Molecular dynamics simulations of nucleotide release from the circadian clock protein KaiC reveal atomic-resolution functional insights.

The cyanobacterial clock proteins KaiA, KaiB, and KaiC form a powerful system to study the biophysical basis of circadian rhythms, because an in vitro mixture of the three proteins is sufficient to generate a robust ∼24-h rhythm in the phosphorylation of KaiC. The nucleotide-bound states of KaiC critically affect both KaiB binding to the N-terminal domain (CI) and the phosphotransfer reactions that (de)phosphorylate the KaiC C-terminal domain (CII). However, the nucleotide exchange pathways associated with transitions among these states are poorly understood. In this study, we integrate recent advances in molecular dynamics methods to elucidate the structure and energetics of the pathway for Mg·ADP release from the CII domain. We find that nucleotide release is coupled to large-scale conformational changes in the KaiC hexamer. Solvating the nucleotide requires widening the subunit interface leading to the active site, which is linked to extension of the A-loop, a structure implicated in KaiA binding. These results provide a molecular hypothesis for how KaiA acts as a nucleotide exchange factor. In turn, structural parallels between the CI and CII domains suggest a mechanism for allosteric coupling between the domains. We relate our results to structures observed for other hexameric ATPases, which perform diverse functions.

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes.

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

Computational Methods Used to Explore Transport Events in Biological Systems.

Transport of various molecules facilitated with membrane proteins is necessary for maintaining homeostasis in living cells. In humans, dysfunction of these proteins leads to many diseases. Thus, understanding how the membrane proteins function may help using them as therapeutic targets. To successfully investigate the mechanistic aspects of transport, the choice of appropriate methods is crucial. We review the computational methods that have proven most effective in investigating transport events, specifically, deterministic time-dependent classical molecular dynamics and its enhanced sampling variants, as well as methods based on Brownian dynamics. We describe technical aspects of these methods and examples of their novel variants or combinations that have been recently and successfully applied in the transport studies. We also discuss the difficulties related to these methods and provide possible solutions to avoid them.

Computational studies of protein aggregation: methods and applications.

Protein aggregation involves the self-assembly of normally soluble proteins into large supramolecular assemblies. The typical end product of aggregation is the amyloid fibril, an extended structure enriched in ß-sheet content. The aggregation process has been linked to a number of diseases, most notably Alzheimer's disease, but fibril formation can also play a functional role in certain organisms. This review focuses on theoretical studies of the process of fibril formation, with an emphasis on the computational models and methods commonly used to tackle this problem.

Free-energy landscapes of transmembrane homodimers by bias-exchange adaptively biased molecular dynamics.

Membrane proteins play essential roles in various biological functions within the cell. One of the most common functional regulations involves the dimerization of two single-pass transmembrane (TM) helices. Glycophorin A (GpA) and amyloid precursor protein (APP) form TM homodimers in the membrane, which have been investigated both experimentally and computationally. The homodimer structures are well characterized using only four collective variables (CVs) when each TM helix is stable. The CVs are the interhelical distance, the crossing angle, and the Crick angles for two TM helices. However, conformational sampling with multi-dimensional replica-exchange umbrella sampling (REUS) requires too many replicas to sample all the CVs for exploring the conformational landscapes. Here, we show that the bias-exchange adaptively biased molecular dynamics (BE-ABMD) with the four CVs effectively explores the free-energy landscapes of the TM helix dimers of GpA, wild-type APP and its mutants in the IMM1 implicit membrane. Compared to the original ABMD, the bias-exchange algorithm in BE-ABMD can provide a more rapidly converged conformational landscape. The BE-ABMD simulations could also reveal TM packing interfaces of the membrane proteins and the dependence of the free-energy landscapes on the membrane thickness. This approach is valuable for numerous other applications, including those involving explicit solvent and a lipid bilayer in all-atom force fields or Martini coarse-grained models, and enhances our understanding of protein-protein interactions in biological membranes.

Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies.

Antimicrobial peptides (AMPs) are increasingly recognized as potent therapeutic agents, with their selective affinity for pathological membranes, low toxicity profile, and minimal resistance development making them particularly attractive in the pharmaceutical landscape. This study offers a comprehensive analysis of the interaction between specific AMPs, including magainin-2, pleurocidin, CM15, LL37, and clavanin, with lipid bilayer models of very different compositions that have been ordinarily used as biological membrane models of healthy mammal, cancerous, and bacterial cells. Employing unbiased molecular dynamics simulations and metadynamics techniques, we have deciphered the intricate mechanisms by which these peptides recognize pathogenic and pathologic lipid patterns and integrate into lipid assemblies. Our findings reveal that the transverse component of the peptide's hydrophobic dipole moment is critical for membrane interaction, decisively influencing the molecule's orientation and expected therapeutic efficacy. Our approach also provides insight on the kinetic and dynamic dependence on the peptide orientation in the axial and azimuthal angles when coming close to the membrane. The aim is to establish a robust framework for the rational design of peptide-based, membrane-targeted therapies, as well as effective quantitative descriptors that can facilitate the automated design of novel AMPs for these therapies using machine learning methods.

