Binding free energies for the SAMPL8 CB8 “Drugs of Abuse” challenge from umbrella sampling combined with Hamiltonian replica exchange

Umbrella sampling along a one-dimensional order parameter in combination with Hamiltonian replica exchange was employed to calculate the binding free energy of five guest molecules with known affinity to cucurbit[8]uril. A simple empirical approach correcting for the overestimation of the affinity by the GAFF force field was proposed and subsequently applied to the seven guest molecules of the “Drugs of Abuse” SAMPL8 challenge. Compared to the uncorrected binding free energies, the systematic error decreased but quantitative agreement with experiment was only reached for a few compounds. From a retrospective analysis a weak point of the correction term was identified.

Interaction of KRSR Peptide with Titanium Dioxide Anatase (100) Surface: A Molecular Dynamics Simulation Study

Due to its tensile strength and excellent biocompatibility, titanium (Ti) is commonly used as an implant material in medicine and dentistry. The success of dental implants depends on the formation of a contact between the oxidized surface of Ti implant and the surrounding bone tissue. The adsorption of proteins and peptides to the implant surface allows the bone-forming osteoblast cells to adhere to such modified surfaces. Recently, it has been observed that tetrapeptide KRSR (Lys-Arg-Ser-Arg) functionalization could promote osteoblast adhesion to implant surfaces. This may facilitate the establishment of an efficient bone-to implant contact and improve implant stability during the healing process. GROMACS, a molecular dynamics software package was used to perform a 200 ns simulation of adsorption of the KRSR peptide to the TiO2 (anatase) surface in an aqueous environment. The molecule conformations were mapped with Replica Exchange Molecular Dynamics (REMD) simulations to assess the possible peptide conformations on the anatase surface, and the umbrella sampling method was used to calculate the binding energy of the most common conformation. The simulations have shown that the KRSR peptide migrates and attaches to the surface in a stable position. The dominant amino acid residue interacting with the TiO2 surface was the N-terminal charged lysine (K) residue. REMD indicated that there is a distinct conformation that is taken by the KRSR peptide. In this conformation the surface interacts only with the lysine residue while the ser (S) and arg (R) residues interact with water molecules farther from the surface. The binding free energy of the most common conformation of KRSR peptide to the anatase (100) surface was ΔG = −8.817 kcal/mol. Our result suggests that the N-terminal lysine residue plays an important role in the adhesion of KRSR to the TiO2 surface and may influence the osseointegration of dental implants.

Optimization of an in silico protocol to characterize membrane PAINS

Membrane Pan-Assay INterference compoundS (PAINS) are a class of molecules that interact non-specifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an in silico protocol based on umbrella sampling (US)/MD simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US-reference position to the P-atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The ISDM-calculated membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicate a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol which can be adopted as a future reference method to identify membrane PAINS.

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field.By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual--water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2--CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS-GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 times compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large scale simulations of biological systems.

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.

Single-Channel Permeability from Markovian Milestoning, Umbrella Sampling, and Voltage Simulations

Permeations of ions and small molecules through membrane channels have diverse functions within cells. Various all-atom molecular dynamics (MD) simulations methods have been developed for computing free energy and crossing rate of permeants. However, a systemic comparison across different methods is scarce. Here, using a carbon nanotube as a model of small conductance (~2 pS) ion channel, we systemically compared three classes of MD-based approaches for computing single-channel permeability for potassium ion: equilibrium free energy-based approach using umbrella sampling, rare-even sampling approach using Markovian milestoning, and steady-state approach using applied voltages. The consistent kinetic results from all three methods demonstrated the robustness of MD-based methods in computing ion channel permeation. Two solvent boundary conditions are tested for milestoning and yield consistent forward and backward mean first passage time (MFPT). The advantages and disadvantages of each method are discussed, with the focus on the future applications of milestoning in more complex systems.

Binding Affinity Estimation From Restrained Umbrella Sampling Simulations

The protein-ligand binding affinity quantifies the binding strength between a protein and its ligand. Computer modeling and simulations can be used to estimate the binding affinity or binding free energy using data- or physics-driven methods or a combination thereof. Here, we discuss a purely physics-based sampling approach based on biased molecular dynamics (MD) simulations, which in spirit is similar to the stratification strategy suggested previously by Woo and Roux. The proposed methodology uses umbrella sampling (US) simulations with additional restraints based on collective variables such as the orientation of the ligand. The novel extension of this strategy presented here uses a simplified and more general scheme that can be easily tailored for any system of interest. We estimate the binding affinity of human fibroblast growth factor 1 (hFGF1) to heparin hexasaccharide based on the available crystal structure of the complex as the initial model and four different variations of the proposed method to compare against the experimentally determined binding affinity obtained from isothermal calorimetry (ITC) experiments. Our results indicate that enhanced sampling methods that sample along the ligand-protein distance without restraining other degrees of freedom do not perform as well as those with additional restraint. In particular, restraining the orientation of the ligands plays a crucial role in reaching a reasonable estimate for binding affinity. The general framework presented here provides a flexible scheme for designing practical binding free energy estimation methods.

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.