References
- Teague SJ . Implications of protein flexibility for drug discovery. Nat. Rev. Drug Discov.2 (7), 527–541 (2003).
- B-Rao C , SubramanianJ, SharmaSD. Managing protein flexibility in docking and its applications. Drug Discov. Today14 (7–8), 394–400 (2009).
- Gallicchio E , LevyRM. Advances in all atom sampling methods for modeling protein-ligand binding affinities. Curr. Opin. Struct. Biol.21, 161–166 (2011).
- Cerqueira NM , GestoD, OliveiraEFet al. Receptor-based virtual screening protocol for drug discovery. Arch. Biochem. Biophys.582, 56–67 (2015).
- Chen YC . Beware of docking. Trends Pharmacol. Sci.36, 78–95 (2015).
- Shin W-H , KimJ-K, KimD-S, SeokC. GalaxyDock2: Protein–ligand docking using beta-complex and global optimization. J. Comput. Chem.34, 2647–2656 (2013).
- Loving KA , LinA, ChengAC. Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility. PLoS Comput. Biol.10, e1003741 (2014).
- Sherman W , DayT, JacobsonMP, FriesnerRA, FaridR. Novel procedure for modeling ligand receptor induced fit effects. J. Med. Chem.49, 534–553 (2006).
- Koska J , SpassovVZ, MaynardAJet al. Fully automated molecular mechanics based induced Fit protein-ligand docking method. J. Chem. Inf. Model.48, 1965–1973 (2008).
- Ivetac A , MccammonJA. Molecular recognition in the case of flexible targets. Curr. Pharm. Des.17, 1663–1671 (2011).
- Bolia A , GerekZN, OzkanSB. BP-Dock: a flexible docking scheme for exploring protein-ligand interactions based on unbound structures. J. Chem. Inf. Model.54, 913–925 (2014).
- Barril X , MorleySD. Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J. Med. Chem.48 (13), 4432–4443 (2005).
- Bolstad ES , AndersonAC. In pursuit of virtual lead optimization: the role of the receptor structure and ensembles in accurate docking. Proteins73 (3), 566–580 (2008).
- Rueda M , TotrovM, AbagyanR. ALiBERO: evolving a team of complementary pocket conformations rather than a single leader. J. Chem. Inf. Model.52, 2705–2714 (2012).
- Forman-Kay JD . The ‘dynamics’ in the thermodynamics of binding. Nat. Struct. Biol.6 (12), 1086–1087 (1999).
- Verkhivker GM , BouzidaD, GehlhaarDK, RejtoPA, FreerST, RosePW. Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective. Curr. Opin. Struct. Biol.12 (2), 197–203 (2002).
- Boehr DD , NussinovR, WrightPE. The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol.5 (11), 789–796 (2009).
- Keskin O . Binding induced conformational changes of proteins correlate with their intrinsic fluctuations: a case study of antibodies. BMC Struct. Biol.7, 31 (2007).
- Kar G , KeskinO, GursoyA, NussinovR. Allostery and population shift in drug discovery. Curr. Opin. Pharmacol.10 (6), 715–722 (2010).
- Csermely P , PalotaiR, NussinovR. Induced fit, conformational selection and independent dynamic segments: an extended view of binding events. Trends Biochem. Sci.35, 539–546 (2010).
- Weikl TR , Von DeusterC. Selected-fit versus induced-fit protein binding: kinetic differences and mutational analysis. Proteins75 (1), 104–110 (2009).
- Nichols SE , BaronR, MccammonA. On the use of molecular dynamics receptor conformations for virtual screning. In : Computational Drug Discovery And Design. BaronR ( Ed.). Springer Science and Business Media, NY, USA, 93–103 (2012).
- Totrov M , AbagyanR. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr. Opin. Struct. Biol.18, 178–184 (2008).
- Rueda M , BottegoniG, AbagyanR. Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model.50 (1), 186–193 (2010).
- Isvoran A , BadelA, CraescuCT, MironS, MitevaMA. Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein–protein interactions. BMC Struct. Biol.11, 24 (2011).
- Vinh NB , SimpsonJS, ScammellsPJ, ChalmersDK. Virtual screening using a conformationally flexible target protein: models for ligand binding to p38α MAPK. J. Comput. Aided Mol. Des.26, 409–423 (2012).
- Cosconati S , MarinelliL, Di LevaFSet al. Protein flexibility in virtual screening: the BACE-1 case study. J. Chem. Inf. Model.52, 2697–2704 (2012).
- Barreca ML , IraciN, ManfroniGet al. Accounting for target flexibility and water molecules by docking to ensembles of target structures: the HCV NS5B palm site I inhibitors case study. J. Chem. Inf. Model.54, 481–497 (2014).
- Huang SY , ZouX. Efficient molecular docking of NMR structures: application to HIV-1 protease. Protein Sci.16 (1), 43–51 (2007).