Constructing conformational library for amyloid-ß42 dimers as the smallest toxic oligomers using two CHARMM force fields.

The study of amyloid-ß (Aß) dimers as the smallest toxic aggregates in the human brain suffering from Alzheimer's disease is of great interest. The structural characterization of the dimers, which is important to rationally design inhibitors for Aß dimerization, is limited by the low stability of these species and their high tendency to aggregate into protofibrils and amyloid fibrils. Therefore, an efficient sampling method is needed for the computational study of the Aß dimers. In this regard, we build a conformational library of the Aß42 dimers by a new computational protocol; the blockwise excursion sampling (BES); with the CHARMM27 and CHARMM36m force fields. The CHARMM27 overestimates helix content and underestimates ß-sheet content, while secondary structure content for the dimers sampled by the CHARMM36m force field is in reasonably consistent with the circular dichroism data. The CHARMM36m force field also generates more Aß42 dimers in line with experimentally measured collision cross sections values relative to the CHARMM27 force field. Our results demonstrate that the BES is an efficient protocol for fast generating a heterogeneous conformational library of the Aß42 dimers in agreement with experimental data. Having a reliable structural library for the Aß42 dimers is very important to identify binding "hot spots" of the dimers versus potential drug candidates using ensemble docking approach.

The prediction of protein-ligand unbinding for modern drug discovery.

INTRODUCTION: Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. AREAS COVERED: In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. EXPERT OPINION: Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Integrating Molecular Docking and Molecular Dynamics Simulations.

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

Enhanced sampling of glutamate receptor ligand-binding domains.

The majority of excitatory synaptic transmission in the central nervous system is mediated by ionotropic glutamate receptors (iGluRs). These membrane-bound protein assemblies consist of modular domains that can be genetically isolated and expressed, which has resulted in a plethora of crystal structures of individual domains in different conformations bound to different ligands. These structures have presented opportunities for molecular dynamics (MD) simulation studies. To examine the free energies that govern molecular behavior, simulation strategies and algorithms have been developed, collectively called enhanced sampling methods This review focuses on the use of enhanced sampling MD simulations of isolated iGluR ligand-binding domains to characterize thermodynamic properties important to receptor function.

Mutagenesis computer experiments in pentameric ligand-gated ion channels: the role of simulation tools with different resolution.

Pentameric ligand-gated ion channels (pLGICs) are an important class of widely expressed membrane neuroreceptors, which play a crucial role in fast synaptic communications and are involved in several neurological conditions. They are activated by the binding of neurotransmitters, which trigger the transmission of an electrical signal via facilitated ion flux. They can also be activated, inhibited or modulated by a number of drugs. Mutagenesis electrophysiology experiments, with natural or unnatural amino acids, have provided a large body of functional data that, together with emerging structural information from X-ray spectroscopy and cryo-electron microscopy, are helping unravel the complex working mechanisms of these neuroreceptors. Computer simulations are complementing these mutagenesis experiments, with insights at various levels of accuracy and resolution. Here, we review how a selection of computational tools, including first principles methods, classical molecular dynamics and enhanced sampling techniques, are contributing to construct a picture of how pLGICs function and can be pharmacologically targeted to treat the disorders they are responsible for.

Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations.

An increasing number of G protein-coupled receptor (GPCR) crystal structures provide important-albeit static-pictures of how small molecules or peptides interact with their receptors. These high-resolution structures represent a tremendous opportunity to apply molecular dynamics (MD) simulations to capture atomic-level dynamical information that is not easy to obtain experimentally. Understanding ligand binding and unbinding processes, as well as the related responses of the receptor, is crucial to the design of better drugs targeting GPCRs. Here, we discuss possible ways to study the dynamics involved in the binding of small molecules to GPCRs, using long timescale MD simulations or metadynamics-based approaches.

Exploring the Alzheimer amyloid-ß peptide conformational ensemble: A review of molecular dynamics approaches.

Alzheimer's disease is one of the most common dementia among elderly worldwide. There is no therapeutic drugs until now to treat effectively this disease. One main reason is due to the poorly understood mechanism of Aß peptide aggregation, which plays a crucial role in the development of Alzheimer's disease. It remains challenging to experimentally or theoretically characterize the secondary and tertiary structures of the Aß monomer because of its high flexibility and aggregation propensity, and its conformations that lead to the aggregation are not fully identified. In this review, we highlight various structural ensembles of Aß peptide revealed and characterized by computational approaches in order to find converging structures of Aß monomer. Understanding how Aß peptide forms transiently stable structures prior to aggregation will contribute to the design of new therapeutic molecules against the Alzheimer's disease.