1
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Liu J, Xue Y, Bai K, Yan F, Long X, Guo H, Yan H, Huang G, Zhou J, Tang Y. Experimental and computational study on anti-gastric cancer activity and mechanism of evodiamine derivatives. Front Pharmacol 2024; 15:1380304. [PMID: 38783957 PMCID: PMC11113551 DOI: 10.3389/fphar.2024.1380304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
Introduction: Human topoisomerase 1 (TOP1) is an important target of various anticancer compounds. The design and discovery of inhibitors targeting TOP1 are of great significance for the development of anticancer drugs. Evodiamine and thieno [2,3-d] pyridine hybrids show potential antitumor activity. Herein, the anti-gastric cancer activities of these hybrids were investigated. Methods: The inhibitory effects of different concentrations of ten evodiamine derivatives on the gastric cancer cell line SGC-7901 were assessed using a methyl thiazolyl tetrazolium assay. Compounds EVO-1 and EVO-6 strongly inhibited gastric cancer cell proliferation, with inhibition rates of 81.17% ± 5.08% and 80.92% ± 2.75%, respectively. To discover the relationship between the structure and activity of these two derivatives, density functional theory was used to investigate their optimized geometries, natural population charges, frontier molecular orbitals, and molecular electrostatic potentials. To clarify their anti-gastric cancer mechanisms, molecular docking, molecular dynamics simulations, and binding free energy calculations were performed against TOP1. Results: The results demonstrated that these compounds could intercalate into the cleaved DNA-binding site to form a TOP1-DNA-ligand ternary complex, and the ligand remained secure at the cleaved DNA-binding site to form a stable ternary complex. As the binding free energy of compound EVO-1 with TOP1 (-38.33 kcal·mol-1) was lower than that of compound EVO-6 (-33.25 kcal·mol-1), compound EVO-1 could be a more potent anti-gastric cancer agent than compound EVO-6. Discussion: Thus, compound EVO-1 could be a promising anti-gastric cancer drug candidate. This study may facilitate the design and development of novel TOP1 inhibitors.
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Affiliation(s)
- Jingli Liu
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Yingying Xue
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Kaidi Bai
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Fei Yan
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Xu Long
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Hui Guo
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Hao Yan
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Guozheng Huang
- College of Chemistry and Chemical Engineering, Anhui University of Technology, Ma’anshan, Anhui, China
| | - Jing Zhou
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Yuping Tang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
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2
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Shi M, Zheng X, Zhou Y, Yin Y, Lu Z, Zou Z, Hu Y, Liang Y, Chen T, Yang Y, Jing M, Lei D, Yang P, Li X. Selectivity Mechanism of Pyrrolopyridone Analogues Targeting Bromodomain 2 of Bromodomain-Containing Protein 4 from Molecular Dynamics Simulations. ACS OMEGA 2023; 8:33658-33674. [PMID: 37744850 PMCID: PMC10515184 DOI: 10.1021/acsomega.3c03935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023]
Abstract
Bromodomain and extra-terminal domain (BET) proteins play an important role in epigenetic regulation and are linked to several diseases; therefore, they are interesting targets. BET has two bromodomains: bromodomain 1 (BD1) and BD2. Selective targeting of BD1 or BD2 may produce different activities and greater effects than pan-BD inhibitors. However, the selective mechanism of the specific core must be studied at the atomic level. This study determined the effectiveness of pyrrolopyridone analogues to selectively inhibit BD2 using a pan-BD inhibitor (ABBV-075) and a selective-BD2 inhibitor (ABBV-744). Molecular dynamics simulations and calculations of binding free energies were used to systematically study the selectivity of BD2 inhibition by the pyrrolopyridone analogues. Overall, the pyrrolopyridone analogue inhibitors targeting BD2 interacted mainly with the following amino acid pairs between bromodomain-containing protein 4 (BRD4)-BD1 and BRD4-BD2 complexes: I146/V439, N140/N433, D144/H437, P82/P375, V87/V380, D88/D381, and Y139/Y432. The pyrrolopyridone analogues targeting BRD4-BD2 were divided into five regions based on selectivity mechanism. These results suggest that the R3 and R5 regions of pyrrolopyridone analogues can be modified to improve the selectivity between BRD4-BD1 and BRD4-BD2. The selectivity of BD2 inhibition by pyrrolopyridone analogues can be used to design novel BD2 inhibitors based on a pyrrolopyridone core.
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Affiliation(s)
- Mingsong Shi
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
- Innovation
Center of Nursing Research, Nursing Key Laboratory of Sichuan Province,
West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xueting Zheng
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yan Zhou
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yuan Yin
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Zhou Lu
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Zhiyan Zou
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yan Hu
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yuanyuan Liang
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Tingting Chen
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Yuhan Yang
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
| | - Meng Jing
- Department
of Pathology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of
China, Mianyang 621099, Sichuan, China
| | - Dan Lei
- School
of Life Science and Engineering, Southwest
University of Science and Technology, Mianyang 621010, Sichuan, China
| | - Pei Yang
- Department
of Hepatobiliary Surgery, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of
China, Mianyang 621099, Sichuan, China
| | - Xiaoan Li
- NHC
Key Laboratory of Nuclear Technology Medical Transformation, Mianyang
Central Hospital, School of Medicine, University
of Electronic Science and Technology of China, Mianyang 621099, Sichuan, China
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3
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Opuu V, Nigro G, Lazennec‐Schurdevin C, Mechulam Y, Schmitt E, Simonson T. Redesigning methionyl-tRNA synthetase for β-methionine activity with adaptive landscape flattening and experiments. Protein Sci 2023; 32:e4738. [PMID: 37518893 PMCID: PMC10451022 DOI: 10.1002/pro.4738] [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: 05/29/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 08/01/2023]
Abstract
Amino acids (AAs) with a noncanonical backbone would be a valuable tool for protein engineering, enabling new structural motifs and building blocks. To incorporate them into an expanded genetic code, the first, key step is to obtain an appropriate aminoacyl-tRNA synthetase. Currently, directed evolution is not available to optimize AAs with noncanonical backbones, since an appropriate selective pressure has not been discovered. Computational protein design (CPD) is an alternative. We used a new CPD method to redesign MetRS and increase its activity towards β-Met, which has an extra backbone methylene. The new method considered a few active site positions for design and used a Monte Carlo exploration of the corresponding sequence space. During the exploration, a bias energy was adaptively learned, such that the free energy landscape of the apo enzyme was flattened. Enzyme variants could then be sampled, in the presence of the ligand and the bias energy, according to their β-Met binding affinities. Eighteen predicted variants were chosen for experimental testing; 10 exhibited detectable activity for β-Met adenylation. Top predicted hits were characterized experimentally in detail. Dissociation constants, catalytic rates, and Michaelis constants for both α-Met and β-Met were measured. The best mutant retained a preference for α-Met over β-Met; however, the preference was reduced, compared to the wildtype, by a factor of 29. For this mutant, high resolution crystal structures were obtained in complex with both α-Met and β-Met, indicating that the predicted, active conformation of β-Met in the active site was retained.
