Finally, we examine 40 diverse PDB complexes, which exhibit a range of errors in standard GLIDE docking, with many cases in the intermediate range of 1.0C3.0 ? RMSD from the experimental crystal structure. charges, we avoid the problem of the quality of the force-field charge model for NVP-BGJ398 phosphate a wide range of medicinal chemistry compounds. Furthermore, employment of QM/MM techniques enables the charge calculations for the ligand to be performed in the protein environment, thus incorporating polarization effects in a natural (and accurate) fashion; the QM model is able to reliably reproduce, for arbitrary ligand chemistry, the response to an external electric field. Because the protein and ligand are not covalently attached, definition of the QM/MM interface is straightforward, and the computational cost of evaluating the charges (requiring only a NVP-BGJ398 phosphate single point calculation, as opposed to geometry optimization) is reasonable, particularly in a lead optimization context where hundreds or thousands, as opposed to millions, of ligands are to be studied. As the present article represents an initial effort to investigate this topic, we confine our studies to native redocking, as opposed to cross docking (which will be investigated in a subsequent publication). Within this restricted regime, we address two fundamental questions: If the ligand charges are optimized for the cocrystallized structure via a QM/MM calculation on the native complex, will subsequent redocking of the ligand yield superior structures as compared to the use of force-field based charges, which do not include polarization? Clearly if this objective is not satisfied, further investigation of the use of more accurate charges in the context of current rigid receptor docking models is unlikely to be profitable. Is it possible, starting with no knowledge of the cocrystallized ligand geometry, to improve binding mode prediction by multiple cycles of docking, recomputation of charges, and redocking, selecting at the end of the process the lowest energy structure (taking into account the charge polarization)? Our algorithmic approach to this problem is rather primitive, and could almost certainly be quantitatively improved, but even with a first generation methodology, in which only one iterative cycle of docking, charge recomputation, and redocking, is employed, dramatic improvements in the prediction of ligand binding modes are obtained. The article is organized as follows. We first briefly review our underlying docking methodology (implemented in the GLIDE program) and QM/MM approach (implemented in the QSITE program), and discuss how we have coupled these two programs together to develop a methodology in which docking and charge computation can, in principle, be iterated to convergence (although the effects of only a single iteration are examined in the present article). We then examine three relatively simple test suites for trypsin cocrystals, t-RNA, and for sugar-binding proteins. For these test cases, GLIDE performs reasonably well using force-field charges; NVP-BGJ398 phosphate charge recomputation is shown to increase robustness and accuracy with remarkable consistency. Finally, we examine 40 diverse PDB complexes, which exhibit a range of errors in standard GLIDE docking, with many cases in the intermediate range of 1.0C3.0 ? RMSD from the experimental crystal structure. For errors of this magnitude (which make up a substantial fraction of the errors in GLIDE native redocking), it is reasonable to hope that the initial guess for the geometry is good enough to allow an iterative protocol to succeed, assuming that improved charge distributions can.We believe that the first step should be to expand our set of test cases (particularly those involving cross docking); however, assuming the methodology is successful on such an expanded test set, development of a next generation version should be straightforward. A more general question is whether there are similar problems associated with the use of fixed charge force-fields in explicit solvent molecular dynamics and Monte Carlo simulations. docking methods for lead optimization applications. quantum chemical approach (DFT) to determine the ligand NVP-BGJ398 phosphate charges, we avoid the problem of the quality of the force-field charge model for a wide range of medicinal chemistry compounds. Furthermore, employment of QM/MM techniques enables the charge calculations for the ligand to be performed in the protein environment, thus incorporating polarization effects in a natural (and accurate) fashion; the QM model is able to reliably reproduce, for arbitrary ligand chemistry, the response to an external electric field. Because the protein and ligand are not covalently attached, definition of the QM/MM interface is straightforward, and the computational cost of evaluating the charges (requiring only a single point calculation, as opposed to geometry optimization) is reasonable, particularly in a lead optimization context where hundreds or thousands, as opposed to millions, of ligands are to be studied. As the present article represents an initial effort to investigate this topic, we confine our studies to native redocking, as opposed to cross docking (which will be investigated in a subsequent publication). Within this restricted regime, we address two fundamental questions: If the ligand charges are optimized for the cocrystallized structure via a QM/MM calculation on the native complex, will subsequent redocking of the ligand yield superior structures as compared to the use of force-field based charges, which do not include polarization? Clearly if this objective is not satisfied, further investigation of the use of more accurate charges in the context of current rigid receptor docking models is unlikely to be profitable. Is Rabbit Polyclonal to RHOB it possible, starting with no knowledge of the cocrystallized ligand geometry, to improve binding mode prediction by multiple cycles of docking, recomputation of charges, and redocking, selecting at the end of the process the lowest energy structure (taking into account the charge polarization)? Our algorithmic approach to this problem is rather primitive, and could almost certainly be quantitatively improved, but even with a first generation methodology, in which only one iterative cycle of docking, charge recomputation, and redocking, is employed, dramatic improvements in the prediction of ligand binding modes are obtained. The article is organized as follows. We first briefly review our underlying docking methodology (implemented in the GLIDE program) and QM/MM approach (implemented in the QSITE program), and discuss how we have coupled these two programs together to develop a methodology in which docking and charge computation can, in principle, be iterated to convergence (although the effects of only a single iteration are examined in the present article). We then examine three relatively simple test suites for trypsin cocrystals, t-RNA, and for sugar-binding proteins. For these test NVP-BGJ398 phosphate cases, GLIDE performs reasonably well using force-field charges; charge recomputation is shown to increase robustness and accuracy with remarkable consistency. Finally, we examine 40 diverse PDB complexes, which exhibit a range of errors in standard GLIDE docking, with many instances in the intermediate range of 1.0C3.0 ? RMSD from your experimental crystal structure. For errors of this magnitude (which make up a substantial portion of the errors in GLIDE native redocking), it is sensible to hope that the initial think for the geometry is definitely good enough to allow an iterative protocol to succeed, assuming that improved charge distributions can in fact yield that result. Our results for this statistically significant test suite demonstrate definitively that generation of more accurate costs, which take polarization into account, is definitely a highly encouraging approach to improving docking accuracy. Finally, in the conclusion, we outline long term directions. Methods Docking Method We.
