Biology subjects

Heo, L.

Publications and source records attributed to Heo, L..

3 recordsLinked to original sources

bioRxiv2020

Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement

Protein structures are crucial for understanding their biological activities. Since the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need to understand the biological behavior of the virus and provide a basis for developing effective therapies. Since the proteome of the virus was determined, some of the protein structures could be determined experimentally, and others were predicted via template-based modeling approaches. However, tertiary structures for several proteins are still not available from experiment nor they could be accurately predicted by template-based modeling because of lack of close homolog structures. Previous efforts to predict structures for these proteins include efforts by DeepMind and the Zhang group via machine learning-based structure prediction methods, i.e. AlphaFold and C-I-TASSER. However, the predicted models vary greatly and have not yet been subjected to refinement. Here, we are reporting new predictions from our in-house structure prediction pipeline. The pipeline takes advantage of inter-residue contact predictions from trRosetta, a machine learning-based method. The predicted models were further improved by applying molecular dynamics simulation-based refinement. We also took the AlphaFold models and refined them by applying the same refinement method. Models based on our structure prediction pipeline and the refined AlphaFold models were analyzed and compared with the C-I-TASSER models. All of our models are available at https://github.com/feiglab/sars-cov-2-proteins.

biophysics↗

bioRxiv2019

Direct therapeutic targeting of SWI/SNF induces epigenetic reprogramming and durable tumor regression in rhabdoid tumor

PurposeRhabdoid tumor is a pediatric cancer characterized by the biallelic inactivation of SMARCB1, a subunit of the SWI/SNF chromatin remodeling complex. SMARCB1 inactivation leads to SWI/SNF redistribution to favor a proliferative dedifferentiated cellular state. Although this deletion is the known oncogenic driver, SWI/SNF therapeutic targeting remains a challenge. Experimental DesignWe define a novel epigenetic mechanism for mithramycin using biochemical fractionation, chromatin immunoprecipitation sequencing (ChIP-seq), and a dual spike-in assay for transposase accessible chromatin sequencing (ATAC-seq). We correlate epigenetic reprogramming with changes with chromatin A/B compartments and promoter accessibility with chromHMM models and RNA-seq. Finally, we demonstrate durable, marked tumor response in an intramuscular rhabdoid tumor xenograft model. ResultsHere we show mithramycin and a second-generation analogue EC8042 evict mutated SWI/SNF from chromatin and are effective in rhabdoid tumor. SWI/SNF blockade triggers chromatin compartment remodeling and promoter reprogramming leading to differentiation and amplification of H3K27me3, the catalytic mark of PRC2. Treatment of rhabdoid rumor xenografts with EC8042 leads to marked, durable tumor regression and differentiation of the tumor tissue into benign mesenchymal tissue, including de novo bone formation. ConclusionOverall, this study identifies a novel therapeutic candidate for rhabdoid tumor and an approach that may be applicable to the 20% of cancers characterized by mutated SWI/SNF. STATEMENT OF TRANSLATIONAL RELEVANCEThere is a tremendous need for novel therapeutic approaches for rhabdoid tumor and the more than 20% of human cancers characterized by dysregulation of the SWI/SNF chromatin remodeling complex. While approaches to target associated complexes, such as PRC2, known to be influenced by dysregulated SWI/SNF are currently being evaluated in the clinic, the direct therapeutic targeting of SWI/SNF has not been explored. Here we identify an inhibitor of SWI/SNF and thoroughly explore the therapeutic development of this compound from a mechanistic and translational perspective thus providing insight into the targeting of this complex as well as a dose, schedule, and biomarker of target inhibition that is immediately clinically translatable.

cancer biology↗

bioRxiv2019

High-Accuracy Protein Structures By Combining Machine-Learning With Physics-Based Refinement

Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from co-evoluttionary analysis via machine learning have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences were templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structure, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

biophysics↗