1
|
Akkus E, Tayfuroglu O, Yildiz M, Kocak A. Revisiting MMPBSA by Adoption of MC-Based Surface Area/Volume, ANI-ML Potentials, and Two-Valued Interior Dielectric Constant. J Phys Chem B 2023; 127:4415-4429. [PMID: 37171911 DOI: 10.1021/acs.jpcb.3c00834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Here, we report the accuracy improvements of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM terms, the use of solvent-accessible surface area (SASA) and volume (SAV) values from the Monte Carlo sampling of the probe, and introducing two different interior dielectric constants for electrostatic interactions of protein-ligand (P-L) and polar solvation term in the MMPBSA calculations. Our results show that the Pearson correlation coefficients of MMPBSA-calculated values with respect to experimental binding free energies can be drastically improved from 0.48 to 0.90 by adoption of ANI-ML potentials in replacement of MM energy terms in the equation, referred to as ANI-PBSA. Moreover, we show that the SASA/SAV-combined equation in the scaled particle theory (SPT) can be a better choice to model nonpolar solvation term, reaching nearly the same accuracy by ANI-PBSA calculations. Finally, we introduce two different values of interior dielectric constants, which could be an alternative strategy between the single and variable constant definitions.
Collapse
Affiliation(s)
- Ebru Akkus
- Department of Chemistry, Gebze Technical University, 41400 Kocaeli, Turkey
| | - Omer Tayfuroglu
- Department of Chemistry, Gebze Technical University, 41400 Kocaeli, Turkey
| | - Muslum Yildiz
- Department of Molecular Biology and Genetics, Gebze Technical University, 41400 Kocaeli, Turkey
| | - Abdulkadir Kocak
- Department of Chemistry, Gebze Technical University, 41400 Kocaeli, Turkey
| |
Collapse
|
2
|
de Paula Junior VF, van Tilburg MF, Morais PA, Júnior FFM, Lima EG, Oliveira VTDS, Guedes MIF, Caetano EWS, Freire VN. Quantum Biochemistry and MM-PBSA Description of the ZIKV NS2B-NS3 Protease: Insights into the Binding Interactions beyond the Catalytic Triad Pocket. Int J Mol Sci 2022; 23:ijms231710088. [PMID: 36077486 PMCID: PMC9456192 DOI: 10.3390/ijms231710088] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
The Zika virus protease NS2B-NS3 has a binding site formed with the participation of a H51-D75-S135 triad presenting two forms, active and inactive. Studies suggest that the inactive conformation is a good target for the design of inhibitors. In this paper, we evaluated the co-crystallized structures of the protease with the inhibitors benzoic acid (5YOD) and benzimidazole-1-ylmethanol (5H4I). We applied a protocol consisting of two steps: first, classical molecular mechanics energy minimization followed by classical molecular dynamics were performed, obtaining stabilized molecular geometries; second, the optimized/relaxed geometries were used in quantum biochemistry and molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) calculations to estimate the ligand interactions with each amino acid residue of the binding pocket. We show that the quantum-level results identified essential residues for the stabilization of the 5YOD and 5H4I complexes after classical energy minimization, matching previously published experimental data. The same success, however, was not observed for the MM-PBSA simulations. The application of quantum biochemistry methods seems to be more promising for the design of novel inhibitors acting on NS2B-NS3.
Collapse
Affiliation(s)
- Valdir Ferreira de Paula Junior
- Biotechnology & Molecular Biology Laboratory, State University of Ceará, Fortaleza 60714-903, Brazil
- Correspondence: ; Tel.: +55-859-8541-8255
| | | | - Pablo Abreu Morais
- Federal Institute of Education, Science and Technology of Ceará, Campus Horizonte, Horizonte 62884-105, Brazil
| | - Francisco Franciné Maia Júnior
- Departamento de Ciências Naturais, Matemática e Estatística, Universidade Federal Rural do Semi-Árido, Mossoró 59625-900, Brazil
| | - Elza Gadelha Lima
- Laboratório Central de Saúde Pública do Ceará (LACEN), Fortaleza 60120-002, Brazil
| | | | | | | | | |
Collapse
|
3
|
Zhou X, Shi M, Wang X, Xu D. Exploring the Binding Mechanism of a Supramolecular Tweezer CLR01 to 14-3-3σ Protein via Well-Tempered Metadynamics. Front Chem 2022; 10:921695. [PMID: 35646830 PMCID: PMC9133541 DOI: 10.3389/fchem.2022.921695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 04/25/2022] [Indexed: 11/17/2022] Open
Abstract
Using supramolecules for protein function regulation is an effective strategy in chemical biology and drug discovery. However, due to the presence of multiple binding sites on protein surfaces, protein function regulation via selective binding of supramolecules is challenging. Recently, the functions of 14-3-3 proteins, which play an important role in regulating intracellular signaling pathways via protein–protein interactions, have been modulated using a supramolecular tweezer, CLR01. However, the binding mechanisms of the tweezer molecule to 14-3-3 proteins are still unclear, which has hindered the development of novel supramolecules targeting the 14-3-3 proteins. Herein, the binding mechanisms of the tweezer to the lysine residues on 14-3-3σ (an isoform in 14-3-3 protein family) were explored by well-tempered metadynamics. The results indicated that the inclusion complex formed between the protein and supramolecule is affected by both kinetic and thermodynamic factors. In particular, simulations confirmed that K214 could form a strong binding complex with the tweezer; the binding free energy was calculated to be −10.5 kcal·mol−1 with an association barrier height of 3.7 kcal·mol−1. In addition, several other lysine residues on 14-3-3σ were identified as being well-recognized by the tweezer, which agrees with experimental results, although only K214/tweezer was co-crystallized. Additionally, the binding mechanisms of the tweezer to all lysine residues were analyzed by exploring the representative conformations during the formation of the inclusion complex. This could be helpful for the development of new inhibitors based on tweezers with more functions against 14-3-3 proteins via modifications of CLR01. We also believe that the proposed computational strategies can be extended to understand the binding mechanism of multi-binding sites proteins with supramolecules and will, thus, be useful toward drug design.