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Affiliation(s)
- Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Giuliano Nigro
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Christine Lazennec‐Schurdevin
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Yves Mechulam
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Emmanuelle Schmitt
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
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4
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Zhou Y, Li X, Luo P, Chen H, Zhou Y, Zheng X, Yin Y, Wei H, Liu H, Xia W, Shi M, Li X. Identification of abemaciclib derivatives targeting cyclin-dependent kinase 4 and 6 using molecular dynamics, binding free energy calculation, synthesis, and pharmacological evaluation. Front Pharmacol 2023; 14:1154654. [PMID: 37234717 PMCID: PMC10206264 DOI: 10.3389/fphar.2023.1154654] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
CDK4/6 plays a crucial role in various cancers and is an effective anticancer drug target. However, the gap between clinical requirements and approved CDK4/6 drugs is unresolved. Thus, there is an urgent need to develop selective and oral CDK4/6 inhibitors, particularly for monotherapy. Here, we studied the interaction between abemaciclib and human CDK6 using molecular dynamics simulations, binding free energy calculations, and energy decomposition. V101 and H100 formed stable hydrogen bonds with the amine-pyrimidine group, and K43 interacted with the imidazole ring via an unstable hydrogen bond. Meanwhile, I19, V27, A41, and L152 interacted with abemaciclib through π-alkyl interactions. Based on the binding model, abemaciclib was divided into four regions. With one region modification, 43 compounds were designed and evaluated using molecular docking. From each region, three favorable groups were selected and combined with each other to obtain 81 compounds. Among them, C2231-A, which was obtained by removing the methylene group from C2231, showed better inhibition than C2231. Kinase profiling revealed that C2231-A showed inhibitory activity similar to that of abemaciclib; additionally, C2231-A inhibited the growth of MDA-MB-231 cells to a greater extent than did abemaciclib. Based on molecular dynamics simulation, C2231-A was identified as a promising candidate compound with considerable inhibitory effects on human breast cancer cell lines.
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Affiliation(s)
- Yanting Zhou
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Xiandeng Li
- College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Peifang Luo
- Department of Cardiovascular Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Huiting Chen
- College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Yan Zhou
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Xueting Zheng
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Yuan Yin
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Haoche Wei
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongji Liu
- Department of Ophthalmology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Wen Xia
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Mingsong Shi
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Xiaoan Li
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
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5
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Zou Y, Wang R, Du M, Wang X, Xu D. Identifying Protein-Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network. J Phys Chem B 2023; 127:899-911. [PMID: 36657025 DOI: 10.1021/acs.jpcb.2c07592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Efficient and accurate characterizations of protein-ligand interactions are key to understanding biology at the molecular level. They are particularly useful in pharmaceutical industry applications. They are usually computationally demanding for those widely applied dynamics-based methods in identifying important residues or calculating ligand binding free energy. In this work, we proposed a graph deep learning (DL) framework, namely, the distance self-feedback biomolecular interaction network (DSBIN), in which the relationship between the complex structure and binding affinity can be established by means of a carefully designed distance self-feedback module and interaction layer. Our model can directly provide a quantitative evaluation of inhibitor binding affinities (pKd). More importantly, the DSBIN model efficiently identifies key interactions for inhibitor binding and thus intrinsically bears the interpretability. Its generalization performance was further verified using 1405 unseen structures. The predicted binding free energies' deviations were calculated to be less than 1.37 kcal/mol for more than 55% structures. Moreover, we also compared the DSBIN model with a commonly used theoretical method in calculating the substrate binding free energy, MM/GBSA. Our results show that the current DL model has generally better performance in predicting the binding free energy. For a specific complex system, mannopentaose/TmCBM27, the DSBIN predicted binding free energy is -8.21 kcal/mol, which is very close to experimentally measured -7.76 kcal/mol and MM/GBSA calculated -7.16 kcal/mol. Meanwhile, all important aromatic residues around the binding pocket can be identified by our DL model. Considering the accuracy and efficiency of the newly developed DL model, it may be very helpful in the field of drug design and molecular recognition.
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Affiliation(s)
- Yurong Zou
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Ruihan Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Meng Du
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Xin Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Dingguo Xu
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China.,Research Center for Materials Genome Engineering, Sichuan University, Chengdu, Sichuan610065, PR China
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6
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Michael E, Saint-Jalme R, Mignon D, Simonson T. Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance. Front Mol Biosci 2023; 9:905588. [PMID: 36699702 PMCID: PMC9868620 DOI: 10.3389/fmolb.2022.905588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme "vitality", defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or "redesigned" individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.
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7
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Shi M, Zhou Y, Wei H, Zhang X, Du M, Zhou Y, Yin Y, Li X, Tang X, Sun L, Xu D, Li X. Interactions between curcumin and human salt-induced kinase 3 elucidated from computational tools and experimental methods. Front Pharmacol 2023; 14:1116098. [PMID: 37124223 PMCID: PMC10133576 DOI: 10.3389/fphar.2023.1116098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/03/2023] [Indexed: 05/02/2023] Open
Abstract
Natural products are widely used for treating mitochondrial dysfunction-related diseases and cancers. Curcumin, a well-known natural product, can be potentially used to treat cancer. Human salt-induced kinase 3 (SIK3) is one of the target proteins for curcumin. However, the interactions between curcumin and human SIK3 have not yet been investigated in detail. In this study, we studied the binding models for the interactions between curcumin and human SIK3 using computational tools such as homology modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations. The open activity loop conformation of SIK3 with the ketoenol form of curcumin was the optimal binding model. The I72, V80, A93, Y144, A145, and L195 residues played a key role for curcumin binding with human SIK3. The interactions between curcumin and human SIK3 were also investigated using the kinase assay. Moreover, curcumin exhibited an IC50 (half-maximal inhibitory concentration) value of 131 nM, and it showed significant antiproliferative activities of 9.62 ± 0.33 µM and 72.37 ± 0.37 µM against the MCF-7 and MDA-MB-23 cell lines, respectively. This study provides detailed information on the binding of curcumin with human SIK3 and may facilitate the design of novel salt-inducible kinases inhibitors.
