DNA and RNA Binding Proteins: From Motifs to Roles in Cancer

ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models

Zinc-finger proteins (ZNFs) constitute the largest family of transcription factors and play crucial roles in various cellular processes. Missense mutations in ZNFs significantly alter protein-DNA interactions, potentially leading to the development of various types of cancers. This study presents ZFP-CanPred, a novel deep learning-based model for predicting cancer-associated driver mutations in ZNFs. The representations derived from protein language models (PLMs) from the structural neighbourhood of mutated sites were utilized to train ZFP-CanPred for differentiating between cancer-causing and neutral mutations. ZFP-CanPred, achieved a superior performance with an accuracy of 0.72, F1-score of 0.79, and area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.74, on an independent test set. In a comparative analysis against 11 existing prediction tools using a curated dataset of 331 mutations, ZFP-CanPred demonstrated the highest AU-ROC of 0.74, outperforming both generic and cancer-specific methods. The model's balanced performance across specificity and sensitivity addresses a significant limitation of current methodologies. The source code and other related files are available on GitHub at https://github.com/amitphogat/ZFP-CanPred.git. We envisage that the present study contributes to understand the oncogenic processes and developing targeted therapeutic strategies.

Apolipoprotein C1 and apoprotein E as potential therapeutic and prognostic targets for adrenocortical carcinoma

BackgroundApolipoprotein C1 (APOC1) and Apoprotein E (APOE) play important roles in lipid transport and metabolism. In recent years, APOC1 and APOE have been shown to play key roles in the occurrence and development of various cancers. However, the expression levels, gene regulatory networks, prognostic values, and target predictions of APOC1 and APOE in adrenocortical carcinoma (ACC) remain unclear.MethodsVarious bioinformatics analysis methods were used, including gene expression profiling interactive analysis, the University of Alabama at Birmingham cancer data analysis portal, biomarker exploration of solid tumors software, the BioPortal for Cancer Genomics, search tool for the retrieval of interacting genes/proteins, gene multiple association network integration algorithm, Metascape, transcriptional regulatory relationships unraveled by sentence-based text-mining, LinkedOmics, and genomics of drug sensitivity in cancer analysis.ResultsAPOC1 and APOE expression were strongly downregulated in patients with ACC. APOC1 and APOE expression levels were lower in male patients with ACC than those in female patients. Furthermore, APOC1 and APOE expression levels affected the prognosis of patients with ACC. The main functions of APOC1 and its altered neighboring genes (ANG) were organophosphate ester transport, rRNA processing, and positive regulation of cytokine production. Cytolysis, protein ubiquitination, and histone modification were the main functions of APOE and its ANGs. The transcription factor E2F1, tumor protein p53, miR-182, miR-493, Erb-B2 receptor tyrosine kinase 2, and cyclin dependent kinase 1 were key regulatory targets of APOC1, APOE, and the ANGs. APOC1 and APOE expression in patients with ACC were positively associated with immune cell infiltration. Furthermore, anti-programmed cell death protein 1 immunotherapy strongly downregulated the expression of APOC1 in patients with ACC. Both pilaralisib and elesclomol strongly inhibited SW13 cell growth.ConclusionsThis study preliminarily clarified that APOC1 and APOE might be potential therapeutic and prognostic targets for ACC, and identified new targets and treatment strategies for ACC.

Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences

Computational prediction of nucleic acid-binding residues in protein sequences is an active field of research, with over 80 methods that were released in the past 2 decades. We identify and discuss 87 sequence-based predictors that include dozens of recently published methods that are surveyed for the first time. We overview historical progress and examine multiple practical issues that include availability and impact of predictors, key features of their predictive models, and important aspects related to their training and assessment. We observe that the past decade has brought increased use of deep neural networks and protein language models, which contributed to substantial gains in the predictive performance. We also highlight advancements in vital and challenging issues that include cross-predictions between deoxyribonucleic acid (DNA)-binding and ribonucleic acid (RNA)-binding residues and targeting the two distinct sources of binding annotations, structure-based versus intrinsic disorder-based. The methods trained on the structure-annotated interactions tend to perform poorly on the disorder-annotated binding and vice versa, with only a few methods that target and perform well across both annotation types. The cross-predictions are a significant problem, with some predictors of DNA-binding or RNA-binding residues indiscriminately predicting interactions with both nucleic acid types. Moreover, we show that methods with web servers are cited substantially more than tools without implementation or with no longer working implementations, motivating the development and long-term maintenance of the web servers. We close by discussing future research directions that aim to drive further progress in this area.

