1
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Al Hasan M, Sabirianov M, Redwine G, Goettsch K, Yang SX, Zhong HA. Binding and selectivity studies of phosphatidylinositol 3-kinase (PI3K) inhibitors. J Mol Graph Model 2023; 121:108433. [PMID: 36812742 DOI: 10.1016/j.jmgm.2023.108433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/01/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
Overexpression of the Phosphatidylinositol 3-kinase (PI3K) proteins have been observed in cancer cells. Targeting the phosphatidylinositol 3-kinase (PI3K) signaling transduction pathway by inhibition of the PI3K substrate recognition sites has been proved to be an effective approach to block cancer progression. Many PI3K inhibitors have been developed. Seven drugs have been approved by the US FDA with a mechanism of targeting the phosphatidylinositol 3-kinase/protein kinase-B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway. In this study, we used docking tools to investigate selective binding of ligands toward four different subtypes of PI3Ks (PI3Kα, PI3Kβ, PI3Kγ and PI3Kδ). The affinity predicted from both the Glide dock and the Movable-Type (MT)-based free energy calculations agreed well with the experimental data. The validation of our predicted methods with a large dataset of 147 ligands showed very small mean errors. We identified residues that may dictate the subtype-specific binding. Particularly, residues Asp964, Ser806, Lys890 and Thr886 of PI3Kγ might be utilized for PI3Kγ-selective inhibitor design. Residues Val828, Trp760, Glu826 and Tyr813 may be important for PI3Kδ-selective inhibitor binding.
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Affiliation(s)
- Mohammad Al Hasan
- DSC 309, Department of Chemistry, The University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182, USA
| | - Matthew Sabirianov
- DSC 309, Department of Chemistry, The University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182, USA
| | - Grace Redwine
- DSC 309, Department of Chemistry, The University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182, USA
| | - Kaitlin Goettsch
- DSC 309, Department of Chemistry, The University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182, USA
| | - Stephen X Yang
- Westlake High School, 100 Lakeview Canyon Rd, Thousand Oaks, CA, 91362, USA
| | - Haizhen A Zhong
- DSC 309, Department of Chemistry, The University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182, USA.
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2
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Liu W, Liu Z, Liu H, Westerhoff LM, Zheng Z. Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein–Ligand Sampling. J Chem Inf Model 2022; 62:5645-5665. [PMID: 36282990 PMCID: PMC9709919 DOI: 10.1021/acs.jcim.2c00278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Fast and accurate biomolecular free energy estimation has been a significant interest for decades, and with recent advances in computer hardware, interest in new method development in this field has even grown. Thorough configurational state sampling using molecular dynamics (MD) simulations has long been applied to the estimation of the free energy change corresponding to the receptor-ligand complexing process. However, performing large-scale simulation is still a computational burden for the high-throughput hit screening. Among molecular modeling tools, docking and scoring methods are widely used during the early stages of the drug discovery process in that they can rapidly generate discrete receptor-ligand binding modes and their individual binding affinities. Unfortunately, the lack of thorough conformational sampling in docking and scoring protocols leads to difficulty discovering global minimum binding modes on a complicated energy landscape. The Movable Type (MT) method is a novel absolute binding free energy approach which has demonstrated itself to be robust across a wide range of targets and ligands. Traditionally, the MT method is used with protein-ligand binding modes generated with rigid-receptor or flexible-receptor (induced fit) docking protocols; however, these protocols are by their nature less likely to be effective with more highly flexible targets or with those situations in which binding involves multiple step pathways. In these situations, more thorough samplings are required to better explain the free energy of binding. Therefore, to explore the prediction capability and computational efficiency of the MT method when using more thorough protein-ligand conformational sampling protocols, in the present work, we introduced a series of binding mode modeling protocols ranging from conventional docking routines to single-trajectory conventional molecular dynamics (cMD) and parallel Monte Carlo molecular dynamics (MCMD). Through validation against several structurally and mechanistically diverse protein-ligand test sets, we explore the performance of the MT method as a virtual screening tool to work with the docking protocols and as an MD simulation-based binding free energy tool.
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Affiliation(s)
- Wenlang Liu
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
| | - Zhenhao Liu
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
| | - Hao Liu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
| | | | - Zheng Zheng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
- QuantumBio Inc., State College, Pennsylvania16801, United States
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3
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Borbulevych OY, Martin RI, Westerhoff LM. The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design. J Comput Aided Mol Des 2020; 35:433-451. [PMID: 33108589 PMCID: PMC8018927 DOI: 10.1007/s10822-020-00354-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/12/2020] [Indexed: 12/29/2022]
Abstract
Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.