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Affiliation(s)
- Mingsong Shi
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Yan Zhou
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Haoche Wei
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xinyu Zhang
- West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China
| | - Meng Du
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan, China
| | - Yanting Zhou
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnocentric of Ministry of Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Yuan Yin
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
| | - Xinghui Li
- West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China
| | - Xinyi Tang
- West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China
| | - Liang Sun
- Shenzhen Shuli Tech Co., Ltd, Shenzhen, Guangdong, China
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan, China
- Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan, China
- *Correspondence: Dingguo Xu, ; Xiaoan Li,
| | - Xiaoan Li
- NHC Key Laboratory of Nuclear Technology Medical Transformation, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China
- *Correspondence: Dingguo Xu, ; Xiaoan Li,
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8
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Liu J, Guo H, Zhou J, Wang Y, Yan H, Jin R, Tang Y. Evodiamine and Rutaecarpine as Potential Anticancer Compounds: A Combined Computational Study. Int J Mol Sci 2022; 23:ijms231911513. [PMID: 36232809 PMCID: PMC9570036 DOI: 10.3390/ijms231911513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/18/2022] [Accepted: 09/26/2022] [Indexed: 12/29/2022] Open
Abstract
Evodiamine (EVO) and rutaecarpine (RUT) are the main active compounds of the traditional Chinese medicinal herb Evodia rutaecarpa. Here, we fully optimized the molecular geometries of EVO and RUT at the B3LYP/6-311++G (d, p) level of density functional theory. The natural population analysis (NPA) charges, frontier molecular orbitals, molecular electrostatic potentials, and the chemical reactivity descriptors for EVO and RUT were also investigated. Furthermore, molecular docking, molecular dynamics simulations, and the analysis of the binding free energies of EVO and RUT were carried out against the anticancer target topoisomerase 1 (TOP1) to clarify their anticancer mechanisms. The docking results indicated that they could inhibit TOP1 by intercalating into the cleaved DNA-binding site to form a TOP1−DNA−ligand ternary complex, suggesting that they may be potential TOP1 inhibitors. Molecular dynamics (MD) simulations evaluated the binding stability of the TOP1−DNA−ligand ternary complex. The calculation of binding free energy showed that the binding ability of EVO with TOP1 was stronger than that of RUT. These results elucidated the structure−activity relationship and the antitumor mechanism of EVO and RUT at the molecular level. It is suggested that EVO and RUT may be potential compounds for the development of new anticancer drugs.
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Affiliation(s)
| | - Hui Guo
- Correspondence: (H.G.); (Y.T.)
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9
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Shi M, Wang L, Liu K, Chen Y, Hu M, Yang L, He J, Chen L, Xu D. Molecular Dynamics Simulations of the Conformational Plasticity in the Active Pocket of Salt-Inducible Kinase 2 (SIK2) Multi-State Binding with Bosutinib. Comput Struct Biotechnol J 2022; 20:2574-2586. [PMID: 35685353 PMCID: PMC9160496 DOI: 10.1016/j.csbj.2022.05.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 11/06/2022] Open
Abstract
The kinase domain is highly conserved among protein kinases 'in terms of both sequence and structure. Conformational rearrangements of the kinase domain are affected by the phosphorylation of residues and the binding of kinase inhibitors. Interestingly, the conformational rearrangement of the active pocket plays an important role in kinase activity and can be used to design novel kinase inhibitors. We characterized the conformational plasticity of the active pocket when bosutinib was bound to salt-inducible kinase 2 (SIK2) using homology modeling and molecular dynamics simulations. Ten different initial complex models were constructed using the Morph server, ranging from open to closed conformations of SIK2 binding with bosutinib. Our simulation showed that bosutinib binds SIK2 with up or down conformations of the P-loop and with all the conformations of the activation loop. In addition, the αC-helix conformation was induced by the conformation of the activation loop, and the salt bridge formed only with its open conformation. The binding affinity of the models was also determined using the molecular mechanics generalized Born surface area method. Bosutinib was found to form a strong binding model with SIK2 and hydrophobic interactions were the dominant factor. This discovery may help guide the design of novel SIK2 inhibitors.
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10
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Shi M, He J, Weng T, Shi N, Qi W, Guo Y, Chen T, Chen L, Xu D. The binding mechanism of NHWD-870 to bromodomain-containing protein 4 based on molecular dynamics simulations and free energy calculation. Phys Chem Chem Phys 2022; 24:5125-5137. [PMID: 35156677 DOI: 10.1039/d1cp05490b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Bromodomain and extra-terminal (BET) proteins (BRD2, BRD3, BRD4, and BRDT) are epigenetic readers with tandem bromodomains.
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Affiliation(s)
- Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Jun He
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Tiantian Weng
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Na Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenyan Qi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Yong Guo
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Tao Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Lijuan Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China
- Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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11
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Wang Y, Hu B, Zhang Y, Wang D, Luo Z, Wang J, Zhang F. Perspective of structural flexibility on selective inhibition towards CYP1B1 over CYP1A1 by α-naphthoflavone analogs. Phys Chem Chem Phys 2021; 23:20230-20246. [PMID: 34474468 DOI: 10.1039/d1cp02541d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Research on action selectivity between CYP1A1 and CYP1B1 is particularly valuable for cancer chemoprevention and chemotherapy. However, they share a very close similarity in their ligand-binding pockets that α-naphthoflavone (ANF) is the co-crystal ligand for both isoforms, which poses a major challenge in revealing their selectivity mechanism. Therefore, three selective CYP1B1 inhibitors derived from ANF were selected to illustrate the structural basis for the selectivity between the two isoforms via a comprehensive computational strategy. It was found that the sustainability of the π-π stacking interactions with the phenylalanine residues of the two isoforms, namely, Phe123, Phe224, and Phe258 for CYP1A1, and Phe134, Phe231, and Phe268 for CYP1B1, played a crucial role in determining the selectivity of ligands with a classic aromatic conjugation system like ANF and its derivatives for CYP1B1 versus CYP1A1. Of note, the structural flexibility of the corresponding protein domains mainly orchestrated the sustainability of the corresponding π-π stacking interactions, thereby determining the binding selectivity. Therefore, the structure modification of naphthoflavone lead compounds into preferable binding configurations to satisfy the π-π stacking interactions of the key phenylalanine residues within CYP1B1 would be an inspiring strategy devised to improve the inhibitory selectivity towards CYP1B1. Collectively, this study revealed valuable insight into understanding the selective mechanism between CYP1A1 and CYP1B1 from the perspective of structural flexibility, which sheds light on the future rational design of CYP1B1 selective inhibitors.