Collapse
Affiliation(s)
- Xin Zhou
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, China
| | - Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Wang
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, China
- *Correspondence: Xin Wang, ; Dingguo Xu,
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, China
- Research Center for Material Genome Engineering, Sichuan University, Chengdu, China
- *Correspondence: Xin Wang, ; Dingguo Xu,
| |
Collapse
|
4
|
Jafari S, Tavares Santos YA, Bergmann J, Irani M, Ryde U. Benchmark Study of Redox Potential Calculations for Iron-Sulfur Clusters in Proteins. Inorg Chem 2022; 61:5991-6007. [PMID: 35403427 PMCID: PMC9044450 DOI: 10.1021/acs.inorgchem.1c03422] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Redox potentials
have been calculated for 12 different iron–sulfur
sites of 6 different types with 1–4 iron ions. Structures were
optimized with combined quantum mechanical and molecular mechanical
(QM/MM) methods, and the redox potentials were calculated using the
QM/MM energies, single-point QM methods in a continuum solvent or
by QM/MM thermodynamic cycle perturbations. We show that the best
results are obtained with a large QM system (∼300 atoms, but
a smaller QM system, ∼150 atoms, can be used for the QM/MM
geometry optimization) and a large value of the dielectric constant
(80). For absolute redox potentials, the B3LYP density functional
method gives better results than TPSS, and the results are improved
with a larger basis set. However, for relative redox potentials, the
opposite is true. The results are insensitive to the force field (charges
of the surroundings) used for the QM/MM calculations or whether the
protein and solvent outside the QM system are relaxed or kept fixed
at the crystal structure. With the best approach for relative potentials,
mean absolute and maximum deviations of 0.17 and 0.44 V, respectively,
are obtained after removing a systematic error of −0.55 V.
Such an approach can be used to identify the correct oxidation states
involved in a certain redox reaction. We
have studied redox potentials of 12 iron−sulfur
sites of 6 types with 1−4 iron ions. Structures were optimized
with combined quantum mechanical and molecular mechanical (QM/MM)
methods, and the redox potentials were calculated with QM/MM, QM calculations
in a continuum solvent or by QM/MM thermodynamic cycle perturbations.
The best results are obtained with the second approach using ∼300
atoms in the QM model and a large dielectric constant.
Collapse
Affiliation(s)
- Sonia Jafari
- Department of Chemistry, University of Kurdistan, 66175-416 Sanandaj, Iran.,Department of Theoretical Chemistry, Chemical Centre, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Yakini A Tavares Santos
- Department of Theoretical Chemistry, Chemical Centre, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Justin Bergmann
- Department of Theoretical Chemistry, Chemical Centre, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Mehdi Irani
- Department of Chemistry, University of Kurdistan, 66175-416 Sanandaj, Iran
| | - Ulf Ryde
- Department of Theoretical Chemistry, Chemical Centre, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden
| |
Collapse
|
5
|
Wang E, Fu W, Jiang D, Sun H, Wang J, Zhang X, Weng G, Liu H, Tao P, Hou T. VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations. J Chem Inf Model 2021; 61:2844-2856. [PMID: 34014672 DOI: 10.1021/acs.jcim.1c00091] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.
Collapse
Affiliation(s)
- Ercheng Wang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Weitao Fu
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dejun Jiang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
| | - Huiyong Sun
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Peng Tao
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| |
Collapse
|
6
|
Boz E, Stein M. Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling. Int J Mol Sci 2021; 22:3078. [PMID: 33802920 PMCID: PMC8002627 DOI: 10.3390/ijms22063078] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/13/2021] [Accepted: 03/15/2021] [Indexed: 12/22/2022] Open
Abstract
Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical 'Geometry, Frequency, Noncovalent, eXtended Tight Binding' (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent 'Statistical Assessment of the Modeling of Proteins and Ligands' (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.
Collapse
Affiliation(s)
| | - Matthias Stein
- Max Planck Institute for Dynamics of Complex Technical Systems, Molecular Simulations and Design Group, 39106 Magdeburg, Germany;
| |
Collapse
|
7
|
Kogut MM, Maszota-Zieleniak M, Marcisz M, Samsonov SA. Computational insights into the role of calcium ions in protein–glycosaminoglycan systems. Phys Chem Chem Phys 2021; 23:3519-3530. [DOI: 10.1039/d0cp05438k] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The prediction power of computational methodologies for studying the role of ions in protein–glycosaminoglycan interactions was critically assessed.
Collapse
|
8
|
Španinger E, Potočnik U, Bren U. Molecular Dynamics Simulations Predict That rSNP Located in the HNF‑1α Gene Promotor Region Linked with MODY3 and Hepatocellular Carcinoma Promotes Stronger Binding of the HNF‑4α Transcription Factor. Biomolecules 2020; 10:biom10121700. [PMID: 33371430 PMCID: PMC7767403 DOI: 10.3390/biom10121700] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/06/2020] [Accepted: 12/18/2020] [Indexed: 12/24/2022] Open
Abstract
Our study aims to investigate the impact of the Maturity-onset diabetes of the young 3 disease-linked rSNP rs35126805 located in the HNF-1α gene promotor on the binding of the transcription factor HNF-4α and consequently on the regulation of HNF-1α gene expression. Our focus is to calculate the change in the binding affinity of the transcription factor HNF-4α to the DNA, caused by the regulatory single nucleotide polymorphism (rSNP) through molecular dynamics simulations and thermodynamic analysis of acquired results. Both root-mean-square difference (RMSD) and the relative binding free energy ΔΔGbind reveal that the HNF-4α binds slightly more strongly to the DNA containing the mutation (rSNP) making the complex more stable/rigid, and thereby influencing the expression of the HNF-1α gene. The resulting disruption of the HNF-4α/HNF-1α pathway is also linked to hepatocellular carcinoma metastasis and enhanced apoptosis in pancreatic cancer cells. To the best of our knowledge, this represents the first study where thermodynamic analysis of the results obtained from molecular dynamics simulations is performed to uncover the influence of rSNP on the protein binding to DNA. Therefore, our approach can be generally applied for studying the impact of regulatory single nucleotide polymorphisms on the binding of transcription factors to the DNA.
Collapse
Affiliation(s)
- Eva Španinger
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia; (E.Š.); (U.P.)
| | - Uroš Potočnik
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia; (E.Š.); (U.P.)
- Faculty of Medicine, University of Maribor, Taborska 8, SI-2000 Maribor, Slovenia
| | - Urban Bren
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia; (E.Š.); (U.P.)
