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Binding thermodynamics and interaction patterns of human purine nucleoside phosphorylase-inhibitor complexes from extensive free energy calculations

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Abstract

Human purine nucleoside phosphorylase (hPNP) plays a significant role in the catabolism of deoxyguanosine. The trimeric protein is an important target in the treatment of T-cell cancers and autoimmune disorders. Experimental studies on the inhibition of the hPNP observe that the first ligand bound to one of three subunits effectively inhibits the protein, while the binding of more ligands to the subsequent sites shows negative cooperativities. In this work, we performed extensive end-point and alchemical free energy calculations to determine the binding thermodynamics of the trimeric protein–ligand system. 13 Immucillin inhibitors with experimental results are under calculation. Two widely accepted charge schemes for small molecules including AM1-BCC and RESP are adopted for ligands. The results of RESP are in better agreement with the experimental reference. Further investigations of the interaction networks in the protein–ligand complexes reveal that several residues play significant roles in stabilizing the complex structure. The most commonly observed ones include PHE200, GLU201, MET219, and ASN243. The conformations of the protein in different protein–ligand complexes are observed to be similar. We expect these insights to aid the development of potent drugs targeting hPNP.

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References

  1. Edwards AA, Mason JM, Clinch K, Tyler PC, Evans GB, Schramm VL (2009) Altered enthalpy− entropy compensation in picomolar transition state analogues of human purine nucleoside phosphorylase. Biochemistry 48:5226–5238

    Article  CAS  PubMed  Google Scholar 

  2. Suarez J, Haapalainen AM, Cahill SM, Ho M-C, Yan F, Almo SC, Schramm VL (2013) Catalytic site conformations in human PNP by 19F-NMR and crystallography. Chem Biol 20:212–222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Edwards AA, Tipton JD, Brenowitz MD, Emmett MR, Marshall AG, Evans GB, Tyler PC, Schramm VL (2010) Conformational states of human purine nucleoside phosphorylase at rest, at work, and with transition state analogues. Biochemistry 49:2058–2067

    Article  CAS  PubMed  Google Scholar 

  4. Glavaš-Obrovac L, Suver M, Hikishima S, Hashimoto M, Yokomatsu T, Magnowska L, Bzowska A (2010) Antiproliferative activity of purine nucleoside phosphorylase multisubstrate analogue inhibitors containing difluoromethylene phosphonic acid against leukaemia and lymphoma cells. Chem Biol Drug Des 75:392–399

    Article  PubMed  Google Scholar 

  5. Thomas K, Haapalainen AM, Burgos ES, Evans GB, Tyler PC, Gulab S, Guan R, Schramm VL (2012) Femtomolar inhibitors bind to 5′-methylthioadenosine nucleosidases with favorable enthalpy and entropy. Biochemistry 51:7541–7550

    Article  CAS  PubMed  Google Scholar 

  6. Clinch K, Evans GB, Fröhlich RF, Gulab SA, Gutierrez JA, Mason JM, Schramm VL, Tyler PC, Woolhouse AD (2012) Transition state analogue inhibitors of human methylthioadenosine phosphorylase and bacterial methylthioadenosine/S-adenosylhomocysteine nucleosidase incorporating acyclic ribooxacarbenium ion mimics. Biorg Med Chem 20:5181–5187

    Article  CAS  Google Scholar 

  7. Wielgus-Kutrowska B, Breer K, Hashimoto M, Hikishima S, Yokomatsu T, Narczyk M, Dyzma A, Girstun A, Staroń K, Bzowska A (2012) Trimeric purine nucleoside phosphorylase: exploring postulated one-third-of-the-sites binding in the transition state. Biorg Med Chem 20:6758–6769

    Article  CAS  Google Scholar 

  8. Vetticatt MJ, Itin B, Evans GB, Schramm VL (2013) Distortional binding of transition state analogs to human purine nucleoside phosphorylase probed by magic angle spinning solid-state NMR. Proc Natl Acad Sci USA 110:15991–15996

