Biology subjects

Palepu, K.

Publications and source records attributed to Palepu, K..

3 recordsLinked to original sources

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↗

bioRxiv2022

Efficient Human Germ Cell Specification from Stem Cells via Combinatorial Expression of Transcription Factors

The generation of germline cell types from human induced pluripotent stem cells (hiPSCs) represents a key milestone toward in vitro gametogenesis, which has the potential to transform reproductive modeling and medicine. Methods to recapitulate advanced germline cell specification in vitro have relied on extensive, long term culture methods, the most notable of which is a four-month culture protocol employing xenogeneic reconstituted ovaries with mouse embryonic ovarian somatic cells. Recently, transcription factor (TF)-based methods have demonstrated the feasibility of exogenous factor expression to directly differentiate hiPSCs into cell types of interest, including various ovarian cell types. The protocols leveraged in these studies, however, utilize more local methods of factor selection, such as basic differential gene expression analysis, and lower-throughput screening strategies via iterative testing of a small set of TFs. In this work, we integrate our recently-described graph theory pipeline and highly-parallelized screening protocols to globally identify and screen 46 oogenesis-regulating TFs for their role in human germline formation. We identify ZNF281, LHX8, SOHLH1, ZGLP1, and ANHX whose combinatorial overexpression drives DDX4+ induced oogonia-like cell (iOLC) formation from hiPSCs. In contrast to previous methods, our protocol employs a simple four-day, feeder-free monolayer culture condition. We additionally demonstrate a method of post-isolation, feeder-free expansion of DDX4+ iOLCs that shows retained cell identity in vitro. We additionally identify DLX5, HHEX, and FIGLA whose individual overexpression enhances hPGCLC formation from hiPSCs. We characterize these TF-based iOLCs and hPGCLCs via gene and protein expression analyses and demonstrate their broad similarity to in vivo and in vitro-derived oogonia and primordial germ cells. Together, these results identify new regulatory factors that enhance in vitro human germ cell specification and further establish unique computational and experimental tools for human in vitro oogenesis research.

developmental biology↗

bioRxiv2022

Design of Peptide-Based Protein Degraders via Contrastive Deep Learning

AO_SCPLOWBSTRACTC_SCPLOWTherapeutic modalities targeting pathogenic proteins are the gold standard of treatment for multiple disease indications. Unfortunately, a significant portion of these proteins are considered "undruggable" by standard small molecule-based approaches, largely due to their disordered nature and instability. Designing functional peptides to undruggable targets, either as standalone binders or fusions to effector domains, thus presents a unique opportunity for therapeutic intervention. In this work, we adapt recent models for contrastive language-image pre-training (CLIP) to devise a unified, sequence-based framework to design target-specific peptides. Furthermore, by leveraging known experimental binding proteins as scaffolds, we create a streamlined inference pipeline, termed Cut&CLIP, that efficiently selects peptides for downstream screening. Finally, we experimentally fuse candidate peptides to E3 ubiquitin ligase domains and demonstrate robust intracellular degradation of pathogenic protein targets in human cells, motivating further development of our technology for future clinical translation.

synthetic biology↗