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
- Correspondence: ; Tel.: +386-2-2294-421
| |
Collapse
|
9
|
Wang E, Liu H, Wang J, Weng G, Sun H, Wang Z, Kang Y, Hou T. Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein–Ligand Binding Affinities. J Chem Inf Model 2020; 60:5353-5365. [DOI: 10.1021/acs.jcim.0c00024] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Yu Kang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| |
Collapse
|
10
|
Sasmal S, El Khoury L, Mobley DL. D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors. J Comput Aided Mol Des 2020; 34:163-177. [PMID: 31781990 PMCID: PMC8208075 DOI: 10.1007/s10822-019-00249-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 11/06/2019] [Indexed: 01/05/2023]
Abstract
The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the [Formula: see text]-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.
Collapse
Affiliation(s)
- Sukanya Sasmal
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - Léa El Khoury
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA.
- Department of Chemistry, University of California, Irvine, CA, 92697, USA.
| |
Collapse
|
11
|
Limongelli V. Ligand binding free energy and kinetics calculation in 2020. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1455] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science – Center for Computational Medicine in Cardiology Università della Svizzera italiana (USI) Lugano Switzerland
- Department of Pharmacy University of Naples “Federico II” Naples Italy
| |
Collapse
|
12
|
Wang E, Weng G, Sun H, Du H, Zhu F, Chen F, Wang Z, Hou T. Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions. Phys Chem Chem Phys 2019; 21:18958-18969. [PMID: 31453590 DOI: 10.1039/c9cp04096j] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (rp = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (rp = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.
Collapse
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Hongyan Du
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Feng Zhu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Fu Chen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China. and State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
| |
Collapse
|
13
|
Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem Rev 2019; 119:9478-9508. [DOI: 10.1021/acs.chemrev.9b00055] [Citation(s) in RCA: 578] [Impact Index Per Article: 115.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU−ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200122, China
- Department of Chemistry, New York University, New York, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| |
Collapse
|
14
|
Belinskaia DA, Avdonin PV, Avdonin PP, Jenkins RO, Goncharov NV. Rational in silico design of aptamers for organophosphates based on the example of paraoxon. Comput Biol Chem 2019; 80:452-462. [PMID: 31170561 DOI: 10.1016/j.compbiolchem.2019.05.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 01/20/2019] [Accepted: 05/08/2019] [Indexed: 01/14/2023]
Abstract
Poisoning by organophosphates (OPs) takes one of the leading places in the total number of exotoxicoses. Detoxication of OPs at the first stage of the poison entering the body could be achieved with the help of DNA- or RNA-aptamers, which are able to bind poisons in the bloodstream. The aim of the research was to develop an approach to rational in silico design of aptamers for OPs based on the example of paraoxon. From the published sequence of an aptamer binding organophosphorus pesticides, its three-dimensional model has been constructed. The most probable binding site for paraoxon was determined by molecular docking and molecular dynamics (MD) methods. Then the nucleotides of the binding site were mutated consequently and the values of free binding energy have been calculated using MD trajectories and MM-PBSA approach. On the basis of the energy values, two sequences that bind paraoxon most efficiently have been selected. The value of free binding energy of paraoxon with peripheral anionic site of acetylcholinesterase (AChE) has been calculated as well. It has been revealed that the aptamers found bind paraoxon more effectively than AChE. The peculiarities of paraoxon interaction with the aptamers nucleotides have been analyzed. The possibility of improving in silico approach for aptamer selection is discussed.
Collapse
Affiliation(s)
- Daria A Belinskaia
- Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia.
| | - Pavel V Avdonin
- Koltzov Institute of Developmental Biology, Russian Academy of Sciences, 26 Vavilova str., Moscow 119334, Russia
| | - Piotr P Avdonin
- Koltzov Institute of Developmental Biology, Russian Academy of Sciences, 26 Vavilova str., Moscow 119334, Russia
| | - Richard O Jenkins
- Leicester School of Allied Health Sciences, De Montfort University, The Gateway, Leicester LE1 9BH, UK
| | - Nikolay V Goncharov
- Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia; Research Institute of Hygiene, Occupational Pathology and Human Ecology, bld.93 p.o.Kuz'molovsky, Leningrad Region 188663, Russia
| |
Collapse
|
15
|
Terayama K, Iwata H, Araki M, Okuno Y, Tsuda K. Machine learning accelerates MD-based binding pose prediction between ligands and proteins. Bioinformatics 2018; 34:770-778. [PMID: 29040432 PMCID: PMC6030886 DOI: 10.1093/bioinformatics/btx638] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 10/10/2017] [Indexed: 01/28/2023] Open
Abstract
Motivation Fast and accurate prediction of protein–ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein–ligand binding poses can be improved with multiple runs at different initial velocity. Results This paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources. Availability and implementation Code and data are available on GitHub at https://github.com/tsudalab/bpbi. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Kei Terayama
- Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan
| | - Hiroaki Iwata
- Foundation for Biomedical Research and Innovation, Hyogo 650-0047, Japan
| | - Mitsugu Araki
- RIKEN Advanced Institute for Computational Science, Hyogo 650-0047, Japan
| | - Yasushi Okuno
- RIKEN Advanced Institute for Computational Science, Hyogo 650-0047, Japan.,Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Koji Tsuda
- Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.,Center for Materials Research by Information Integration, NIMS, Ibaraki 305-0047, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| |
Collapse
|
16
|
Ahmad S, Raza S, Abbasi SW, Azam SS. Identification of natural inhibitors against Acinetobacter baumannii d-alanine-d-alanine ligase enzyme: A multi-spectrum in silico approach. J Mol Liq 2018. [DOI: 10.1016/j.molliq.2018.04.124] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
17
|
Izadi S, Harris RC, Fenley MO, Onufriev AV. Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies. J Chem Theory Comput 2018; 14:1656-1670. [PMID: 29378399 DOI: 10.1021/acs.jctc.7b00886] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ Gel) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ Gel estimates varies widely between different GB models and also across different types of biomolecular complexes, with R2 correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R2 = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ Gel. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.
Collapse
Affiliation(s)
- Saeed Izadi
- Early Stage Pharmaceutical Development , Genentech Inc. , 1 DNA Way , South San Francisco , California 94080 , United States
| | - Robert C Harris
- Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States
| | - Marcia O Fenley
- Institute of Molecular Biophysics , Florida State University , Tallahassee , Florida 32306-3408 , United States
| | | |
Collapse
|
18
|
Wang C, Greene D, Xiao L, Qi R, Luo R. Recent Developments and Applications of the MMPBSA Method. Front Mol Biosci 2018; 4:87. [PMID: 29367919 PMCID: PMC5768160 DOI: 10.3389/fmolb.2017.00087] [Citation(s) in RCA: 332] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 11/30/2017] [Indexed: 12/23/2022] Open
Abstract
The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.