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Schramm VL (2011) Enzymatic transition states, transition-state analogs, dynamics, thermodynamics, and lifetimes. Annu Rev Biochem 80:703–732

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Guan R, Tyler PC, Evans GB, Schramm VL (2013) Thermodynamic analysis of transition-state features in picomolar inhibitors of human 5′-methylthioadenosine phosphorylase. Biochemistry 52:8313–8322

    Article  CAS  PubMed  Google Scholar 

  11. Ho M-C, Shi W, Rinaldo-Matthis A, Tyler PC, Evans GB, Clinch K, Almo SC, Schramm VL (2010) Four generations of transition-state analogues for human purine nucleoside phosphorylase. Proc. Natl. Acad. Sci, USA

    Book  Google Scholar 

  12. Breer K, Wielgus-Kutrowska B, Girstun A, Staroń K, Hashimoto M, Hikishima S, Yokomatsu T, Bzowska A (2010) Overexpressed proteins may act as mops removing their ligands from the host cells: a case study of calf PNP. Biochem Biophys Res Commun 391:1203–1209

    Article  CAS  PubMed  Google Scholar 

  13. Guan R, Ho M-C, Brenowitz M, Tyler PC, Evans GB, Almo SC, Schramm VL (2011) Entropy-driven binding of picomolar transition state analogue inhibitors to human 5′-methylthioadenosine phosphorylase. Biochemistry 50:10408–10417

    Article  CAS  PubMed  Google Scholar 

  14. Hooft RW, van Eijck BP, Kroon J (1992) An adaptive umbrella sampling procedure in conformational analysis using molecular dynamics and its application to glycol. J Chem Phys 97:6690–6694

    Article  CAS  Google Scholar 

  15. Mezei M (1987) Adaptive umbrella sampling: self-consistent determination of the non-Boltzmann bias. J Comput Phys 68:237–248

    Article  Google Scholar 

  16. Kästner J (2011) Umbrella sampling. Wiley Interdisip Rev Comput Mol Sci 1:932–942

    Article  Google Scholar 

  17. Sun Z, Wang X, Zhang JZH (2017) Protonation-dependent base flipping in the catalytic triad of a small RNA. Chem Phys Lett 684:239–244

    Article  CAS  Google Scholar 

  18. Sun Z, Zhang JZH (2020) Thermodynamic insights of base flipping in TNA duplex: force fields, salt concentrations, and free-energy simulation methods. CCS Chem 2:1026–1039

    Google Scholar 

  19. Sun Z, Wang X, Zhang JZH, He Q (2019) Sulfur-substitution-induced base flipping in the DNA duplex. Phys Chem Chem Phys 21:14923–14940

    Article  CAS  PubMed  Google Scholar 

  20. Sun Z (2021) SAMPL7 trimertrip host-guest binding poses and binding affinities from spherical-coordinates-biased simulations. J Comput Aided Mol Des 35:105–115

    Article  CAS  PubMed  Google Scholar 

  21. Sun Z, He Q, Li X, Zhu Z (2020) SAMPL6 host–guest binding affinities and binding poses from spherical-coordinates-biased simulations. J Comput Aided Mol Des 34:589–600

    Article  CAS  PubMed  Google Scholar 

  22. Brotzakis ZF, Gehre M, Voets IK, Bolhuis PG (2017) Stability and growth mechanism of self-assembling putative antifreeze cyclic peptides. Phys Chem Chem Phys 19:19032–19042

    Article  CAS  PubMed  Google Scholar 

  23. Brotzakis ZF, Voets IK, Bakker HJ, Bolhuis PG (2018) Water structure and dynamics in the hydration layer of a type III anti-freeze protein. Phys Chem Chem Phys 20:6996–7006

    Article  CAS  PubMed  Google Scholar 

  24. Wang X, Xingzhao T, Boming D, John ZHZ, Sun Z (2019) BAR-based optimum adaptive steered MD for configurational sampling. J. Comput. Chem. 40:1270–1289

