Researchers at the Tokyo Institute of Technology say they have developed a computational method based on large-scale molecular dynamics simulations to predict the cell-membrane permeability of cyclic peptides using a supercomputer.
Their protocol has exhibited promising accuracy and may become a useful tool for the design and discovery of cyclic peptide drugs, which could help researchers reach new therapeutic targets inside cells beyond the capabilities of conventional small-molecule drugs or antibody-based drugs, according to the team.
Cyclic peptide drugs have attracted the attention of major pharmaceutical companies around the world as promising alternatives to conventional small molecule-based drugs. Through proper design, cyclic peptides can be tailored to reach specific targets inside cells, such as protein-protein interactions, which are beyond the scope of small molecules. Unfortunately, it has proven notoriously difficult to design cyclic peptides with high cell-membrane permeability, i.e., cyclic peptides that can easily diffuse through the lipid bilayer that delimits the inside and outside of a cell.
To address this bottleneck, scientists at the Middle Molecule IT-based Drug Discovery Laboratory (MIDL) have been working on a computational method for predicting cell-membrane permeability.
In a study (“Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations”) published in the Journal of Chemical Information and Modeling, Yutaka Akiyama, PhD, and colleagues from MIDL and Tokyo Tech have developed a protocol for predicting the cell-membrane permeability of cyclic peptides using molecular dynamics simulations. Such simulations constitute a widely accepted computational approach for predicting and reproducing the dynamics of atoms and molecules by sequentially solving Newton’s laws of motion at short time intervals, said Akiyama, who added that even a single simulation for predicting the permeability of a cyclic peptide with only eight amino acids takes a tremendous amount of time and resources.
“Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs. However, membrane permeation mechanisms are poorly understood. To investigate common features of permeable (and nonpermeable) designs, it is necessary to reproduce the membrane permeation process of cyclic peptides through the lipid bilayer. We simulated the membrane permeation process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular dynamics (MD) and replica-exchange umbrella sampling simulations and predicted membrane permeability using the inhomogeneous solubility-diffusion model and a modified version of it,” write the investigators.
“Furthermore, we confirmed the effectiveness of this protocol by predicting the membrane permeability of 56 eight-residue cyclic peptides with diverse chemical structures, including some confidential designs from a pharmaceutical company. As a result, a reasonable correlation between experimentally assessed and calculated membrane permeability of cyclic peptides was observed for the peptide libraries, except for strongly hydrophobic peptides.
“Our analysis of the MD trajectory demonstrated that most peptides were stabilized in the boundary region between bulk water and membrane and that for most peptides, the process of crossing the center of the membrane is the main obstacle to membrane permeation. The height of this barrier is well correlated with the electrostatic interaction between the peptide and the surrounding media.
“The structural and energetic features of the representative peptide at each vertical position within the membrane were also analyzed, revealing that peptides permeate the membrane by changing their orientation and conformation according to the surrounding environment.”
“Our study marks the first time comprehensive simulations were performed for as many as 156 different cyclic peptides,” notes Akiyama, “The simulation of each cyclic peptide using the protocol we developed took about 70 hours per peptide using 28 GPUs on the TSUBAME 3.0 supercomputer at Tokyo Tech.”
The researchers verified the predicted permeability values with experimentally derived ones and confirmed an acceptable correlation coefficient of R = 0.63 under the best conditions, showcasing the potential of their protocol. Moreover, after a detailed analysis of the peptide conformation and energy values obtained from the trajectory data, Akiyama’s team found that the strength of the electrostatic interactions between the atoms constituting the cyclic peptide and the surrounding media, namely lipid membrane and water molecules, are strongly related to the membrane permeability value.
The simulations also revealed the way in which peptides permeate through the membrane by changing their orientation and conformation according to their surroundings.
“Our results shed some light on the mechanisms of cell-membrane permeability and provide a guideline for designing molecules that can get inside cells more efficiently. This will greatly contribute to the development of next-generation peptide drugs,” remarked Masatake Sugita, PhD, the first author of the study.
The researchers are already working on a more advanced simulation protocol that will enable more accurate predictions. They are also trying to incorporate artificial intelligence into the picture by adopting deep learning techniques, which could increase both accuracy and speed.