Evaluation of pH change effects on the HSA folding and its drug binding characteristics, a computational biology investigation.
computational biology
drug metabolism
human serum albumin
pH
protein folding
Journal
Proteins
ISSN: 1097-0134
Titre abrégé: Proteins
Pays: United States
ID NLM: 8700181
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
revised:
24
04
2022
received:
21
11
2021
accepted:
26
04
2022
pubmed:
16
5
2022
medline:
13
10
2022
entrez:
15
5
2022
Statut:
ppublish
Résumé
The binding of therapeutics to human serum albumin (HSA), which is an abundant protein in plasma poses a major challenge in drug discovery. Although HSA has several binding pockets, the binding site I on D2 and binding site II on D3 are the main binding pockets of HSA. To date, a few experiments have been conducted to examine the effects of the potential of hydrogen (pH) changes on HSA attributes. In the present investigation, the effect of acidic (pH 7.1) and basic states (pH 7.7) on HSA structure and its drug binding potency were examined in comparison with the physiological state (pH 7.4). For this purpose, molecular dynamics (MD), free energy landscape (FEL), principal component analysis (PCA), probability distribution function (PDF), tunnel-cavity investigation, secondary structure analysis, docking study, and free energy investigation were employed to investigate the effect of pH changes on the structural characteristics of HSA at the atomic level. The results obtained from this study revealed the significant effect of pH alterations on the secondary and tertiary structure of HSA. In addition, HSA stability and its drug binding ability can be severely affected following pH changes. Given that pH change frequently occurs in various diseases such as cancer, diabetes, and kidney failure, therefore, pharmaceutical companies should allocate specific consideration to this subject throughout their drug design experiments.
Substances chimiques
Hydrogen
7YNJ3PO35Z
Serum Albumin, Human
ZIF514RVZR
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1908-1925Informations de copyright
© 2022 Wiley Periodicals LLC.
Références
Scheife RT. Protein binding: what does it mean? DICP. 1989;23(7-8 suppl):S27-S31.
Tayyab S, Zaroog MS, Feroz SR, Mohamad SB, Malek SNA. Exploring the interaction between the antiallergic drug, tranilast and human serum albumin: insights from calorimetric, spectroscopic and modeling studies. Int J Pharm. 2015;491(1-2):352-358.
Agbanyim AN, Okoro O, Iheanacho GC. The effect of pH on the binding characteristics of paracetamol to bovine serum albumin. Int J Sci Res Publ. 2016;6(1):598-601.
Yang F, Zhang Y, Liang H. Interactive association of drugs binding to human serum albumin. Int J Mol Sci. 2014;15(3):3580-3595.
Brodersen R, Robertson A. Ceftriaxone binding to human serum albumin: competition with bilirubin. Mol Pharmacol. 1989;36(3):478-483.
Hinderling PH, Hartmann D. The pH dependency of the binding of drugs to plasma proteins in man. Ther Drug Monit. 2005;27(1):71-85.
Santos J, Iglesias V, Santos-Suárez J, et al. pH-dependent aggregation in intrinsically disordered proteins is determined by charge and lipophilicity. Cell. 2020;9(1):145-159.
Khan AY, Suresh Kumar G. Exploring the binding interaction of potent anticancer drug topotecan with human serum albumin: spectroscopic, calorimetric and fibrillation study. J Biomol Struct Dyn. 2018;36(9):2463-2473.
Varshney A, Sen P, Ahmad E, Rehan M, Subbarao N, Khan RH. Ligand binding strategies of human serum albumin: how can the cargo be utilized? Chirality. 2010;22(1):77-87.
Baler K, Martin OA, Carignano MA, Ameer GA, Vila JA, Szleifer I. Electrostatic unfolding and interactions of albumin driven by pH changes: a molecular dynamics study. J Phys Chem B. 2014;118(4):921-930.
Esmaeilzadeh S, Valizadeh H, Zakeri-Milani P. The effects of pH, temperature and protein concentration on the in vitro binding of flutamide to human serum albumin. Pharm Dev Technol. 2017;22(8):982-991.
Charlwood P, Ens A. Effect of pH on the sedimentation of serum albumins and ovalbumin. Can J Chem. 1957;35(1):101-103.
Vallianatou T, Lambrinidis G, Tsantili-Kakoulidou A. In silico prediction of human serum albumin binding for drug leads. Expert Opin Drug Discov. 2013;8(5):583-595.
