The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

Molecular self-assembly is pivotal for the formation of ordered nanostructures, yet the structural diversity obtained by the use of a single type of building block is limited. Multicomponent coassembly, utilized to expand the architectural space, is principally based on empirical observations rather than rational design. Here we report large-scale molecular dynamics simulations of the coassembly of diphenylalanine (FF) and triphenylalanine (FFF) peptides at various mass ratios. Our simulations show that FF and FFF can co-organize into both canonical and noncanonical assemblies. Strikingly, toroid nanostructures, which were rarely observed for the extensively studied FF or FFF, are often seen in the FF-FFF coassembly simulations and later corroborated by scanning electron microscopy. Our simulations demonstrate a wide ratio-dependent variation of nanostructure morphologies including hollow and solid assemblies, much richer than those formed by each individual moiety. The hollow-solid structural transformation displays a discontinuous transition feature, and the toroids appear to be an obligatory intermediate for the structural transition. Interaction analysis reveals that the hollow-solid structural transition is mostly dominated by FF-FFF interactions, while the nanotoroid formation is determined by the competition between FF-water and FFF-water interactions. This study provides both structural and mechanistic insights into the coassembly of FF and FFF peptides, thus offering a molecular basis for the rational design of bionanomaterials utilizing peptide coassembly.

Summary The Hippo pathway plays a crucial role in organ size control and tumor suppression, but its precise regulation has not been fully understood. In this study, we discovered phosphatidic acid (PA)-related lipid signaling as a key regulator of the Hippo pathway. Supplementing PA in various Hippo-activating conditions activates YAP. This PA-related lipid signaling is involved in the Rho-mediated YAP activation. Mechanistically, PA directly interacts with Hippo components LATS and NF2 to respectively disrupt the LATS-MOB1 complex formation and NF2-mediated LATS membrane translocation and activation. Inhibition of phospholipase D (PLD)-dependent PA production suppresses YAP oncogenic activities. PLD1 is highly expressed in breast cancer and positively correlates with YAP activation, suggesting their pathological relevance in breast cancer development. Taken together, our study not only reveals a role of PLD-PA lipid signaling in regulation of the Hippo pathway, but also indicates the PLD-PA-YAP axis as a potential therapeutic target for cancer treatment.

Experiments suggested that the fibrillation of the 11-25 fragment (hIAPP(11-25)) of human islet amyloid polypeptide (hIAPP or amylin) involves the formation of transient α-helical intermediates, followed by conversion to β-sheet-rich structure. However, atomic details of α-helical intermediates and the transition mechanism are mostly unknown. We investigated the structural properties of the monomer and dimer in atomistic detail by replica exchange molecular dynamics (REMD) simulations. Transient α-helical monomers and dimers were both observed in the REMD trajectories. Our calculated H(α) chemical shifts based on the monomer REMD run are in agreement with the solution-state NMR experimental observations. Multiple 300 ns MD simulations at 310 K show that α-helix-to-β-sheet transition follows two mechanisms: the first involved direct transition of the random coil part of the helical conformation into antiparallel β-sheet, and in the second, the α-helical conformation unfolded and converted into antiparallel β-sheet. In both mechanisms, the α-helix-to-β-sheet transition occurred via random coil, and the transition was accompanied by an increase of interpeptide contacts. In addition, our REMD simulations revealed different temperature dependencies of helical and β-structures. Comparison with experimental data suggests that the propensity for hIAPP(11-25) to form α-helices and amyloid structures is concentration- and temperature-dependent.

Protein aggregation is associated with many human diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and type II diabetes (T2D). Understanding the molecular mechanism of protein aggregation is essential for therapy development. Molecular dynamics (MD) simulations have been shown as powerful tools to study protein aggregation. However, conventional MD simulations can hardly sample the whole conformational space of complex protein systems within acceptable simulation time as it can be easily trapped in local minimum-energy states. Many enhanced sampling methods have been developed. Among these, the replica exchange molecular dynamics (REMD) method has gained great popularity. By combining MD simulation with the Monte Carlo algorithm, the REMD method is capable of overcoming high energy-barriers easily and of sampling sufficiently the conformational space of proteins. In this chapter, we present a brief introduction to REMD method and a practical application protocol with a case study of the dimerization of the 11-25 fragment of human islet amyloid polypeptide (hIAPP(11-25)), using the GROMACS software. We also provide solutions to problems that are often encountered in practical use, and provide some useful scripts/commands from our research that can be easily adapted to other systems.