Recent Posts
- Kramer and coworkers continued to develop an in depth 3D pharmacophore (QSAR) conformational model for rabbit Asbt substrates using schooling sets of varied bile acid-based inhibitors as well as the CATALYST software program (Baringhaus et al
- The main impurity (*) was seen as a peptide mass fingerprinting and is most probably to become an Cap-DNA recognition protein (gi:2098303), in keeping with the observed molecular mass of 24?kDa
- In addition, they have decreased positive charge and does not have the lipophilic fatty acid part chain; therefore, there is absolutely no dose-dependent nephrotoxicity59
- Collecting and screening blood for the presence of COVID-19 antibodies in serum on a mass screening is easier than molecular screening for the computer virus
- Transient lymphopenia was observed at the peak of viremia (day 6 p
Recent Comments
Categories
- Orexin Receptors
- Orexin, Non-Selective
- Orexin1 Receptors
- ORL1 Receptors
- Ornithine Decarboxylase
- Orphan 7-TM Receptors
- Orphan 7-Transmembrane Receptors
- Orphan G-Protein-Coupled Receptors
- Orphan GPCRs
- OT Receptors
- Other Acetylcholine
- Other Adenosine
- Other Apoptosis
- Other ATPases
- Other Calcium Channels
- Other Channel Modulators
- Other Dehydrogenases
- Other Hydrolases
- Other Ion Pumps/Transporters
- Other Kinases
- Other MAPK
- Other Nitric Oxide
- Other Nuclear Receptors
- Other Oxygenases/Oxidases
- Other Peptide Receptors
- Other Pharmacology
- Other Product Types
- Other Proteases
- Other RTKs
- Other Synthases/Synthetases
- Other Tachykinin
- Other Transcription Factors
- Other Transferases
- Other Wnt Signaling
- OX1 Receptors
- OXE Receptors
- Oxidative Phosphorylation
- Oxoeicosanoid receptors
- Oxygenases/Oxidases
- Oxytocin Receptors
- P-Glycoprotein
- P-Selectin
- P-Type ATPase
- P-Type Calcium Channels
- p14ARF
- p160ROCK
- P2X Receptors
- P2Y Receptors
- p38 MAPK
- p53
- p56lck
- p60c-src
- p70 S6K
- p75
- p90 Ribosomal S6 Kinase
- PAC1 Receptors
- PACAP Receptors
- PAF Receptors
- PAO
- PAR Receptors
- Parathyroid Hormone Receptors
- PARP
- PC-PLC
- PDE
- PDGFR
- PDK1
- PDPK1
- Peptide Receptor, Other
- Peptide Receptors
- Peroxisome-Proliferating Receptors
- PGF
- PGI2
- Phosphatases
- Phosphodiesterases
- Phosphoinositide 3-Kinase
- Phosphoinositide-Specific Phospholipase C
- Phospholipase A
- Phospholipase C
- Phospholipases
- Phosphorylases
- Photolysis
- PI 3-Kinase
- PI 3-Kinase/Akt Signaling
- PI-PLC
- PI3K
- Pim Kinase
- Pim-1
- PIP2
- Pituitary Adenylate Cyclase Activating Peptide Receptors
- PKA
- PKB
- PKC
- PKD
- PKG
- PKM
- PKMTs
- PLA
- Plasmin
- Platelet Derived Growth Factor Receptors
- Platelet-Activating Factor (PAF) Receptors
- Uncategorized