1
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Modi V, Dunbrack RL. Kincore: a web resource for structural classification of protein kinases and their inhibitors. Nucleic Acids Res 2022; 50:D654-D664. [PMID: 34643709 PMCID: PMC8728253 DOI: 10.1093/nar/gkab920] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.
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Affiliation(s)
- Vivek Modi
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19148, USA
| | - Roland L Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19148, USA
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2
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Burggraaff L, Lenselink EB, Jespers W, van Engelen J, Bongers BJ, González MG, Liu R, Hoos HH, van Vlijmen HWT, IJzerman AP, van Westen GJP. Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors. J Chem Inf Model 2020; 60:4283-4295. [PMID: 32343143 PMCID: PMC7525794 DOI: 10.1021/acs.jcim.9b01204] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
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Kinases are frequently
studied in the context of anticancer drugs.
Their involvement in cell responses, such as proliferation, differentiation,
and apoptosis, makes them interesting subjects in multitarget drug
design. In this study, a workflow is presented that models the bioactivity
spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC,
and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and
PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding
inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based
models were included, which were thoroughly benchmarked and optimized.
A virtual screening was performed to test the workflow for one of
the main targets, RET kinase. This resulted in 5 novel and chemically
dissimilar RET inhibitors with remaining RET activity of <60% (at
a concentration of 10 μM) and similarities with known RET inhibitors
from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors
were assessed in a concentration range and proved to be modestly active
with a pIC50 value of 5.1 for the most active compound.
The experimental validation of inhibitors for RET strongly indicates
that the multitarget workflow is able to detect novel inhibitors for
kinases, and hence, this workflow can potentially be applied in polypharmacology
modeling. We conclude that this approach can identify new chemical
matter for existing targets. Moreover, this workflow can easily be
applied to other targets as well.
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Affiliation(s)
- Lindsey Burggraaff
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Eelke B Lenselink
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Willem Jespers
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Department of Cell and Molecular Biology, Uppsala University, Uppsala 75124, Sweden
| | - Jesper van Engelen
- Department of Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Brandon J Bongers
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Marina Gorostiola González
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Rongfang Liu
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Holger H Hoos
- Department of Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Herman W T van Vlijmen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Janssen Research & Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Adriaan P IJzerman
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
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3
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Alibay I, Bryce RA. Ring Puckering Landscapes of Glycosaminoglycan-Related Monosaccharides from Molecular Dynamics Simulations. J Chem Inf Model 2019; 59:4729-4741. [PMID: 31609614 DOI: 10.1021/acs.jcim.9b00529] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The conformational flexibility of the glycosaminoglycans (GAGs) is known to be key in their binding and biological function, for example in regulating coagulation and cell growth. In this work, we employ enhanced sampling molecular dynamics simulations to probe the ring conformations of GAG-related monosaccharides, including a range of acetylated and sulfated GAG residues. We first perform unbiased MD simulations of glucose anomers and the epimers glucuronate and iduronate. These calculations indicate that in some cases, an excess of 15 μs is required for adequate sampling of ring pucker due to the high energy barriers between states. However, by applying our recently developed msesMD simulation method (multidimensional swarm-enhanced sampling molecular dynamics), we were able to quantitatively and rapidly reproduce these ring pucker landscapes. From msesMD simulations, the puckering free energy profiles were then compared for 15 further monosaccharides related to GAGs; this includes to our knowledge the first simulation study of sulfation effects on β-GalNAc ring puckering. For the force field employed, we find that in general the calculated pucker free energy profiles for sulfated sugars were similar to the corresponding unsulfated profiles. This accords with recent experimental studies suggesting that variation in ring pucker of sulfated GAG residues is primarily dictated by interactions with surrounding residues rather than by intrinsic conformational preference. As an exception to this, however, we predict that 4-O-sulfation of β-GalNAc leads to reduced ring rigidity, with a significant lowering in energy of the 1C4 ring conformation; this observation may have implications for understanding the structural basis of the biological function of β-GalNAc-containing glycosaminoglycans such as dermatan sulfate.
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Affiliation(s)
- Irfan Alibay
- Division of Pharmacy and Optometry, School of Health Sciences , University of Manchester , Oxford Road , Manchester M13 9PT , U.K.,Structural Bioinformatics and Computational Biochemistry Unit, Department of Biochemistry , University of Oxford , South Parks Road , Oxford OX1 3QU , U.K
| | - Richard A Bryce
- Division of Pharmacy and Optometry, School of Health Sciences , University of Manchester , Oxford Road , Manchester M13 9PT , U.K
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4
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Shkurti A, Styliari ID, Balasubramanian V, Bethune I, Pedebos C, Jha S, Laughton CA. CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space. J Chem Theory Comput 2019; 15:2587-2596. [PMID: 30620585 DOI: 10.1021/acs.jctc.8b00657] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
CoCo ("complementary coordinates") is a method for ensemble enrichment based on principal component analysis (PCA) that was developed originally for the investigation of NMR data. Here we investigate the potential of the CoCo method, in combination with molecular dynamics simulations (CoCo-MD), to be used more generally for the enhanced sampling of conformational space. Using the alanine penta-peptide as a model system, we find that an iterative workflow, interleaving short multiple-walker MD simulations with long-range jumps through conformational space informed by CoCo analysis, can increase the rate of sampling of conformational space up to 10 times for the same computational effort (total number of MD timesteps). Combined with the reservoir-REMD method, free energies can be readily calculated. An alternative, approximate but fast and practically useful, alternative approach to unbiasing CoCo-MD generated data is also described. Applied to cyclosporine A, we can achieve far greater conformational sampling than has been reported previously, using a fraction of the computational resource. Simulations of the maltose binding protein, begun from the "open" state, effectively sample the "closed" conformation associated with ligand binding. The PCA-based approach means that optimal collective variables to enhance sampling need not be defined in advance by the user but are identified automatically and are adaptive, responding to the characteristics of the developing ensemble. In addition, the approach does not require any adaptations to the associated MD code and is compatible with any conventional MD package.
