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Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

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Article: 2362788 | Received 28 Nov 2023, Accepted 29 May 2024, Published online: 10 Jun 2024

References

  • Carter PJ. Potent antibody therapeutics by design. Nat Rev Immunol. 2006;6(5):343–20. doi:10.1038/nri1837.
  • Lobo ED, Hansen RJ, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharmaceut Sci [Internet]. 2004;93(11):2645–68. doi:10.1002/jps.20178.
  • Reichert JM. Which are the antibodies to watch in 2013? MAbs. 2013;5(1):1–4. doi:10.4161/mabs.22976.
  • Reichert JM. Antibodies to watch in 2016. MAbs. 2016;8(2):197–204. doi:10.1080/19420862.2015.1125583.
  • Bailly M, Mieczkowski C, Juan V, Metwally E, Tomazela D, Baker J, Uchida M, Kofman E, Raoufi F, Motlagh S, et al. Predicting antibody developability profiles through early stage discovery screening. MAbs. 2020;12(1):1743053. doi:10.1080/19420862.2020.1743053.
  • Zarzar J, Khan T, Bhagawati M, Weiche B, Sydow-Andersen J, Sreedhara A. High concentration formulation developability approaches and considerations. MAbs. 2023;15(1):2211185. doi:10.1080/19420862.2023.2211185.
  • Starr CG, Tessier PM. Selecting and engineering monoclonal antibodies with drug-like specificity. Curr Opinion Biotechnol [Internet]. 2019;60:119–27. doi:10.1016/j.copbio.2019.01.008.
  • Ollier R, Fuchs A, Gauye F, Piorkowska K, Menant S, Ratnam M, Montanari P, Guilhot F, Phillipe D, Audrain M, et al. Improved antibody pharmacokinetics by disruption of contiguous positive surface potential and charge reduction using alternate human framework. MAbs. 2023;15(1). doi:10.1080/19420862.2023.2232087.
  • Stüber JC, Rechberger KF, Miladinović SM, Pöschinger T, Zimmermann T, Villenave R, Eigenmann MJ, Kraft TE, Shah DK, Kettenberger H, et al. Impact of charge patches on tumor disposition and biodistribution of therapeutic antibodies. AAPS Open [Internet]. 2022;8(1):3. doi:10.1186/s41120-021-00048-9.
  • Peng H-P, Lee KH, Jian J-W, Yang A-S. Origins of specificity and affinity in antibody–protein interactions. Proc Natl Acad Sci USA. 2014;111(26):E2656–65. doi:10.1073/pnas.1401131111.
  • Kringelum JV, Nielsen M, Padkjær SB, Lund O. Structural analysis of b-cell epitopes in antibody: protein complexes. Molecul Immunol [Internet]. 2013;53(1–2):24–34. doi:10.1016/j.molimm.2012.06.001.
  • Ramaraj T, Angel T, Dratz EA, Jesaitis AJ, Mumey B. Antigen–antibody interface properties: composition, residue interactions, and features of 53 non-redundant structures. Biochimica et Biophysica Acta (BBA) - Proteins Proteom [Internet]. 2012;1824(3):520–32. doi:10.1016/j.bbapap.2011.12.007.
  • Kayser V, Chennamsetty N, Voynov V, Helk B, Trout BL. Conformational stability and aggregation of therapeutic monoclonal antibodies studied with ANS and thioflavin t binding. MAbs. 2011;3(4):408–11. doi:10.4161/mabs.3.4.15677.
  • Manning MC, Chou DK, Murphy BM, Payne RW, Katayama DS. Stability of protein pharmaceuticals: an update. Pharm Res. 2010;27(4):544–75. doi:10.1007/s11095-009-0045-6.
  • Roberts CJ. Protein aggregation and its impact on product quality. Cur Opin Biotechnol [Internet]. 2014;30:211–17. doi:10.1016/j.copbio.2014.08.001.
  • Yadav S, Shire SJ, Kalonia DS. Factors affecting the viscosity in high concentration solutions of different monoclonal antibodies. J Pharm Sci. 2010;99(12):4812–29. doi:10.1002/jps.22190.