Re-appraising the evidence for the source, regulation and function of p53-family isoforms

The p53 family of proteins evolved from a common ancestor into three separate genes encoding proteins that act as transcription factors with distinct cellular roles. Isoforms of each member that lack specific regions or domains are suggested to result from alternative transcription start sites, alternative splicing or alternative translation initiation, and have the potential to exponentially increase the functional repertoire of each gene. However, evidence supporting the presence of individual protein variants at functional levels is often limited and is inferred by mRNA detection using highly sensitive amplification techniques. We provide a critical appraisal of the current evidence for the origins, expression, functions and regulation of p53-family isoforms. We conclude that despite the wealth of publications, several putative isoforms remain poorly established. Future research with improved technical approaches and the generation of isoform-specific protein detection reagents is required to establish the physiological relevance of p53-family isoforms in health and disease. In addition, our analyses suggest that p53-family variants evolved partly through convergent rather than divergent evolution from the ancestral gene.

m6A modification of CDC5L promotes lung adenocarcinoma progression through transcriptionally regulating WNT7B expression

Cell division cycle 5-like (CDC5L) protein is implicated in the development of various cancers. However, its role in the progression of lung adenocarcinoma (LUAD) remains uncertain. Our findings revealed frequent upregulation of CDC5L in LUAD, which correlated with poorer overall survival rates and advanced clinical stages. In vitro experiments demonstrated that CDC5L overexpression stimulated the proliferation, migration, and invasion of LUAD cells, whereas CDC5L knockdown exerted suppressive effects on these cellular processes. Furthermore, silencing CDC5L significantly inhibited tumor growth and metastasis in a xenograft mouse model. Mechanistically, CDC5L activates the Wnt/β-catenin signaling pathway by transcriptionally regulating WNT7B, thereby promoting LUAD progression. Besides, METTL14-mediated m6A modification contributed to CDC5L upregulation in an IGF2BP2-dependent manner. Collectively, our study uncovers a novel molecular mechanism by which the m6A-induced CDC5L functions as an oncogene in LUAD by activating the Wnt/β-catenin pathway through transcriptional regulation of WNT7B, suggesting that CDC5L may serve as a promising prognostic marker and therapeutic target for LUAD.

Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation

DNA motifs are crucial patterns in gene regulation. DNA-binding proteins (DBPs), including transcription factors, can bind to specific DNA motifs to regulate gene expression and other cellular activities. Past studies suggest that DNA shape features could be subtly involved in DNA-DBP interactions. Therefore, the shape motif annotations based on intrinsic DNA topology can deepen the understanding of DNA-DBP binding. Nevertheless, high-throughput tools for DNA shape motif discovery that incorporate multiple features altogether remain insufficient. To address it, we propose a series of methods to discover non-redundant DNA shape motifs with the generalization to multiple motifs in multiple shape features. Specifically, an existing Gibbs sampling method is generalized to multiple DNA motif discovery with multiple shape features. Meanwhile, an expectation-maximization (EM) method and a hybrid method coupling EM with Gibbs sampling are proposed and developed with promising performance, convergence capability, and efficiency. The discovered DNA shape motif instances reveal insights into low-signal ChIP-seq peak summits, complementing the existing sequence motif discovery works. Additionally, our modelling captures the potential interplays across multiple DNA shape features. We provide a valuable platform of tools for DNA shape motif discovery. An R package is built for open accessibility and long-lasting impact: https://zenodo.org/doi/10.5281/zenodo.10558980.