My primary research interest are in the development and application of computational approaches to drug discovery, drug design and target prediction. I have pioneered new computational approaches to determine drug actions based on chemical networks (https://services.mbi.ucla.edu/CSNAP/) and applied this method to discover new drugs inhibiting cell divisions and cancers. My current research at the Altman's lab focuses on developing novel computational methods for predicting drug actions, interactions, side-effects and drug repurposing. By correlating low-level structural data with high-level functional biology and clinical outcomes, I will apply system-based approach to engineer safe and effective medicine for disease treatments.
Honors & Awards
Stanford School of Medicine Dean's Postdoctoral Fellowship, Stanford University (2017-2018)
Genentech Early Research and Development (gERD) Postdoctoral Fellowship, Genentech Inc. (2016)
CINF Scholarship for Scientific Excellence Award, American Chemical Society (2016)
Final Paper, Annual Review of Translational Bioinformatics (2016)
Featured Paper, PLOS Computational Biology (2015)
HHMI Awards Nominee, UCLA (2013)
IDRE Scholarship Award, UCLA (2011)
Honor in Bioengineering, UC Berkeley (2006)
Member of Bioengineering Honor Society, UC Berkeley (2004-2006)
Boards, Advisory Committees, Professional Organizations
Judge, ACS ENVR Certificate of Merit (2017 - Present)
Committee Member, ISMB/ECCB (2017 - Present)
Member, American Chemical Society (2010 - Present)
Member, American Association of Clinical Chemistry (2008 - Present)
Member, Biomedical Engineering Society (2004 - Present)
Doctor of Philosophy, University of California Los Angeles (2016)
Master of Science, University of California Los Angeles, Biomedical Engineering (2010)
Bachelor of Science, University of California Berkeley, Biomedical Engineering (2006)
Community and International Work
Stanford MeDRA Medical Terminology Coordinator
Opportunities for Student Involvement
Field Mentor-Molecular and Cellular Bioengineering, UCLA
Opportunities for Student Involvement
Lo Y.C., Senese S., Damoiseaux R., Torres J.Z.. "United States Patent 62/022.976 Microtubins: a novel class of anticancer agents", UCLA
Lo Y.C., McNamara D., Senese S., Yeates T.O., Damouseux R., Torres Z. J.. "United StatesMi-181: A novel microtubule targeting agent", UCLA
Machine learning in chemoinformatics and drug discovery.
Drug discovery today
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
View details for DOI 10.1016/j.drudis.2018.05.010
View details for PubMedID 29750902
Computational Cell Cycle Profiling of Cancer Cells for Prioritizing FDA-Approved Drugs with Repurposing Potential.
2017; 7 (1): 11261
Discovery of first-in-class medicines for treating cancer is limited by concerns with their toxicity and safety profiles, while repurposing known drugs for new anticancer indications has become a viable alternative. Here, we have developed a new approach that utilizes cell cycle arresting patterns as unique molecular signatures for prioritizing FDA-approved drugs with repurposing potential. As proof-of-principle, we conducted large-scale cell cycle profiling of 884 FDA-approved drugs. Using cell cycle indexes that measure changes in cell cycle profile patterns upon chemical perturbation, we identified 36 compounds that inhibited cancer cell viability including 6 compounds that were previously undescribed. Further cell cycle fingerprint analysis and 3D chemical structural similarity clustering identified unexpected FDA-approved drugs that induced DNA damage, including clinically relevant microtubule destabilizers, which was confirmed experimentally via cell-based assays. Our study shows that computational cell cycle profiling can be used as an approach for prioritizing FDA-approved drugs with repurposing potential, which could aid the development of cancer therapeutics.
View details for DOI 10.1038/s41598-017-11508-2
View details for PubMedID 28900159
Microtubins: a novel class of small synthetic microtubule targeting drugs that inhibit cancer cell proliferation.
2017; 8 (61): 104007–21
Microtubule targeting drugs like taxanes, vinca alkaloids, and epothilones are widely-used and effective chemotherapeutic agents that target the dynamic instability of microtubules and inhibit spindle functioning. However, these drugs have limitations associated with their production, solubility, efficacy and unwanted toxicities, thus driving the need to identify novel antimitotic drugs that can be used as anticancer agents. We have discovered and characterized the Microtubins (Microtubule inhibitors), a novel class of small synthetic compounds, which target tubulin to inhibit microtubule polymerization, arrest cancer cells predominantly in mitosis, activate the spindle assembly checkpoint and trigger an apoptotic cell death. Importantly, the Microtubins do not compete for the known vinca or colchicine binding sites. Additionally, through chemical synthesis and structure-activity relationship studies, we have determined that specific modifications to the Microtubin phenyl ring can activate or inhibit its bioactivity. Combined, these data define the Microtubins as a novel class of compounds that inhibit cancer cell proliferation by perturbing microtubule polymerization and they could be used to develop novel cancer therapeutics.