  • Yadav S, Laue TM, Kalonia DS, Singh SN, Shire SJ. The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions. Mol Pharmaceut. 2012;9(4):791–802. doi:10.1021/mp200566k.
  • Yadav S, Sreedhara A, Kanai S, Liu J, Lien S, Lowman H, Kalonia DS, Shire SJ. Establishing a link between amino acid sequences and self-associating and viscoelastic behavior of two closely related monoclonal antibodies. Pharm Res. 2011;28(7):1750–64. doi:10.1007/s11095-011-0410-0.
  • Yadav S, Shire SJ, Kalonia DS. Viscosity behavior of high-concentration monoclonal antibody solutions: correlation with interaction parameter and electroviscous effects. J Pharm Sci. 2012;101(3):998–1011. doi:10.1002/jps.22831.
  • Lilyestrom WG, Yadav S, Shire SJ, Scherer TM. Monoclonal antibody self-association, cluster formation, and rheology at high concentrations. J Phys Chem B. 2013;117(21):6373–84. doi:10.1021/jp4008152.
  • Connolly Brian B, Petry C, Yadav S, Demeule B, Ciaccio N, Moore Jamie JR, Shire Steven S, Gokarn Yatin Y. Weak interactions govern the viscosity of concentrated antibody solutions: high-throughput analysis using the diffusion interaction parameter. Biophys J [Internet]. 2012;103(1):69–78. doi:10.1016/j.bpj.2012.04.047.
  • Shire SJ. 3 - stability of monoclonal antibodies (mAbs) [Internet]. In: Shire S, editor. Monoclonal antibodies. Woodhead Publishing; 2015. p. 45–92. https://www.sciencedirect.com/science/article/pii/B9780081002964000038.
  • Irudayanathan FJ, Zarzar J, Lin J, Izadi S. Deciphering deamidation and isomerization in therapeutic proteins: effect of neighboring residue. MAbs. 2022;14(1):2143006. doi:10.1080/19420862.2022.2143006.
  • Freed AS, Cramer SM. Protein−surface interaction maps for Ion-exchange chromatography. Langmuir [Internet]. 2011;27(7):3561–68. doi:10.1021/la104641z.
  • Yao Y, Lenhoff AM. Electrostatic contributions to protein retention in ion-exchange chromatography. 1. Cytochrome c variants. Anal Chem. 2004;76(22):6743–52. doi:10.1021/ac049327z.
  • Jain T, Boland T, Vásquez M. Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches. MAbs. 2023;15(1):2200540. doi:10.1080/19420862.2023.2200540.
  • Jain T, Sun T, Durand S, Hall A, Houston NR, Nett JH, Sharkey B, Bobrowicz B, Caffry I, Yu Y, et al. Biophysical properties of the clinical-stage antibody landscape. Proc Natl Acad Sci USA. 2017;114(5):944–49. doi:10.1073/pnas.1616408114.
  • Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, Bujotzek A, Shi J, Deane CM. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci USA. 2019;116(10):4025–30. doi:10.1073/pnas.1810576116.
  • Sharma VK, Patapoff TW, Kabakoff B, Pai S, Hilario E, Zhang B, Li C, Borisov O, Kelley RF, Chorny I, et al. In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability. Proc Natl Acad Sci USA. 2014;111(52):18601–06. doi:10.1073/pnas.1421779112.
  • Shire SJ, Shahrokh Z, Liu J. Challenges in the development of high protein concentration formulations. J Pharm Sci. 2004;93(6):1390–402. doi:10.1002/jps.20079.
  • Cunningham O, Scott M, Zhou ZS, Finlay WJJ. Polyreactivity and polyspecificity in therapeutic antibody development: risk factors for failure in preclinical and clinical development campaigns. MAbs. 2021;13(1):1999195. doi:10.1080/19420862.2021.1999195.
  • Agrawal NJ, Helk B, Kumar S, Mody N, Sathish HA, Samra HS, Buck PM, Li L, Trout BL. Computational tool for the early screening of monoclonal antibodies for their viscosities. MAbs. 2016;8(1):43–48. doi:10.1080/19420862.2015.1099773.