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Affiliation(s)
- Ardita Shkurti
- School of Pharmacy and Centre for Biomolecular Sciences , University of Nottingham , University Park, Nottingham NG7 2RD , U.K
| | - Ioanna Danai Styliari
- School of Pharmacy and Centre for Biomolecular Sciences , University of Nottingham , University Park, Nottingham NG7 2RD , U.K
| | - Vivek Balasubramanian
- Department of Electrical and Computer Engineering , Rutgers University , Piscataway , New Jersey 08854 , United States
| | - Iain Bethune
- EPCC , The University of Edinburgh , James Clerk Maxwell Building, Peter Guthrie Tait Road , Edinburgh EH9 3FD , U.K
| | - Conrado Pedebos
- School of Pharmacy and Centre for Biomolecular Sciences , University of Nottingham , University Park, Nottingham NG7 2RD , U.K.,CAPES Foundation , Ministry of Education of Brazil , Brasília 70040 , Brazil
| | - Shantenu Jha
- Department of Electrical and Computer Engineering , Rutgers University , Piscataway , New Jersey 08854 , United States
| | - Charles A Laughton
- School of Pharmacy and Centre for Biomolecular Sciences , University of Nottingham , University Park, Nottingham NG7 2RD , U.K
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5
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Alibay I, Burusco KK, Bruce NJ, Bryce RA. Identification of Rare Lewis Oligosaccharide Conformers in Aqueous Solution Using Enhanced Sampling Molecular Dynamics. J Phys Chem B 2018; 122:2462-2474. [PMID: 29419301 DOI: 10.1021/acs.jpcb.7b09841] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Determining the conformations accessible to carbohydrate ligands in aqueous solution is important for understanding their biological action. In this work, we evaluate the conformational free-energy surfaces of Lewis oligosaccharides in explicit aqueous solvent using a multidimensional variant of the swarm-enhanced sampling molecular dynamics (msesMD) method; we compare with multi-microsecond unbiased MD simulations, umbrella sampling, and accelerated MD approaches. For the sialyl Lewis A tetrasaccharide, msesMD simulations in aqueous solution predict conformer landscapes in general agreement with the other biased methods and with triplicate unbiased 10 μs trajectories; these simulations find a predominance of closed conformer and a range of low-occupancy open forms. The msesMD simulations also suggest closed-to-open transitions in the tetrasaccharide are facilitated by changes in ring puckering of its GlcNAc residue away from the 4C1 form, in line with previous work. For sialyl Lewis X tetrasaccharide, msesMD simulations predict a minor population of an open form in solution corresponding to a rare lectin-bound pose observed crystallographically. Overall, from comparison with biased MD calculations, we find that triplicate 10 μs unbiased MD simulations may not be enough to fully sample glycan conformations in aqueous solution. However, the computational efficiency and intuitive approach of the msesMD method suggest potential for its application in glycomics as a tool for analysis of oligosaccharide conformation.
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Affiliation(s)
- Irfan Alibay
- Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre , University of Manchester , Oxford Road , Manchester M13 9PL , U.K
| | - Kepa K Burusco
- Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre , University of Manchester , Oxford Road , Manchester M13 9PL , U.K
| | - Neil J Bruce
- Heidelberg Institute for Theoretical Studies , Schloss-Wolfsbrunnenweg 35 , Heidelberg 69118 , Germany
| | - Richard A Bryce
- Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre , University of Manchester , Oxford Road , Manchester M13 9PL , U.K
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6
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Exploiting computationally derived out-of-the-box protein conformations for drug design. Future Med Chem 2016; 8:1887-1897. [PMID: 27629935 DOI: 10.4155/fmc-2016-0098] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Structural plasticity is an intrinsic property of proteins that allows each gene product to accomplish its tasks in a strictly regulated manner at a precise time and cellular location. Moreover, protein motions allow protein-ligand and protein-protein recognition. The knowledge of the conformational ensemble that a drug target populates may be crucial for the design of small molecules that can differently modulate its function. X-ray crystallography and NMR have endlessly provided snapshots of protein states. However, experimental structure determination is not always straightforward. Therefore, attempts have been made to depict protein conformational landscapes through molecular dynamics and enhanced sampling methods. Here, we review how accounting for protein dynamics through in silico generated out-of-the-box protein conformations has started to impact on drug discovery.
