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publications
The assembly of β-barrel membrane proteins by BAM and SAM
Published in Molecular Microbiology, 2020
Gram-negative bacteria, mitochondria, and chloroplasts all possess an outer membrane populated with a host of β-barrel outer-membrane proteins (βOMPs)… Here, we will review these recent studies and highlight their contributions toward understanding βOMP biogenesis in Gram-negative bacteria and in mitochondria.
Recommended citation: Recommended citation: Y Lundquist, K, Billings, E, Bi, M, Wellnitz, J, Noinaj, N. The assembly of β-barrel membrane proteins by BAM and SAM. Mol Microbiol. 2021; 115: 425–435. https://onlinelibrary.wiley.com/doi/full/10.1111/mmi.14666
The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation
Published in Arxiv Preprint, 2023
We extend these previously defined validation strategies for QSAR modeling of binary mixtures to the more complex case of general, N-ary mixtures and argue that these strategies are applicable to many modeling tasks beyond simple chemical mixtures.
Recommended citation: Travis Maxfield, Joshua Hochuli, James Wellnitz, Cleber Melo-Filho, Konstantin I. Popov, Eugene Muratov, & Alex Tropsha. (2023). The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation. https://arxiv.org/abs/2308.06347
Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds
Published in Journal of Medicinal Chemistry, 2023
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed “Liability Predictor,” a free web tool to predict HTS artifacts.
Recommended citation: Alves, V. M., Yasgar, A., Wellnitz, J., Rai, G., Rath, M., Braga, R. C., … Tropsha, A. (2023). Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds. Journal of Medicinal Chemistry, 66(18), 12828–12839. https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482#
Hit Discovery using Docking ENriched by GEnerative Modeling (HIDDEN GEM): A Novel Computational Workflow for Accelerated Virtual Screening of Ultra-large Chemical Libraries
Published in Molecular Informatics, 2023
We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow.
Recommended citation: Wellnitz, J., Popov, K.I., Maxfield, T, and Tropsha, A. (2023), Hit Discovery using Docking ENriched by GEnerative Modeling (HIDDEN GEM): A Novel Computational Workflow for Accelerated Virtual Screening of Ultra-large Chemical Libraries.. Mol. Inf.. Accepted Author Manuscript. https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.202300207
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
CBMC/BOIC 805 Molecular Modeling
Graduate course, UNC Chapel Hill, Eshelman School of Pharmacy , 1900
Teaching assistant for introduction class on molecular modeling, 2022 and 2023