Biology subjects

Tan, C. S. H.

Publications and source records attributed to Tan, C. S. H..

4 recordsLinked to original sources

bioRxiv2023

Cellular protein painting for structural and binding sites analysis via lysine reactivity profiling with o-phthalaldehyde

The three-dimensional structure and the molecular interaction of proteins determine their roles in many cellular processes. Chemical protein painting with protein mass spectrometry can identify changes in structural conformations and molecular interactions of proteins including their binding sites. Nevertheless, most current protein painting techniques identified protein targets and binding sites of drugs in vitro using cell lysate or purified protein. Here, we screened 11 membrane-permeable lysine-reactive chemical probes for intracellular covalent labeling of endogenous proteins, which reveals ortho-phthalaldehyde (OPA) as the most reactive probe in intracellular environment. An MS workflow was developed and coupled with a new data analysis strategy termed RAPID (Reactive Amino acid Profiling by Inverse Detection) to enhance detection sensitivity. RAPID-OPA successfully identified structural change induced by allosteric drug TEPP-46 on its target protein PKM2, and was applied to profile conformation change of the proteome occurring in cells during thermal denaturation. Application of RAPID-OPA on cells treated with geldanamycin, selumetinib, and staurosporine successfully revealed their binding sites on target proteins. Thus, RAPID-OPA for cellular protein painting permits the identification of ligand-binding sites and detection of protein structural changes occurring in cells. Significance StatementProtein painting can be used to identify changes in the three-dimensional structure and molecular interaction of proteins that govern many cellular processes but are mostly applied to cell lysate or purified protein. We identified lysine reactive probes for the intracellular labeling of endogenous proteins, and developed an MS procedure with new data analysis strategy termed RAPID-OPA to characterize the intracellular conformation change of the proteome during thermal denaturation, and identified structural change mediated by allosteric regulator TEPP-46 on target protein PKM2. Furthermore, the approach could identify ligand binding sites exemplified by labeling of target proteins in cells treated with geldanamycin, selumetinib and staurosporine. Overall, RAPID-OPA for cellular protein painting enables the detection of protein structural changes happening in cells as well as the identification of ligand-binding sites.

cell biology↗

bioRxiv2023

Fully automated and integrated proteomics sample preparation platform for high-throughput drug target identification

With the increased demand of large-cohort proteomic analysis, fast and reproducible sample preparation has become the critical issue that needs to be solved. Herein, we developed a fully automated and integrated proteomics sample preparation workflow (autoSISPROT), enabling the simultaneous processing of 96 samples in less than 2.5 hours. Benefiting from its 96-channel all-in-tip operation, protein digestion, peptide desalting, and TMT labeling could be achieved in a fully automated manner. The autoSISPROT demonstrated good sample preparation performances, including >94% of digestion efficiency, nearly 100% of alkylation efficiency, >98% of TMT labeling efficiency, and >0.9 of intra- and inter-batch Pearson correlation coefficients. Furthermore, by combining with cellular thermal shift assay-coupled to mass spectrometry (CETSA-MS), the autoSISPROT was able to process and TMT-label 40 samples automatically and accurately identify the known target of methotrexate. Importantly, taking advantage of the data independent acquisition and isothermal CETSA-MS, the autoSISPROT was well applied for identifying known targets and potential off-targets of 20 kinase inhibitors by automatedly processing 87 samples, affording over a 10-fold improvement in throughput when compared to classical CETSA-MS. Collectively, we developed a fully automated and integrated workflow for high-throughput proteomics sample preparation and drug target identification.

systems biology↗

bioRxiv2023

Improved in situ Characterization of Proteome-wide Protein Complex Dynamics with Thermal Proximity Co-Aggregation

Vast majority of cellular activities are carried out by protein complexes that assembled dynamically in response to cellular needs and environmental cues. Large scale efforts had uncovered a large repertoire of functionally uncharacterized protein complexes which necessitate new strategies to delineate their roles in various cellular activities and diseases. Thermal proximity co-aggregation profiling could be readily deployed to simultaneously characterize the dynamics for hundreds to thousands of protein complexes in situ across different cellular conditions. Toward this goal, we had optimized the original method both experimentally and computationally. In this new iteration termed Slim-TPCA, fewer temperatures are used which increase throughputs by over 3X, while coupled with new scoring metrics and statistical evaluation resulted in minimal compromise in coverage and the detection of more relevant protein complexes. Overall, less samples are needed, false positives from batch effects are minimized and statistical evaluation time is reduced by two orders of magnitude. We applied Slim-TPCA to profile state of protein complexes in K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated based on TPCA signature in accordance with expected downregulation of most cellular activities. These complexes include 55S ribosome and various respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. On other hand, protein complexes involved in protein transport and degradation are found increasingly associated revealing their involvement in metabolic reprogramming during glucose deprivation. In summary. Slim-TPCA is an efficient strategy for proteome-wide characterization of protein complexes. The various algorithmic improvement of Slim-TPCA is available as Python package at https://pypi.org/project/Slim-TPCA/

bioinformatics↗

bioRxiv2023

A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks

Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein-protein interaction (PPI) data enables the identification of protein complexes computationally. Although various computational approaches have been proposed to detect protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply cocomplex relationships, taking into account the signs of PPIs can benefit the detection of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing protein complex identification algorithms are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering model (PS-SNC) for detecting protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that PS-SNC outperforms other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.

bioinformatics↗