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Affiliation(s)
- Ying Wang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.,School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Yupeng Zhang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Dong Wang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Zhaohu Luo
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.,School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Fengjiao Zhang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
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12
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Shi M, Zhao M, Wang L, Liu K, Li P, Liu J, Cai X, Chen L, Xu D. Exploring the stability of inhibitor binding to SIK2 using molecular dynamics simulation and binding free energy calculation. Phys Chem Chem Phys 2021; 23:13216-13227. [PMID: 34086021 DOI: 10.1039/d1cp00717c] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Salt inducible kinase 2 (SIK2) is a calcium/calmodulin-dependent protein kinase-like kinase that is implicated in a variety of biological phenomena, including cellular metabolism, growth, and apoptosis. SIK2 is the key target for various cancers, including ovarian, breast, prostate, and lung cancers. Although potent inhibitors of SIK2 are being developed, their binding stability and functional role are not presently known. In this work, we studied the detailed interactions between SIK2 and four of its inhibitors, HG-9-91-01, KIN112, MRT67307, and MRT199665, using molecular docking, molecular dynamics simulation, binding free energy calculation, and interaction fingerprint analysis. Intermolecular interactions revealed that HG-9-91-01 and KIN112 have stronger interactions with SIK2 than those of MRT199665 and MRT67307. The key residues involved in binding with SIK2 are conserved among all four inhibitors. Our results explain the detailed interaction of SIK2 with its inhibitors at the molecular level, thus paving the way for the development of targeted efficient anti-cancer drugs.
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Affiliation(s)
- Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Min Zhao
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lun Wang
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Kongjun Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Penghui Li
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China.
| | - Jiang Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Xiaoying Cai
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lijuan Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China. and Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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13
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Polydorides S, Archontis G. Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2. Biophys J 2021; 120:2859-2871. [PMID: 33984310 PMCID: PMC8110322 DOI: 10.1016/j.bpj.2021.02.049] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/19/2021] [Accepted: 02/15/2021] [Indexed: 01/15/2023] Open
Abstract
The coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the coronavirus disease 2019 pandemic, and the closely related SARS-CoV coronavirus enter cells by binding at the human angiotensin converting enzyme 2 (hACE2). The stronger hACE2 affinity of SARS-CoV-2 has been connected with its higher infectivity. In this work, we study hACE2 complexes with the receptor-binding domains (RBDs) of the human SARS-CoV-2 and human SARS-CoV viruses, using all-atom molecular dynamics simulations and computational protein design with a physics-based energy function. The molecular dynamics simulations identify charge-modifying substitutions between the CoV-2 and CoV RBDs, which either increase or decrease the hACE2 affinity of the SARS-CoV-2 RBD. The combined effect of these mutations is small, and the relative affinity is mainly determined by substitutions at residues in contact with hACE2. Many of these findings are in line and interpret recent experiments. Our computational protein design calculations redesign positions 455, 493, 494, and 501 of the SARS-CoV-2 receptor binding motif, which contact hACE2 in the complex and are important for ACE2 recognition. Sampling is enhanced by an adaptive importance sampling Monte Carlo method. Sequences with increased affinity replace CoV-2 glutamine by a negative residue at position 493; serine by a nonpolar or aromatic residue or an asparagine at position 494; and asparagine by valine or threonine at position 501. Substitutions at positions 455 and 501 have a smaller effect on affinity. Substitutions suggested by our design are seen in viral sequences encountered in other species, including bat and pangolin. Our results might be used to identify potential virus strains with higher human infectivity and assist in the design of peptide-based or peptidomimetic compounds with the potential to inhibit SARS-CoV-2 binding at hACE2.
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14
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Shi M, Wang L, Li P, Liu J, Chen L, Xu D. Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations. ACS OMEGA 2021; 6:11025-11038. [PMID: 34056256 PMCID: PMC8153941 DOI: 10.1021/acsomega.1c00947] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/06/2021] [Indexed: 02/08/2023]
Abstract
![]()
Salt-inducible kinases
(SIKs) are calcium/calmodulin-dependent
protein kinase (CAMK)-like (CAMKL) family members implicated in insulin
signal transduction, metabolic regulation, inflammatory response,
and other processes. Here, we focused on SIK2, which is a target of
the Food and Drug Administration (FDA)-approved pan inhibitor N-(2-chloro-6-methylphenyl)-2-(6-(4-(2-hydroxyethyl)piperazin-1-yl)-2-methylpyrimidin-4-ylamino)thiazole-5-carboxamide
(dasatinib), and constructed four representative SIK2 structures by
homology modeling. We investigated the interactions between dasatinib
and SIK2 via molecular docking, molecular dynamics simulation, and
binding free energy calculation and found that dasatinib showed strong
binding affinity for SIK2. Binding free energy calculations suggested
that the modification of various dasatinib regions may provide useful
information for drug design and to guide the discovery of novel dasatinib-based
SIK2 inhibitors.
