Multi‐layer sequential network analysis improves protein<scp>3D</scp>structural classification

Newaz, Khalique; Piland, Jacob; Clark, Patricia L.; Emrich, Scott J.; Li, Jun; Milenković, Tijana

doi:10.1002/prot.26349

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…Given a pair of co-crystallized proteins p 1 and p 2 , we define an RRI between residues r 1 from p 1 and r 2 from p 2 , if and only if the 3D Euclidean distance between any of the heavy atoms (i.e., Carbon, Nitrogen, Oxygen, and Sulphur) of r 1 is within 6 Å to any of the heavy atoms of r 2 . This definition of RRIs is consistent with existing studies [22,30,43–46].…”

Section: Methodssupporting

confidence: 90%

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

Liebold,

Del Moral-Morales,

Manalastas-Cantos

et al. 2024

Preprint

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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.

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Section: Methodssupporting

confidence: 90%

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

Liebold,

Del Moral-Morales,

Manalastas-Cantos

et al. 2024

Preprint

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“…Two exons were considered to interact if at least one amino acid of an exon was in contact with at least one amino acid of the other exon. Contacts between any two amino acids were defined based on the spatial distance of 6Å between any of their heavy atoms, as is typically done to obtain biologically relevant protein contact maps [24][25][26][27] . The above procedure resulted in an EEI network (EEIN) with 16,012 EEIs among 9,442 exons.…”

Section: Definitions Of Exon-exon Interactionsmentioning

confidence: 99%

Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer

Newaz,

Schaefers,

Weisel

et al. 2024

Preprint

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Aberrant alternative splicing (AS) is a prominent hallmark of cancer. AS can perturb protein-protein interactions (PPIs) by adding or removing interface regions of protein complexes encoded by individual exons. Identifying prognostic exon-exon interactions (EEIs) from PPI interfaces can help discover AS-affected cancer-driving PPIs that can serve as potential drug targets. Here, we assessed the prognostic significance of EEIs across 15 cancer types by integrating RNA-seq data with three-dimensional structures of protein complexes. By analyzing the resulting EEI network we identified patient-specific perturbed EEIs (i.e., EEIs present in healthy samples but absent from the paired cancer samples or vice versa) that were significantly associated with survival. We provide the first evidence that EEIs can be used as prognostic biomarkers for cancer patient survival. Our findings provide mechanistic insights into AS-affected protein-protein interaction interfaces. Given the ongoing expansion of available RNA-seq data and the number of structurally-resolved (or confidently predicted) protein complexes, our computational framework will help accelerate the discovery of clinically important cancer-promoting AS events.

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“…However, proteins (including TFs) perform functions in their 3-dimensional (3D) structural forms. Using 3D structural features of proteins has already been shown to outperform sequence features in several other tasks, including protein structural classification (Newaz et al, 2022; Guo et al, 2019; Newaz et al, 2020). Hence, in the light of an exponential increase in the number of predicted 3D protein structures (Jumper et al, 2021; Lin et al, 2023) of near experimental quality (Akdel et al, 2022), using 3D protein structural features of proteins can further improve upon the current state-of-the-art TF prediction methods, which is what we show in this study.…”

Section: Introductionmentioning

confidence: 99%

Transcription factor prediction using protein 3D secondary structures

Liebold,

Neuhaus,

Geiser

et al. 2024

Preprint

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Motivation: Transcription factors (TFs) are DNA-binding proteins that regulate expressions of genes in an organism. Hence, it is important to identify novel TFs. Traditionally, novel TFs have been identified by their sequence similarity to the DNA-binding domains (DBDs) of known TFs. However, this approach can miss to identify a novel TF that is not sequence similar to any of the known DBDs. Hence, computational methods have been developed for the TF prediction task that, instead of relying on known DBDs, use sequence features of proteins to train a machine learning model, in order to capture sequence patterns that distinguish TFs from other proteins. Because 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures can more correctly predict novel TFs. Results: We propose the first deep learning-based TF prediction method (named StrucTFactor) based on 3D protein structures. We compare StrucTFactor with a recent state-of-the-art TF prediction method that relies only on protein sequences. We evaluate the considered methods on ~550,000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy and 3D protein structural quality) that can influence a method's performance. We find that StrucTFactor significantly (p-value < 0.001) outperforms the existing state-of-the-art TF prediction method, improving performance by up to 23% based on Matthews correlation coefficient. Our results show the importance of using 3D protein structures to predict novel TFs. We provide StrucTFactor as a computational pipeline.

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Multi‐layer sequential network analysis improves protein3Dstructural classification

Cited by 6 publications

References 43 publications

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

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

Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer

Transcription factor prediction using protein 3D secondary structures

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