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Affiliation(s)
- Oleg Y Borbulevych
- QuantumBio Inc, 2790 West College Ave, Suite 900, State College, PA, 16801, USA
| | - Roger I Martin
- QuantumBio Inc, 2790 West College Ave, Suite 900, State College, PA, 16801, USA
| | - Lance M Westerhoff
- QuantumBio Inc, 2790 West College Ave, Suite 900, State College, PA, 16801, USA.
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4
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Zheng Z, Borbulevych OY, Liu H, Deng J, Martin RI, Westerhoff LM. MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment. J Chem Inf Model 2020; 60:5437-5456. [PMID: 32791826 PMCID: PMC7781189 DOI: 10.1021/acs.jcim.0c00618] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
For decades, the
complicated energy surfaces found in macromolecular
protein:ligand structures, which require large amounts of computational
time and resources for energy state sampling, have been an inherent
obstacle to fast, routine free energy estimation in industrial drug
discovery efforts. Beginning in 2013, the Merz research group addressed
this cost with the introduction of a novel sampling methodology termed
“Movable Type” (MT). Using numerical integration methods,
the MT method reduces the computational expense for energy state sampling
by independently calculating each atomic partition function from an
initial molecular conformation in order to estimate the molecular
free energy using ensembles of the atomic partition functions. In
this work, we report a software package, the DivCon Discovery Suite
with the MovableType module from QuantumBio Inc., that performs this
MT free energy estimation protocol in a fast, fully encapsulated manner.
We discuss the computational procedures and improvements to the original
work, and we detail the corresponding settings for this software package.
Finally, we introduce two validation benchmarks to evaluate the overall
robustness of the method against a broad range of protein:ligand structural
cases. With these publicly available benchmarks, we show that the
method can use a variety of input types and parameters and exhibits
comparable predictability whether the method is presented with “expensive”
X-ray structures or “inexpensively docked” theoretical
models. We also explore some next steps for the method. The MovableType
software is available at http://www.quantumbioinc.com/
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Affiliation(s)
- Zheng Zheng
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States.,School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Oleg Y Borbulevych
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| | - Hao Liu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Jianpeng Deng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Roger I Martin
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| | - Lance M Westerhoff
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
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5
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Rizzi A, Jensen T, Slochower DR, Aldeghi M, Gapsys V, Ntekoumes D, Bosisio S, Papadourakis M, Henriksen NM, de Groot BL, Cournia Z, Dickson A, Michel J, Gilson MK, Shirts MR, Mobley DL, Chodera JD. The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations. J Comput Aided Mol Des 2020; 34:601-633. [PMID: 31984465 DOI: 10.1007/s10822-020-00290-5] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 01/13/2020] [Indexed: 12/22/2022]
Abstract
Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.
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Affiliation(s)
- Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA.
| | - Travis Jensen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Matteo Aldeghi
- Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany
| | - Vytautas Gapsys
- Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany
| | - Dimitris Ntekoumes
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Michail Papadourakis
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Atomwise, 717 Market St Suite 800, San Francisco, CA, 94103, USA
| | - Bert L de Groot
- Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Alex Dickson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California, 92697, USA.
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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6
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Slochower DR, Henriksen NM, Wang LP, Chodera JD, Mobley DL, Gilson MK. Binding Thermodynamics of Host-Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative. J Chem Theory Comput 2019; 15:6225-6242. [PMID: 31603667 PMCID: PMC7328435 DOI: 10.1021/acs.jctc.9b00748] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small-molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a druglike guest molecule have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root-mean-square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.