  • Molecular Operating Environment (MOE). 8, Chemical computing group ULC, 1010 Sherbrooke St, West, suite 910. Montreal, QC, Canada, H3A 2R7; 2013.
  • Søndergaard CR, Olsson MHM, Rostkowski M, Jensen JH. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J Chem Theory Comput. 2011;7(7):2284–95. doi:10.1021/ct200133y.
  • Labute P. Protonate3D: assignment of ionization states and hydrogen coordinates to macromolecular structures. Proteins. 2009;75(1):187–205. doi:10.1002/prot.22234.
  • Davis ME, McCammon JA. Electrostatics in biomolecular structure and dynamics. Chem Rev. 1990;90(3):509–21. doi:10.1021/cr00101a005.
  • Hoerschinger VJ, Waibl F, Pomarici ND, Loeffler JR, Deane CM, Georges G, Kettenberger H, Fernández-Quintero ML, Liedl KR. PEP-patch: electrostatics in protein–protein recognition, specificity, and antibody developability. J Chem Inf Model. 2023;63(22):6964–71. doi:10.1021/acs.jcim.3c01490.
  • Onufriev AV, Izadi S. Water models for biomolecular simulations. WIREs Comput Mol Sci. 2018;8(2):e1347. doi:10.1002/wcms.1347.
  • Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci USA. 2009;106(29):11937–42. doi:10.1073/pnas.0904191106.
  • Sankar K, Krystek SR, Carl SM, Day T, Maier JK. AggScore: prediction of aggregation-prone regions in proteins based on the distribution of surface patches. Proteins Struct Funct Bioinf. 2018;86(11):1147–56. doi:10.1002/prot.25594.
  • Coffman J, Marques B, Orozco R, Aswath M, Mohammad H, Zimmermann E, Khouri J, Griesbach J, Izadi S, Williams A, et al. Highland games: a benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences. Biotech & Bioengg. 2020;117(7):2100–15. doi:10.1002/bit.27349.
  • Etha A, Thorsteinson N, Jmeian Y, Jeganathan A, Giblin P, Fransson J. Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies. MAbs. 2018;10(6):890–900. doi:10.1080/19420862.2018.1475871.
  • Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1PII of original article: S0169-409X(96)00423-1. The article was originally published in advanced drug delivery reviews 23 (1997) 3–25.1. Advanced Drug Delivery Rev [Internet]. 2001;46:3–26. https://www.sciencedirect.com/science/article/pii/S0169409X00001290.
  • Eisenberg D, McLachlan A. Solvation energy in protein folding and binding. Nature [Internet]. 1986;319(6050):199–203. doi:10.1038/319199a0.
  • Black SD, Mould DR. Development of hydrophobicity parameters to analyze proteins which bear post- or cotranslational modifications. Analytical Biochem [Internet]. 1991;193(1):72–82. doi:10.1016/0003-2697(91)90045-U.
  • Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Molecul Biol [Internet]. 1982;157(1):105–32. doi:10.1016/0022-2836(82)90515-0.
  • Jain T, Boland T, Lilov A, Burnina I, Brown M, Xu Y, Vásquez M, Valencia A. Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning. Bioinformat [Internet]. 2017;33(23):3758–66. doi:10.1093/bioinformatics/btx519.
  • Waibl F, Fernández-Quintero ML, Wedl FS, Kettenberger H, Georges G, Liedl KR. Comparison of hydrophobicity scales for predicting biophysical properties of antibodies. Front Mol Biosci. 2022;9. doi:10.3389/fmolb.2022.960194.
  • Jaszczyszyn I, Bielska W, Gawlowski T, Dudzic P, Satława T, Kończak J, Wilman W, Janusz B, Wróbel S, Chomicz D, et al. Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery. Front Molecul Biosci [Internet]. 2023;10. https://www.frontiersin.org/articles/10.3389/fmolb.2023.1214424.
  • Abanades B, Wong WK, Boyles F, Georges G, Bujotzek A, Deane CM. ImmuneBuilder: deep-learning models for predicting the structures of immune proteins. Commun Biol. 2023;6(1):575. doi:10.1038/s42003-023-04927-7.
