Using DelPhi Capabilities to Mimic Protein's Conformational Reorganization with Amino Acid Specific Dielectric Constants

Using DelPhi Capabilities to Mimic Protein's Conformational Reorganization with Amino Acid Specific Dielectric Constants

Year:    2013

Communications in Computational Physics, Vol. 13 (2013), Iss. 1 : pp. 13–30

Abstract

Many molecular events are associated with small or large conformational changes occurring in the corresponding proteins. Modeling such changes is a challenge and requires significant amount of computing time. From point of view of electrostatics, these changes can be viewed as a reorganization of local charges and dipoles in response to the changes of the electrostatic field, if the cause is insertion or deletion of a charged amino acid. Here we report a large scale investigation of modeling the changes of the folding energy due to single mutations involving charged group. This allows the changes of the folding energy to be considered mostly electrostatics in origin and to be calculated with DelPhi assigning residue-specific value of the internal dielectric constant of protein. The predicted energy changes are benchmarked against experimentally measured changes of the folding energy on a set of 257 single mutations. The best fit between experimental values and predicted changes is used to find out the effective value of the internal dielectric constant for each type of amino acid. The predicted folding free energy changes with the optimal, amino acid specific, dielectric constants are within RMSD=0.86 kcal/mol from experimentally measured changes.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.300611.120911s

Communications in Computational Physics, Vol. 13 (2013), Iss. 1 : pp. 13–30

Published online:    2013-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    18

Keywords:   

  1. pKa predictions for proteins, RNAs, and DNAs with the Gaussian dielectric function using DelPhi pKa

    Wang, Lin | Li, Lin | Alexov, Emil

    Proteins: Structure, Function, and Bioinformatics, Vol. 83 (2015), Iss. 12 P.2186

    https://doi.org/10.1002/prot.24935 [Citations: 104]
  2. Modeling the electrostatic potential of asymmetric lipopolysaccharide membranes: The MEMPOT algorithm implemented in DelPhi

    Dias, Roberta P. | Li, Lin | Soares, Thereza A. | Alexov, Emil

    Journal of Computational Chemistry, Vol. 35 (2014), Iss. 19 P.1418

    https://doi.org/10.1002/jcc.23632 [Citations: 18]
  3. Interaction entropy for computational alanine scanning in protein–protein binding

    Qiu, Linqiong | Yan, Yuna | Sun, Zhaoxi | Song, Jianing | Zhang, John Z.H.

    WIREs Computational Molecular Science, Vol. 8 (2018), Iss. 2

    https://doi.org/10.1002/wcms.1342 [Citations: 44]
  4. NQO1 rs1800566 polymorph is more prone to NOx induced lung injury: Endorsing deleterious functionality through informatics approach

    Basharat, Zarrin | Messaoudi, Abdelmonaem | Ruba, Sehrish | Yasmin, Azra

    Gene, Vol. 591 (2016), Iss. 1 P.14

    https://doi.org/10.1016/j.gene.2016.06.048 [Citations: 6]
  5. Reproducing the Ensemble Average Polar Solvation Energy of a Protein from a Single Structure: Gaussian-Based Smooth Dielectric Function for Macromolecular Modeling

    Chakravorty, Arghya | Jia, Zhe | Li, Lin | Zhao, Shan | Alexov, Emil

    Journal of Chemical Theory and Computation, Vol. 14 (2018), Iss. 2 P.1020

    https://doi.org/10.1021/acs.jctc.7b00756 [Citations: 15]
  6. Design and Performance Analysis of Symmetrical and Asymmetrical Triple Gate Dopingless Vertical TFET for Biorecognition

    Wadhera, Tanu | Wadhwa, Girish | Bhardwaj, Tarun Kumar | Kakkar, Deepti | Raj, Balwinder

    Silicon, Vol. 13 (2021), Iss. 11 P.4057

    https://doi.org/10.1007/s12633-020-00686-w [Citations: 14]
  7. Electrostatic component of binding energy: Interpreting predictions from poisson–boltzmann equation and modeling protocols

    Chakavorty, Arghya | Li, Lin | Alexov, Emil

    Journal of Computational Chemistry, Vol. 37 (2016), Iss. 28 P.2495

    https://doi.org/10.1002/jcc.24475 [Citations: 18]
  8. On the Dielectric “Constant” of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi

    Li, Lin | Li, Chuan | Zhang, Zhe | Alexov, Emil

    Journal of Chemical Theory and Computation, Vol. 9 (2013), Iss. 4 P.2126

    https://doi.org/10.1021/ct400065j [Citations: 446]
  9. Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