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Affiliation(s)
- Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lun Wang
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Penghui Li
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China
| | - Jiang Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lijuan Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Dingguo Xu
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China
- Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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15
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Horvath D, Marcou G, Varnek A. "Big Data" Fast Chemoinformatics Model to Predict Generalized Born Radius and Solvent Accessibility as a Function of Geometry. J Chem Inf Model 2020; 60:2951-2965. [PMID: 32374171 DOI: 10.1021/acs.jcim.9b01172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of the degree of burial of an atom and not very sensitive to small changes of geometry: in molecular dynamics, the costly EBR update procedure is not mandatory at every step. This work however aims at implementing a GB model into the Sampler for Multiple Protein-Ligand Entities (S4MPLE) evolutionary algorithm with mandatory EBR updates at each step triggering arbitrarily large geometric changes. Therefore, a quantitative structure-property relationship has been developed in order to express the EBRs as a linear function of both the topological neighborhood and geometric occupancy of the space around atoms. A training set of 810 molecular systems, starting from fragment-like to drug-like compounds, proteins, host-guest systems, and ligand-protein complexes, has been compiled. For each species, S4MPLE generated several hundreds of random conformers. For each atom in each geometry of each species, its "standard" EBR was calculated by numeric integration and associated to topological and geometric descriptors of the atom neighborhood. This training set (EBR, atom descriptors) involving >5 M entries was subjected to a boot-strapping multilinear regression process with descriptor selection. In parallel, the strategy was repurposed to also learn atomic solvent-accessible areas (SA) based on the same descriptors. Resulting linear equations were challenged to predict EBR and SA values for a similarly compiled external set of >2000 new molecular systems. Solvation energies calculated with estimated EBR and SA match "standard" energies within the typical error of a force-field-based approach (a few kilocalories per mole). Given the extreme diversity of molecular systems covered by the model, this simple EBR/SA estimator covers a vast applicability domain.
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Affiliation(s)
- Dragos Horvath
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
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16
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Wang Y, Joseph J, Gao Y, Hu B, Geng X, Wu D, Wang J, Zhang F. Revealing the interaction modes of 5-HT2A receptor antagonists and the Structure-Based virtual screening from FDA and TCMNP database. J Biomol Struct Dyn 2020; 39:3681-3692. [PMID: 32406337 DOI: 10.1080/07391102.2020.1768900] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
5-hydroxytryptamine 2A (5-HT2A) receptor is emerging as an important target for numerous psychoactive drugs due to its imperative roles in psychological diseases. In fact, multiple 5-HT2A receptor antagonists were developed to treat numerous psychiatric disorders, however, their clinical outcome was far from ideal probably due to a blurry information of the exact interaction modes between the receptor and its antagonists. Impressively, with a recent release of its crystal structure, we carefully analyzed the receptor-ligand interactions with Protein Contacts Atlas, structure-based pharmacophore models, and molecular dynamics (MD) simulations to sum up the chemical features for antagonists interacting with 5-HT2A receptor. Moreover, the molecular docking-based virtual screening was applied to discover potential 5-HT2A receptor antagonists from FDA and TCMNP databases. Intriguingly, after a systematic assessment of the docking scores, binding modes and free energies, as well as their MD simulations performances, three compounds in TCMNP database were highlighted to be potential 5-HT2A receptor antagonists. Fascinatedly, these three hits also exhibited highly binding affinities with dopamine D2 receptor (D2R) due to the similarity of the ligand binding pockets of the receptors, indicating them to be promising dual target molecules that are of great benefit for anti-psychotic-drug research and development. In addition, ADME/Tox predictions were conducted for a primary evaluation of their developing potential. Together, this study not only revealed the exact interaction modes between 5-HT2A receptor and its antagonists, which shed a light on a better access for developing its novel antagonists, but also provided promising dual D2 and 5-HT2A receptor antagonists.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ying Wang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Johnson Joseph
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Yinli Gao
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Xiaohui Geng
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Di Wu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang, People's Republic of China.,School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
| | - Fengjiao Zhang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, People's Republic of China
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17
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Carvedilol serves as a novel CYP1B1 inhibitor, a systematic drug repurposing approach through structure-based virtual screening and experimental verification. Eur J Med Chem 2020; 193:112235. [DOI: 10.1016/j.ejmech.2020.112235] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/22/2020] [Accepted: 03/11/2020] [Indexed: 01/07/2023]
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18
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Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power. PLoS Comput Biol 2020; 16:e1007600. [PMID: 31917825 PMCID: PMC7041857 DOI: 10.1371/journal.pcbi.1007600] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 02/25/2020] [Accepted: 12/11/2019] [Indexed: 01/30/2023] Open
Abstract
Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are a complementary route. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We have now extended the method to design an enzyme for specific transition state binding, i.e., for its catalytic power. We considered methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We suggest the present method should become the paradigm for computational enzyme design. Designed enzymes are of major interest. Experimental directed evolution still has significant limitations, and computational approaches are another route. Enzymes must be stable, bind substrates, and be powerful catalysts. It is challenging to design for all these properties. A method to design substrate binding was proposed recently. It used an adaptive Monte Carlo method to explore mutations of a few amino acids near the substrate. A bias energy was gradually “learned” such that, in the absence of the ligand, the simulation visited most of the possible protein mutations with comparable probabilities. Remarkably, a simulation of the protein:ligand complex, including the bias, will then preferentially sample tight-binding sequences. We generalized the method to design binding specificity. We tested it for the methionyl-tRNA synthetase enzyme, which has been engineered in order to expand the genetic code. We redesigned the enzyme to obtain variants with low activation free energies for the catalytic step. The variants proposed by the simulations were shown experimentally to be active, and the predicted activation free energies were in reasonable agreement with the experimental values. We expect the new method will become the paradigm for computational enzyme design.
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19
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Villa F, Simonson T. Protein pKa’s from Adaptive Landscape Flattening Instead of Constant-pH Simulations. J Chem Theory Comput 2018; 14:6714-6721. [DOI: 10.1021/acs.jctc.8b00970] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Francesco Villa
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
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20
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Villa F, Panel N, Chen X, Simonson T. Adaptive landscape flattening in amino acid sequence space for the computational design of protein:peptide binding. J Chem Phys 2018; 149:072302. [PMID: 30134674 DOI: 10.1063/1.5022249] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
For the high throughput design of protein:peptide binding, one must explore a vast space of amino acid sequences in search of low binding free energies. This complex problem is usually addressed with either simple heuristic scoring or expensive sequence enumeration schemes. Far more efficient than enumeration is a recent Monte Carlo approach that adaptively flattens the energy landscape in sequence space of the unbound peptide and provides formally exact binding free energy differences. The method allows the binding free energy to be used directly as the design criterion. We propose several improvements that allow still more efficient sampling and can address larger design problems. They include the use of Replica Exchange Monte Carlo and landscape flattening for both the unbound and bound peptides. We used the method to design peptides that bind to the PDZ domain of the Tiam1 signaling protein and could serve as inhibitors of its activity. Four peptide positions were allowed to mutate freely. Almost 75 000 peptide variants were processed in two simulations of 109 steps each that used 1 CPU hour on a desktop machine. 96% of the theoretical sequence space was sampled. The relative binding free energies agreed qualitatively with values from experiment. The sampled sequences agreed qualitatively with an experimental library of Tiam1-binding peptides. The main assumption limiting accuracy is the fixed backbone approximation, which could be alleviated in future work by using increased computational resources and multi-backbone designs.