  • Ruffolo JA, Sulam J, Gray JJ. Antibody structure prediction using interpretable deep learning. Patterns [Internet]. 2022;3(2):100406. doi:10.1016/j.patter.2021.100406.
  • Lee JH, Yadollahpour P, Watkins A, Frey NC, Leaver-Fay A, Ra S, Cho K, Gligorijevic V, Regev A, Bonneau R. EquiFold: protein structure prediction with a novel coarse-grained structure representation. bioRxiv. 2022. https://www.biorxiv.org/content/early/2022/10/08/2022.10.07.511322.
  • Fernández-Quintero ML, Kokot J, Waibl F, Fischer ALM, Quoika PK, Deane CM, Liedl KR. Challenges in antibody structure prediction. MAbs. 2023;15(1):2175319. doi:10.1080/19420862.2023.2175319.
  • Fernández-Quintero ML, Math BA, Loeffler JR, Liedl KR. Transitions of CDR-L3 loop canonical cluster conformations on the micro-to-millisecond timescale. Front Immunol. 2019;10. doi:10.3389/fimmu.2019.02652.
  • Waibl F, Fernández-Quintero ML, Kamenik AS, Kraml J, Hofer F, Kettenberger H, Georges G, Liedl KR. Conformational ensembles of antibodies determine their hydrophobicity. Biophys J [Internet]. 2021;120(1):143–57. doi:10.1016/j.bpj.2020.11.010.
  • Tucs A, Tsuda K, Sljoka A. Probing conformational dynamics of antibodies with geometric simulations. Methods Mol Biol. 2023;2552:125–39.
  • Miao Y, Feher VA, McCammon JA. Gaussian accelerated molecular dynamics: unconstrained enhanced sampling and free energy calculation. J Chem Theory Comput. 2015;11(8):3584–95. doi:10.1021/acs.jctc.5b00436.
  • Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci USA. 2001;98(18):10037–41. doi:10.1073/pnas.181342398.
  • Wimley W, White S. Experimentally determined hydrophobicity scale for proteins at membrane interfaces. Nature Struct Molecul Biol. 1996;3(10):842–48. doi:10.1038/nsb1096-842.
  • Salgado JC, Rapaport I, Asenjo JA. Predicting the behaviour of proteins in hydrophobic interaction chromatography: 2. Using a statistical description of their surface amino acid distribution. J Chromat [Internet]. 2006;1107(1–2):120–29. doi:10.1016/j.chroma.2005.12.033.
  • Chari R, Jerath K, Badkar AV, Kalonia DS. Long- and short-range electrostatic interactions affect the rheology of highly concentrated antibody solutions. Pharm Res. 2009;26(12):2607–18. doi:10.1007/s11095-009-9975-2.
  • Chaudhri A, Zarraga IE, Yadav S, Patapoff TW, Shire SJ, Voth GA. The role of amino acid sequence in the self-association of therapeutic monoclonal antibodies: insights from coarse-grained modeling. J Phys Chem B. 2013;117(5):1269–79. doi:10.1021/jp3108396.
  • Yadav S, Laue TM, Kalonia DS, Singh SN, Shire SJ. The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions. Mol Pharm. 2012;9(4):791–802. doi:10.1021/mp200566k.
  • Liu J, Nguyen MDH, Andya JD, Shire SJ. Reversible self-association increases the viscosity of a concentrated monoclonal antibody in aqueous solution. J Pharm Sci. 2005;94(9):1928–40. doi:10.1002/jps.20347.
  • Cholpon Tilegenova C, Izadi S, Yin PJ, Huang CS, Wu J, Ellerman D, Hymowitz SG, Walters B, Salisbury C, Carter PJ. Dissecting the molecular basis of high viscosity of monospecific and bispecific IgG antibodies. MAbs. 2020;12(1):1692764. doi:10.1080/19420862.2019.1692764.
  • Pin-Kuang Lai P-K, Gallegos A, Mody N, Sathish HA, Trout BL. Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics. MAbs. 2022;14(1):2026208. doi:10.1080/19420862.2022.2026208.
  • Jing Dai JZ, Izadi S, Zarzar PJ, Wu P, Oh A, Carter PJ. Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG1 antibody. MAbs. 2024;16(1):2304282. doi:10.1080/19420862.2024.2304282.
