Binding free energy calculation of human beta defensin 3 with negatively charged lipid bilayer using free energy perturbation method

Peptide Drug Design Using Alchemical Free Energy Calculation: An Application and Validation on Agonists of Ghrelin Receptor

With recent large-scale applications and validations, the relative binding free energy (RBFE) calculated using alchemical free energy methods has been proven to be an accurate measure to probe the binding of small-molecule drug candidates. On the other hand, given the flexibility of peptides, it is of great interest to find out whether sufficient sampling could be achieved within the typical time scale of such calculation, and a similar level of accuracy could be reached for peptide drugs. However, the systematic evaluation of such calculations on protein-peptide systems has been less reported. Most reported studies of peptides were restricted to a limited number of data points or lacking experimental support. To demonstrate the applicability of the alchemical free energy method for protein-peptide systems in a typical real-world drug discovery project, we report an application of the thermodynamic integration (TI) method to the RBFE calculation of ghrelin receptor and its peptide agonists. Along with the calculation, the synthesis and in vitro EC50 activity of relamorelin and 17 new peptide derivatives were also reported. A cost-effective criterion to determine the data collection time was proposed for peptides in the TI simulation. The average of three TI repeats yielded a mean absolute error of 0.98 kcal/mol and Pearson's correlation coefficient (R) of 0.77 against the experimental free energy derived from the in vitro EC50 activity, showing good repeatability of the proposed method and a slightly better agreement than the results obtained from the arbitrary time frames up to 20 ns. Although it is limited by having one target and a deduced binding pose, we hope that this study can add some insights into alchemical free energy calculation of protein-peptide systems, providing theoretical assistance to the development of peptide drugs.

Molecular Dynamics Simulations on Spike Protein Mutants Binding with Human β Defensin Type 2

Human β defensin type 2 (hBD-2), a cationic cysteine-rich peptide secreted from the human innate immune system, can bind Spike-RBD at the same site as receptor ACE2, thus blocking viral entry into ACE2-expressing cells. In order to find out the impact of CoV-2 mutations on hBD-2's antiviral activity, it is important to investigate the binding and interaction of hBD-2 with RBD mutants. All-atom molecular dynamics simulations were conducted on typical RBD mutants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N, and N501Y-E484K-K417N, binding with hBD-2. Starting from the stable binding structure of hBD-2 and wt-RBD and ClusPro and HADDOCK docking-predicted initial structures, the RBD variants bound with hBD-2 simulations were set up, and NAMD simulations were conducted. Based on the structure and dynamics analysis, it was found that most RBD variants can still form a similar number of hydrogen bonds with hBD-2, in addition to having a similar-sized buried surface area (BSA) and a similar binding interface to the RBD wildtype. However, the RBD triple mutant formed a less stable binding structure with hBD-2 than other variants. Additionally, the free energy perturbation (FEP) method was applied to calculate the contribution of key mutant residues to the binding and the free energy change caused by the mutations. The result shows that N439K, K417N, and the trimutation increase the binding free energy of RBD with hBD-2; thus, RBD should bind less stably with hBD-2. E484K decreases the binding free energy, thus it should bind more stably with hBD-2, while N501Y, S477N, T478I, and P479S almost do not change the binding free energy with hBD-2. The MM-GBSA method predicted the binding interaction energy which shows that the trimutant should be able to escape the binding with hBD-2 but N501Y should not. The result can provide insight into understanding the functional mechanism of hBD-2 combating SARS-CoV-2 mutants.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor

The ongoing COVID-19 pandemic continues to infect people worldwide, and the virus continues to evolve in significant ways which can pose challenges to the efficiency of available vaccines and therapeutic drugs and cause future pandemic. Therefore, it is important to investigate the binding and interaction of ACE2 with different RBD variants. A comparative study using all-atom MD simulations was conducted on ACE2 binding with 8 different RBD variants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N and N501Y-E484K-K417N on RBD. Based on the RMSD, RMSF, and DSSP results, overall the binding of RBD variants with ACE2 is stable, and the secondary structure of RBD and ACE2 are consistent after the point mutation. Besides that, a similar buried surface area, a consistent binding interface and a similar amount of hydrogen bonds formed between RBD and ACE2 although the exact residue pairs on the binding interface were modified. The change of binding free energy from point mutation was predicted using the free energy perturbation (FEP) method. It is found that N501Y, N439K, and K417N can strengthen the binding of RBD with ACE2, while E484K and P479S weaken the binding, and S477N and T478I have negligible effect on the binding. Point mutations modified the dynamic correlation of residues in RBD based on the dihedral angle covariance matrix calculation. Doing dynamic network analysis, a common intrinsic network community extending from the tail of RBD to central, then to the binding interface region was found, which could communicate the dynamics in the binding interface region to the tail thus to the other sections of S protein. The result can supply unique methodology and molecular insight on studying the molecular structure and dynamics of possible future pandemics and design novel drugs.

Interaction of Human β Defensin Type 3 (hBD-3) with Different PIP2-Containing Membranes, a Molecular Dynamics Simulation Study

Human β defensin type 3 (hBD-3) is a cysteine-rich small antibacterial peptide. It belongs to the human innate immune system. hBD-3 has six cysteine residues, which form three pairs of disulfide bonds, and those bonds break in the reducing condition. It is known that hBD-3 can interact with bacterial membrane, and even eukaryotic cell membrane, which has a low concentration of phosphatidylinositol 4,5-bisphosphate (PIP2) lipids. PIP2 is a vital component in cell membranes and has been found to play important roles during antimicrobial peptide (AMP) interaction with membranes. To understand the functional mechanism of hBD-3 interacting with PIP2-containing membranes, the binding structures of hBD-3 on 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers mixed with 10% of PIP2 were predicted using two kinds of methods. The first one is by placing the hBD-3 monomer in different orientations above the POPC + 10%PIP2 membrane to set up five different initial simulation systems and performing long-term simulations on each to predict the most stable binding structure. It was found that hBD-3 analogue binds on the mixed lipid membrane on the two loop regions. The second method is by running long-term simulations on one or nine hBD-3 dimers binding on POPC mixed with 10%PIP2 lipid bilayer starting from the solid-state NMR (ssNMR)-suggested orientation. The dimer dissociated, and the most stable binding of hBD-3 in wild-type on the mixed membrane is also through the two loop regions, which agrees with the prediction from both the first method and the lipid self-assembly result. The PIP2 lipids can form long-lasting hydrogen bonds with positively charged residues such as Arg and Lys on hBD-3, thus forming clusters with hBD-3. As a comparison, hBD-3 dimers binding with a combined bilayer having 1,2-palmitoyl-oleoyl-sn-glycero-3-phosphoserine (POPS) on the upper and POPC on the lower leaflets and the combined POPS + POPC bilayer mixing with 10%PIP2 were also studied. The long-term simulation result shows that hBD-3 can bind with the heads of negatively charged POPS and PIP2 lipids and form hydrogen bonds. The stable binding sites of hBD-3 on PIP2 or POPS mixed bilayers are still on the two loop regions. On the combined POPS + POPC mixed with 10%PIP2 bilayer, the binding of hBD-3 with PIP2 lipids became less stable and fewer because of the competition of binding with the POPS lipids. Besides that, binding with hBD-3 can decrease the membrane thickness of the POPC + PIP2, POPS + POPC, and POPS + POPC + PIP2 bilayers and make POPS and PIP2 lipids more flexible based on the order parameter calculations. Our results supply molecular insight on AMP binding with different membranes and can help understand the functional mechanism of hBD-3 disrupting PIP2-containing membranes.