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Affiliation(s)
- Francesco Villa
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Xingyu Chen
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
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21
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Setiawan D, Brender J, Zhang Y. Recent advances in automated protein design and its future challenges. Expert Opin Drug Discov 2018; 13:587-604. [PMID: 29695210 DOI: 10.1080/17460441.2018.1465922] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.
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Affiliation(s)
- Dani Setiawan
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA
| | - Jeffrey Brender
- b Radiation Biology Branch , Center for Cancer Research, National Cancer Institute - NIH , Bethesda , MD , USA
| | - Yang Zhang
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA.,c Department of Biological Chemistry , University of Michigan , Ann Arbor , MI , USA
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22
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Gaillard T, Simonson T. Full Protein Sequence Redesign with an MMGBSA Energy Function. J Chem Theory Comput 2017; 13:4932-4943. [DOI: 10.1021/acs.jctc.7b00202] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Thomas Gaillard
- Laboratoire de Biochimie
(CNRS UMR7654), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie
(CNRS UMR7654), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France
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23
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Michael E, Polydorides S, Simonson T, Archontis G. Simple models for nonpolar solvation: Parameterization and testing. J Comput Chem 2017; 38:2509-2519. [PMID: 28786118 DOI: 10.1002/jcc.24910] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Revised: 07/19/2017] [Accepted: 07/20/2017] [Indexed: 12/13/2022]
Abstract
Implicit solvent models are important for many biomolecular simulations. The polarity of aqueous solvent is essential and qualitatively captured by continuum electrostatics methods like Generalized Born (GB). However, GB does not account for the solvent-induced interactions between exposed hydrophobic sidechains or solute-solvent dispersion interactions. These "nonpolar" effects are often modeled through surface area (SA) energy terms, which lack realism, create mathematical singularities, and have a many-body character. We have explored an alternate, Lazaridis-Karplus (LK) gaussian energy density for nonpolar effects and a dispersion (DI) energy term proposed earlier, associated with GB electrostatics. We parameterized several combinations of GB, SA, LK, and DI energy terms, to reproduce 62 small molecule solvation free energies, 387 protein stability changes due to point mutations, and the structures of 8 protein loops. With optimized parameters, the models all gave similar results, with GBLK and GBDILK giving no performance loss compared to GBSA, and mean errors of 1.7 kcal/mol for the stability changes and 2 Å deviations for the loop conformations. The optimized GBLK model gave poor results in MD of the Trpcage mini-protein, but parameters optimized specifically for MD performed well for Trpcage and three other small proteins. Overall, the LK and DI nonpolar terms are valid alternatives to SA treatments for a range of applications. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Eleni Michael
- Department of Physics, University of Cyprus, PO20537, Nicosia, CY1678, Cyprus
| | - Savvas Polydorides
- Department of Physics, University of Cyprus, PO20537, Nicosia, CY1678, Cyprus.,Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Georgios Archontis
- Department of Physics, University of Cyprus, PO20537, Nicosia, CY1678, Cyprus
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24
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Villa F, Mignon D, Polydorides S, Simonson T. Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design. J Comput Chem 2017; 38:2396-2410. [PMID: 28749575 DOI: 10.1002/jcc.24898] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 06/30/2017] [Accepted: 07/06/2017] [Indexed: 12/13/2022]
Abstract
Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Francesco Villa
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
| | - David Mignon
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
| | - Savvas Polydorides
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
| | - Thomas Simonson
- Ecole Polytechnique, Laboratoire de Biochimie (CNRS UMR7654), Palaiseau, 91128, France
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25
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Mignon D, Panel N, Chen X, Fuentes EJ, Simonson T. Computational Design of the Tiam1 PDZ Domain and Its Ligand Binding. J Chem Theory Comput 2017; 13:2271-2289. [PMID: 28394603 DOI: 10.1021/acs.jctc.6b01255] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
PDZ domains direct protein-protein interactions and serve as models for protein design. Here, we optimized a protein design energy function for the Tiam1 and Cask PDZ domains that combines a molecular mechanics energy, Generalized Born solvent, and an empirical unfolded state model. Designed sequences were recognized as PDZ domains by the Superfamily fold recognition tool and had similarity scores comparable to natural PDZ sequences. The optimized model was used to redesign the two PDZ domains, by gradually varying the chemical potential of hydrophobic amino acids; the tendency of each position to lose or gain a hydrophobic character represents a novel hydrophobicity index. We also redesigned four positions in the Tiam1 PDZ domain involved in peptide binding specificity. The calculated affinity differences between designed variants reproduced experimental data and suggest substitutions with altered specificities.
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Affiliation(s)
- David Mignon
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Xingyu Chen
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
| | - Ernesto J Fuentes
- Department of Biochemistry, Roy J. & Lucille A. Carver College of Medicine and Holden Comprehensive Cancer Center, University of Iowa , Iowa City, Iowa 52242-1109, United States
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique , Palaiseau, France
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26
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Choong YS, Lee YV, Soong JX, Law CT, Lim YY. Computer-Aided Antibody Design: An Overview. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1053:221-243. [PMID: 29549642 DOI: 10.1007/978-3-319-72077-7_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.
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Affiliation(s)
- Yee Siew Choong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia.
| | - Yie Vern Lee
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Jia Xin Soong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Cheh Tat Law
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Yee Ying Lim
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
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27
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Park H, Bradley P, Greisen P, Liu Y, Mulligan VK, Kim DE, Baker D, DiMaio F. Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules. J Chem Theory Comput 2016; 12:6201-6212. [PMID: 27766851 PMCID: PMC5515585 DOI: 10.1021/acs.jctc.6b00819] [Citation(s) in RCA: 275] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.
