Labs

Laboratory Collaborations

Research collaborations across computational peptide drug design and developmental neuroscience at UC Santa Cruz and UC Davis.

Lokey Lab

UC Santa Cruz

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Lab Focus

The Lokey Lab focuses on cyclic peptide natural products and their application to challenging drug targets. Their research addresses three main areas: (1) understanding how naturally occurring cyclic peptides achieve unexpected pharmacokinetic properties despite being outside conventional drug-likeness parameters, (2) developing DNA-encoded cyclic peptide libraries for targeting protein-protein interactions related to uncontrolled cell proliferation, and (3) designing and optimizing targeted protein degraders (molecular glues and PROTACs) with improved ADME profiles. The lab's work bridges structural biology, medicinal chemistry, and high-throughput screening to enable modulation of difficult targets like protein-protein interactions and allosteric binding sites.

My Work

Developed transformer models for molecular property prediction and cheminformatics applications. Created optimization pipelines for peptide library design and combinatorial chemistry. Built analysis pipelines for high-throughput, single-molecule fidelity DNA-encoded library (DEL) assessment. Designed innovative tools for analyzing massive combinatorial library datasets. Initially joined through the ACCESS Summer REU program developing deep learning models for predicting cell membrane permeability of cyclic peptides.

Kim Lab

UC Santa Cruz

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Lab Focus

The Kim Lab investigates the organizational logic of long-distance cortical circuits and their developmental mechanisms using mouse visual cortex as a model system. Their research addresses two fundamental questions: In the mature brain, what are the connectivity and functional bases of neural circuits at the cellular level? In the developing brain, what mechanisms match gene expression to neuronal connectivity? The lab employs cutting-edge techniques including novel trans-synaptic viral tracers, mouse genetics, single-cell genome-wide sequencing, and in vivo imaging to understand how specific long-distance neural circuits develop and organize at single-cell resolution. This work provides insights into neurodevelopmental disorders such as autism and schizophrenia.

My Work

Building a from-scratch intrinsic signal imaging macroscope system including optics setup, custom compute workstation, and full stack software development. Quantification and statistical analysis of viral axon tracing, automated cell counting, and Allen Brain Atlas data integration. Clustering and information-theoretic analysis of datasets to understand cortico-cortical connectivity patterns in the developing visual cortex.

Yarov-Yarovoy Lab

UC Davis

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Lab Focus

The Yarov-Yarovoy Lab specializes in ion channel structure and the design of subtype-specific modulators. Their research encompasses structure-function studies of voltage-gated ion channels, computational design of channel modulators, development of computational methods for membrane protein structure prediction, and evolutionary analysis of human voltage-gated ion channels. A primary focus is designing novel subtype-selective neuronal sodium channel blockers for treating pain and epilepsy—major unmet medical needs affecting millions worldwide. Using innovative computational approaches combined with electrophysiology, biochemistry, and molecular biology, the lab designs and validates therapeutically useful drugs with high efficacy and minimal side effects, providing fundamental insights into neuromodulation and signal transduction.

My Work

Co-authored computational protein design research with David Baker, 2024 Nobel Prize laureate in Chemistry, focusing on NaV1.7 sodium channel binders. Authored the most-read paper in Channels journal for 2024 on ion channel structural modeling using AlphaFold2, RoseTTAFold2, and ESMFold. Engineered novel protein binders for ion channels using state-of-the-art ML techniques including ProteinMPNN and RFDiffusion. Built comprehensive workflows from target input to optimized, validated protein designs.