Biology subjects

Newaz, K.

Publications and source records attributed to Newaz, K..

3 recordsLinked to original sources

bioRxiv2024

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

Aberrant alternative splicing (AS) is a prominent hallmark of cancer. AS can perturb protein-protein interactions (PPIs) by adding or removing interface regions 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 (3D) 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 PPI interfaces. Given the ongoing expansion of available RNA-seq data and the number of 3D structurally-resolved (or confidently predicted) protein complexes, our computational framework will help accelerate the discovery of clinically important cancer-promoting AS events.

bioinformatics↗

bioRxiv2024

Transcription factor prediction using protein 3D structures

MotivationTranscription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs. ResultsWe propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on[~] 525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a methods performance. We find that StrucTFactor significantly (p-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient. AvailabilityData and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor/ Contactkhalique.newaz@uni-hamburg.de Supplementary informationIncluded

bioinformatics↗

bioRxiv2024

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

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.

bioinformatics↗