Collapse
Affiliation(s)
- Changhao Wang
- Chemical and Materials Physics Graduate Program, University of California, Irvine, Irvine, CA, United States
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
- Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, United States
| | - D'Artagnan Greene
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
| | - Li Xiao
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Ruxi Qi
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
| | - Ray Luo
- Chemical and Materials Physics Graduate Program, University of California, Irvine, Irvine, CA, United States
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Chemical Engineering and Materials Science, University of California, Irvine, Irvine, CA, United States
| |
Collapse
|
19
|
Olsson MA, García-Sosa AT, Ryde U. Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking. J Comput Aided Mol Des 2018; 32:211-224. [PMID: 28879536 PMCID: PMC5767205 DOI: 10.1007/s10822-017-0056-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 08/29/2017] [Indexed: 01/07/2023]
Abstract
We have studied the binding of 102 ligands to the farnesoid X receptor within the D3R Grand Challenge 2016 blind-prediction competition. First, we employed docking with five different docking software and scoring functions. The selected docked poses gave an average root-mean-squared deviation of 4.2 Å. Consensus scoring gave decent results with a Kendall's τ of 0.26 ± 0.06 and a Spearman's ρ of 0.41 ± 0.08. For a subset of 33 ligands, we calculated relative binding free energies with free-energy perturbation. Five transformations between the ligands involved a change of the net charge and we implemented and benchmarked a semi-analytic correction (Rocklin et al., J Chem Phys 139:184103, 2013) for artifacts caused by the periodic boundary conditions and Ewald summation. The results gave a mean absolute deviation of 7.5 kJ/mol compared to the experimental estimates and a correlation coefficient of R 2 = 0.1. These results were among the four best in this competition out of 22 submissions. The charge corrections were significant (7-8 kJ/mol) and always improved the results. By employing 23 intermediate states in the free-energy perturbation, there was a proper overlap between all states and the precision was 0.1-0.7 kJ/mol. However, thermodynamic cycles indicate that the sampling was insufficient in some of the perturbations.
Collapse
Affiliation(s)
- Martin A Olsson
- Department of Theoretical Chemistry, Chemical Centre, Lund University, P. O. Box 124, 221 00, Lund, Sweden
| | | | - Ulf Ryde
- Department of Theoretical Chemistry, Chemical Centre, Lund University, P. O. Box 124, 221 00, Lund, Sweden.
| |
Collapse
|
20
|
Manzoni F, Uranga J, Genheden S, Ryde U. Can System Truncation Speed up Ligand-Binding Calculations with Periodic Free-Energy Simulations? J Chem Inf Model 2017; 57:2865-2873. [PMID: 29076739 DOI: 10.1021/acs.jcim.7b00324] [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
We have investigated whether alchemical free-energy perturbation calculations of relative binding energies can be sped up by simulating a truncated protein. Previous studies with spherical nonperiodic systems showed that the number of simulated atoms could be reduced by a factor of 26 without affecting the calculated binding free energies by more than 0.5 kJ/mol on average ( Genheden, S.; Ryde, U. J. Chem. Theory Comput. 2012 , 8 , 1449 ), leading to a 63-fold decrease in the time consumption. However, such simulations are rather slow, owing to the need of a large cutoff radius for the nonbonded interactions. Periodic simulations with the electrostatics treated by Ewald summation are much faster. Therefore, we have investigated if a similar speed-up can be obtained also for periodic simulations. Unfortunately, our results show that it is harder to truncate periodic systems and that the truncation errors are larger for these systems. In particular, residues need to be removed from the calculations, which means that atoms have to be restrained to avoid that they move in an unrealistic manner. The results strongly depend on the strength on this restraint. For the binding of seven ligands to dihydrofolate reductase and ten inhibitors of blood-clotting factor Xa, the best results are obtained with a small restraining force constant. However, the truncation errors were still significant (e.g., 1.5-2.9 kJ/mol at a truncation radius of 10 Å). Moreover, the gain in computer time was only modest. On the other hand, if the snapshots are truncated after the MD simulations, the truncation errors are small (below 0.9 kJ/mol even for a truncation radius of 10 Å). This indicates that postprocessing with a more accurate energy function (e.g., with quantum chemistry) on truncated snapshots may be a viable approach.
Collapse
Affiliation(s)
- Francesco Manzoni
- Department of Theoretical Chemistry, Lund University, Chemical Centre , P.O. Box 124, SE-221 00 Lund, Sweden
| | - Jon Uranga
- Department of Theoretical Chemistry, Lund University, Chemical Centre , P.O. Box 124, SE-221 00 Lund, Sweden
| | - Samuel Genheden
- Department of Theoretical Chemistry, Lund University, Chemical Centre , P.O. Box 124, SE-221 00 Lund, Sweden
| | - Ulf Ryde
- Department of Theoretical Chemistry, Lund University, Chemical Centre , P.O. Box 124, SE-221 00 Lund, Sweden
| |
Collapse
|
21
|
Ben-Shalom IY, Pfeiffer-Marek S, Baringhaus KH, Gohlke H. Efficient Approximation of Ligand Rotational and Translational Entropy Changes upon Binding for Use in MM-PBSA Calculations. J Chem Inf Model 2017; 57:170-189. [DOI: 10.1021/acs.jcim.6b00373] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Ido Y. Ben-Shalom
- Institute
for Pharmaceutical and Medicinal Chemistry, Department of Mathematics
and Natural Sciences, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Stefania Pfeiffer-Marek
- LGCR/Pharmaceutical
Sciences Operations, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Karl-Heinz Baringhaus
- R&D Resources/Site Direction, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Holger Gohlke
- Institute
for Pharmaceutical and Medicinal Chemistry, Department of Mathematics
and Natural Sciences, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| |
Collapse
|
22
|
Zhang H, Yin C, Yan H, van der Spoel D. Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host–Guest Complexes. J Chem Inf Model 2016; 56:2080-2092. [DOI: 10.1021/acs.jcim.6b00418] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Haiyang Zhang
- Department
of Biological Science and Engineering, School of Chemistry and Biological
Engineering, University of Science and Technology Beijing, 100083 Beijing, China
| | - Chunhua Yin
- Department
of Biological Science and Engineering, School of Chemistry and Biological
Engineering, University of Science and Technology Beijing, 100083 Beijing, China
| | - Hai Yan
- Department
of Biological Science and Engineering, School of Chemistry and Biological
Engineering, University of Science and Technology Beijing, 100083 Beijing, China
| | - David van der Spoel
- Uppsala
Center for Computational Chemistry, Science for Life Laboratory, Department
of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box
596, SE-75124 Uppsala, Sweden
| |
Collapse
|
23
|
Misini Ignjatović M, Caldararu O, Dong G, Muñoz-Gutierrez C, Adasme-Carreño F, Ryde U. Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. J Comput Aided Mol Des 2016; 30:707-730. [PMID: 27565797 PMCID: PMC5078160 DOI: 10.1007/s10822-016-9942-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/17/2016] [Indexed: 11/25/2022]
Abstract
We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.