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Affiliation(s)
- Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Philip Bradley
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., Seattle, Washington 98019, USA
| | - Per Greisen
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Yuan Liu
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Vikram Khipple Mulligan
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - David E. Kim
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Howard Hughes Medical Institute, University of Washington, Box 357370, Seattle, Washington 98195, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Howard Hughes Medical Institute, University of Washington, Box 357370, Seattle, Washington 98195, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
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28
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Druart K, Bigot J, Audit E, Simonson T. A Hybrid Monte Carlo Scheme for Multibackbone Protein Design. J Chem Theory Comput 2016; 12:6035-6048. [DOI: 10.1021/acs.jctc.6b00421] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Karen Druart
- Laboratoire
de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
- Maison
de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Julien Bigot
- Maison
de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Edouard Audit
- Maison
de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Thomas Simonson
- Laboratoire
de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
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29
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Xun S, Jiang F, Wu YD. Intrinsically disordered regions stabilize the helical form of the C-terminal domain of RfaH: A molecular dynamics study. Bioorg Med Chem 2016; 24:4970-4977. [DOI: 10.1016/j.bmc.2016.08.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 08/07/2016] [Accepted: 08/08/2016] [Indexed: 02/07/2023]
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30
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Mignon D, Simonson T. Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic. J Comput Chem 2016; 37:1781-93. [PMID: 27197555 DOI: 10.1002/jcc.24393] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Revised: 02/26/2016] [Accepted: 03/27/2016] [Indexed: 01/11/2023]
Abstract
Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- David Mignon
- Laboratoire De Biochimie (UMR CNRS 7654), Department Of Biology, Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire De Biochimie (UMR CNRS 7654), Department Of Biology, Ecole Polytechnique, Palaiseau, France
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31
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Gaillard T, Panel N, Simonson T. Protein side chain conformation predictions with an MMGBSA energy function. Proteins 2016; 84:803-19. [PMID: 26948696 DOI: 10.1002/prot.25030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 02/22/2016] [Accepted: 02/27/2016] [Indexed: 12/17/2022]
Abstract
The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Thomas Gaillard
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France
| | - Nicolas Panel
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France
| | - Thomas Simonson
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France
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32
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Polydorides S, Michael E, Mignon D, Druart K, Archontis G, Simonson T. Proteus and the Design of Ligand Binding Sites. Methods Mol Biol 2016; 1414:77-97. [PMID: 27094287 DOI: 10.1007/978-1-4939-3569-7_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This chapter describes the organization and use of Proteus, a multitool computational suite for the optimization of protein and ligand conformations and sequences, and the calculation of pK α shifts and relative binding affinities. The software offers the use of several molecular mechanics force fields and solvent models, including two generalized Born variants, and a large range of scoring functions, which can combine protein stability, ligand affinity, and ligand specificity terms, for positive and negative design. We present in detail the steps for structure preparation, system setup, construction of the interaction energy matrix, protein sequence and structure optimizations, pK α calculations, and ligand titration calculations. We discuss illustrative examples, including the chemical/structural optimization of a complex between the MHC class II protein HLA-DQ8 and the vinculin epitope, and the chemical optimization of the compstatin analog Ac-Val4Trp/His9Ala, which regulates the function of protein C3 of the complement system.
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Affiliation(s)
- Savvas Polydorides
- Theoretical and Computational Biophysics Group, Department of Physics, University of Cyprus, 1678, Nicosia, Cyprus
| | - Eleni Michael
- Theoretical and Computational Biophysics Group, Department of Physics, University of Cyprus, 1678, Nicosia, Cyprus
| | - David Mignon
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
| | - Karen Druart
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
| | - Georgios Archontis
- Theoretical and Computational Biophysics Group, Department of Physics, University of Cyprus, 1678, Nicosia, Cyprus.
| | - Thomas Simonson
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
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33
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Niu Y, Pan D, Shi D, Bai Q, Liu H, Yao X. Influence of Chirality of Crizotinib on Its MTH1 Protein Inhibitory Activity: Insight from Molecular Dynamics Simulations and Binding Free Energy Calculations. PLoS One 2015; 10:e0145219. [PMID: 26677850 PMCID: PMC4683072 DOI: 10.1371/journal.pone.0145219] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 11/30/2015] [Indexed: 12/12/2022] Open
Abstract
As a promising target for the treatment of lung cancer, the MutT Homolog 1 (MTH1) protein can be inhibited by crizotinib. A recent work shows that the inhibitory potency of (S)-crizotinib against MTH1 is about 20 times over that of (R)-crizotinib. But the detailed molecular mechanism remains unclear. In this study, molecular dynamics (MD) simulations and free energy calculations were used to elucidate the mechanism about the effect of chirality of crizotinib on the inhibitory activity against MTH1. The binding free energy of (S)-crizotinib predicted by the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Adaptive biasing force (ABF) methodologies is much lower than that of (R)-crizotinib, which is consistent with the experimental data. The analysis of the individual energy terms suggests that the van der Waals interactions are important for distinguishing the binding of (S)-crizotinib and (R)-crizotinib. The binding free energy decomposition analysis illustrated that residues Tyr7, Phe27, Phe72 and Trp117 were important for the selective binding of (S)-crizotinib to MTH1. The adaptive biasing force (ABF) method was further employed to elucidate the unbinding process of (S)-crizotinib and (R)-crizotinib from the binding pocket of MTH1. ABF simulation results suggest that the reaction coordinates of the (S)-crizotinib from the binding pocket is different from (R)-crizotinib. The results from our study can reveal the details about the effect of chirality on the inhibition activity of crizotinib to MTH1 and provide valuable information for the design of more potent inhibitors.
