Benchmarking pKa prediction methods for Lys115 in acetoacetate decarboxylase

被引:2
|
作者
Liu, Yuli [1 ]
Patel, Anand H. G. [1 ]
Burger, Steven K. [1 ]
Ayers, Paul W. [1 ]
机构
[1] McMaster Univ, Dept Chem & Chem Biol, Hamilton, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Protein pKa; Acetoacetate decarboxylase; Net-event kinetic Monte Carlo; POISSON-BOLTZMANN EQUATION; ACTIVE-SITE; CONFORMATIONAL FLEXIBILITY; ENZYMATIC DECARBOXYLATION; IONIZATION-CONSTANT; IONIZABLE GROUPS; REPORTER GROUP; MONTE-CARLO; PROTEINS; VALUES;
D O I
10.1007/s00894-017-3324-x
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Three different pK(a) prediction methods were used to calculate the pK(a) of Lys115 in acetoacetate decarboxylase (AADase): the empirical method PROPKA, the multiconformation continuum electrostatics (MCCE) method, and the molecular dynamics/thermodynamic integration (MD/ TI) method with implicit solvent. As expected, accurate pK(a) prediction of Lys115 depends on the protonation patterns of other ionizable groups, especially the nearby Glu76. However, since the prediction methods do not explicitly sample the protonation patterns of nearby residues, this must be done manually. When Glu76 is deprotonated, all three methods give an incorrect pK(a) value for Lys115. If protonated, Glu76 is used in an MD/TI calculation, the pK(a) of Lys115 is predicted to be 5.3, which agrees well with the experimental value of 5.9. This result agrees with previous site-directed mutagenesis studies, where the mutation of Glu76 (negative charge when deprotonated) to Gln (neutral) causes no change in K-m, suggesting that Glu76 has no effect on the pK(a) shift of Lys115. Thus, we postulate that the pK(a) of Glu76 is also shifted so that Glu76 is protonated (neutral) in AADase.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Bond-Valence methods for pKa prediction:: critical reanalysis and a new approach
    Bickmore, BR
    Tadanier, CJ
    Rosso, KM
    Monn, WD
    Eggett, DL
    GEOCHIMICA ET COSMOCHIMICA ACTA, 2004, 68 (09) : 2025 - 2042
  • [22] Prediction of pKa Values for Druglike Molecules Using Semiempirical Quantum Chemical Methods
    Jensen, Jan H.
    Swain, Christopher J.
    Olsen, Lars
    JOURNAL OF PHYSICAL CHEMISTRY A, 2017, 121 (03): : 699 - 707
  • [23] Prediction of pKa values of nido-carboranes by density functional theory methods
    Farras, Pau
    Teixidor, Francesc
    Branchadell, Vicenc
    INORGANIC CHEMISTRY, 2006, 45 (19) : 7947 - 7954
  • [24] Prediction and rationalization of protein pKa values using QM and QM/MM methods
    Jensen, JH
    Li, H
    Robertson, AD
    Molina, PA
    JOURNAL OF PHYSICAL CHEMISTRY A, 2005, 109 (30): : 6634 - 6643
  • [25] Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction
    Bahai, Akash
    Kwoh, Chee Keong
    Mu, Yuguang
    Li, Yinghui
    PLOS COMPUTATIONAL BIOLOGY, 2024, 20 (12)
  • [26] A low pressure axial fan for benchmarking prediction methods for aerodynamic performance and sound
    Carolus, Thomas
    Zhu, Tao
    Sturm, Michael
    NOISE CONTROL ENGINEERING JOURNAL, 2015, 63 (06) : 537 - 545
  • [27] CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction
    Puton, Tomasz
    Kozlowski, Lukasz P.
    Rother, Kristian M.
    Bujnicki, Janusz M.
    NUCLEIC ACIDS RESEARCH, 2013, 41 (07) : 4307 - 4323
  • [28] Statistical Study For The prediction of pKa Values of Substituted Benzaldoxime Based on Quantum Chemicals Methods
    Al-Hyali, Emad A. S.
    Al-Azzawi, Nezar A.
    Al-Abady, Faiz M. H.
    JOURNAL OF THE KOREAN CHEMICAL SOCIETY-DAEHAN HWAHAK HOE JEE, 2011, 55 (05): : 733 - 740
  • [29] Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods
    Mengshan Li
    Huaijing Zhang
    Bingsheng Chen
    Yan Wu
    Lixin Guan
    Scientific Reports, 8
  • [30] Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods
    Li, Mengshan
    Zhang, Huaijing
    Chen, Bingsheng
    Wu, Yan
    Guan, Lixin
    SCIENTIFIC REPORTS, 2018, 8