Biology subjects

Chong, H.

Publications and source records attributed to Chong, H..

8 recordsLinked to original sources

bioRxiv2024

Systematic Targeting of Protein Complexes with Molecular COUPLrs

Molecular glues that engage protein complexes have transformed the study of cell biology and have had a direct impact on clinical oncology. However, the identification of new glue classes and their corresponding protein complexes has remained largely serendipitous. To overcome this challenge, we report the development of molecular COUPLrs, elaborated small molecules flanked by two cysteine-reactive warheads, as well as CONNECT, an integrated chemical proteomic platform for target deconvolution. By profiling a library of molecular COUPLrs across 13 cancer cell lines, we uncovered hundreds of proteins that can be coupled together, including in some cases in mutant selective fashions. We develop an advanced COUPLr for the oncogene EML4-ALK, which engages the fusion outside of its kinase domain, restricts protein dynamics, and disrupts EML4-ALK signaling. Collectively, molecular COUPLrs substantially expand the scope of proteins that can be chemically connected, providing an unbiased approach to identify small molecules that target protein complexes.

systems biology↗

bioRxiv2023

A strict requirement in proteasome substrates for spacing between ubiquitin tag and degradation initiation elements

Proteins are typically targeted to the proteasome for degradation through the attachment of ubiquitin chains and the proteasome initiates degradation at a disordered region within the target protein. Yet some proteins with ubiquitin chains and disordered regions escape degradation. Here we investigate how the position of the ubiquitin chain on the target protein relative to the disordered region modulates degradation and show that the distance between the two determines whether a protein is degraded efficiently. This distance depends on the type of the degradation tag and is likely a result of the separation on the proteasome between the receptor that binds the tag and the site that engages the disordered region.

biochemistry↗

bioRxiv2021

Enabling technology for microbial source tracking based on transfer learning: From ontology-aware general knowledge to context-aware expert systems

Microbial source tracking quantifies the potential origin of microbial communities, facilitates better understanding of how the taxonomic structure and community functions were formed and maintained. However, previous methods involve a tradeoff between speed and accuracy, and have encountered difficulty in source tracking under many context-dependent settings. Here, we present EXPERT for context-aware microbial source tracking, in which we adopted a Transfer Learning approach to profoundly elevate and expand the applicability of source tracking, enabling biologically informed novel microbial knowledge discovery. We demonstrate that EXPERT can predict microbial sources with performance superior to other methods in efficiency and accuracy. More importantly, we demonstrate EXPERTs context-aware ability on several applications, including tracking the progression of infant gut microbiome development and monitoring the changes of gut microbiome for colorectal cancer patients. Broadly, transfer learning enables accurate and context-aware microbial source tracking and has the potential for novel microbial knowledge discovery.

bioinformatics↗

bioRxiv2020

Analytical validation and performance characteristics of a 48-gene next-generation sequencing panel for detecting potentially actionable genomic alterations in myeloid neoplasms

Identification of genomic mutations by molecular testing plays an important role in diagnosis, prognosis, and treatment of myeloid neoplasms. Next-generation sequencing (NGS) is an efficient method for simultaneous detection of clinically significant genomic mutations with high sensitivity. However, due to lack of standard NGS protocols, the application of NGS for hematologic malignancies into clinical settings remains limited. We report development and validation of a 48-gene NGS panel for molecular profiling of myeloid neoplasms including acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and myeloproliferative neoplasms (MPN). Target regions were captured by hybridization with complementary biotinylated DNA baits, and NGS was performed on an Illumina NextSeq500 instrument. A bioinformatics pipeline that was developed in-house was used to detect single nucleotide variations (SNVs), insertions/deletions (indels), and FLT3 internal tandem duplications (FLT3-ITD). An analytical validation study was performed on 184 unique specimens for variants with allele frequencies [≥]5%. Variants identified by the 48-gene panel were compared to those identified by a 35-gene hematologic neoplasms panel using an additional 137 unique specimens. The developed assay was applied to a large cohort (n=2,053) of patients with suspected myeloid neoplasms. Analytical validation yielded 99.6% sensitivity (95% CI: 98.9-99.9%) and 100% specificity (95% CI: 100%). Concordance of variants detected by the 2 tested panels was 100%. Among patients with suspected myeloid neoplasms (n=2,053), 54.5% patients harbored at least one clinically significant mutation: 77% in AML patients, 48% in MDS, and 45% in MPN. Together, these findings demonstrate that the assay can identify mutations associated with diagnosis, prognosis, and treatment options of myeloid neoplasms.

cancer biology↗

bioRxiv2020

Meta-Prism 2.0: Enabling algorithm for ultra-fast, accurate and memory-efficient search among millions of microbial community samples