View details for DOI 10.18632/oncotarget.21945
View details for PubMedID 29262617
View details for PubMedCentralID PMC5732783
The X-Linked-Intellectual-Disability-Associated Ubiquitin Ligase Mid2 Interacts with Astrin and Regulates Astrin Levels to Promote Cell Division
2016; 14 (2): 180-188
Mid1 and Mid2 are ubiquitin ligases that regulate microtubule dynamics and whose mutation is associated with X-linked developmental disorders. We show that astrin, a microtubule-organizing protein, co-purifies with Mid1 and Mid2, has an overlapping localization with Mid1 and Mid2 at intercellular bridge microtubules, is ubiquitinated by Mid2 on lysine 409, and is degraded during cytokinesis. Mid2 depletion led to astrin stabilization during cytokinesis, cytokinetic defects, multinucleated cells, and cell death. Similarly, expression of a K409A mutant astrin in astrin-depleted cells led to the accumulation of K409A on intercellular bridge microtubules and an increase in cytokinetic defects, multinucleated cells, and cell death. These results indicate that Mid2 regulates cell division through the ubiquitination of astrin on K409, which is critical for its degradation and proper cytokinesis. These results could help explain how mutation of MID2 leads to misregulation of microtubule organization and the downstream disease pathology associated with X-linked intellectual disabilities.
View details for DOI 10.1016/j.celrep.2015.12.035
View details for Web of Science ID 000368101600002
View details for PubMedID 26748699
View details for PubMedCentralID PMC4724641
- Chemical Similarity Networks for Drug Discovery Special Topics in Drug Discovery [ISBN 978-953-51-2800-7] edited by Chen, T. InTech . 2016; 1
- Quantitative Methods in System-Based Drug Discovery Complex Systems, Sustainability and Innovation [ISBN: 978-953-51-2842-7] edited by Thomas, C. InTech. 2016; 1
- Computer-Aided Biosensor Design Computer-aided Technologies - Applications in Engineering and Medicine [ISBN: 978-953-51-2788-8 ] edited by Udroiu, R. Intech. 2016; 1
3D Chemical Similarity Networks for Structure-based Target Prediction and Scaffold Hopping.
ACS chemical biology
Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using in-vitro microtubule polymerization assays and cell-based assays.
View details for DOI 10.1021/acschembio.6b00253
View details for PubMedID 27285961
Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens
PLOS COMPUTATIONAL BIOLOGY
2015; 11 (3)
Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).
View details for DOI 10.1371/journal.pcbi.1004153
View details for Web of Science ID 000352195700042
View details for PubMedID 25826798
Chemical dissection of the cell cycle: probes for cell biology and anti-cancer drug development
CELL DEATH & DISEASE
Cancer cell proliferation relies on the ability of cancer cells to grow, transition through the cell cycle, and divide. To identify novel chemical probes for dissecting the mechanisms governing cell cycle progression and cell division, and for developing new anti-cancer therapeutics, we developed and performed a novel cancer cell-based high-throughput chemical screen for cell cycle modulators. This approach identified novel G1, S, G2, and M-phase specific inhibitors with drug-like properties and diverse chemotypes likely targeting a broad array of processes. We further characterized the M-phase inhibitors and highlight the most potent M-phase inhibitor MI-181, which targets tubulin, inhibits tubulin polymerization, activates the spindle assembly checkpoint, arrests cells in mitosis, and triggers a fast apoptotic cell death. Importantly, MI-181 has broad anti-cancer activity, especially against BRAF(V600E) melanomas.
View details for DOI 10.1038/cddis.2014.420
View details for Web of Science ID 000344994000034
View details for PubMedID 25321469
The STARD9/Kif16a Kinesin Associates with Mitotic Microtubules and Regulates Spindle Pole Assembly
2011; 147 (6): 1309-1323
During cell division, cells form the microtubule-based mitotic spindle, a highly specialized and dynamic structure that mediates proper chromosome transmission to daughter cells. Cancer cells can show perturbed mitotic spindles and an approach in cancer treatment has been to trigger cell killing by targeting microtubule dynamics or spindle assembly. To identify and characterize proteins necessary for spindle assembly, and potential antimitotic targets, we performed a proteomic and genetic analysis of 592 mitotic microtubule copurifying proteins (MMCPs). Screening for regulators that affect both mitosis and apoptosis, we report the identification and characterization of STARD9, a kinesin-3 family member, which localizes to centrosomes and stabilizes the pericentriolar material (PCM). STARD9-depleted cells have fragmented PCM, form multipolar spindles, activate the spindle assembly checkpoint (SAC), arrest in mitosis, and undergo apoptosis. Interestingly, STARD9-depletion synergizes with the chemotherapeutic agent taxol to increase mitotic death, demonstrating that STARD9 is a mitotic kinesin and a potential antimitotic target.