    Petukh, Marharyta | Li, Minghui | Alexov, Emil | MacKerell, Alexander

    PLOS Computational Biology, Vol. 11 (2015), Iss. 7 P.e1004276

    https://doi.org/10.1371/journal.pcbi.1004276 [Citations: 97]
  10. NanoShaper–VMD interface: computing and visualizing surfaces, pockets and channels in molecular systems

    Decherchi, Sergio | Spitaleri, Andrea | Stone, John | Rocchia, Walter | Valencia, Alfonso

    Bioinformatics, Vol. 35 (2019), Iss. 7 P.1241

    https://doi.org/10.1093/bioinformatics/bty761 [Citations: 24]
  11. SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties

    Raffo, Andrea | Fugacci, Ulderico | Biasotti, Silvia | Rocchia, Walter | Liu, Yonghuai | Otu, Ekpo | Zwiggelaar, Reyer | Hunter, David | Zacharaki, Evangelia I. | Psatha, Eleftheria | Laskos, Dimitrios | Arvanitis, Gerasimos | Moustakas, Konstantinos | Aderinwale, Tunde | Christoffer, Charles | Shin, Woong-Hee | Kihara, Daisuke | Giachetti, Andrea | Nguyen, Huu-Nghia | Nguyen, Tuan-Duy | Nguyen-Truong, Vinh-Thuyen | Le-Thanh, Danh | Nguyen, Hai-Dang | Tran, Minh-Triet

    Computers & Graphics, Vol. 99 (2021), Iss. P.1

    https://doi.org/10.1016/j.cag.2021.06.010 [Citations: 11]
  12. Electrostatic effects on the folding stability of FKBP12

    Batra, Jyotica | Tjong, Harianto | Zhou, Huan-Xiang

    Protein Engineering Design and Selection, Vol. 29 (2016), Iss. 8 P.301

    https://doi.org/10.1093/protein/gzw014 [Citations: 6]
  13. Progress in developing Poisson-Boltzmann equation solvers

    Li, Chuan | Li, Lin | Petukh, Marharyta | Alexov, Emil

    Computational and Mathematical Biophysics, Vol. 1 (2013), Iss. 2013 P.42

    https://doi.org/10.2478/mlbmb-2013-0002 [Citations: 24]
  14. Computing the Differences between Asn-X and Gln-X Deamidation and Their Impact on Pharmaceutical and Physiological Proteins: A Theoretical Investigation Using Model Dipeptides

    Lawson, Katherine E. | Evans, Megan N. | Dekle, Joseph K. | Adamczyk, Andrew J.

    The Journal of Physical Chemistry A, Vol. 127 (2023), Iss. 1 P.57

    https://doi.org/10.1021/acs.jpca.2c06511 [Citations: 2]
  15. SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach

    Getov, Ivan | Petukh, Marharyta | Alexov, Emil

    International Journal of Molecular Sciences, Vol. 17 (2016), Iss. 4 P.512

    https://doi.org/10.3390/ijms17040512 [Citations: 71]
  16. Towards pharmaceutical protein stabilization: DFT and statistical learning studies on non-enzymatic peptide hydrolysis degradation mechanisms

    Lawson, Katherine E. | Dekle, Joseph K. | Adamczyk, Andrew J.

    Computational and Theoretical Chemistry, Vol. 1218 (2022), Iss. P.113938

    https://doi.org/10.1016/j.comptc.2022.113938 [Citations: 1]
  17. Deamidation reaction network mapping of pharmacologic and related proteins: impact of solvation dielectric on the degradation energetics of asparagine dipeptides

    Lawson, Katherine E. | Dekle, Joseph K. | Evans, Megan N. | Adamczyk, Andrew J.

    Reaction Chemistry & Engineering, Vol. 7 (2022), Iss. 7 P.1525

    https://doi.org/10.1039/D2RE00110A [Citations: 6]
  18. PypKa: A Flexible Python Module for Poisson–Boltzmann-Based pKa Calculations

    Reis, Pedro B. P. S. | Vila-Viçosa, Diogo | Rocchia, Walter | Machuqueiro, Miguel

    Journal of Chemical Information and Modeling, Vol. 60 (2020), Iss. 10 P.4442

    https://doi.org/10.1021/acs.jcim.0c00718 [Citations: 44]
  19. Monte carlo simulations of proteins at constant pH with generalized born solvent, flexible sidechains, and an effective dielectric boundary

    Polydorides, Savvas | Simonson, Thomas

    Journal of Computational Chemistry, Vol. 34 (2013), Iss. 31 P.2742

    https://doi.org/10.1002/jcc.23450 [Citations: 30]