4 publications

(3 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These changes can be due to the disruptive effects of cancer mutations and aberrant alternative splicing. So, a natural extension of our analysis is the system-level study of protein-protein interaction (PPI) network rewiring [ 81 , 82 , 83 ].…”

Section: Discussionmentioning

confidence: 99%

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Narykov,

Zhu,

Brettin

et al. 2023

Cancers

Self Cite

View full text Add to dashboard Cite

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

show abstract

Section: Discussionmentioning

confidence: 99%

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Narykov,

Zhu,

Brettin

et al. 2023

Cancers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using wet lab experiments to study PPI changes is expensive and time-consuming [5]. Hence, computational methods to study AS-related PPI changes were recently developed [6,7], which rely on existing knowledge about protein 3-dimensional (3D) structural domain-domain interactions (DDIs). One such recent method is ALTernatively spliced INteraction prediction (ALT-IN) [6], which extracts biochemical or DDI features of proteins to train a machine-learning model based on ground truth knowledge about existing AS-affected PPIs [1].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, computational methods to study AS-related PPI changes were recently developed [6,7], which rely on existing knowledge about protein 3-dimensional (3D) structural domain-domain interactions (DDIs). One such recent method is ALTernatively spliced INteraction prediction (ALT-IN) [6], which extracts biochemical or DDI features of proteins to train a machine-learning model based on ground truth knowledge about existing AS-affected PPIs [1]. Then, ALT-IN uses the trained model to predict whether or not a PPI disappears when, for any of the participating proteins, the original isoform is replaced by an alternative isoform.…”

Section: Introductionmentioning

confidence: 99%

The power and limits of predicting exon-exon interactions using protein 3D structures

Liebold,

Del Moral-Morales,

Manalastas-Cantos

et al. 2024

Preprint

View full text Add to dashboard Cite

Alternative splicing (AS) effects on cellular functions can be captured by studying changes in the underlying protein-protein interactions (PPIs). Because AS results in the gain or loss of exons, existing methods for predicting AS-related PPI changes utilize known PPI interfacing exon-exon interactions (EEIs), which only cover ~5% of known human PPIs. Hence, there is a need to extend the existing limited EEI knowledge to advance the functional understanding of AS. In this study, we explore whether existing computational PPI interface prediction (PPIIP) methods, originally designed to predict residue-residue interactions (RRIs), can be used to predict EEIs. We evaluate three recent state-of-the-art PPIIP methods for the RRI- as well as EEI-prediction tasks using known protein complex structures, covering ~230,000 RRIs and ~27,000 EEIs. Our results provide the first evidence that existing PPIIP methods can be extended for the EEI prediction task, showing F-score, precision, and recall performances of up to ~38%, ~63%, and ~28%, respectively, with a false discovery rate of less than 5%. Our study provides insights into the power and limits of existing PPIIP methods to predict EEIs, thus guiding future developments of computational methods for the EEI prediction task. We provide streamlined computational pipelines integrating each of the three considered PPIIP methods for the EEI prediction task to be utilized by the scientific community.

show abstract

RNAi-based drug design: considerations and future directions

Tang,

Khvorova

2024

Nat Rev Drug Discov

View full text Add to dashboard Cite

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Cited by 4 publications

References 106 publications

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

The power and limits of predicting exon-exon interactions using protein 3D structures

RNAi-based drug design: considerations and future directions

Contact Info

Product

Resources

About