Collapse
Affiliation(s)
- Majda Misini Ignjatović
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Octav Caldararu
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Geng Dong
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden
| | - Camila Muñoz-Gutierrez
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Francisco Adasme-Carreño
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Ulf Ryde
- Department of Theoretical Chemistry, Lund University, Chemical Centre, P. O. Box 124, 221 00, Lund, Sweden.
| |
Collapse
|
24
|
Ryde U, Söderhjelm P. Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods. Chem Rev 2016; 116:5520-66. [DOI: 10.1021/acs.chemrev.5b00630] [Citation(s) in RCA: 175] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ulf Ryde
- Department of Theoretical
Chemistry and ‡Department of Biophysical Chemistry, Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Pär Söderhjelm
- Department of Theoretical
Chemistry and ‡Department of Biophysical Chemistry, Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
| |
Collapse
|
25
|
Homeyer N, Ioannidis H, Kolarov F, Gauglitz G, Zikos C, Kolocouris A, Gohlke H. Interpreting Thermodynamic Profiles of Aminoadamantane Compounds Inhibiting the M2 Proton Channel of Influenza A by Free Energy Calculations. J Chem Inf Model 2016; 56:110-26. [PMID: 26690735 DOI: 10.1021/acs.jcim.5b00467] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The development of novel anti-influenza drugs is of great importance because of the capability of influenza viruses to occasionally cross interspecies barriers and to rapidly mutate. One class of anti-influenza agents, aminoadamantanes, including the drugs amantadine and rimantadine now widely abandoned due to virus resistance, bind to and block the pore of the transmembrane domain of the M2 proton channel (M2TM) of influenza A. Here, we present one of the still rare studies that interprets thermodynamic profiles from isothermal titration calorimetry (ITC) experiments in terms of individual energy contributions to binding, calculated by the computationally inexpensive implicit solvent/implicit membrane molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach, for aminoadamantane compounds binding to M2TM of the avian "Weybridge" strain. For all eight pairs of aminoadamantane compounds considered, the trend of the predicted relative binding free energies and their individual components, effective binding energies and changes in the configurational entropy, agrees with experimental measures (ΔΔG, ΔΔH, TΔΔS) in 88, 88, and 50% of the cases. In addition, information yielded by the MM-PBSA approach about determinants of binding goes beyond that available in component quantities (ΔH, ΔS) from ITC measurements. We demonstrate how one can make use of such information to link thermodynamic profiles from ITC with structural causes on the ligand side and, ultimately, to guide decision making in lead optimization in a prospective manner, which results in an aminoadamantane derivative with improved binding affinity against M2TM(Weybridge).
Collapse
Affiliation(s)
- Nadine Homeyer
- Mathematisch-Naturwissenschaftliche Fakultät, Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf , 40225 Düsseldorf, Germany
| | - Harris Ioannidis
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , 15771 Athens, Greece
| | - Felix Kolarov
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität Tübingen , 72076 Tübingen, Germany
| | - Günter Gauglitz
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität Tübingen , 72076 Tübingen, Germany
| | - Christos Zikos
- Demokritos, National Center for Scientific Research , 15310 Athens, Greece
| | - Antonios Kolocouris
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , 15771 Athens, Greece
| | - Holger Gohlke
- Mathematisch-Naturwissenschaftliche Fakultät, Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf , 40225 Düsseldorf, Germany
| |
Collapse
|
26
|
Wickstrom L, Deng N, He P, Mentes A, Nguyen C, Gilson MK, Kurtzman T, Gallicchio E, Levy RM. Parameterization of an effective potential for protein-ligand binding from host-guest affinity data. J Mol Recognit 2015; 29:10-21. [PMID: 26256816 DOI: 10.1002/jmr.2489] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 06/06/2015] [Accepted: 06/07/2015] [Indexed: 12/13/2022]
Abstract
Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.
Collapse
Affiliation(s)
- Lauren Wickstrom
- Borough of Manhattan Community College, Department of Science, The City University of New York, New York, NY, 10007, USA
| | - Nanjie Deng
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Peng He
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Ahmet Mentes
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Crystal Nguyen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093-0736, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093-0736, USA
| | - Tom Kurtzman
- Department of Chemistry, Lehman College, The City University of New York, Bronx, NY, 10468, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, NY, 11210, USA
| | - Ronald M Levy
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| |
Collapse
|
27
|
Sun H, Li Y, Tian S, Xu L, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys Chem Chem Phys 2015; 16:16719-29. [PMID: 24999761 DOI: 10.1039/c4cp01388c] [Citation(s) in RCA: 553] [Impact Index Per Article: 61.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
By using different evaluation strategies, we systemically evaluated the performance of Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodologies based on more than 1800 protein-ligand crystal structures in the PDBbind database. The results can be summarized as follows: (1) for the one-protein-family/one-binding-ligand case which represents the unbiased protein-ligand complex sampling, both MM/GBSA and MM/PBSA methodologies achieve approximately equal accuracies at the interior dielectric constant of 4 (with rp = 0.408 ± 0.006 of MM/GBSA and rp = 0.388 ± 0.006 of MM/PBSA based on the minimized structures); while for the total dataset (1864 crystal structures), the overall best Pearson correlation coefficient (rp = 0.579 ± 0.002) based on MM/GBSA is better than that of MM/PBSA (rp = 0.491 ± 0.003), indicating that biased sampling may significantly affect the accuracy of the predicted result (some protein families contain too many instances and can bias the overall predicted accuracy). Therefore, family based classification is needed to evaluate the two methodologies; (2) the prediction accuracies of MM/GBSA and MM/PBSA for different protein families are quite different with rp ranging from 0 to 0.9, whereas the correlation and ranking scores (an averaged rp/rs over a list of protein folds and also representing the unbiased sampling) given by MM/PBSA (rp-score = 0.506 ± 0.050 and rs-score = 0.481 ± 0.052) are comparable to those given by MM/GBSA (rp-score = 0.516 ± 0.047 and rs-score = 0.463 ± 0.047) at the fold family level; (3) for the overall prediction accuracies, molecular dynamics (MD) simulation may not be quite necessary for MM/GBSA (rp-minimized = 0.579 ± 0.002 and rp-1ns = 0.564 ± 0.002), but is needed for MM/PBSA (rp-minimized = 0.412 ± 0.003 and rp-1ns = 0.491 ± 0.003). However, for the individual systems, whether to use MD simulation is depended. (4) both MM/GBSA and MM/PBSA may be unable to give successful predictions for the ligands with high formal charges, with the Pearson correlation coefficient ranging from 0.621 ± 0.003 (neutral ligands) to 0.125 ± 0.142 (ligands with a formal charge of 5). Therefore, it can be summarized that, although MM/GBSA and MM/PBSA perform similarly in the unbiased dataset, for the currently available crystal structures in the PDBbind database, compared with MM/GBSA, which may be used in multi-target comparisons, MM/PBSA is more sensitive to the investigated systems, and may be more suitable for individual-target-level binding free energy ranking. This study may provide useful guidance for the post-processing of docking based studies.