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7
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Abstract
Interest in the application of molecular dynamics (MD) simulations has increased in the field of protein kinase (PK) drug discovery. PKs belong to an important drug target class because they are directly involved in a number of diseases, including cancer. MD methods simulate dynamic biological and chemical events at an atomic level. This information can be combined with other in silico and experimental methods to efficiently target selected receptors. In this review, we present common and advanced methods of MD simulations and we focus on the recent applications of MD-based methodologies that provided significant insights into the elucidation of biological mechanisms involving PKs and into the discovery of novel kinase inhibitors.
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8
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Bosc N, Wroblowski B, Aci-Sèche S, Meyer C, Bonnet P. A Proteometric Analysis of Human Kinome: Insight into Discriminant Conformation-dependent Residues. ACS Chem Biol 2015; 10:2827-40. [PMID: 26411811 DOI: 10.1021/acschembio.5b00555] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Because of the success of imatinib, the first type-II kinase inhibitor approved by the FDA in 2001, sustained efforts have been made by the pharmaceutical industry to discover novel compounds stabilizing the inactive conformation of protein kinases. On the seven type-II inhibitors having reached the market, four were released in 2012, suggesting an acceleration of the research of such a class of compounds. Still, they represent less than a third of the protein kinase inhibitors available to patients today. The identification of key residues involved in the binding of this type of ligands in the kinase active site might ease the design of potent and selective type-II inhibitors. In order to identify those discriminant residues, we have developed a proteometric approach combining residue descriptors of protein kinase sequences and biological activities of various type-II kinase inhibitors. We applied Partial Least Squares (PLS) regression to identify 29 key residues that influence the binding of four type-II inhibitors to most proteins of the kinome. The gatekeeper residue was found to be the most relevant, confirming an essential role in ligand binding as well as in protein kinase conformational changes. Using the newly developed proteometric model, we predicted the propensity of each protein kinase to be inhibited by type-II ligands. The model was further validated using an external data set of protein/ligand activity pairs. Other residues present in the kinase domain, and more specifically in the binding site, have been highlighted by this approach, but their role in biological mechanisms is still unknown.
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Affiliation(s)
- Nicolas Bosc
- Institut
de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d’Orléans 7311, Université d’Orléans
BP 6759, 45067 Orléans
Cedex 2, France
| | - Berthold Wroblowski
- Janssen Research & Development, a division of Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Samia Aci-Sèche
- Institut
de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d’Orléans 7311, Université d’Orléans
BP 6759, 45067 Orléans
Cedex 2, France
| | - Christophe Meyer
- Centre de Recherche Janssen-Cilag, Campus de Maigremont - CS
10615, 27106 Val de
Reuil Cedex, France
| | - Pascal Bonnet
- Institut
de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d’Orléans 7311, Université d’Orléans
BP 6759, 45067 Orléans
Cedex 2, France
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9
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Burusco KK, Bruce NJ, Alibay I, Bryce RA. Free Energy Calculations using a Swarm-Enhanced Sampling Molecular Dynamics Approach. Chemphyschem 2015; 16:3233-41. [PMID: 26418190 PMCID: PMC4676921 DOI: 10.1002/cphc.201500524] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Indexed: 11/19/2022]
Abstract
Free energy simulations are an established computational tool in modelling chemical change in the condensed phase. However, sampling of kinetically distinct substates remains a challenge to these approaches. As a route to addressing this, we link the methods of thermodynamic integration (TI) and swarm-enhanced sampling molecular dynamics (sesMD), where simulation replicas interact cooperatively to aid transitions over energy barriers. We illustrate the approach by using alchemical alkane transformations in solution, comparing them with the multiple independent trajectory TI (IT-TI) method. Free energy changes for transitions computed by using IT-TI grew increasingly inaccurate as the intramolecular barrier was heightened. By contrast, swarm-enhanced sampling TI (sesTI) calculations showed clear improvements in sampling efficiency, leading to more accurate computed free energy differences, even in the case of the highest barrier height. The sesTI approach, therefore, has potential in addressing chemical change in systems where conformations exist in slow exchange.
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Affiliation(s)
- Kepa K Burusco
- Manchester Pharmacy School, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Neil J Bruce
- Manchester Pharmacy School, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.,Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany
| | - Irfan Alibay
- Manchester Pharmacy School, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Richard A Bryce
- Manchester Pharmacy School, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
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10
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Park IH, Venable JD, Steckler C, Cellitti SE, Lesley SA, Spraggon G, Brock A. Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis. J Chem Inf Model 2015; 55:1914-25. [PMID: 26241692 DOI: 10.1021/acs.jcim.5b00185] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.
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Affiliation(s)
- In-Hee Park
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - John D Venable
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Caitlin Steckler
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States.,Joint Center for Structural Genomics , La Jolla, California 92037, United States
| | - Susan E Cellitti
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Scott A Lesley
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States.,Department of Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, California 92037, United States.,Joint Center for Structural Genomics , La Jolla, California 92037, United States
| | - Glen Spraggon
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Ansgar Brock
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
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