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Xiaoqiang Huang

Researcher at University of Michigan

Publications -  41
Citations -  1007

Xiaoqiang Huang is an academic researcher from University of Michigan. The author has contributed to research in topics: Protein design & Biology. The author has an hindex of 14, co-authored 33 publications receiving 604 citations. Previous affiliations of Xiaoqiang Huang include Tsinghua University.

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Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1.

TL;DR: The reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions and suggests the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.
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De novo design of protein peptides to block association of the SARS-CoV-2 spike protein with human ACE2.

TL;DR: This study demonstrates a new avenue to utilize computationally designed peptide motifs to treat the COVID-19 disease by blocking the critical spike-RBD and hACE2 interactions.
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Effects of SARS-CoV-2 mutations on protein structures and intraviral protein-protein interactions.

TL;DR: Overall, the findings suggest that SARS‐CoV‐2 mutations could change viral protein structure, binding affinity, and hot spots of the interface, thereby might have impacts on Sars‐Co V‐2 transmission, diagnosis, and treatment of COVID‐19.
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EvoEF2: accurate and fast energy function for computational protein design.

TL;DR: The study demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than Evo EF, which was optimized on thermodynamic mutation data.
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EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function.

TL;DR: A new EvoDesign server is proposed, which extends the principles of evolution-based design to design protein-protein interactions and should become a useful online tool for functional protein design and drug discovery studies.