FusOn-pLM: a fusion oncoprotein-specific language model via adjusted rate masking.

Fusion oncoproteins, a class of chimeric proteins arising from chromosomal translocations, are major drivers of various pediatric cancers. These proteins are intrinsically disordered and lack druggable pockets, making them highly challenging therapeutic targets for both small molecule-based and structure-based approaches. Protein language models (pLMs) have recently emerged as powerful tools for capturing physicochemical and functional protein features but have yet to be trained on fusion oncoprotein sequences.

Rapid human oogonia-like cell specification via transcription factor-directed differentiation.

The generation of germline cells from human induced pluripotent stem cells (hiPSCs) represents a milestone toward in vitro gametogenesis. Methods to recapitulate germline development beyond primordial germ cells in vitro have relied on long-term cell culture, such as 3-dimensional organoid co-culture for ~four months. Using a pipeline with highly parallelized screening, this study identifies combinations of TFs that directly and rapidly convert hiPSCs to induced oogonia-like cells (iOLCs).

De novo design of peptide binders to conformationally diverse targets with contrastive language modeling.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery. In this work, we provide an 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 sequences for target-selective interaction activity via a contrastive language-image pretraining (CLIP)-based contrastive learning architecture.

Programmable protein degraders enable selective knockdown of pathogenic β-catenin subpopulations and .

Aberrant activation of Wnt signaling results in unregulated accumulation of cytosolic β-catenin, which subsequently enters the nucleus and promotes transcription of genes that contribute to cellular proliferation and malignancy. Here, we sought to eliminate pathogenic β-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 β-catenin, which were subsequently fused to the catalytic domain of a human E3 called C-terminus of Hsp70-interacting protein (CHIP).

Programmable Protein Stabilization with Language Model-Derived Peptide Guides.

Dysregulated protein degradation via the ubiquitin-proteasomal pathway can induce numerous disease phenotypes, including cancer, neurodegeneration, and diabetes. Stabilizing improperly ubiquitinated proteins via target-specific deubiquitination is thus a critical therapeutic goal. Building off the major advances in targeted protein degradation (TPD) using bifunctional small-molecule degraders, targeted protein stabilization (TPS) modalities have been described recently.

Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling.

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.

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.

The Known and Unknown: Investigating the Carcinogenic Potential of Plastic Additives.

Microplastics are routinely ingested and inhaled by humans and other organisms. Despite the frequency of plastic exposure, little is known about its health consequences. Of particular concern are plastic additives─chemical compounds that are intentionally or unintentionally added to plastics to improve functionality or as residual components of plastic production.

PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling.

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation.

Corrigendum: Psymberin, a marine-derived natural product, induces cancer cell growth arrest and protein translation inhibition.

[This corrects the article DOI: 10.3389/fmed.2022.

Psymberin, a marine-derived natural product, induces cancer cell growth arrest and protein translation inhibition.

Colorectal cancer (CRC) is the third most prevalent form of cancer in the United States and results in over 50,000 deaths per year. Treatments for metastatic CRC are limited, and therefore there is an unmet clinical need for more effective therapies. In our prior work, we coupled high-throughput chemical screens with patient-derived models of cancer to identify new potential therapeutic targets for CRC.