    Article  PubMed  Google Scholar 

  25. Wang X, Sun Z (2019) Determination of base flipping free energy landscapes from nonequilibrium stratification. J Chem Inf Model 59:2980–2994

    Article  CAS  PubMed  Google Scholar 

  26. Zhaoxi S (2021) A benchmark test on the leapfrog integrator and its middle alternative

  27. Wang X, Deng B, Sun Z (2019) Thermodynamics of helix formation in small peptides of varying length in vacuo, in implicit solvent, and in explicit solvent. J Mol Model 25:3

    Article  Google Scholar 

  28. Ozer G, Quirk S, Hernandez R (2012) Adaptive steered molecular dynamics: Validation of the selection criterion and benchmarking energetics in vacuum. J Chem Phys 136:215104

    Article  PubMed  Google Scholar 

  29. Wang X, He Q, Sun Z (2019) BAR-based multi-dimensional nonequilibrium pulling for indirect construction of a QM/MM free energy landscape. Phys Chem Chem Phys 21:6672–6688

    Article  CAS  PubMed  Google Scholar 

  30. Sun Z, Wang X (2019) Thermodynamics of Helix formation in small peptides of varying length in vacuo, implicit solvent and explicit solvent: Comparison between AMBER force fields. J Theor Comput Chem 3:1950015

    Article  Google Scholar 

  31. Barducci A, Bonomi M, Parrinello M (2011) Metadynamics. Wiley Interdisip Rev Comput Mol Sci 1:826–843

    Article  CAS  Google Scholar 

  32. Tiwary P, van de Walle A (2013) Accelerated molecular dynamics through stochastic iterations and collective variable based basin identification. Phys Rev B 87:094304

    Article  Google Scholar 

  33. Fukunishi H, Watanabe O, Takada S (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J Chem Phys 116:9058–9067

    Article  CAS  Google Scholar 

  34. Itoh SG, Damjanovic A, Brooks BR (2011) pH replica-exchange method based on discrete protonation states. Proteins 79:3420–3436

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151

    Article  CAS  Google Scholar 

  36. Sugita Y, Kitao A, Okamoto Y (2000) Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 113:6042–6051

    Article  CAS  Google Scholar 

  37. Swope WC (1982) A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: application to small water clusters. J Chem Phys 76:637

    Article  CAS  Google Scholar 

  38. Pham TT, Shirts MR (2011) Identifying low variance pathways for free energy calculations of molecular transformations in solution phase. J Chem Phys 135:034114

    Article  PubMed  Google Scholar 

  39. Procacci P, Chelli R (2017) Statistical mechanics of ligand-receptor noncovalent association, revisited: binding site and standard state volumes in modern alchemical theories. J Chem Theory Comput 13:1924–1933

    Article  CAS  PubMed  Google Scholar 

  40. Wang B, Qi Y, Gao Y, Zhang JZH (2020) A method for efficient calculation of thermal stability of proteins upon point mutations. Phys Chem Chem Phys 22:8461–8466

    Article  CAS  PubMed  Google Scholar 

  41. Munoz M, Cardenas C (2017) How predictive could alchemical derivatives be? Phys Chem Chem Phys 19:16003–16012

    Article  CAS  PubMed  Google Scholar 

  42. Li Z, Bao J, Qi Y, Zhang JZH (2020) Computational approaches to studying methylated H4K20 recognition by DNA repair factor 53BP1. Phys Chem Chem Phys 22:6136–6144

    Article  CAS  PubMed  Google Scholar 

  43. Pearlman DA, Kollman PA (1989) The lag between the Hamiltonian and the system configuration in free energy perturbation calculations. J Chem Phys 91:7831–7839

    Article  CAS  Google Scholar 

  44. Ravishanker G, Mezei M, Beveridge DL (1986) Conformational stability and flexibility of the ala dipeptide in free space and water: Monte Carlo computer simulation studies. J Comput Chem 7:345–348