View details for DOI 10.1016/j.cell.2011.11.020
View details for Web of Science ID 000298148100020
View details for PubMedID 22153075
View details for PubMedCentralID PMC4180425
Fatostatin inhibits cancer cell proliferation by affecting mitotic microtubule spindle assembly and cell division.
The Journal of biological chemistry
The sterol regulatory element binding protein (SREBP) transcription factors have become attractive targets for pharmacological inhibition in the treatment of metabolic diseases and cancer. SREBPs are critical for the production and metabolism of lipids and cholesterol, which are essential for cellular homeostasis and cell proliferation. Fatostatin was recently discovered as a specific inhibitor of SCAP (SREBP cleavage-activating protein), which is required for SREBP activation. Fatostatin possesses antitumor properties including the inhibition of cancer cell proliferation, invasion and migration, and it arrests cancer cells in G2/M phase. Although Fatostatin has been viewed as an antitumor agent due to its inhibition of SREBP and its effect on lipid metabolism, we show that Fatostatin's anticancer properties can also be attributed to its inhibition of cell division. We analyzed the effect of SREBP activity inhibitors including Fatostatin, PF-429242 and Betulin on the cell cycle and determined that only Fatostatin possessed antimitotic properties. Fatostatin inhibited Tubulin polymerization, arrested cells in mitosis, activated the spindle assembly checkpoint and triggered mitotic catastrophe and reduced cell viability. Thus Fatostatin's ability to inhibit SREBP activity and cell division could prove beneficial in treating aggressive types of cancers like glioblastomas that have elevated lipid metabolism, fast proliferation rates and often develop resistance to current anticancer therapies.
View details for DOI 10.1074/jbc.C116.737346
View details for PubMedID 27378817
Tctex1d2 associates with short-rib polydactyly syndrome proteins and is required for ciliogenesis
2015; 14 (7): 1116-1125
Short-rib polydactyly syndromes (SRPS) arise from mutations in genes involved in retrograde intraflagellar transport (IFT) and basal body homeostasis, which are critical for cilia assembly and function. Recently, mutations in WDR34 or WDR60 (candidate dynein intermediate chains) were identified in SRPS. We have identified and characterized Tctex1d2, which associates with Wdr34, Wdr60 and other dynein complex 1 and 2 subunits. Tctex1d2 and Wdr60 localize to the base of the cilium and their depletion causes defects in ciliogenesis. We propose that Tctex1d2 is a novel dynein light chain important for trafficking to the cilium and potentially retrograde IFT and is a new molecular link to understanding SRPS pathology.
View details for DOI 10.4161/15384101.2014.985066
View details for Web of Science ID 000352606600028
View details for PubMedID 25830415
View details for PubMedCentralID PMC4614626
A unique insertion in STARD9's motor domain regulates its stability
MOLECULAR BIOLOGY OF THE CELL
2015; 26 (3): 440-452
STARD9 is a largely uncharacterized mitotic kinesin and putative cancer target that is critical for regulating pericentriolar material cohesion during bipolar spindle assembly. To begin to understand the mechanisms regulating STARD9 function and their importance to cell division, we took a multidisciplinary approach to define the cis and trans factors that regulate the stability of the STARD9 motor domain. We show that, unlike the other ∼50 mammalian kinesins, STARD9 contains an insertion in loop 12 of its motor domain (MD). Working with the STARD9-MD, we show that it is phosphorylated in mitosis by mitotic kinases that include Plk1. These phosphorylation events are important for targeting a pool of STARD9-MD for ubiquitination by the SCFβ-TrCP ubiquitin ligase and proteasome-dependent degradation. Of interest, overexpression of nonphosphorylatable/nondegradable STARD9-MD mutants leads to spindle assembly defects. Our results with STARD9-MD imply that in vivo the protein levels of full-length STARD9 could be regulated by Plk1 and SCFβ-TrCP to promote proper mitotic spindle assembly.
View details for DOI 10.1091/mbc.E14-03-0829
View details for Web of Science ID 000348857300006
View details for PubMedID 25501367