1
|
Monari L, Galentino K, Cecchini M. ChemFlow_py: a flexible toolkit for docking and rescoring. J Comput Aided Mol Des 2023; 37:565-572. [PMID: 37620503 DOI: 10.1007/s10822-023-00527-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .
Collapse
Affiliation(s)
- Luca Monari
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Katia Galentino
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.
| |
Collapse
|
2
|
Tam C, Iwasaki W. AlphaCutter: Efficient removal of non-globular regions from predicted protein structures. Proteomics 2023; 23:e2300176. [PMID: 37309722 DOI: 10.1002/pmic.202300176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/14/2023]
Abstract
A huge number of high-quality predicted protein structures are now publicly available. However, many of these structures contain non-globular regions, which diminish the performance of downstream structural bioinformatic applications. In this study, we develop AlphaCutter for the removal of non-globular regions from predicted protein structures. A large-scale cleaning of 542,380 predicted SwissProt structures highlights that AlphaCutter is able to (1) remove non-globular regions that are undetectable using pLDDT scores and (2) preserve high integrity of the cleaned domain regions. As useful applications, AlphaCutter improved the folding energy scores and sequence recovery rates in the re-design of domain regions. On average, AlphaCutter takes less than 3 s to clean a protein structure, enabling efficient cleaning of the exploding number of predicted protein structures. AlphaCutter is available at https://github.com/johnnytam100/AlphaCutter. AlphaCutter-cleaned SwissProt structures are available for download at https://doi.org/10.5281/zenodo.7944483.
Collapse
Affiliation(s)
- Chunlai Tam
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Chiba, Japan
| | - Wataru Iwasaki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Chiba, Japan
| |
Collapse
|
3
|
Bonaventura P, Alcazer V, Mutez V, Tonon L, Martin J, Chuvin N, Michel E, Boulos RE, Estornes Y, Valladeau-Guilemond J, Viari A, Wang Q, Caux C, Depil S. Identification of shared tumor epitopes from endogenous retroviruses inducing high-avidity cytotoxic T cells for cancer immunotherapy. SCIENCE ADVANCES 2022; 8:eabj3671. [PMID: 35080970 PMCID: PMC8791462 DOI: 10.1126/sciadv.abj3671] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human endogenous retroviruses (HERVs) represent 8% of the human genome. HERV products may represent tumor antigens relevant for cancer immunotherapy. We developed a bioinformatic approach to identify shared CD8+ T cell epitopes derived from cancer-associated HERVs in solid tumors. Six candidates among the most commonly shared HLA-A2 epitopes with evidence of translation were selected for immunological evaluation. In vitro priming assays confirmed the immunogenicity of these epitopes, which induced high-avidity CD8+ T cell clones. These T cells specifically recognize and kill HLA-A2+ tumor cells presenting HERV epitopes on HLA molecules, as demonstrated by mass spectrometry. Furthermore, epitope-specific CD8+ T cells were identified by dextramer staining among tumor-infiltrating lymphocytes from HLA-A2+ patients with breast cancer. Last, we showed that HERV-specific T cells lyse patient-derived organoids. These shared virus-like epitopes are of major interest for the development of cancer vaccines or T cell-based immunotherapies, especially in tumors with low/intermediate mutational burden.
Collapse
Affiliation(s)
- Paola Bonaventura
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
- Centre Léon Bérard, Lyon, France
| | - Vincent Alcazer
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
| | | | - Laurie Tonon
- Synergie Lyon Cancer, Plateforme de bioinformatique « Gilles Thomas », Lyon, France
| | - Juliette Martin
- CNRS-Institut de Biologie et Chimie des Protéines UMR 5086, Lyon, France
| | | | | | | | | | | | - Alain Viari
- Synergie Lyon Cancer, Plateforme de bioinformatique « Gilles Thomas », Lyon, France
| | | | - Christophe Caux
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
- Centre Léon Bérard, Lyon, France
| | - Stéphane Depil
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
- Centre Léon Bérard, Lyon, France
- ErVaccine Technologies, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
- Corresponding author.
| |
Collapse
|