    Article  CAS  Google Scholar 

  45. Cross AJ (1986) Influence of Hamiltonian parameterization on convergence of Kirkwood free energy calculations. Chem Phys Lett 128:198–202

    Article  CAS  Google Scholar 

  46. Steinbrecher T, Mobley DL, Case DA (2007) Nonlinear scaling schemes for Lennard–Jones interactions in free energy calculations. J Chem Phys 127:214108

    Article  PubMed  Google Scholar 

  47. Zacharias M, Straatsma TP, Mccammon JA (1994) Separation-shifted scaling, a new scaling method for Lennard-Jones interactions in thermodynamic integration. J Chem Phys 100:9025–9031

    Article  CAS  Google Scholar 

  48. Beutler TC, Mark AE, Schaik RCV, Gerber PR, Gunsteren WFV (1994) Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations. Chem Phys Lett 222:529–539

    Article  CAS  Google Scholar 

  49. Pitera JW, van Gunsteren WF (2002) A comparison of non-bonded scaling approaches for free energy calculations. Mol Simul 28:45–65

    Article  CAS  Google Scholar 

  50. Bitetti R (2003) Generalized ensembles serve to improve the convergence of free energy simulations. Chem Phys Lett 377:633–641

    Article  Google Scholar 

  51. Chipot C, Rozanska X, Dixit SB (2005) Can free energy calculations be fast and accurate at the same time? Binding of low-affinity, non-peptide inhibitors to the SH2 domain of the src protein. J Comput Aided Mol Des 19:765–770

    Article  CAS  PubMed  Google Scholar 

  52. Fowler PW, Jha S, Coveney PV (2005) Grid-based steered thermodynamic integration accelerates the calculation of binding free energies. Philos Trans R Soc A 363:1999–2015

    Article  CAS  Google Scholar 

  53. Rocklin GJ, Mobley DL, Dill KA, Hünenberger PH (2013) Calculating the binding free energies of charged species based on explicit-solvent simulations employing lattice-sum methods: an accurate correction scheme for electrostatic finite-size effects. J Chem Phys 139:184103

    Article  PubMed  PubMed Central  Google Scholar 

  54. Wang X, Tu X, Zhang JZH, Sun Z (2018) BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation: the nonequilibrium stratification. Phys Chem Chem Phys 20:2009–2021

    Article  CAS  PubMed  Google Scholar 

  55. Shirts MR, Pande VS (2005) Solvation free energies of amino acid side chain analogs for common molecular mechanics water models. J Chem Phys 122:134508

    Article  PubMed  Google Scholar 

  56. Hummer G, Pratt LR, Garcia AE (1995) Hydration free energy of water. J Phys Chem 99:14188–14194

    Article  CAS  Google Scholar 

  57. Mobley DL, Dumont E, Chodera JD, Dill KA (2007) Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J Phys Chem B 111:2242–2254

    Article  CAS  PubMed  Google Scholar 

  58. Sun Z, Wang X, Song J (2017) Extensive assessment of various computational methods for aspartate’s pKa shift. J Chem Inf Model 57:1621–1639

    Article  CAS  PubMed  Google Scholar 

  59. Sun ZX, Wang XH, Zhang JZH (2017) BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation. Phys Chem Chem Phys 19:15005–15020

    Article  CAS  PubMed  Google Scholar 

  60. Gallicchio E, Levy RM (2011) Advances in all atom sampling methods for modeling protein-ligand binding affinities. Curr Opin Struct Biol 21:161–166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 21:150–160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Parenti MD, Rastelli G (2012) Advances and applications of binding affinity prediction methods in drug discovery. Biotechnol Adv 30:244–250

    Article  CAS  PubMed  Google Scholar 

  63. Boyce SE, Mobley DL, Rocklin GJ, Graves AP, Dill KA, Shoichet BK (2009) Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site. J Mol Biol 394:747–763

