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Yi J, Shi S, Fu L, Yang Z, Nie P, Lu A, Wu C, Deng Y, Hsieh C, Zeng X, Hou T, Cao D. OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds. Nat Protoc 2024; 19:1105-1121. [PMID: 38263521 DOI: 10.1038/s41596-023-00942-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 10/27/2023] [Indexed: 01/25/2024]
Abstract
Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.
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Affiliation(s)
- Jiacai Yi
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Shaohua Shi
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
| | - Ziyi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
| | - Pengfei Nie
- National Supercomputer Center in Tianjin, Tianjin, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, China
| | - Chengkun Wu
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
| | - Changyu Hsieh
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiangxiang Zeng
- Deparment of Computer Science, Hunan University, Changsha, China
| | - Tingjun Hou
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China.
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China.
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Chen SH, Weiss KL, Stanley C, Bhowmik D. Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing. Protein Sci 2023; 32:e4772. [PMID: 37646172 PMCID: PMC10503416 DOI: 10.1002/pro.4772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/01/2023]
Abstract
Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.
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Affiliation(s)
- Serena H. Chen
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
| | - Kevin L. Weiss
- Neutron Scattering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
| | - Christopher Stanley
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
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Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol 2023; 14:1228873. [PMID: 37781387 PMCID: PMC10539903 DOI: 10.3389/fimmu.2023.1228873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Affiliation(s)
- Zahra S. Ghoreyshi
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Jason T. George
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Engineering Medicine Program, Texas A&M University, Houston, TX, United States
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
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