Li Z, Beeram SR, Bi C, Suresh D, Zheng X, Hage DS. High-performance affinity chromatography: applications in drug-protein binding studies and personalized medicine. Adv Protein Chem Struct Biol. 2016;102:1-39.
Ascoli GA, Domenici E, Bertucci C. Drug binding to human serum albumin: abridged review of results obtained with high-performance liquid chromatography and circular dichroism. Chirality. 2006;18(9):667-679.
Estrada E, Uriarte E, Molina E, Simón-Manso Y, Milne GWA. An integrated in silico analysis of drug-binding to human serum albumin. J Chem Inf Model. 2006;46(6):2709-2724.
Sugiki T, Furuita K, Fujiwara T, Kojima C. Current NMR techniques for structure-based drug discovery. Molecules. 2018;23(1):148-175.
Zheng H, Handing KB, Zimmerman MD, Shabalin IG, Almo SC, Minor W. X-ray crystallography over the past decade for novel drug discovery - where are we heading next? Expert Opin Drug Discov. 2015;10(9):975-989.
Sebille B. Methods of drug protein binding determinations. Fundam Clin Pharmacol. 1990;4(suppl 2):151s-161s.
Mahmoudi Gomari M, Saraygord-Afshari N, Farsimadan M, Rostami N, Aghamiri S, Farajollahi MM. Opportunities and challenges of the tag-assisted protein purification techniques: applications in the pharmaceutical industry. Biotechnol Adv. 2020;45:107653.
Larsen FG, Larsen CG, Andersen S, Nørgaard A, Hansen HE, Brodersen R. Warfarin binding to plasma albumin, measured in patients and related to fatty acid concentrations. Eur J Clin Invest. 1986;16(1):22-27.
Ghuman J, Zunszain PA, Petitpas I, Bhattacharya AA, Otagiri M, Curry S. Structural basis of the drug-binding specificity of human serum albumin. J Mol Biol. 2005;353(1):38-52.
Yamasaki K, Chuang VTG, Maruyama T, Otagiri M. Albumin-drug interaction and its clinical implication. Biochim Biophys Acta. 2013;1830(12):5435-5443.
Araujo F. Uses and limits of the clinical laboratory in the COVID-19 pandemic: a didactic review. Rev Assoc Med Bras (1992). 2020;66(12):1718-1724.
Mahmoudi Gomari M, Rostami N, Ghodrati A, et al. Implementation of docking, molecular dynamics and free energy to investigate drug potency of novel BCR-ABLT315I inhibitors as an alternative to ponatinib. Comput Toxicol. 2021;20:100180.
Mahmoudi Gomari M, Rostami N, Omidi-Ardali H, Arab SS. Insight into molecular characteristics of SARS-CoV-2 spike protein following D614G point mutation, a molecular dynamics study. J Biomol Struct Dyn. 2021;39:1-9.
Jafari D, Malih S, Gomari MM, Safari M, Jafari R, Farajollahi MM. Designing a chimeric subunit vaccine for influenza virus, based on HA2, M2e and CTxB: a bioinformatics study. BMC Mol Cell Biol. 2020;21(1):89.
Rostami N, Choupani E, Hernandez Y, Arab SS, Jazayeri SM, Gomari MM. SARS-CoV-2 spike evolutionary behaviors; simulation of N501Y mutation outcomes in terms of immunogenicity and structural characteristic. J Cell Biochem. 2021;123:417-430.
Dokholyan NV, Buldyrev SV, Stanley HE, Shakhnovich EI. Discrete molecular dynamics studies of the folding of a protein-like model. Fold Des. 1998;3(6):577-587.
Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design-a review. Curr Top Med Chem. 2010;10(1):95-115.
Seidel T, Schuetz DA, Garon A, Langer T. The pharmacophore concept and its applications in computer-aided drug design. Prog Chem Org Nat Prod. 2019;110:99-141.
Ferreira LLG, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug Discov Today. 2019;24(5):1157-1165.
Khan MF, Verma G, Akhtar W, et al. Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1,3,4-thiadiazole amides and sulfonamides as antitubulin agents. Arab J Chem. 2019;12(8):5000-5018.
Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K. In silico methods for design of kinase inhibitors as anticancer drugs. Front Chem. 2019;7:873.
Cucchiari D, Podestà MA, Merizzoli E, et al. Dose-related effects of metformin on acid-base balance and renal function in patients with diabetes who develop acute renal failure: a cross-sectional study. Acta Diabetol. 2016;53(4):551-558.