Collapse
Affiliation(s)
- Huiyong Sun
- Institute of Functional Nano & Soft Materials (FUNSOM) and Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, Jiangsu 215123, China.
| | | | | | | | | |
Collapse
|
28
|
Abstract
INTRODUCTION The molecular mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to estimate the free energy of the binding of small ligands to biological macromolecules. They are typically based on molecular dynamics simulations of the receptor-ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchemical perturbation methods. They have been applied to a large number of systems with varying success. AREAS COVERED The authors review the use of MM/PBSA and MM/GBSA methods to calculate ligand-binding affinities, with an emphasis on calibration, testing and validation, as well as attempts to improve the methods, rather than on specific applications. EXPERT OPINION MM/PBSA and MM/GBSA are attractive approaches owing to their modular nature and that they do not require calculations on a training set. They have been used successfully to reproduce and rationalize experimental findings and to improve the results of virtual screening and docking. However, they contain several crude and questionable approximations, for example, the lack of conformational entropy and information about the number and free energy of water molecules in the binding site. Moreover, there are many variants of the method and their performance varies strongly with the tested system. Likewise, most attempts to ameliorate the methods with more accurate approaches, for example, quantum-mechanical calculations, polarizable force fields or improved solvation have deteriorated the results.
Collapse
Affiliation(s)
- Samuel Genheden
- University of Southampton, School of Chemistry, Highfield, SO17 1BJ, Southampton, UK
| | - Ulf Ryde
- Lund University, Chemical Centre, Department of Theoretical Chemistry, P. O. Box 124, SE-221 00 Lund, Sweden+46 46 2224502; +46 46 2228648;
| |
Collapse
|
29
|
Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015; 28:581-604. [PMID: 25808539 DOI: 10.1002/jmr.2471] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 12/11/2022]
Abstract
Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.
Collapse
Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jessica Holien
- ACRF Rational Drug Discovery Centre and Structural Biology Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Victoria, 3004, Australia.,Department of Surgery Austin Health, University of Melbourne, Melbourne, Victoria, 3084, Australia.,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Victoria, 3004, Australia.,School of Biomedical Sciences, CHIRI Biosciences, Curtin University, Perth, Western Australia, 6845, Australia
| |
Collapse
|
30
|
Mikulskis P, Genheden S, Ryde U. A large-scale test of free-energy simulation estimates of protein-ligand binding affinities. J Chem Inf Model 2014; 54:2794-806. [PMID: 25264937 DOI: 10.1021/ci5004027] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We have performed a large-scale test of alchemical perturbation calculations with the Bennett acceptance-ratio (BAR) approach to estimate relative affinities for the binding of 107 ligands to 10 different proteins. Employing 20-Å truncated spherical systems and only one intermediate state in the perturbations, we obtain an error of less than 4 kJ/mol for 54% of the studied relative affinities and a precision of 0.5 kJ/mol on average. However, only four of the proteins gave acceptable errors, correlations, and rankings. The results could be improved by using nine intermediate states in the simulations or including the entire protein in the simulations using periodic boundary conditions. However, 27 of the calculated affinities still gave errors of more than 4 kJ/mol, and for three of the proteins the results were not satisfactory. This shows that the performance of BAR calculations depends on the target protein and that several transformations gave poor results owing to limitations in the molecular-mechanics force field or the restricted sampling possible within a reasonable simulation time. Still, the BAR results are better than docking calculations for most of the proteins.
Collapse
Affiliation(s)
- Paulius Mikulskis
- Department of Theoretical Chemistry, Lund University, Chemical Centre , P.O. Box 124, SE-221 00 Lund, Sweden
| | | | | |
Collapse
|
31
|
Homeyer N, Stoll F, Hillisch A, Gohlke H. Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context. J Chem Theory Comput 2014; 10:3331-44. [PMID: 26588302 DOI: 10.1021/ct5000296] [Citation(s) in RCA: 119] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.
Collapse
Affiliation(s)
- Nadine Homeyer
- Institute for Pharmaceutical and Medicinal Chemistry, Department of Mathematics and Natural Sciences, Heinrich-Heine-University Düsseldorf , Universitätsstr.1, 40225 Düsseldorf, Germany
| | - Friederike Stoll
- Global Drug Discovery, Medicinal Chemistry, Bayer Pharma AG , Aprather Weg 18A, 42113 Wuppertal, Germany
| | - Alexander Hillisch
- Global Drug Discovery, Medicinal Chemistry, Bayer Pharma AG , Aprather Weg 18A, 42113 Wuppertal, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Department of Mathematics and Natural Sciences, Heinrich-Heine-University Düsseldorf , Universitätsstr.1, 40225 Düsseldorf, Germany
| |
Collapse
|
32
|
Mikulskis P, Genheden S, Ryde U. Effect of explicit water molecules on ligand-binding affinities calculated with the MM/GBSA approach. J Mol Model 2014; 20:2273. [DOI: 10.1007/s00894-014-2273-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 04/24/2014] [Indexed: 12/24/2022]
|
33
|
Mikulskis P, Cioloboc D, Andrejić M, Khare S, Brorsson J, Genheden S, Mata RA, Söderhjelm P, Ryde U. Free-energy perturbation and quantum mechanical study of SAMPL4 octa-acid host–guest binding energies. J Comput Aided Mol Des 2014; 28:375-400. [DOI: 10.1007/s10822-014-9739-x] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 02/24/2014] [Indexed: 01/09/2023]
|
34
|
Pope D, Madura JD, Cascio M. β-Amyloid and neprilysin computational studies identify critical residues implicated in binding specificity. J Chem Inf Model 2014; 54:1157-65. [PMID: 24650257 DOI: 10.1021/ci500015m] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The zinc metalloprotease neprilysin (NEP) promiscuously degrades small bioactive peptides. NEP is among a select group of metalloenzymes that degrade the amyloid beta-peptide (Aβ) in vivo and in situ. Since accumulation of neurotoxic Aβ aggregates in the brain appears to be a causative agent in the pathophysiology of Alzheimer's disease (AD), increased clearance of Aβ resulting from overexpression of NEP exhibits therapeutic potential for AD. However, higher NEP peptidase activity may be harmful without an increased specificity for Aβ over other competing substrates. Crystal structures of NEP-inhibitor complexes and their characterization have highlighted potential amino acid interactions involved in substrate binding and are used as templates to guide our methodology in docking Aβ in NEP. Results from protein-ligand docking calculations predict S2' subsite residues Arg 102 and Arg 110 of NEP participate in specific interactions with Aβ. These interactions provide insight into developing NEP specificity for Aβ.