  • Apgar JR, Tam AS, Sorm R, Moesta S, King AC, Yang H, Kelleher K, Murphy D, D’Antona AM, Yan G, et al. Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design. PLOS ONE. 2020;15(5):15. doi:10.1371/journal.pone.0232713.
  • Rai BK, Apgar JR, Bennett EM. Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation. Sci Rep. 2023;13(1). doi:10.1038/s41598-023-28841-4.
  • Hötzel I, Theil F-P, Bernstein LJ, Prabhu S, Deng R, Quintana L, Lutman J, Sibia R, Chan P, Bumbaca D, et al. A strategy for risk mitigation of antibodies with fast clearance. MAbs. 2012;4(6):753–60. doi:10.4161/mabs.22189.
  • Wu H, Pfarr DS, Johnson S, Brewah YA, Woods RM, Patel NK, White WI, Young JF, Kiener PA. Development of motavizumab, an ultra-potent antibody for the prevention of respiratory syncytial virus infection in the upper and lower respiratory tract. J Molecul Biol [Internet]. 2007;368(3):652–65. doi:10.1016/j.jmb.2007.02.024.
  • Grinshpun B, Thorsteinson N, Pereira JN, Rippmann F, Nannemann D, Sood VD, Fomekong Nanfack Y. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. MAbs. 2021;13(1). doi:10.1080/19420862.2021.1932230.
  • Kraft TE, Richter WF, Emrich T, Knaupp A, Schuster M, Wolfert A, Kettenberger H. Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis. MAbs. 2020;12(1):1683432. doi:10.1080/19420862.2019.1683432.
  • Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. Mabs-austin. 2021;13(1):1981805. doi:10.1080/19420862.2021.1981805.
  • Geng SB, Cheung JK, Narasimhan C, Shameem M, Tessier PM. Improving monoclonal antibody selection and engineering using measurements of colloidal protein interactions. J Pharmaceut Sci [Internet]. 2014;103(11):3356–63. doi:10.1002/jps.24130.
  • Sormanni P, Aprile FA, Vendruscolo M. The CamSol method of rational design of protein mutants with enhanced solubility. J Molecul Biol [Internet]. 2015;427(2):478–90. doi:10.1016/j.jmb.2014.09.026.
  • Estep P, Caffry I, Yu Y, Sun T, Cao Y, Lynaugh H, Jain T, Vásquez M, Tessier PM, Xu Y. An alternative assay to hydrophobic interaction chromatography for high-throughput characterization of monoclonal antibodies. MAbs. 2015;7(3):553–61. doi:10.1080/19420862.2015.1016694.
  • Haverick M, Mengisen S, Shameem M, Ambrogelly A. Separation of mAbs molecular variants by analytical hydrophobic interaction chromatography HPLC: overview and applications. MAbs. 2014;6(4):852–58.
  • Olsen TH, Boyles F, Deane CM. Observed antibody space: a diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Sci. 2022;31(1):141–46. doi:10.1002/pro.4205.
  • Sievers SA, Scharf L, West AP, Bjorkman PJ. Antibody engineering for increased potency, breadth and half-life. Curr Opin HIV AIDS. 2015;10(3):151–59. doi:10.1097/COH.0000000000000148.
  • Li L, Li C, Zhang Z, Alexov E. On the dielectric “constant” of proteins: smooth dielectric function for macromolecular modeling and its implementation in DelPhi. J Chem Theory Comput. 2013;9(4):2126–36. doi:10.1021/ct400065j.
  • Vicatos S, Roca M, Warshel A. Effective approach for calculations of absolute stability of proteins using focused dielectric constants. Proteins: Struct, Funct, and Bioinfor [Internet]. 2009;77(3):670–84. doi:10.1002/prot.22481.
  • Isom DG, Castañeda CA, Cannon BR, Velu PD, García-Moreno B. Charges in the hydrophobic interior of proteins. Proc Natl Acad Sci USA. 2010;107(37):16096–100. doi:10.1073/pnas.1004213107.