Abdulkadir AA, Thanoon IA. Comparative effects of glibenclamide and metformin on C-reactive protein and oxidant/antioxidant status in patients with type II diabetes mellitus. Sultan Qaboos Univ Med J. 2012;12(1):55-61.
Dick SM, Queiroz M, Bernardi BL, Dall'Agnol A, Brondani LA, Silveiro SP. Update in diagnosis and management of primary aldosteronism. Clin Chem Lab Med. 2018;56(3):360-372.
Bar A, Cies J, Stapleton K, Tauber D, Chopra A, Shore PM. Acetazolamide therapy for metabolic alkalosis in critically ill pediatric patients. Pediatr Crit Care Med. 2015;16(2):e34-e40.
Gilchrist M, Kem DC, Washburn D. The effect of acidosis and alkalosis on in vitro aldosterone production. Metabolism. 1983;32(7):697-700.
Kaschula CH, Tuveri R, Ngarande E, et al. The garlic compound ajoene covalently binds vimentin, disrupts the vimentin network and exerts anti-metastatic activity in cancer cells. BMC Cancer. 2019;19(1):248.
Rostami N, Davarnejad R. Characterization of folic acid-functionalized PLA-PEG nanomicelle to deliver Letrozole: a nanoinformatics study. IET Nanobiotechnol. 2021;16(4):103-114.
Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(Database issue):D901-D906.
Kaczmarek E, Hauser CJ, Kwon WY, et al. A subset of five human mitochondrial formyl peptides mimics bacterial peptides and functionally deactivates human neutrophils. J Trauma Acute Care Surg. 2018;85(5):936-943.
Li H, Liu S, Yang X, et al. Cellular processes involved in Jurkat cells exposed to nanosecond pulsed electric field. Int J Mol Sci. 2019;20(23):5847-5892.
Wang W, Xia M, Chen J, et al. Data set for phylogenetic tree and RAMPAGE Ramachandran plot analysis of SODs in Gossypium raimondii and G. arboreum. Data Brief. 2016;9:345-348.
Taheri-Anganeh M, Amiri A, Movahedpour A, et al. In silico evaluation of PLAC1-fliC as a chimeric vaccine against breast cancer. Iran Biomed J. 2019;24:173-182.
Bhattacharya M, Malick RC, Mondal N, et al. Computational characterization of epitopic region within the outer membrane protein candidate in Flavobacterium columnare for vaccine development. J Biomol Struct Dyn. 2019;38(2):450-459.
Kota P, Ding F, Ramachandran S, Dokholyan NV. Gaia: automated quality assessment of protein structure models. Bioinformatics. 2011;27(16):2209-2215.
Ramachandran S, Kota P, Ding F, Dokholyan NV. Automated minimization of steric clashes in protein structures. Proteins. 2011;79(1):261-270.
Kraut JA, Madias NE. Metabolic acidosis: pathophysiology, diagnosis and management. Nat Rev Nephrol. 2010;6(5):274-285.
Khanna A, Kurtzman NA. Metabolic alkalosis. J Nephrol. 2006;19(suppl 9):S86-S96.
Harrison RES, Mohan RR, Gorham RD Jr, Kieslich CA, Morikis D. AESOP: a python library for investigating electrostatics in protein interactions. Biophys J. 2017;112(9):1761-1766.
Santos J, Iglesias V, Pintado C, Santos-Suárez J, Ventura S. DispHred: a server to predict pH-dependent order-disorder transitions in intrinsically disordered proteins. Int J Mol Sci. 2020;21(16):5814-5826.
Zerze GH, Uz B, Mittal J. Folding thermodynamics of beta-hairpins studied by replica-exchange molecular dynamics simulations. Proteins. 2015;83(7):1307-1315.
Mukund V, Behera SK, Alam A, Nagaraju GP. Molecular docking analysis of nuclear factor-kappaB and genistein interaction in the context of breast cancer. Bioinformation. 2019;15(1):11-17.
Thielemann HC, Cardellini A, Fasano M, et al. From GROMACS to LAMMPS: GRO2LAM. J Mol Model. 2019;25(6):147.
Muhseen ZT, Li G. Promising terpenes as natural antagonists of cancer: an in-silico approach. Molecules. 2019;25(1):155-172.
Maiorov VN, Crippen GM. Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. J Mol Biol. 1994;235(2):625-634.