Collapse
Affiliation(s)
- Darrick Pope
- Department of Chemistry and Biochemistry and Center for Computational Sciences, Duquesne University , 600 Forbes Avenue, 331 Mellon Hall, Pittsburgh, Pennsylvania 15282, United States
| | | | | |
Collapse
|
35
|
Sandberg L. Predicting hydration free energies with chemical accuracy: the SAMPL4 challenge. J Comput Aided Mol Des 2014; 28:211-9. [PMID: 24550133 DOI: 10.1007/s10822-014-9725-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 01/30/2014] [Indexed: 12/14/2022]
Abstract
An implicit solvent model described by a non-simple dielectric medium is used for the prediction of hydration free energies on the dataset of 47 molecules in the SAMPL4 challenge. The solute is represented by a minimal parameter set model based on a new all atom force-field, named the liquid simulation force-field. The importance of a first solvation shell correction to the hydration free energy prediction is discussed and two different approaches are introduced to address it: either with an empirical correction to a few functional groups (alcohol, ether, ester, amines and aromatic nitrogen), or an ab initio correction based on the formation of a solute/explicit water complex. Both approaches give equally good predictions with an average unsigned error <1 kcal/mol. Chemical accuracy is obtained.
Collapse
Affiliation(s)
- Lars Sandberg
- Division of Biological Chemistry and Drug Discovery College of Life Sciences, University of Dundee, Dundee, UK,
| |
Collapse
|
36
|
Subramanian G, Rao SN. Comprehending renin inhibitor's binding affinity using structure-based approaches. Bioorg Med Chem Lett 2013; 23:6667-72. [PMID: 24239018 DOI: 10.1016/j.bmcl.2013.10.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2013] [Revised: 10/19/2013] [Accepted: 10/22/2013] [Indexed: 10/26/2022]
Abstract
The performance of several structure-based design (SBD) approaches in predicting the binding affinity of diverse small molecule inhibitors co-crystallized to human renin was assessed to ascertain the modeling tool and method of choice required when dealing with structure-based lead optimization projects. Most of the SBD approaches investigated here were able to provide qualitative guidance, but quantitative accuracy as well as decisive discrimination between [in]actives is still not within reach. Such an outcome suggests that the current methods need improvement to capture the overall physics of the binding phenomenon for consistent applications in a lead optimization setting.
Collapse
|
37
|
Wickstrom L, He P, Gallicchio E, Levy RM. Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process. J Chem Theory Comput 2013; 9:3136-3150. [PMID: 25147485 DOI: 10.1021/ct400003r] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Host-guest inclusion complexes are useful models for understanding the structural and energetic aspects of molecular recognition. Due to their small size relative to much larger protein-ligand complexes, converged results can be obtained rapidly for these systems thus offering the opportunity to more reliably study fundamental aspects of the thermodynamics of binding. In this work, we have performed a large scale binding affinity survey of 57 β-cyclodextrin (CD) host guest systems using the binding energy distribution analysis method (BEDAM) with implicit solvation (OPLS-AA/AGBNP2). Converged estimates of the standard binding free energies are obtained for these systems by employing techniques such as parallel Hamitionian replica exchange molecular dynamics, conformational reservoirs and multistate free energy estimators. Good agreement with experimental measurements is obtained in terms of both numerical accuracy and affinity rankings. Overall, average effective binding energies reproduce affinity rank ordering better than the calculated binding affinities, even though calculated binding free energies, which account for effects such as conformational strain and entropy loss upon binding, provide lower root mean square errors when compared to measurements. Interestingly, we find that binding free energies are superior rank order predictors for a large subset containing the most flexible guests. The results indicate that, while challenging, accurate modeling of reorganization effects can lead to ligand design models of superior predictive power for rank ordering relative to models based only on ligand-receptor interaction energies.