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Affiliation(s)
- David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Lee-Ping Wang
- Department of Chemistry , University of California , Davis , California 95616 , United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center , New York , New York 10065 , United States
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry , University of California , Irvine , California 92697 , United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
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7
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Application of the Movable Type Free Energy Method to the Caspase-Inhibitor BindingAffinity Study. Int J Mol Sci 2019; 20:ijms20194850. [PMID: 31569580 PMCID: PMC6801467 DOI: 10.3390/ijms20194850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 09/24/2019] [Accepted: 09/25/2019] [Indexed: 11/16/2022] Open
Abstract
Many studies have provided evidence suggesting that caspases not only contribute to the neurodegeneration associated with Alzheimer’s disease (AD) but also play essential roles in promoting the underlying pathology of this disease. Studies regarding the caspase inhibition draw researchers’ attention through time due to its therapeutic value in the treatment of AD. In this work, we apply the “Movable Type” (MT) free energy method, a Monte Carlo sampling method extrapolating the binding free energy by simulating the partition functions for both free-state and bound-state protein and ligand configurations, to the caspase-inhibitor binding affinity study. Two test benchmarks are introduced to examine the robustness and sensitivity of the MT method concerning the caspase inhibition complexing. The first benchmark employs a large-scale test set including more than a hundred active inhibitors binding to caspase-3. The second benchmark includes several smaller test sets studying the relative binding free energy differences for minor structural changes at the caspase-inhibitor interaction interfaces. Calculation results show that the RMS errors for all test sets are below 1.5 kcal/mol compared to the experimental binding affinity values, demonstrating good performance in simulating the caspase-inhibitor complexing. For better understanding the protein-ligand interaction mechanism, we then take a closer look at the global minimum binding modes and free-state ligand conformations to study two pairs of caspase-inhibitor complexes with (1) different caspase targets binding to the same inhibitor, and (2) different polypeptide inhibitors targeting the same caspase target. By comparing the contact maps at the binding site of different complexes, we revealed how small structural changes affect the caspase-inhibitor interaction energies. Overall, this work provides a new free energy approach for studying the caspase inhibition, with structural insight revealed for both free-state and bound-state molecular configurations.
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8
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Fernández-Quintero ML, Kraml J, Georges G, Liedl KR. CDR-H3 loop ensemble in solution - conformational selection upon antibody binding. MAbs 2019; 11:1077-1088. [PMID: 31148507 PMCID: PMC6748594 DOI: 10.1080/19420862.2019.1618676] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
We analyzed pairs of protein-binding, peptide-binding and hapten-binding antibodies crystallized as complex and in the absence of the antigen with and without conformational differences upon binding in the complementarity-determining region (CDR)-H3 loop. Here, we introduce a molecular dynamics-based approach to capture a diverse conformational ensemble of the CDR-H3 loop in solution. The results clearly indicate that the inherently flexible CDR-H3 loop indeed needs to be characterized as a conformational ensemble. The conformational changes of the CDR-H3 loop in all antibodies investigated follow the paradigm of conformation selection, because we observe the experimentally determined binding competent conformation without the presence of the antigen within the ensemble of pre-existing conformational states in solution before binding. We also demonstrate for several examples that the conformation observed in the antibody crystal structure without antigen present is actually selected to bind the carboxyterminal tail region of the antigen-binding fragment (Fab). Thus, special care must be taken when characterizing antibody CDR-H3 loops by Fab X-ray structures, and the possibility that pre-existing conformations are present should always be considered.
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Affiliation(s)
- Monica L Fernández-Quintero
- a Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck , Innsbruck , Austria
| | - Johannes Kraml
- a Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck , Innsbruck , Austria
| | - Guy Georges
- b Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg , Germany
| | - Klaus R Liedl
- a Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck , Innsbruck , Austria
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9
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Kellett K, Duggan BM, Gilson MK. Facile synthesis of a diverse library of mono-3-substituted β-cyclodextrin analogues. Supramol Chem 2019. [DOI: 10.1080/10610278.2018.1562191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- K. Kellett
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - B. M. Duggan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - M. K. Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
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10
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Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL, Chodera JD. Overview of the SAMPL6 host-guest binding affinity prediction challenge. J Comput Aided Mol Des 2018; 32:937-963. [PMID: 30415285 PMCID: PMC6301044 DOI: 10.1007/s10822-018-0170-6] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 10/07/2018] [Indexed: 10/27/2022]
Abstract
Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.
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Affiliation(s)
- Andrea Rizzi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - Steven Murkli
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - John N McNeill
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - Wei Yao
- Department of Chemistry, Tulane University, Louisiana, LA, 70118, USA
| | - Matthew Sullivan
- Department of Chemistry, Tulane University, Louisiana, LA, 70118, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael W Chiu
- Qualcomm Institute, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Lyle Isaacs
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA
| | - Bruce C Gibb
- Department of Chemistry, Tulane University, Louisiana, LA, 70118, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California, 92697, USA.
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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11
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Zheng Z, Pei J, Bansal N, Liu H, Song LF, Merz KM. Generation of Pairwise Potentials Using Multidimensional Data Mining. J Chem Theory Comput 2018; 14:5045-5067. [PMID: 30183299 DOI: 10.1021/acs.jctc.8b00516] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.
