Buffer Screening of Protein Formulations Using a Coarse-Grained Protocol Based on Medicinal Chemistry Interactions

被引:0
作者
Petris, Panagiotis C. [1 ]
Sweere, Augustinus J. M. [1 ]
机构
[1] Siemens Ind Software Netherlands BV, NL-2595 BN The Hague, Netherlands
关键词
AUTOMATED-FRAGMENTATION; FORCE-FIELD; AGGREGATION; DYNAMICS; PARAMETRIZATION; COEFFICIENTS; SIMULATIONS; PREDICTION; CHARMM; ENERGY;
D O I
10.1021/acs.jpcb.4c04105
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In drug and vaccine development, the designed protein formulation should be highly stable against the temperature, pH, buffer, excipients, and other environmental settings. Similarly, in a sensing unit, one needs to know how strongly two biomolecules bind to guide the design of the biorecognition unit accordingly. Typically, the community performs a series of experiments to thoroughly examine the parameter space, the so-called design-of-experiment (DoE) method, to identify the optimal formulation conditions. Unfortunately, extensive physical testing entails high costs, repeatability issues, and a lack of in-depth knowledge of the underlying mechanisms that affect the final outcome. To address these challenges, we developed a physics-based simulation protocol for buffer screening of protein formulations. We are introducing a coarse-grained molecular simulation protocol that consists of six different interactions. The so-called medicinal chemistry interactions (electrostatics, hydrophobicity, hydrogen bonding propensity, disulfide bonding, and water-water) are based on the physical nature of the protein's amino acid and the partitioning/polarity of any other chemical constituent. The protocol is applied in immunoglobulin-based monoclonal antibodies. We have analyzed the protein behavior as a function of acidity (pH) to discover the isoelectric point by solving the Poisson-Boltzmann equation in a mesoscale grid. To identify the conditions under which the protein oligomerizes in a given buffer, pH, temperature, and ionic strength, we are performing dissipative particle dynamics (DPD) simulations. The protocol allows researchers to reach the high time/space scales required to study protein formulations in their full complexity. Combined with the disruptive protein folding artificial intelligence (AI) algorithms that have been recently developed, the protocol creates a powerful digital framework for cultivating advanced pharmaceutical and biological applications.
引用
收藏
页码:9353 / 9362
页数:10
相关论文
共 38 条
[1]   Accurate prediction of protein structures and interactions using a three-track neural network [J].
Baek, Minkyung ;
DiMaio, Frank ;
Anishchenko, Ivan ;
Dauparas, Justas ;
Ovchinnikov, Sergey ;
Lee, Gyu Rie ;
Wang, Jue ;
Cong, Qian ;
Kinch, Lisa N. ;
Schaeffer, R. Dustin ;
Millan, Claudia ;
Park, Hahnbeom ;
Adams, Carson ;
Glassman, Caleb R. ;
DeGiovanni, Andy ;
Pereira, Jose H. ;
Rodrigues, Andria V. ;
van Dijk, Alberdina A. ;
Ebrecht, Ana C. ;
Opperman, Diederik J. ;
Sagmeister, Theo ;
Buhlheller, Christoph ;
Pavkov-Keller, Tea ;
Rathinaswamy, Manoj K. ;
Dalwadi, Udit ;
Yip, Calvin K. ;
Burke, John E. ;
Garcia, K. Christopher ;
Grishin, Nick V. ;
Adams, Paul D. ;
Read, Randy J. ;
Baker, David .
SCIENCE, 2021, 373 (6557) :871-+
[2]   CHARMM - A PROGRAM FOR MACROMOLECULAR ENERGY, MINIMIZATION, AND DYNAMICS CALCULATIONS [J].
BROOKS, BR ;
BRUCCOLERI, RE ;
OLAFSON, BD ;
STATES, DJ ;
SWAMINATHAN, S ;
KARPLUS, M .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 1983, 4 (02) :187-217
[3]   Effect of Buffer on Protein Stability in Aqueous Solutions: A Simple Protein Aggregation Model [J].
Brudar, Sandi ;
Hribar-Lee, Barbara .
JOURNAL OF PHYSICAL CHEMISTRY B, 2021, 125 (10) :2504-2512
[4]   Weak Interactions Govern the Viscosity of Concentrated Antibody Solutions: High-Throughput Analysis Using the Diffusion Interaction Parameter [J].
Connolly, Brian D. ;
Petry, Chris ;
Yadav, Sandeep ;
Demeule, Barthelemy ;
Ciaccio, Natalie ;
Moore, Jamie M. R. ;
Shire, Steven J. ;
Gokarn, Yatin R. .
BIOPHYSICAL JOURNAL, 2012, 103 (01) :69-78
[5]   Improved Parameters for the Martini Coarse-Grained Protein Force Field [J].
de Jong, Djurre H. ;
Singh, Gurpreet ;
Bennett, W. F. Drew ;
Arnarez, Clement ;
Wassenaar, Tsjerk A. ;
Schafer, Lars V. ;
Periole, Xavier ;
Tieleman, D. Peter ;
Marrink, Siewert J. .
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2013, 9 (01) :687-697
[6]   A SMOOTH PARTICLE MESH EWALD METHOD [J].
ESSMANN, U ;
PERERA, L ;
BERKOWITZ, ML ;
DARDEN, T ;
LEE, H ;
PEDERSEN, LG .
JOURNAL OF CHEMICAL PHYSICS, 1995, 103 (19) :8577-8593
[7]   Calculation of Diffusion Coefficients through Coarse-Grained Simulations Using the Automated-Fragmentation-Parametrization Method and the Recovery of Wilke-Chang Statistical Correlation [J].
Fraaije, Johannes G. E. M. ;
van Male, Jan ;
Becherer, Paul ;
Gracia, Ruben Serral .
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2018, 14 (02) :479-485
[8]   Coarse-Grained Models for Automated Fragmentation and Parametrization of Molecular Databases [J].
Fraaije, Johannes G. E. M. ;
van Male, Jan ;
Becherer, Paul ;
Gracia, Ruben Serral .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2016, 56 (12) :2361-2377
[9]   A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations [J].
Frappier, Vincent ;
Najmanovich, Rafael J. .
PLOS COMPUTATIONAL BIOLOGY, 2014, 10 (04)
[10]   Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation [J].
Groot, RD ;
Warren, PB .
JOURNAL OF CHEMICAL PHYSICS, 1997, 107 (11) :4423-4435