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Affiliation(s)
- Yuzhen Niu
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou, 730000, China
| | - Dabo Pan
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou, 730000, China
| | - Danfeng Shi
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou, 730000, China
| | - Qifeng Bai
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou, 730000, China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
- The Separating Scientific Institute of Lanzhou, Lanzhou, 730000, China
| | - Xiaojun Yao
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou, 730000, China
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Lanzhou University, Lanzhou, 730000, China
- * E-mail:
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34
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Druart K, Palmai Z, Omarjee E, Simonson T. Protein:Ligand binding free energies: A stringent test for computational protein design. J Comput Chem 2015; 37:404-15. [PMID: 26503829 DOI: 10.1002/jcc.24230] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 10/01/2015] [Accepted: 10/02/2015] [Indexed: 01/29/2023]
Abstract
A computational protein design method is extended to allow Monte Carlo simulations where two ligands are titrated into a protein binding pocket, yielding binding free energy differences. These provide a stringent test of the physical model, including the energy surface and sidechain rotamer definition. As a test, we consider tyrosyl-tRNA synthetase (TyrRS), which has been extensively redesigned experimentally. We consider its specificity for its substrate l-tyrosine (l-Tyr), compared to the analogs d-Tyr, p-acetyl-, and p-azido-phenylalanine (ac-Phe, az-Phe). We simulate l- and d-Tyr binding to TyrRS and six mutants, and compare the structures and binding free energies to a more rigorous "MD/GBSA" procedure: molecular dynamics with explicit solvent for structures and a Generalized Born + Surface Area model for binding free energies. Next, we consider l-Tyr, ac- and az-Phe binding to six other TyrRS variants. The titration results are sensitive to the precise rotamer definition, which involves a short energy minimization for each sidechain pair to help relax bad contacts induced by the discrete rotamer set. However, when designed mutant structures are rescored with a standard GBSA energy model, results agree well with the more rigorous MD/GBSA. As a third test, we redesign three amino acid positions in the substrate coordination sphere, with either l-Tyr or d-Tyr as the ligand. For two, we obtain good agreement with experiment, recovering the wildtype residue when l-Tyr is the ligand and a d-Tyr specific mutant when d-Tyr is the ligand. For the third, we recover His with either ligand, instead of wildtype Gln.
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Affiliation(s)
- Karen Druart
- Laboratoire De Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, Palaiseau, France
| | - Zoltan Palmai
- Laboratoire De Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, Palaiseau, France
| | - Eyaz Omarjee
- Laboratoire De Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire De Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, Palaiseau, France
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35
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LuCore SD, Litman JM, Powers KT, Gao S, Lynn AM, Tollefson WTA, Fenn TD, Washington MT, Schnieders MJ. Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures. Biophys J 2015; 109:816-26. [PMID: 26287633 PMCID: PMC4547145 DOI: 10.1016/j.bpj.2015.06.062] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 06/07/2015] [Accepted: 06/29/2015] [Indexed: 11/15/2022] Open
Abstract
A balance of van der Waals, electrostatic, and hydrophobic forces drive the folding and packing of protein side chains. Although such interactions between residues are often approximated as being pairwise additive, in reality, higher-order many-body contributions that depend on environment drive hydrophobic collapse and cooperative electrostatics. Beginning from dead-end elimination, we derive the first algorithm, to our knowledge, capable of deterministic global repacking of side chains compatible with many-body energy functions. The approach is applied to seven PCNA x-ray crystallographic data sets with resolutions 2.5-3.8 Å (mean 3.0 Å) using an open-source software. While PDB_REDO models average an Rfree value of 29.5% and MOLPROBITY score of 2.71 Å (77th percentile), dead-end elimination with the polarizable AMOEBA force field lowered Rfree by 2.8-26.7% and improved mean MOLPROBITY score to atomic resolution at 1.25 Å (100th percentile). For structural biology applications that depend on side-chain repacking, including x-ray refinement, homology modeling, and protein design, the accuracy limitations of pairwise additivity can now be eliminated via polarizable or quantum mechanical potentials.
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Affiliation(s)
- Stephen D LuCore
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Jacob M Litman
- Department of Biochemistry, University of Iowa, Iowa City, Iowa
| | - Kyle T Powers
- Department of Biochemistry, University of Iowa, Iowa City, Iowa
| | - Shibo Gao
- Department of Biochemistry, University of Iowa, Iowa City, Iowa
| | - Ava M Lynn
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | | | | | | | - Michael J Schnieders
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa; Department of Biochemistry, University of Iowa, Iowa City, Iowa.
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36
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Spiriti J, Zuckerman DM. Tunable Coarse Graining for Monte Carlo Simulations of Proteins via Smoothed Energy Tables: Direct and Exchange Simulations. J Chem Theory Comput 2014; 10:5161-5177. [PMID: 25400525 PMCID: PMC4230378 DOI: 10.1021/ct500622z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Indexed: 12/03/2022]
Abstract
Many commonly used coarse-grained models for proteins are based on simplified interaction sites and consequently may suffer from significant limitations, such as the inability to properly model protein secondary structure without the addition of restraints. Recent work on a benzene fluid (Lettieri S.; Zuckerman D. M.J. Comput. Chem.2012, 33, 268-275) suggested an alternative strategy of tabulating and smoothing fully atomistic orientation-dependent interactions among rigid molecules or fragments. Here we report our initial efforts to apply this approach to the polar and covalent interactions intrinsic to polypeptides. We divide proteins into nearly rigid fragments, construct distance and orientation-dependent tables of the atomistic interaction energies between those fragments, and apply potential energy smoothing techniques to those tables. The amount of smoothing can be adjusted to give coarse-grained models that range from the underlying atomistic force field all the way to a bead-like coarse-grained model. For a moderate amount of smoothing, the method is able to preserve about 70-90% of the α-helical structure while providing a factor of 3-10 improvement in sampling per unit computation time (depending on how sampling is measured). For a greater amount of smoothing, multiple folding-unfolding transitions of the peptide were observed, along with a factor of 10-100 improvement in sampling per unit computation time, although the time spent in the unfolded state was increased compared with less smoothed simulations. For a β hairpin, secondary structure is also preserved, albeit for a narrower range of the smoothing parameter and, consequently, for a more modest improvement in sampling. We have also applied the new method in a "resolution exchange" setting, in which each replica runs a Monte Carlo simulation with a different degree of smoothing. We obtain exchange rates that compare favorably to our previous efforts at resolution exchange (Lyman E.; Zuckerman D. M.J. Chem. Theory Comput.2006, 2, 656-666).
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Affiliation(s)
- Justin Spiriti
- Department of Computational
and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
| | - Daniel M. Zuckerman
- Department of Computational
and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
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