MotivationMicrobial community samples and sequencing data have been accumulated at a speed faster than ever, with tens of thousands of samples been sequenced each year. Mining such a huge amount of multi-source heterogeneous data is becoming more and more difficult. Among several sample mining bottlenecks, efficient and accurate search of samples is one of the most prominent: Faced with millions of samples in the data repository, traditional sample comparison and search approaches fall short in speed and accuracy. ResultsHere we proposed Meta-Prism 2.0, a microbial community sample search method based on smart pair-wise sample comparison, which pushed the time and memory efficiency to a new limit, without the compromise of accuracy. Based on memory-saving data structure, time-saving instruction pipeline, and boost scheme optimization, Meta-Prism 2.0 has enabled ultra-fast, accurate and memory-efficient search among millions of samples. Meta-Prism 2.0 has been put to test on several datasets, with largest containing one million samples. Results have shown that firstly, as a distance-based method, Meta-Prism 2.0 is not only faster than other distance-based methods, but also faster than unsupervised methods. Its 0.00001s per sample pair search speed, as well as 8GB memory needs for searching against one million samples, have enabled it to be the most efficient method for sample comparison. Additionally, Meta-Prism 2.0 could achieve the comparison accuracy and search precision that are comparable or better than other contemporary methods. Thirdly, Meta-Prism 2.0 can precisely identify the original biome for samples, thus enabling sample source tracking. ConclusionIn summary, Meta-Prism 2.0 can perform accurate searches among millions of samples with very low memory cost and fast speed, enabling knowledge discovery from samples at a massive scale. It has changed the traditional resource-intensive sample comparison and search scheme to a cheap and effective procedure, which could be conducted by researchers everyday even on a laptop, for insightful sample search and knowledge discovery. Meta-Prism 2.0 could be accessed at: https://github.com/HUST-NingKang-Lab/Meta-Prism-2.0.

bioinformatics↗

bioRxiv2020

Ontology-Aware Deep Learning Enables Ultrafast, Accurate and Interpretable Source Tracking among Sub-Million Microbial Community Samples from Hundreds of Niches

The taxonomical structure of microbial community sample is highly habitat-specific, making it possible for source tracking niches where samples are originated. Current methods face challenges when the number of samples and niches are magnitudes more than current in use, under which circumstances they are unable to accurately source track samples in a timely manner, rendering them difficult in knowledge discovery from sub-million heterogeneous samples. Here, we introduce a deep learning method based on Ontology-aware Neural Network approach, ONN4MST (https://github.com/HUST-NingKang-Lab/ONN4MST), which takes into consideration the ontology structure of niches and the relationship of samples from these ontologically-organized niches. ONN4MSTs superiority in accuracy, speed and robustness have been proven, for example with an accuracy of 0.99 and AUC of 0.97 in a microbial source tracking experiment that 125,823 samples and 114 niches were involved. Moreover, ONN4MST has been utilized on several source tracking applications, showing that it could provide highly-interpretable results from samples with previously less-studied niches, detect microbial contaminants, and identify similar samples from ontologically-remote niches, with high fidelity.

bioinformatics↗

bioRxiv2020

Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals

The current COVID-19 pandemic, caused by a novel coronavirus SARS-CoV-2, poses serious threats to public health and social stability, calling for urgent need for vaccines and therapeutics. SARS-CoV-2 is genetically close to SARS-CoV, thus it is important to define the between antigenic cross-reactivity and neutralization. In this study, we firstly analyzed 20 convalescent serum samples collected from SARS-CoV infected individuals during the 2003 SARS outbreak. All patient sera reacted strongly with the S1 subunit and receptor-binding domain (RBD) of SARS-CoV, cross-reacted with the S ectodomain, S1, RBD, and S2 proteins of SARS-CoV-2, and neutralized both SARS-CoV and SARS-CoV-2 S protein-driven infections. Multiple panels of antisera from mice and rabbits immunized with a full-length S and RBD immunogens of SARS-CoV were also characterized, verifying the cross-reactive neutralization against SARS-CoV-2. Interestingly, we found that a palm civet SARS-CoV-derived RBD elicited more potent cross-neutralizing responses in immunized animals than the RBD from a human SARS-CoV strain, informing a strategy to develop a universe vaccine against emerging CoVs. SummarySerum antibodies from SARS-CoV infected patients and immunized animals cross-neutralize SARS-CoV-2 suggests strategies for universe vaccines against emerging CoVs.

microbiology↗

bioRxiv2020

Design of potent membrane fusion inhibitors against SARS-CoV-2, an emerging coronavirus with high fusogenic activity

The coronavirus disease COVID-19, caused by emerging SARS-CoV-2, has posed serious threats to global public health, economic and social stabilities, calling for the prompt development of therapeutics and prophylactics. In this study, we firstly verified that SARS-CoV-2 uses human ACE2 as a cell receptor and its spike (S) protein mediates high membrane fusion activity. Comparing to that of SARS-CoV, the heptad repeat 1 (HR1) sequence in the S2 fusion protein of SARS-CoV-2 possesses markedly increased -helicity and thermostability, as well as a higher binding affinity with its corresponding heptad repeat 2 (HR1) site. Then, we designed a HR2 sequence-based lipopeptide fusion inhibitor, termed IPB02, which showed highly poent activities in inibibiting the SARS-CoV-2 S protein-mediated cell-cell fusion and pseudovirus infection. IPB02 also inhibited the SARS-CoV pseudovirus efficiently. Moreover, the strcuture and activity relationship (SAR) of IPB02 were characterzized with a panel of truncated lipopeptides, revealing the amino acid motifs critical for its binding and antiviral capacities. Therefore, the presented results have provided important information for understanding the entry pathway of SARS-CoV-2 and the design of antivirals that target the membrane fusion step.

microbiology↗