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Affiliation(s)
- Zheng Zheng
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Jun Pei
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Nupur Bansal
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Hao Liu
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Lin Frank Song
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
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12
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Bansal N, Zheng Z, Song LF, Pei J, Merz KM. The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation. J Am Chem Soc 2018; 140:5434-5446. [PMID: 29607642 DOI: 10.1021/jacs.8b00743] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MTFlex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MTFlex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.
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Affiliation(s)
- Nupur Bansal
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Zheng Zheng
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Lin Frank Song
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Jun Pei
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States.,Institute for Cyber Enabled Research , Michigan State University , 567 Wilson Road , East Lansing , Michigan 48824 , United States
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13
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Zhong HA, Santos EM, Vasileiou C, Zheng Z, Geiger JH, Borhan B, Merz KM. Free-Energy-Based Protein Design: Re-Engineering Cellular Retinoic Acid Binding Protein II Assisted by the Moveable-Type Approach. J Am Chem Soc 2018; 140:3483-3486. [PMID: 29480012 DOI: 10.1021/jacs.7b10368] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
How to fine-tune the binding free energy of a small-molecule to a receptor site by altering the amino acid residue composition is a key question in protein engineering. Indeed, the ultimate solution to this problem, to chemical accuracy (±1 kcal/mol), will result in profound and wide-ranging applications in protein design. Numerous tools have been developed to address this question using knowledge-based models to more computationally intensive molecular dynamics simulations-based free energy calculations, but while some success has been achieved there remains room for improvement in terms of overall accuracy and in the speed of the methodology. Here we report a fast, knowledge-based movable-type (MT)-based approach to estimate the absolute and relative free energy of binding as influenced by mutations in a small-molecule binding site in a protein. We retrospectively validate our approach using mutagenesis data for retinoic acid binding to the Cellular Retinoic Acid Binding Protein II (CRABPII) system and then make prospective predictions that are borne out experimentally. The overall performance of our approach is supported by its success in identifying mutants that show high or even sub-nano-molar binding affinities of retinoic acid to the CRABPII system.
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Affiliation(s)
- Haizhen A Zhong
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States.,Department of Chemistry , University of Nebraska at Omaha , Omaha , Nebraska 68182 , United States
| | - Elizabeth M Santos
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Chrysoula Vasileiou
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Zheng Zheng
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - James H Geiger
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Babak Borhan
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry , Michigan State University , East Lansing , Michigan 48824 , United States
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14
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Cang Z, Wei GW. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 2017; 13:e1005690. [PMID: 28749969 PMCID: PMC5549771 DOI: 10.1371/journal.pcbi.1005690] [Citation(s) in RCA: 159] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 08/08/2017] [Accepted: 07/18/2017] [Indexed: 11/18/2022] Open
Abstract
Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. AVAILABILITY weilab.math.msu.edu/TDL/.
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Affiliation(s)
- Zixuan Cang
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
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15
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Wang B, Zhao Z, Nguyen DD, Wei GW. Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies. Theor Chem Acc 2017. [DOI: 10.1007/s00214-017-2083-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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16
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Incorporation of side chain flexibility into protein binding pockets using MTflex. Bioorg Med Chem 2016; 24:4978-4987. [DOI: 10.1016/j.bmc.2016.08.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 08/16/2016] [Accepted: 08/18/2016] [Indexed: 01/15/2023]
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17
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Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge. J Comput Aided Mol Des 2016; 31:71-85. [PMID: 27677749 DOI: 10.1007/s10822-016-9968-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/08/2016] [Indexed: 12/11/2022]
Abstract
Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett's acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.
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18
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Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK. Overview of the SAMPL5 host-guest challenge: Are we doing better? J Comput Aided Mol Des 2016; 31:1-19. [PMID: 27658802 DOI: 10.1007/s10822-016-9974-4] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 09/14/2016] [Indexed: 01/18/2023]
Abstract
The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host-guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host-guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.
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Affiliation(s)
- Jian Yin
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Niel M Henriksen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Michael W Chiu
- Qualcomm Institute, University of California, San Diego, La Jolla, CA, 92093, USA
| | - David L Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, CA, 92697, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA.