Collapse
Affiliation(s)
- Lauren Wickstrom
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854 ; Department of Chemistry, Lehman College, The City University of New York, Bronx, NY 10468
| | - Peng He
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854
| | - Emilio Gallicchio
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854
| | - Ronald M Levy
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854
| |
Collapse
|
38
|
Kumar A, Ito A, Hirohama M, Yoshida M, Zhang KYJ. Identification of Sumoylation Activating Enzyme 1 Inhibitors by Structure-Based Virtual Screening. J Chem Inf Model 2013; 53:809-20. [DOI: 10.1021/ci300618e] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Ashutosh Kumar
- Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako,
Saitama 351-0198, Japan
| | - Akihiro Ito
- Chemical Genetics Laboratory/Chemical
Genomics Research Group, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Mikako Hirohama
- Chemical Genetics Laboratory/Chemical
Genomics Research Group, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Minoru Yoshida
- Chemical Genetics Laboratory/Chemical
Genomics Research Group, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kam Y. J. Zhang
- Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, 2-1 Hirosawa, Wako,
Saitama 351-0198, Japan
| |
Collapse
|
39
|
Hotiana HA, Haider MK. Structural modeling of HCV NS3/4A serine protease drug-resistance mutations using end-point continuum solvation and side-chain flexibility calculations. J Chem Inf Model 2013; 53:435-51. [PMID: 23305404 DOI: 10.1021/ci3004754] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Computational methods of modeling protein-ligand interactions have gained widespread application in modern drug discovery. In continuum solvation-based methods of binding affinity estimation, limited description of solvent environment and protein flexibility is traded for a time scale that fits medicinal chemistry test cycles. The results of this speed-accuracy trade-off have been promising in terms of modeling structure-activity relationships of ligand series against protein targets. The potential of these approaches in recapitulating structural and energetic effects of resistance mutations, which involve large changes in binding affinity, remains relatively unexplored. We used continuum solvation binding affinity predictions and graph theory-based flexibility calculations to model thirteen drug resistance mutations in HCV NS3/4A serine protease, against three small-molecule inhibitors, with a 2-fold objective: quantitative assessment of binding energy predictions against experimental data and elucidation of structural/energetic determinants of resistance. The results show statistically significant correlation between predicted and experimental binding affinities, with R(2) and predictive index of up to 0.83 and 0.91, respectively. The level of accuracy was consistent with what has been reported for the inverse problem of binding affinity estimation of congeneric ligands against the same target. The quality of predictions was poor for mutations involving induced-fit effects, primarily, because of the lack of entropy terms. Flexibility analysis explained this discrepancy by indicating characteristic changes in side-chain mobility of a key binding site residue. The combined results from two approaches provide novel insights regarding the molecular mechanism of resistance. NS3/4A inhibitors, with large P2 substituents, derive high affinity with optimal van der Waals interactions in the S2 subsite, in order to overcome unfavorable desolvation and entropic cost of induced-fit effects. High-level resistance mutations tend to increase the desolvation and/or entropic barrier to ligand binding. The lead optimization strategies should, therefore, address the balance of these opposing energetic contributions in both the wild-type and mutant target.
Collapse
Affiliation(s)
- Hajira Ahmed Hotiana
- Undergraduate Program in Science, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore 54792, Pakistan
| | | |
Collapse
|
40
|
Genheden S. Are homology models sufficiently good for free-energy simulations? J Chem Inf Model 2012; 52:3013-21. [PMID: 23113602 DOI: 10.1021/ci300349s] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In this paper, I evaluate the usefulness of protein homology models in rigorous free-energy simulations to determine ligand affinities. Two templates were used to create models of the factor Xa protein and one template was used for dihydrofolate reductase from Plasmodium falciparum. Then, the relative free energies for several pairs of ligands were estimated using thermodynamic integration with the homology models as starting point of the simulation. These binding affinities were compared to affinities obtained when using published crystal structures as starting point of the simulations. Encouragingly, the differences between the affinities obtained when starting from either homology models or crystal structure were not statistical significant for a majority of the considered pairs of ligands. Differences between 1 and 2 kJ/mol were observed for the dihydrofolate reductase ligands and differences between 0 and 8 kJ/mol were observed for the factor Xa ligands. The largest difference for factor Xa was caused by an erroneous modeling of a loop region close to two of the ligands, and it was only observed when using one of the templates. Therefore, it is advisible to always use more than one template when creating homology models if they should be used in free-energy simulations.
Collapse
Affiliation(s)
- Samuel Genheden
- Division of Theoretical Chemistry, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden.
| |
Collapse
|
41
|
Uranga J, Mikulskis P, Genheden S, Ryde U. Can the protonation state of histidine residues be determined from molecular dynamics simulations? COMPUT THEOR CHEM 2012. [DOI: 10.1016/j.comptc.2012.09.025] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
42
|
Genheden S, Kuhn O, Mikulskis P, Hoffmann D, Ryde U. The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant. J Chem Inf Model 2012; 52:2079-88. [DOI: 10.1021/ci3001919] [Citation(s) in RCA: 112] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Samuel Genheden
- Department of Theoretical Chemistry,
Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Oliver Kuhn
- Department of Bioinformatics,
Center for Medical Biotechnology, University of Duisburg-Essen, Universitätsstraβe
1-5, 45117 Essen, Germany
| | - Paulius Mikulskis
- Department of Theoretical Chemistry,
Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
| | - Daniel Hoffmann
- Department of Bioinformatics,
Center for Medical Biotechnology, University of Duisburg-Essen, Universitätsstraβe
1-5, 45117 Essen, Germany
| | - Ulf Ryde
- Department of Theoretical Chemistry,
Lund University, Chemical Centre, P.O. Box 124, SE-221 00 Lund, Sweden
| |
Collapse
|
43
|
Genheden S, Ryde U. Comparison of end-point continuum-solvation methods for the calculation of protein-ligand binding free energies. Proteins 2012; 80:1326-42. [PMID: 22274991 DOI: 10.1002/prot.24029] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Revised: 12/16/2011] [Accepted: 12/16/2011] [Indexed: 11/10/2022]
Abstract
We have compared the predictions of ligand-binding affinities from several methods based on end-point molecular dynamics simulations and continuum solvation, that is, methods related to MM/PBSA (molecular mechanics combined with Poisson-Boltzmann and surface area solvation). Two continuum-solvation models were considered, viz., the Poisson-Boltzmann (PB) and generalised Born (GB) approaches. The nonelectrostatic energies were also obtained in two different ways, viz., either from the sum of the bonded, van der Waals, nonpolar solvation energies, and entropy terms (as in MM/PBSA), or from the scaled protein-ligand van der Waals interaction energy (as in the linear interaction energy approach, LIE). Three different approaches to calculate electrostatic energies were tested, viz., the sum of electrostatic interaction energies and polar solvation energies, obtained either from a single simulation of the complex or from three independent simulations of the complex, the free protein, and the free ligand, or the linear-response approximation (LRA). Moreover, we investigated the effect of scaling the electrostatic interactions by an effective internal dielectric constant of the protein (ϵ(int) ). All these methods were tested on the binding of seven biotin analogues to avidin and nine 3-amidinobenzyl-1H-indole-2-carboxamide inhibitors to factor Xa. For avidin, the best results were obtained with a combination of the LIE nonelectrostatic energies with the MM+GB electrostatic energies from a single simulation, using ϵ(int) = 4. For fXa, standard MM/GBSA, based on one simulation and using ϵ(int) = 4-10 gave the best result. The optimum internal dielectric constant seems to be slightly higher with PB than with GB solvation.
Collapse
Affiliation(s)
- Samuel Genheden
- Department of Theoretical Chemistry, Lund University, Chemical Centre, SE-221 00 Lund, Sweden
| | | |
Collapse
|