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Huang N, Kalyanaraman C, Bernacki K, Jacobson MP (2006) Molecular mechanics methods for predicting protein–ligand binding. Phys Chem Chem Phys 8:5166–5177

    Article  CAS  PubMed  Google Scholar 

  65. Sun Z, Yan YN, Yang M, Zhang JZ (2017) Interaction entropy for protein-protein binding. J Chem Phys 146:124124

    Article  PubMed  Google Scholar 

  66. Qiu L, Yan Y, Sun Z, Song J, Zhang JZH (2017) Interaction entropy for computational alanine scanning in protein–protein binding. Wiley Interdiscip Rev

  67. Huai Z, Yang H, Li X, Sun Z (2021) SAMPL7 TrimerTrip host-guest binding affinities from extensive alchemical and end-point free energy calculations. J Comput Aided Mol Des 35:117–129

    Article  CAS  PubMed  Google Scholar 

  68. Kilburg D, Gallicchio E (2018) Assessment of a single decoupling alchemical approach for the calculation of the absolute binding free energies of protein-peptide complexes. Front Mol Biosci 5:22

    Article  PubMed  PubMed Central  Google Scholar 

  69. Gohlke H, Kiel C, Case DA (2003) Insights into protein–protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras–RalGDS complexes. J Mol Biol 330:891–913

    Article  CAS  PubMed  Google Scholar 

  70. Bai HJ (2010) Protein-protein interactions:interface analysis, binding free energy calculation and interaction design. Acta Physico-Chim Sin 26:1988–1997

    Article  CAS  Google Scholar 

  71. Bruckner S, Boresch S (2011) Efficiency of alchemical free energy simulations. II. Improvements for thermodynamic integration. J Comput Chem 32:1320–1333

    Article  CAS  PubMed  Google Scholar 

  72. Resat H, Mezei M (1993) Studies on free energy calculations. I. Thermodynamic integration using a polynomial path. J. Chem. Phys. 99:6052–6061

    Article  CAS  Google Scholar 

  73. Resat H, Mezei M (1994) Studies on free energy calculations. II. A theoretical approach to molecular solvation. J Chem Phys 101:6126–6140

    Article  CAS  Google Scholar 

  74. Zwanzig RW (1954) High temperature equation of state by a perturbation method. J Chem Phys 22:1420–1426

    Article  CAS  Google Scholar 

  75. Hummer G (2001) Fast-growth thermodynamic integration: error and efficiency analysis. J Chem Phys 114:7330–7337

    Article  CAS  Google Scholar 

  76. Eastwood MP, Hardin C, Luthey-Schulten Z, Wolynes PG (2002) Statistical mechanical refinement of protein structure prediction schemes: cumulant expansion approach. J Chem Phys 117:4602–4615

    Article  CAS  Google Scholar 

  77. Shirts MR, Bair E, Hooker G, Pande VS (2003) Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods. Phys Rev Lett 91:140601

    Article  PubMed  Google Scholar 

  78. Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22:245–268

    Article  Google Scholar 

  79. Fenwick MK, Escobedo FA (2004) On the use of Bennett’s acceptance ratio method in multi-canonical-type simulations. J Chem Phys 120:3066–3074

    Article  CAS  PubMed  Google Scholar 

  80. Tan Z (2004) On a likelihood approach for Monte Carlo integration. J Am Stat Assoc 99:1027–1036

    Article  Google Scholar 

  81. Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129:124105

    Article  PubMed  PubMed Central  Google Scholar 

  82. Wang X, Sun Z (2018) A theoretical interpretation of variance-based convergence citeria in perturbation-based theories. arXiv:1803.03123

  83. Jarzynski C (1997) Equilibrium free-energy differences from nonequilibrium measurements: a master-equation approach. Phys Rev E 56:5018–5035

    Article  CAS  Google Scholar 

  84. Jarzynski C (1997) A nonequilibrium equality for free energy differences. Phys Rev Lett 78:2690–2693

    Article  CAS  Google Scholar 

  85. Crooks GE (1998) Nonequilibrium measurements of free energy differences for microscopically reversible Markovian systems. J Stat Phys 90:1481–1487