- Miteva MA , RobertCH, MaréchalJD, PerahiaD. Receptor flexibility in ligand docking and virtual screening. In : In Silico Lead Discovery. MitevaMA ( Ed.). 99–117 (2011).
- Osguthorpe DJ , ShermanW, HaglerAT. Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering. Chem. Biol. Drug Des.80, 182–193 (2012).
- Cavasotto CN , OrryAJ, AbagyanR. The challenge of considering receptor flexibility in ligand docking and virtual screening. Curr. Comput. Aided Drug Design1, 423–440 (2005).
- Yuriev E , RamslandPA. Latest developments in molecular docking: 2010–2011 in review. J. Mol. Recognit.26, 215–239 (2013).
- Cheng LS , AmaroRE, XuD, LiWW, ArzbergerPW, MccammonJA. Ensemble-based virtual screening reveals potential novel antiviral compounds for avian influenza neuraminidase. J. Med. Chem.51 (13), 3878–3894 (2008).
- Xu Y , ColletierJP, JiangH, SilmanI, SussmanJL, WeikM. Induced-fit or preexisting equilibrium dynamics? Lessons from protein crystallography and MD simulations on acetylcholinesterase and implications for structure-based drug design. Protein Sci.17, 601–605 (2008).
- Asses Y , VenkatramanV, LerouxV, RitchieDW, MaigretB. Exploring c-Met kinase flexibility by sampling and clustering its conformational space. Proteins80, 1227–1238 (2012).
- Proctor EA , YinS, TropshaA, DokholyanNV. Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets. Biophys. J.102 (1), 144–151 (2012).
- Machado KS , SchroederEK, RuizDD, CohenEM, De SouzaON. FReDoWS: a method to automate molecular docking simulations with explicit receptor flexibility and snapshots selection. BMC Genomics12 (4), S6 (2011).
- Degliesposti G , PortioliC, ParentiMD, RastelliG. BEAR, a novel virtual screening methodology for drug discovery. J. Biomol. Screen.16, 129–133 (2011).
- Hou T , WangJ, LiY, WangW. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model.51, 69–82 (2011).
- Lin JH , PerrymanAL, SchamesJR, McCammonJA. Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J. Am. Chem. Soc.124 (20), 5632–5633 (2002).
- Amaro RE , BaronR, McCammonJA. An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J. Comput. Aided Mol. Des.22 (9), 693–705 (2008).
- Xu M , LillMA. Utilizing experimental data for reducing ensemble size in flexible-protein docking. J. Chem. Inf. Model.52, 187–198 (2012).
- Martiny VY , CarbonellP, LagorceD, VilloutreixBO, MoroyG, MitevaMA. In silico mechanistic profiling to probe small molecule binding to sulfotransferases. PLoS ONE8, e73587 (2013).
- Rueda M , BottegoniG, AbagyanR. Consistent improvement of cross-docking results using binding site ensembles generated with elastic network normal modes. J. Chem. Inf. Model.49 (3), 716–725 (2009).
- Bahar I , LezonTR, YangLW, EyalE. Global dynamics of proteins: bridging detween structure and function. Ann. Rev. Biophys.39, 23–42 (2010).
- Sperandio O , MouawadL, PintoE, VilloutreixBO, PerahiaD, MitevaMA. How to choose relevant multiple receptor conformations for virtual screening: a test case of CDK2 and normal mode analysis. Eur. Biophys. J.39, 1365–1372 (2010).
- Leis S , ZachariasM. Efficient inclusion of receptor flexibility in grid-based protein–ligand docking. J. Comput. Chem.32, 3433–3439 (2011).
- Nichols SE , BaronR, IvetacA, McCammonJA. Predictive power of molecular dynamics receptor structures in virtual screening. J. Chem. Inf. Model.51, 1439–1446 (2011).
- Korb O , OlssonTS, BowdenSJet al. Potential and limitations of ensemble docking. J. Chem. Inf. Model.52, 1262–1274 (2012).
- Sgobba M , CaporuscioF, AnighoroA, PortioliC, RastelliG. Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. Eur. J. Med. Chem.58, 431–440 (2012).
- Tarcsay A , ParagiG, VassM, JójártB, BogárF, KeserűGM. The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J. Chem. Inf. Model.53, 2990–2999 (2013).
- Moroy G , MartinyVY, VayerP, VilloutreixBO, MitevaMA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov. Today17 (1–2), 44–55 (2012).
- Brooks BR , BrooksCL3rd, MackerellADJret al. CHARMM: the biomolecular simulation program. J. Comput. Chem.30 (10), 1545–1614 (2009).
- Bas DC , RogersDM, JensenJH. Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins73, 765–783 (2008).
- Jain AN . Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des.21 (5), 281–306 (2007).
- Pettersen EF , GoddardTD, HuangCCet al. UCSF Chimera – a visualization system for exploratory research and analysis. J. Comput. Chem.25 (13), 1605–1612 (2004).
