Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction
The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusio...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/12/1/3 |
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| author | Moses N. Arthur Kristeen Bebla Emmanuel Broni Carolyn Ashley Miriam Velazquez Xianin Hua Ravi Radhakrishnan Samuel K. Kwofie Whelton A. Miller |
| author_facet | Moses N. Arthur Kristeen Bebla Emmanuel Broni Carolyn Ashley Miriam Velazquez Xianin Hua Ravi Radhakrishnan Samuel K. Kwofie Whelton A. Miller |
| author_sort | Moses N. Arthur |
| collection | DOAJ |
| description | The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of −11.0, −10.7, −10.6, −10.2, −10.2, −9.9, −9.9, −9.9, −9.9, and −9.9 kcal/mol, respectively. To confirm the stability of the menin–ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia. |
| first_indexed | 2024-03-08T11:01:26Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-08T11:01:26Z |
| publishDate | 2023-12-01 |
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| record_format | Article |
| series | Computation |
| spelling | doaj.art-bffe6fc9bb894e218cfe1010b92813a52024-01-26T15:51:34ZengMDPI AGComputation2079-31972023-12-01121310.3390/computation12010003Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia InteractionMoses N. Arthur0Kristeen Bebla1Emmanuel Broni2Carolyn Ashley3Miriam Velazquez4Xianin Hua5Ravi Radhakrishnan6Samuel K. Kwofie7Whelton A. Miller8Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Legon, Accra LG 581, GhanaDepartment of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USADepartment of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USADepartment of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USADepartment of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USADepartment of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic & Applied Sciences, University of Ghana, Legon, Accra LG 77, GhanaDepartment of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USAThe prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of −11.0, −10.7, −10.6, −10.2, −10.2, −9.9, −9.9, −9.9, −9.9, and −9.9 kcal/mol, respectively. To confirm the stability of the menin–ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia.https://www.mdpi.com/2079-3197/12/1/3meninmixed-lineage leukemiamolecular dynamicscomputer-aided drug design |
| spellingShingle | Moses N. Arthur Kristeen Bebla Emmanuel Broni Carolyn Ashley Miriam Velazquez Xianin Hua Ravi Radhakrishnan Samuel K. Kwofie Whelton A. Miller Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction Computation menin mixed-lineage leukemia molecular dynamics computer-aided drug design |
| title | Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction |
| title_full | Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction |
| title_fullStr | Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction |
| title_full_unstemmed | Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction |
| title_short | Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction |
| title_sort | design of inhibitors that target the menin mixed lineage leukemia interaction |
| topic | menin mixed-lineage leukemia molecular dynamics computer-aided drug design |
| url | https://www.mdpi.com/2079-3197/12/1/3 |
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