    Article  Google Scholar 

  86. Aqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–91

    Article  CAS  PubMed  Google Scholar 

  87. Carlson HA, Jorgensen WL (1995) An extended linear response method for determining free energies of hydration. J Phys Chem 99:10667–10673

    Article  CAS  Google Scholar 

  88. Wang W, Wang J, Kollman PA (1999) What determines the van der Waals coefficient β in the LIE (linear interaction energy) method to estimate binding free energies using molecular dynamics simulations? Proteins 34:395–402

    Article  CAS  PubMed  Google Scholar 

  89. Leach AR (2001) Molecular modeling principles & applications. Pearson education, New York

    Google Scholar 

  90. Lee FS, Chu ZT, Bolger MB, Warshel A (1992) Calculations of antibody-antigen interactions: microscopic and semi-microscopic evaluation of the free energies of binding of phosphorylcholine analogs to McPC603. Protein Eng 5:215–228

    Article  CAS  PubMed  Google Scholar 

  91. Ferrari AM, Degliesposti G, Sgobba M, Rastelli G (2007) Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors. Biorg Med Chem 15:7865–7877

    Article  CAS  Google Scholar 

  92. Rapp C, Kalyanaraman C, Schiffmiller A, Schoenbrun EL, Jacobson MP (2011) A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. J Chem Inf Model 51:2082–2089

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Miller BR, Mcgee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA.py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8:3314–3321

    Article  CAS  PubMed  Google Scholar 

  94. Honig B, Nicholls A (1995) Classical electrostatics in biology and chemistry. Science 268:1144–1149

    Article  CAS  PubMed  Google Scholar 

  95. Su PC, Tsai CC, Mehboob S, Hevener KE, Johnson ME (2015) Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F. tularensis enoyl-ACP reductase (F. abI). J Comput Chem 36:1859–1873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Sangpheak W, Khuntawee W, Wolschann P, Pongsawasdi P, Rungrotmongkol T (2014) Enhanced stability of a naringenin/2, 6-dimethyl β-cyclodextrin inclusion complex: Molecular dynamics and free energy calculations based on MM-and QM-PBSA/GBSA. J Mol Graph Model 50:10–15

    Article  CAS  PubMed  Google Scholar 

  97. Tsitsanou KE, Hayes JM, Keramioti M, Mamais M, Oikonomakos NG, Kato A, Leonidas DD, Zographos SE (2013) Sourcing the affinity of flavonoids for the glycogen phosphorylase inhibitor site via crystallography, kinetics and QM/MM-PBSA binding studies: comparison of chrysin and flavopiridol. Food Chem Toxicol 61:14–27

    Article  CAS  PubMed  Google Scholar 

  98. Yang Y-P, He L-P, Bao J-X, Qi Y-F, Zhang JZ (2019) Computational analysis for residue-specific CDK2-inhibitor bindings. Chin J Chem Phys 32:134

    Article  CAS  Google Scholar 

  99. Chen J, Pang L, Wang W, Wang L, Zhang JZ, Zhu T (2019) Decoding molecular mechanism of inhibitor bindings to CDK2 using molecular dynamics simulations and binding free energy calculations. J Biomol Struct Dyn 38:1–23

    Google Scholar 

  100. Kohut G, Liwo A, Bosze S, Beke-Somfai T, Samsonov SA (2018) Protein-ligand interaction energy-based entropy calculations: fundamental challenges for flexible systems. J Phys Chem B 122:7821–7827

    Article  CAS  PubMed  Google Scholar 

  101. Hirschi JS, Arora K, Brooks CL III, Schramm VL (2010) Conformational dynamics in human purine nucleoside phosphorylase with reactants and transition-state analogues. J Phys Chem B 114:16263–16272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Caceres RA, Timmers LFSM, Pauli I, Gava LM, Ducati RG, Basso LA, Santos DS, de Azevedo Jr WF (2010) Crystal structure and molecular dynamics studies of human purine nucleoside phosphorylase complexed with 7-deazaguanine. J Struct Biol 169:379–388