There are synergistic effects of Aβ and tau protein in Alzheimer's disease. Aβ 1−42 protofibril seeds induce conversion of human tau protein into β-sheet-rich toxic tau oligomers. However, the molecular mechanisms underlying such a conformational conversion are unclear. Here, we use extensive all atom replica exchange molecular dynamics simulations to investigate the effects of preformed Aβ 1−42 protofibril on two monomeric tau constructs: K18 and K19. We found that Aβ oligomer stretches tau conformation and drastically reduces the metastable secondary structures/hydrogen bonding/salt-bridge networks in tau monomers and exposes their fibril nucleating motifs 275 VQIINK 280 and 306 VQIVYK 311 . Aβ interacting patches around Tyr10/Ile41 contribute significantly to the interactions with K18 and K19. Aβ cross-seeded tau aggregation can adopt a "stretching-and-packing" mechanism, paving the way for the next, prion-like growth step. The results provide a mechanism on the atomic level to experimental observations that tau pathogenesis is promoted by Aβ 1−42 but not by Aβ 1−40 .A myloid β (Aβ) forms extracellular senile plaques and tau protein simultaneously forms intracellular neurofibrillary tangles (NFTs) in the brain of Alzheimer's disease patients. 1,2 There is increasing evidence of independent and synergistic effects of Aβ and tau. 3−5 Classically, Aβ and tau are respectively deposited extra-and intracellularly. However, Aβ also accumulates intraneuronally 2,6 and it binds to tau to form insoluble complexes within the same neurons. 7 In the Aβ-induced tau pathology, Aβ accumulation occurs prior to tau in AD pathogenesis and triggers the conversion of tau from normal to toxic states. 3,4,6,8−15 Although mechanisms linking Aβ and tau-pathology have not been conclusively identified, several potential mechanisms have been postulated. These posit that (1) Aβ interacts directly with neuronal receptors and membranes, (2) Aβ induces inflammation via glial cells, and (3) Aβ directly seeds and propagates tau aggregation. 4 The third mechanism has recently drawn increasing attention. 4,8,16−18 Propagation of toxic, misfolded Aβ and tau bears a striking resemblance to that of the prion protein. 16,17,19 Misfolded Aβ promotes tau aggregation in vitro through direct, intermolecular interaction. 15,20,21 Intriguingly, injection of preaggregated synthetic amyloid peptides 9 or aggregated amyloid peptide enriched brain extracts 22 induced tau aggregation not only at the injection site but also in functionally connected brain regions remote from the site, which indicates that amyloid peptides can initiate and propagate tau aggregation in these regions as well. 9,22 This prion-like behavior 23,24 of Aβ and tau underscores the need for an in-depth understanding of the seeding mechanism through which Aβ induces tau pathology.It has been shown that Aβ 1−42 oligomer seeds induce the conversion of unstructured, monomeric human recombinant tau into β-sheet-rich toxic tau oligomers. 20 Previous simulations of the interactions of Aβ fibri...

Molecular dynamics simulations of biomolecules have been widely adopted in biomedical studies. As classical point-charge models continue to be used in routine biomolecular applications, there have been growing demands on developing polarizable force fields for handling more complicated biomolecular processes. Here, we focus on a recently proposed polarizable Gaussian Multipole (pGM) model for biomolecular simulations. A key benefit of pGM is its screening of all short-range electrostatic interactions in a physically consistent manner, which is critical for stable charge-fitting and is needed to reproduce molecular anisotropy. Another advantage of pGM is that each atom's multipoles are represented by a single Gaussian function or its derivatives, allowing for more efficient electrostatics than other Gaussian-based models. In this study, we present an efficient formulation for the pGM model defined with respect to a local frame formed with a set of covalent basis vectors. The covalent basis vectors are chosen to be along each atom's covalent bonding directions. The new local frame can better accommodate the fact that permanent dipoles are primarily aligned along covalent bonds due to the differences in electronegativity of bonded atoms. It also allows molecular flexibility during molecular simulations and facilitates an efficient formulation of analytical electrostatic forces without explicit torque computation. Subsequent numerical tests show that analytical atomic forces agree excellently with numerical finite-difference forces for the tested system. Finally, the new pGM electrostatics algorithm is interfaced with the particle mesh Ewald (PME) implementation in Amber for molecular simulations under the periodic boundary conditions. To validate the overall pGM/PME electrostatics, we conducted an NVE simulation for a small water box of 512 water molecules. Our results show that to achieve energy conservation in the polarizable model, it is important to ensure enough accuracy on both PME and induction iteration. It is hoped that the reformulated pGM model will facilitate the development of future force fields based on the pGM electrostatics for applications in biomolecular systems and processes where polarization plays crucial roles.

Membrane-bound protein receptors are a primary biological drug target, but the computational analysis of membrane proteins has been limited. In order to improve molecular mechanics Poisson-Boltzmann surface area (MMPBSA) binding free energy calculations for membrane protein-ligand systems, we have optimized a new heterogeneous dielectric implicit membrane model, with respect to free energy simulations in explicit membrane and explicit water, and implemented it into the Amber software suite. This new model supersedes our previous uniform, single dielectric implicit membrane model by allowing the dielectric constant to vary with depth within the membrane. We calculated MMPBSA binding free energies for the human purinergic platelet receptor (P2Y 12 R) and two of the muscarinic acetylcholine receptors (M2R and M3R) bound to various antagonist ligands using both membrane models, and we found that the heterogeneous dielectric membrane model has a stronger correlation with experimental binding affinities compared to the older model under otherwise identical conditions. This improved membrane model increases the utility of MMPBSA calculations for the rational design and improvement of future drug candidates.