Kuzmanic A, Zagrovic B. Determination of ensemble-average pairwise root mean-square deviation from experimental B-factors. Biophys J. 2010;98(5):861-871.
Chen H, Panagiotopoulos AZ. Molecular modeling of surfactant micellization using solvent-accessible surface area. Langmuir. 2019;35(6):2443-2450.
Ahmed MC, Crehuet R, Lindorff-Larsen K. Computing, analyzing, and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins. Methods Mol Biol. 2020;2141:429-445.
Chong SH, Ham S. Folding free energy landscape of ordered and intrinsically disordered proteins. Sci Rep. 2019;9(1):14927.
Ringnér M. What is principal component analysis? Nat Biotechnol. 2008;26(3):303-304.
Oya M, Taniguchi Y, Fujimura N, Miyamoto K, Oya M. Kinetic analysis of hemoglobin detergency by probability density functional method. PLoS One. 2020;15(8):e0237255.
Colloc'h N, Carpentier P, Montemiglio LC, Vallone B, Prangé T. Mapping hydrophobic tunnels and cavities in neuroglobin with noble gas under pressure. Biophys J. 2017;113(10):2199-2206.
Eriksson AE, Baase WA, Zhang XJ, et al. Response of a protein structure to cavity-creating mutations and its relation to the hydrophobic effect. Science. 1992;255(5041):178-183.
Xue M, Wakamoto T, Kejlberg C, et al. How internal cavities destabilize a protein. Proc Natl Acad Sci. 2019;116(42):21031-21036.
Stourac J, Vavra O, Kokkonen P, et al. Caver Web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport. Nucleic Acids Res. 2019;47(W1):W414-W422.
Iyer M, Li Z, Jaroszewski L, Sedova M, Godzik A. Difference contact maps: from what to why in the analysis of the conformational flexibility of proteins. PLoS One. 2020;15(3):e0226702.
Koch O. Use of secondary structure element information in drug design: polypharmacology and conserved motifs in protein-ligand binding and protein-protein interfaces. Future Med Chem. 2011;3(6):699-708.
Zacharias J, Knapp E-W. Protein secondary structure classification revisited: processing DSSP information with PSSC. J Chem Inf Model. 2014;54(7):2166-2179.
Heinig M, Frishman D. STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res. 2004;32(Web Server issue):W500-W502.
Cuff JA, Barton GJ. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins: Struct Funct Bioinformatics. 1999;34(4):508-519.
Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins. 1995;23(4):566-579.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.
Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016;11(5):905-919.
Wang J, Dokholyan NV. MedusaDock 2.0: efficient and accurate protein-ligand docking with constraints. J Chem Inf Model. 2019;59(6):2509-2515.
Ding F, Dokholyan NV. Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark. J Chem Inf Model. 2013;53(8):1871-1879.
Kumari R, Kumar R, Lynn A. g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model. 2014;54(7):1951-1962.
Buß O, Rudat J, Ochsenreither K. FoldX as protein engineering tool: better than random based approaches? Comput Struct Biotechnol J. 2018;16:25-33.
Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3(8):711-716.
Panjehshahin MR, Yates MS, Bowmer CJ. A comparison of drug binding sites on mammalian albumins. Biochem Pharmacol. 1992;44(5):873-879.
Fehske K, Müller W, Wollert U. Direct demonstration of the highly reactive tyrosine residue of human serum albumin located in fragment 299-585. Arch Biochem Biophys. 1980;205(1):217-221.
O'Brien EP, Brooks BR, Thirumalai D. Effects of pH on proteins: predictions for ensemble and single-molecule pulling experiments. J Am Chem Soc. 2012;134(2):979-987.
Idrees D, Shahbaaz M, Bisetty K, Islam A, Ahmad F, Hassan MI. Effect of pH on structure, function, and stability of mitochondrial carbonic anhydrase VA. J Biomol Struct Dyn. 2017;35(2):449-461.
Spadaccini R, Leone S, Rega MF, Richter C, Picone D. Influence of pH on the structure and stability of the sweet protein MNEI. FEBS Lett. 2016;590(20):3681-3689.
Talley K, Alexov E. On the pH-optimum of activity and stability of proteins. Proteins. 2010;78(12):2699-2706.
Watkins AM, Wuo MG, Arora PS. Protein-protein interactions mediated by helical tertiary structure motifs. J Am Chem Soc. 2015;137(36):11622-11630.