    Article  CAS  PubMed  Google Scholar 

  103. Caceres RA, Timmers LFSM, Ducati RG, da Silva DON, Basso LA, de Azevedo WF, Santos DS (2012) Crystal structure and molecular dynamics studies of purine nucleoside phosphorylase from Mycobacterium tuberculosis associated with acyclovir. Biochimie 94:155–165

    Article  CAS  PubMed  Google Scholar 

  104. Antoniou D, Basner J, Núñez S, Schwartz SD (2006) Computational and theoretical methods to explore the relation between enzyme dynamics and catalysis. Chem Rev 106:3170–3187

    Article  CAS  PubMed  Google Scholar 

  105. Rocha JA, Rego NCS, Carvalho BTS, Silva FI, Sousa JA, Ramos RM, Passos ING, de Moraes J, Leite JRSA, Lima FCA (2018) Computational quantum chemistry, molecular docking, and ADMET predictions of imidazole alkaloids of Pilocarpus microphyllus with schistosomicidal properties. PLoS ONE 13:e0198476

    Article  PubMed  PubMed Central  Google Scholar 

  106. Isaksen GV, Hopmann KH, Åqvist J, Brandsdal BO (2016) Computer simulations reveal substrate specificity of glycosidic bond cleavage in native and mutant human purine nucleoside phosphorylase. Biochemistry 55:2153–2162

    Article  CAS  PubMed  Google Scholar 

  107. Núñez S, Wing C, Antoniou D, Schramm VL, Schwartz SD (2006) Insight into catalytically relevant correlated motions in human purine nucleoside phosphorylase. J Phys Chem A 110:463–472

    Article  PubMed  Google Scholar 

  108. Decherchi S, Berteotti A, Bottegoni G, Rocchia W, Cavalli A (2015) The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning. Nat Commun 6:6155

    Article  CAS  PubMed  Google Scholar 

  109. Ghanem M, Zhadin N, Callender R, Schramm VL (2009) Loop-tryptophan human purine nucleoside phosphorylase reveals submillisecond protein dynamics. Biochemistry 48:3658–3668

    Article  CAS  PubMed  Google Scholar 

  110. Zanchi FB, Caceres RA, Stabeli RG, de Azevedo WF (2010) Molecular dynamics studies of a hexameric purine nucleoside phosphorylase. J Mol Model 16:543–550

    Article  CAS  PubMed  Google Scholar 

  111. Timmers LFSM, Caceres RA, Dias R, Basso LA, Santos DS, de Azevedo WF (2009) Molecular modeling, dynamics and docking studies of Purine Nucleoside Phosphorylase from Streptococcus pyogenes. Biophys Chem 142:7–16

    Article  CAS  PubMed  Google Scholar 

  112. Saen-Oon S, Ghanem M, Schramm VL, Schwartz SD (2008) Remote mutations and active site dynamics correlate with catalytic properties of purine nucleoside phosphorylase. Biophys J 94:4078–4088

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Isaksen GV, Åqvist J, Brandsdal BO (2017) Thermodynamics of the purine nucleoside phosphorylase reaction revealed by computer simulations. Biochemistry 56:306–312

    Article  CAS  PubMed  Google Scholar 

  114. Pyrka M, Maciejczyk M (2020) Why purine nucleoside phosphorylase ribosylates 2,6-diamino-8-azapurine in noncanonical positions? A molecular modeling study. J Chem Inf Model 60:1595–1606

    Article  CAS  PubMed  Google Scholar 

  115. Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges AM1-BCC model: II. Parameterization and validation. J Comput Chem 23:1623–41

    Article  CAS  PubMed  Google Scholar 

  116. Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J. Comput. Chem. 21:132–146

