Biology subjects

Vincoff, S.

Publications and source records attributed to Vincoff, S..

4 recordsLinked to original sources

bioRxiv2024

Programmable protein degraders enable selective knockdown of pathogenic β-catenin subpopulations in vitro and in vivo

Aberrant activation of Wnt signaling results in unregulated accumulation of cytosolic {beta}-catenin, which subsequently enters the nucleus and promotes transcription of genes that contribute to cellular proliferation and malignancy. Here, we sought to eliminate pathogenic {beta}-catenin from the cytosol using designer ubiquibodies (uAbs), chimeric proteins composed of an E3 ubiquitin ligase and a target-binding domain that redirect intracellular proteins to the proteasome for degradation. To accelerate uAb development, we leveraged a protein language model (pLM)-driven algorithm called SaLT&PepPr to computationally design "guide" peptides with affinity for {beta}-catenin, which were subsequently fused to the catalytic domain of a human E3 called C-terminus of Hsp70-interacting protein (CHIP). Expression of the resulting peptide-guided uAbs in colorectal cancer cells led to the identification of several designs that significantly reduced the abnormally stable pool of free {beta}-catenin in the cytosol and nucleus while preserving the normal membrane-associated subpopulation. This selective knockdown of pathogenic {beta}-catenin suppressed Wnt/{beta}-catenin signaling and impaired tumor cell survival and proliferation. Furthermore, one of the best degraders selectively decreased cytosolic but not membrane-associated {beta}-catenin levels in livers of BALB/c mice following delivery as a lipid nanoparticle (LNP)-encapsulated mRNA. Collectively, these findings reveal the unique ability of uAbs to selectively eradicate abnormal proteins in vitro and in vivo and open the door to peptide-programmable biologic modulators of other disease-causing proteins.

bioengineering↗

bioRxiv2024

FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking

Fusion oncoproteins, a class of chimeric proteins arising from chromosomal translocations, drive and sustain various cancers, particularly those impacting children. Unfortunately, due to their intrinsically disordered nature, large size, and lack of well-defined, druggable pockets, they have been historically challenging to target therapeutically: neither small molecule-based methods nor structure-based approaches for binder design are strong options for this class of molecules. Recently, protein language models (pLMs) have demonstrated success at representing protein sequences with information-rich embeddings, enabling downstream design applications from sequence alone. However, no current pLM has been trained on fusion oncoprotein sequences and thus may not produce optimal representations for these proteins. In this work, we introduce FusOn-pLM, a novel pLM that fine-tunes the state-of-the-art ESM-2 model on fusion oncoprotein sequences. We specifically introduce a novel masked language modeling (MLM) strategy, employing a binding-site probability predictor to focus masking on key amino acid residues, thereby generating more optimal fusion oncoprotein-aware embeddings. Our model improves performance on both fusion oncoprotein-specific benchmarks and disorder prediction tasks in comparison to baseline ESM-2 representations, as well as manually-constructed biophysical embeddings, motivating downstream usage of FusOn-pLM embeddings for therapeutic design tasks targeting these fusions. We have made our model publicly available to the community at https://huggingface.co/ChatterjeeLab/FusOn-pLM.

bioinformatics↗

bioRxiv2023

Toxicogenomic analysis of the carcinogenic potential of plastic additives

Plastics are the most prevalent human-made substance in the world and are ubiquitous throughout all ecosystems. Microscopic plastic particles are routinely ingested and inhaled by humans and other organisms. Despite the frequency of plastic exposures, little is known about their health consequences. Of particular concern are plastic additives -chemical compounds that are mixed into plastics to improve functionality or are unintentionally acquired during plastic production and use. Additives are loosely bound to the plastic polymer and may be released during plastic exposures. These compounds may pose health concerns, such as cancer, but little is known about the potential impact of these chemicals on health. To better understand the health effects of plastic additives, we performed an integrated toxicogenomic analysis on 2,712 additives, focusing on cancer as a well-studied toxicological endpoint. Screening these substances across three chemical databases revealed two key observations: 1) over 150 plastic additives have known carcinogenicity and 2) the majority ([~]90%) of plastic additives lack data on carcinogenic endpoints. Analyses of additive usage patterns pinpointed specific polymers, functions, and products in which carcinogenic additives reside. Based on published chemical-gene interactions, both carcinogenic additives and additives with unknown carcinogenicity impacted similar biological pathways. The predominant pathways involved DNA damage, apoptosis, immune response, viral diseases, and cancer. This study underscores the urgent need for systematic and comprehensive carcinogenicity assessment of plastic additives and regulatory responses to mitigate the potential health risks of plastic exposure.

pharmacology and toxicology↗

bioRxiv2023

De Novo Generation and Prioritization of Target-Binding Peptide Motifs from Sequence Alone

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

bioengineering↗