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19
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Zheng Z, Wang T, Li P, Merz KM. KECSA-Movable Type Implicit Solvation Model (KMTISM). J Chem Theory Comput 2016; 11:667-82. [PMID: 25691832 PMCID: PMC4325602 DOI: 10.1021/ct5007828] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Indexed: 11/30/2022]
Abstract
![]()
Computation
of the solvation free energy for chemical and biological
processes has long been of significant interest. The key challenges
to effective solvation modeling center on the choice of potential
function and configurational sampling. Herein, an energy sampling
approach termed the “Movable Type” (MT) method, and
a statistical energy function for solvation modeling, “Knowledge-based
and Empirical Combined Scoring Algorithm” (KECSA) are developed
and utilized to create an implicit solvation model: KECSA-Movable
Type Implicit Solvation Model (KMTISM) suitable for the study of chemical
and biological systems. KMTISM is an implicit solvation model, but
the MT method performs energy sampling at the atom pairwise level.
For a specific molecular system, the MT method collects energies from
prebuilt databases for the requisite atom pairs at all relevant distance
ranges, which by its very construction encodes all possible molecular
configurations simultaneously. Unlike traditional statistical energy
functions, KECSA converts structural statistical information into
categorized atom pairwise interaction energies as a function of the
radial distance instead of a mean force energy function. Within the
implicit solvent model approximation, aqueous solvation free energies
are then obtained from the NVT ensemble partition function generated
by the MT method. Validation is performed against several subsets
selected from the Minnesota Solvation Database v2012. Results are
compared with several solvation free energy calculation methods, including
a one-to-one comparison against two commonly used classical implicit
solvation models: MM-GBSA and MM-PBSA. Comparison against a quantum
mechanics based polarizable continuum model is also discussed (Cramer
and Truhlar’s Solvation Model 12).
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Affiliation(s)
- Zheng Zheng
- Institute for Cyber Enabled Research, Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
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20
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Yilmazer ND, Korth M. Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods. Int J Mol Sci 2016; 17:ijms17050742. [PMID: 27196893 PMCID: PMC4881564 DOI: 10.3390/ijms17050742] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 04/18/2016] [Accepted: 05/03/2016] [Indexed: 11/16/2022] Open
Abstract
We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.
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Affiliation(s)
- Nusret Duygu Yilmazer
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
| | - Martin Korth
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
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21
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Pan LL, Zheng Z, Wang T, Merz KM. Free Energy-Based Conformational Search Algorithm Using the Movable Type Sampling Method. J Chem Theory Comput 2015; 11:5853-64. [PMID: 26605406 DOI: 10.1021/acs.jctc.5b00930] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this article, we extend the movable type (MT) sampling method to molecular conformational searches (MT-CS) on the free energy surface of the molecule in question. Differing from traditional systematic and stochastic searching algorithms, this method uses Boltzmann energy information to facilitate the selection of the best conformations. The generated ensembles provided good coverage of the available conformational space including available crystal structures. Furthermore, our approach directly provides the solvation free energies and the relative gas and aqueous phase free energies for all generated conformers. The method is validated by a thorough analysis of thrombin ligands as well as against structures extracted from both the Protein Data Bank (PDB) and the Cambridge Structural Database (CSD). An in-depth comparison between OMEGA and MT-CS is presented to illustrate the differences between the two conformational searching strategies, i.e., energy-based versus free energy-based searching. These studies demonstrate that our MT-based ligand conformational search algorithm is a powerful approach to delineate the conformational ensembles of molecular species on free energy surfaces.
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Affiliation(s)
- Li-Li Pan
- Department of Chemistry, Michigan State University , 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zheng Zheng
- Department of Chemistry, Michigan State University , 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Ting Wang
- Department of Chemistry, Michigan State University , 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University , 578 South Shaw Lane, East Lansing, Michigan 48824, United States.,Institute for Cyber Enabled Research, Michigan State University , 567 Wilson Road, Room 1440, East Lansing, Michigan 48824, United States
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22
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Affiliation(s)
- Jie Liu
- State
Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative
Innovation Center of Chemistry for Life Sciences, Shanghai Institute
of Organic Chemistry, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Renxiao Wang
- State
Key Laboratory of Bioorganic and Natural Products Chemistry, Collaborative
Innovation Center of Chemistry for Life Sciences, Shanghai Institute
of Organic Chemistry, Chinese Academy of Sciences, Shanghai, People’s Republic of China
- State
Key Laboratory of Quality Research in Chinese Medicine, Macau Institute
for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, People’s Republic of China
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