  • Ahmed L, Gupta P, Martin KP, Scheer JM, Nixon AE, Kumar S. Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proc Natl Acad Sci USA. 2021;118(37):e2020577118. doi:10.1073/pnas.2020577118.
  • Raybould MIJ, Marks C, Lewis AP, Shi J, Bujotzek A, Taddese B, Deane CM. Thera-SAbDab: the therapeutic structural antibody database. Nucleic Acids Res [Internet]. 2020;48(D1):D383–8. doi:10.1093/nar/gkz827.
  • Pindrus M, Shire SJ, Kelley RF, Demeule B, Wong R, Xu Y, Yadav S. Solubility challenges in high concentration monoclonal antibody formulations: relationship with amino acid sequence and intermolecular interactions. Mol Pharmaceut. 2015;12(11):3896–907. doi:10.1021/acs.molpharmaceut.5b00336.
  • Leem J, Dunbar J, Georges G, Shi J, Deane CM. ABodyBuilder: automated antibody structure prediction with data–driven accuracy estimation. MAbs. 2016;8(7):1259–68. doi:10.1080/19420862.2016.1205773.
  • Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res [Internet]. 2004;32(Web Server):W665–7. doi:10.1093/nar/gkh381.
  • Wang J, Cieplak P, Kollman PA. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem. 2000;21(12):1049–74. https://onlinelibrary.wiley.com.
  • Li H, Robertson AD, Jensen JH. Very fast empirical prediction and rationalization of protein pKa values. Proteins: Struct, Funct, and Bioinfor [Internet]. 2005;61(4):704–21. doi:10.1002/prot.20660.
  • Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696–713. doi:10.1021/acs.jctc.5b00255.
  • Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys [Internet]. 1983;79(2):926–35. doi:10.1063/1.445869.
  • Hopkins CW, Le Grand S, Walker RC, Roitberg AE. Long-time-step molecular dynamics through hydrogen mass repartitioning. J Chem Theory Comput. 2015;11(4):1864–74. doi:10.1021/ct5010406.
  • Case DA, Belfon K, Ben-Shalom IY, Brozell SR, Cerutti DS III, Cruzeiro TEC, Darden VWD, Duke TA, Giambasu RE, et al. Amber 2020. San Francisco: University of California; 2020.
  • Grand S, Goetz AW, Walker RC. SPFP: speed without compromise - a mixed precision model for GPU accelerated molecular dynamics simulations. cpc. 2013;184:374–80.
  • Darden T, York D, Pedersen L. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems. J Chem Phys. 1993;98(12):10089–92. doi:10.1063/1.464397.
  • Pastor RW, Brooks BR, Szabo A. An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol Phys. 1988;65(6):1409–19. doi:10.1080/00268978800101881.
  • Decherchi S, Colmenares J, Catalano CE, Spagnuolo M, Alexov E, Rocchia W. Between algorithm and model: different molecular surface definitions for the Poisson-Boltzmann based electrostatic characterization of biomolecules in solution. Commun Comput Phys. 2013;13(1):61. doi:10.4208/cicp.050711.111111s.
  • Dunbar J, Deane CM. ANARCI: antigen receptor numbering and receptor classification. Bioinformat [Internet]. 2016;32(2):298–300. doi:10.1093/bioinformatics/btv552.
  • Shrake A, Rupley JA. Environment and exposure to solvent of protein atoms. Lysozyme and insulin. J Molecul Biol [Internet]. 1973;79(2):351–71. doi:10.1016/0022-2836(73)90011-9.
  • Yang R, Tang Y, Zhang B, Lu X, Liu A, Zhang YT. High resolution separation of recombinant monoclonal antibodies by size-exclusion ultra-high performance liquid chromatography (SE-UHPLC). J Pharm Biomed Anal [Internet]. 2015;109:52–61. doi:10.1016/j.jpba.2015.02.032.
  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.
  • Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods. 2020;17(3):261–72. doi:10.1038/s41592-019-0686-2.
  • Ghose S, Tao Y, Conley L and Cecchini D. (2013). Purification of monoclonal antibodies by hydrophobic interaction chromatography under no-salt conditions. mAbs, 5(5), 795–800. 10.4161/mabs.25552