    Article  CAS  Google Scholar 

  117. Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 23:1623–1641

    Article  CAS  PubMed  Google Scholar 

  118. He X, Man VH, Yang W, Lee T-S, Wang J (2020) A fast and high-quality charge model for the next generation general AMBER force field. J Chem Phys 153:114502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Fiser A, Do RKG (2000) Modeling of loops in protein structures. Protein Sci 9:1753–1773

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Tian C, Kasavajhala K, Belfon KA, Raguette L, Huang H, Migues AN, Bickel J, Wang Y, Pincay J, Wu Q (2019) ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J Chem Theory Comput 16:528–552

    Article  PubMed  Google Scholar 

  121. Huai Z, Shen Z, Sun Z (2021) Binding thermodynamics and interaction patterns of inhibitor-major urinary protein-I binding from extensive free-energy calculations: benchmarking AMBER force fields. J Chem Inf Model 61:284–297

    Article  CAS  PubMed  Google Scholar 

  122. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Sun Z, Wang X, Zhang JZ (2020) Theoretical understanding of the thermodynamics and interactions in transcriptional regulator TtgR-ligand binding. Phys Chem Chem Phys 22:1511–1524

    Article  CAS  PubMed  Google Scholar 

  124. Sun Z, Wang X, Zhao Q, Zhu T (2019) Understanding Aldose Reductase-Inhibitors interactions with free energy simulation. J Mol Graph Model 91:10–21

    Article  CAS  PubMed  Google Scholar 

  125. Wang X, Sun Z (2019) Understanding PIM-1 kinase inhibitor interactions with free energy simulation. Phys Chem Chem Phys 21:7544–7558

    Article  CAS  PubMed  Google Scholar 

  126. Gouda H, Kuntz ID, Case DA, Kollman PA (2003) Free energy calculations for theophylline binding to an RNA aptamer: comparison of MM-PBSA and thermodynamic integration methods. Biopolymers 68:16–34

    Article  CAS  PubMed  Google Scholar 

  127. Gohlke H, Case DA (2004) Converging free energy estimates: MM-PB (GB) SA studies on the protein–protein complex Ras-Raf. J Comput Chem 25:238–250

    Article  CAS  PubMed  Google Scholar 

  128. Genheden S, Ryde U (2010) How to obtain statistically converged MM/GBSA results. J Comput Chem 31:837–846

    CAS  PubMed  Google Scholar 

  129. Song J, Qiu L, Zhang JZ (2018) An efficient method for computing excess free energy of liquid. Sci China Chem 61:135–140

    Article  CAS  Google Scholar 

  130. Pastor RW, Brooks BR, Szabo A (1988) An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol Phys 65:1409–1419

    Article  Google Scholar 

  131. Case DA, Cheatham TE, Tom D, Holger G, Luo R, Merz KM, Alexey O, Carlos S, Bing W, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9:3084–3095

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Part of the simulation was performed on the high-performance computing platform of the Center for Life Science (Peking University). Dr. Zhaoxi Sun is supported by the PKU-Boya Postdoctoral Fellowship. We are grateful for many valuable and insightful comments from the anonymous reviewers.

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The variation of \({\langle \frac{\partial U}{\partial }\rangle }_{i}\) along the alchemical pathway, the time-evolution of \({\frac{\partial U}{\partial }|}_{i}\), the time series of secondary structures with two charge schemes, the time series and the average number of hydrogen bonds formed between the ligand and its surroundings in protein–ligand and solvated-ligand systems with two charge schemes, the detailed results of free energy estimates obtained from end-point and alchemical free energy calculations, the relative difference between the ESP produced by the two charge models and the HF ESP, and the comparison of the dipole under two charge schemes are given in the supporting information.. Supplementary file1 (PDF 5039 kb)

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Huai, Z., Yang, H. & Sun, Z. Binding thermodynamics and interaction patterns of human purine nucleoside phosphorylase-inhibitor complexes from extensive free energy calculations. J Comput Aided Mol Des 35, 643–656 (2021). https://doi.org/10.1007/s10822-021-00382-w

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