Biology subjects

Biggar, K. K.

Publications and source records attributed to Biggar, K. K..

7 recordsLinked to original sources

bioRxiv2025

Leveraging learned representations and multitask learning for lysine methylation site discovery

Lysine methylation is a dynamic and reversible post-translational modification of proteins carried out by lysine methyltransferase enzymes. The role of this modification in epigenetics and gene regulation is relatively well understood, but our understanding of the extent and the role of lysine methylation of non-histone substrates remains fairly limited. Several lysine methyltransferases which methylate non-histone substrates are overexpressed in a number of cancers and are believed to be key drivers of cancer progression. There is great incentive to identify the lysine methylome, as this is a key step in identifying drug targets. While numerous computational models have been developed in the last decade to identify novel lysine methylation sites, the accuracy of these model has been modest, leaving much room for improvement. In this work, we leverage the most recent advancements in deep learning and present a transformer-based model for lysine methylation site prediction which achieves state-of-the-art accuracy. In addition, we show that other post-translational modifications of lysine are informative and that multitask learning is an effective way to integrate this prior knowledge into our lysine methylation site predictor, MethylSight 2.0. Finally, we validate our model by means of mass spectrometry experiments and identify 68 novel lysine methylation sites. This work constitutes another contribution towards the completion of a comprehensive map of the lysine methylome.

bioinformatics↗

bioRxiv2024

AuroraB-kinase methylation by SETD6 regulates cytokinesis and protects cells from chromosomal instability

SETD6 is a non-histone lysine methylatransferase, previously shown to participate in several housekeeping signaling pathways such as the NFkB pathway, Wnt signaling pathway, mitosis and more. In the current study we show evidence that SETD6 methylation is involved in the regulation of cytokinesis - the final process that divides cell contents into two daughter cells. SETD6 depleted HeLa cells presented high levels of chromatin bridges and actin patches, which are commonly observed following chromosomal segregation errors. In a proteomic screen we identified Aurora-B as a novel SETD6 substrate. Aurora-B kinase is an essential regulator of cytokinesis, known to actively delay cytokinesis as a response to the presence of chromatin in the midzone. We found that SETD6 binds and methylates Aurora-B on two adjacent lysine residues. Upon replication stress, Aurora-B methylation by SETD6 increases but is abolished when the two lysine methylation targets are substituted. In addition, replication stress led to a high tendency of SETD6 depleted cells to multinucleate, a major chromosomal-instability (CIN) phenotype. We detected a significant reduction in the Aurora-B kinase activity during cytokinesis in SETD6 knockout cells upon replication stress, which could be the mechanism underlying the accumulation of CIN phenotypes in these cells. CIN is a hallmark of cancer and is associated with tumor cell malignancy. Our findings suggest that Aurora-B methylation by SETD6 carries meaningful implications on tumorigenic cellular pathways.

cell biology↗

bioRxiv2021

Computational design of peptide therapeutics: how useful are sequence-based protein-protein interaction predictors?

Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein-protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein-protein interaction predictors are more suitable for this purpose than the state-of-the-art deep learning methods publicly available at the time of writing. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein-protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.

bioinformatics↗

bioRxiv2020

Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 ("coronavirus disease 2019"), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. Given the known interaction between the human ACE2 ("Angiotensin-converting enzyme 2") protein and the SARS-CoV virus (responsible for the coronavirus outbreak circa. 2003), considerable focus has been directed towards the putative interaction between the SARS-CoV-2 Spike protein and ACE2. However, a more holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises additional putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics. To that end, we leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Of these, we identify the high-scoring subset of human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins by both methods, comprising 279 high-confidence putative interactions involving 225 human proteins. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors, corroborating existing evidence for this PPI. Furthermore, the PIPE-Sites algorithm was used to predict the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions. We hereby publicly release the comprehensive set of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: 10.5683/SP2/JZ77XA. All data and metadata are released under a CC-BY 4.0 licence. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

bioinformatics↗

bioRxiv2019

PeSA: A Software Tool for Peptide Specificity Analysis

The discovery of molecular interactions is crucial towards a better understanding of complex biological functions. Particularly protein-protein interactions (i.e., PPIs), which are responsible for a variety of cellular functions from epigenetic modifications to enzyme-substrate specificity, have been studied extensively over the past decades. Position-specific scoring matrices (PSSM) in particular are used extensively to help determine interaction specificity or candidate interaction motifs. However, not all studies successfully report their results as a candidate interaction motif. In many cases, this is the result of a lack of analysis tools for simple analysis and motif generation. Peptide Specificity Analyst (PeSA) is developed with the goal of filling this gap and providing an analysis software to aid peptide array analysis and subsequent motif generation. PeSA utilizes two models of motif creation: (1) frequency-based using a peptide list, and (2) weight-based using a quantified matrix. The ability to generate motifs effortlessly will make analyzing, interpreting and sharing peptide specificity study results in a simple and straightforward process.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC=\"FIGDIR/small/760140v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@1668389org.highwire.dtl.DTLVardef@145bbfeorg.highwire.dtl.DTLVardef@1392eaeorg.highwire.dtl.DTLVardef@127e2c3_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIBiological motifs are widely used representations for peptide specificity analysis.\nC_LIO_LIPeSA populates a list of peptides matching a set threshold from a quantified matrix.\nC_LIO_LIFrequency-based motif using a peptide list to spot residue patterns.\nC_LIO_LIUse of quantified matrices to create weight-based motifs using residue positions.\nC_LI

bioinformatics↗

bioRxiv2019

An essential RNA-binding lysine residue in the Nab3 RRM domain undergoes mono and tri-methylation

The Nrd1-Nab3-Sen1 (NNS) complex integrates molecular inputs to direct termination of noncoding transcription in budding yeast. NNS is positively regulated by methylation of histone H3 lysine-4 as well as through Nrd1 binding to the initiating form of RNA PolII. These cues collaborate with Nrd1 and Nab3 binding to target RNA sequences in nascent transcripts through their RRM RNA recognition motifs. In this study, we identify nine lysine residues distributed amongst Nrd1, Nab3, and Sen1 that are mono-, di-, or trimethylated, suggesting novel molecular inputs for NNS regulation. One of these methylated residues, Nab3 lysine-363 (K363), resides within its RRM, and is known to physically contact target RNA. Although mutation of Nab3-K363 to arginine (Nab3-K363R) causes a severe growth defect, it nevertheless produces a stable protein that is incorporated into the NNS complex, suggesting that RNA binding through Nab3-K363 is crucial for NNS function. Consistent with this hypothesis, K363R mutation decreases RNA binding of the Nab3 RRM in vitro and causes transcription termination defects in vivo. These findings reveal crucial roles for Nab3-K363 and suggest that methylation of this residue may modulate NNS activity through its impact on Nab3 RNA binding.

molecular biology↗

bioRxiv2018

MethylSight: Taking a wider view of lysine methylation through computer-aided discovery to provide insight into the human methyl-lysine proteome

Protein Lys methylation plays a critical role in numerous cellular processes, yet it has been challenging to identify Lys methylation in a systematic manner. We present here an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We have developed MethylSight, a program that predicts Kme events solely on physicochemical and biochemical properties of putative methylation sites, which can then be validated by targeted MS. Using this approach, we have identified 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts ~50,000 Kme sites in non-histone proteins with high confidence, suggesting that Lys methylation is a prevalent post-translational modification. Our work provides a useful resource for systematic exploration of the role of Lys methylation in human health and disease.

biochemistry↗