- Volkamer A , KuhnD, RippmannF, RareyM. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics28 (15), 2074–2075 (2012).
- Mackerell AD , BashfordD, BellottRet al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B102, 3586–3616 (1998).
- Haberthur U , CaflischA. FACTS: fast analytical continuum treatment of solvation. J. Comput. Chem.29 (5), 701–715 (2008).
- Kleinjung J , FraternaliF. Design and application of implicit solvent models in biomolecular simulations. Curr. Opin. Struct. Biol.25, 126–134 (2014).
- Ryckaert J-P , CiccottiG, BerendsenHJC. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys.23, 327–341 (1977).
- Perahia D , MouawadL. Computation of low-frequency normal modes in macromolecules: improvements to the method of diagonalization in a mixed basis and application to hemoglobin. Comput. Chem.19 (3), 241–246 (1995).
- Cui Q , LiG, MaJ, KarplusM. A normal mode analysis of structural plasticity in the biomolecular motor F(1)-ATPase. J. Mol. Biol.340 (2), 345–372 (2004).
- Mouawad L , PerahiaD. Motions in hemoglobin studied by normal mode analysis and energy minimization: evidence for the existence of tertiary T-like, quaternary R-like intermediate structures. J. Mol. Biol.258 (2), 393–410 (1996).
- Tama F , GadeaFX, MarquesO, SanejouandYH. Building-block approach for determining low-frequency normal modes of macromolecules. Proteins41 (1), 1–7 (2000).
- Floquet N DP , MaigretB, BadetB, Badet-DenisotM-A, PerahiaD. Collective motions in glucosamine-6-phosphate synthase: influence of ligand binding and role in ammonia channelling and opening of the fructose-6-phosphate binding site. J. Mol. Biol.385, 653–664 (2009).
- R Development Core Team . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2009). www.r-project.org/.
- Huang N , ShoichetBK, IrwinJJ. Benchmarking sets for molecular docking. J. Med. Chem.49 (23), 6789–6801 (2006).
- Chemaxon. www.chemaxon.com.
- Lagorce D , MaupetitJ, BaellJet al. The FAF-Drugs2 server: a multi-step engine to prepare electronic chemical compound collections. Bioinformatics27, 2018–2020 (2011).
- CADD Group Chemoinformatics Tools and User Services . http://cactus.nci.nih.gov/.
- Miteva MA , GuyonF, TufferyP. Frog2: efficient 3D conformation ensemble generator for small compounds. Nucleic Acids Res.38, W622–W627 (2010).
- Trott O , OlsonAJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem.31 (2), 455–461 (2010).
- Ben Nasr N , GuillemainH, LagardeN, ZaguryJF, MontesM. Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query. J. Chem. Inf. Model.53, 293–311 (2013).
- Xu X , GammonMD, WetmurJGet al. A functional 19-base pair deletion polymorphism of dihydrofolate reductase (DHFR) and risk of breast cancer in multivitamin users. Am. J. Clin. Nutr.85, 1098–1102 (2007).
- Obeid R , HerrmannW. The emerging role of unmetabolized folic acid in human diseases: myth or reality?Curr. Drug Metab.13, 1184–1195 (2012).
- Morgan DO . Principles of CDK regulation. Nature374 (6518), 131–134 (1995).
- Sielecki TM , BoylanJF, BenfieldPA, TrainorGL. Cyclin-dependent kinase inhibitors: useful targets in cell cycle regulation. J. Med. Chem.43 (1), 1–18 (2000).
- Davies TG , BentleyJ, ArrisCEet al. Structure-based design of a potent purine-based cyclin-dependent kinase inhibitor. Nat. Struct. Biol.9 (10), 745–749 (2002).
- Hardcastle IR , ArrisCE, BentleyJet al. N2-substituted O6-cyclohexylmethylguanine derivatives: potent inhibitors of cyclin-dependent kinases 1 and 2. J. Med. Chem.47 (15), 3710–3722 (2004).
- Chang MW , AyeniC, BreuerS, TorbettBE. Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina. PLoS ONE5, e11955 (2010).
- Huse M , KuriyanJ. The conformational plasticity of protein kinases. Cell109 (3), 275–282 (2002).
- Subramanian J , SharmaS, CBR. A novel computational analysis of ligand-induced conformational changes in the ATP binding sites of cyclin dependent kinases. J. Med. Chem.49 (18), 5434–5441 (2006).
- May A , ZachariasM. Protein-ligand docking accounting for receptor side chain and global flexibility in normal modes: evaluation on kinase inhibitor cross docking. J. Med. Chem.51 (12), 3499–3506 (2008).
- Costa MGS , BatistaPR, BischPM, PerahiaD. Exploring free energy landscapes of large conformational changes: molecular dynamics with excited normal modes. J. Chem. Theory Comput.11, 2755–2767 (2015).
- Perot S , SperandioO, MitevaMA, CamprouxAC, VilloutreixBO. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov. Today15 (15–16), 656–667 (2010).