24:T467,Dysregulated transcription factors (TFs) that rewire gene expression circuitry are frequently identified as key players in disease. Although several TFs have been drugged with small molecules, the majority of oncogenic TFs are not currently pharmaceutically tractable due to their paucity of ligandable pockets. The first generation of transcription factor targeting chimeras (TRAFTACs) was developed to target TFs for proteasomal degradation by exploiting their DNA binding ability. In the current study, we have developed the second generation TRAFTACs ("oligoTRAFTACs") composed of a TF-binding oligonucleotide and an E3 ligase-recruiting ligand. Herein, we demonstrate the development of oligoTRAFTACs to induce the degradation of two oncogenic TFs, c-Myc and brachyury. In addition, we show that brachyury can be successfully degraded by oligoTRAFTACs in chordoma cell lines. Furthermore, zebrafish experiments demonstrate in vivo oligoTRAFTAC activity. Overall, our data demonstrate oligoTRAFTACs as a generalizable platform towards difficult-to-drug TFs and their degradability via the proteasomal pathway.25:T47d,Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.26:T493,Proteolysis-targeting chimeras or PROTACs are hetero-bifunctional molecules designed to mediate the disposal of a target protein via recruitment of the ubiquitination-proteasome degradation machinery. Because of the chimeric nature of such molecules, their synthesis requires a key step of "assembling" whether in the lab or in situ. Furthermore, targeted PROTACs often are hetero-trifunctional and require a second "assembling" step. Click chemistry has the unique advantages of tethering two or more molecular entities of choice under near physiological conditions and therefore has been applied to the development of PROTACs in various ways. This review provides a succinct summary of this field with a critical analysis of various factors that need to be considered for optimal results. Specifically, we examine issues including applications of click chemistry in in situ assembly for improved delivery, conjugation with a targeting group for selectivity, rapid synthesis for linker optimization, and lysosomal degradation of extracellular and membrane-associated proteins. We also examine reaction kinetics issues whenever possible or warranted.27:T5b1,

#	Concept	H-Index	Publications	Citations
1	Medicine	11	12	290
2	Natural Sciences	9	9	284
3	Biochemistry	7	7	253
4	Engineering	3	3	138
5	Biomedical Engineering	1	1	19

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