Exploration of the binding modes of buffalo PGRP1 receptor complexed with meso-diaminopimelic acid and lysine-type peptidoglycans by molecular dynamics simulation and free energy calculation

Experimentally verified flexible molecular docking and dynamic simulation of aptamer with intracellular proteins based on direct DNA 3D structure prediction

Despite the rising spotlight on biotargeting aptamers, their mechanism of regulating cellular functions remained elusive due to lack of systematic method to investigate their intracellular behavior. This study systematically established a complete workflow including DNA secondary and 3D structure prediction, flexibilization, docking, experimental validation, and molecular dynamic (MD) simulation. RNAfold was demonstrated to provide more accurate ssDNA secondary structure predictions and compatibility for flexible docking. Feasibility of a novel direct prediction tool of DNA 3D structure, 3dDNA, has first been proven with similar reliability and better data stability in flexible docking compared to indirect prediction by RNAComposer. Flexible docking by AutoDock Vina exhibited higher reliability, while rigid docking was less reliable. Docking results were influenced by secondary and 3D structures, but the proteins' inherent affinity to nucleic acids was the key determinant. Aptamer bound to proteins with non-specificity (KD > 100 nM) and affinities (Rmax) exponentially correlated to flexible docking scores, necessitating further MD validation and identification of binding sites. Via the established workflow, binding sites of stem cell-recruiting aptamer Apt-19s on its known target (ALPL) was identified, Sec24B was first screened as its potential intracellular targets, providing theoretical guidance and feasible methodology for future exploration of aptamer biotargeting mechanisms.

Structural Interaction of Apolipoprotein A-I Mimetic Peptide with Amyloid-β Generates Toxic Hetero-oligomers

Apolipoproteins are involved in pathological conditions of Alzheimer's disease (AD), and it has been reported that truncated apolipoprotein fragments and β-amyloid (Aβ) peptides coexist as neurotoxic heteromers within the plaques. Therefore, it is important to investigate these complexes at the molecular level to better understand their properties and roles in the pathology of AD. Here, we present a mechanistic insight into such heteromerization using a structurally homologue apolipoprotein fragment of apoA-I (4F) complexed with Aβ(M1-42) and characterize their toxicity. The 4F peptide slows down the aggregation kinetics of Aβ(M1-42) by constraining its structural plasticity. NMR and CD experiments identified 4F-Aβ(M1-42) heteromers comprised of unstructured Aβ(M1-42) and helical 4F. A uniform two-fold reduction in ¹⁵N/¹H NMR signal intensities of Aβ(M1-42) with no observable chemical shift perturbation indicated the formation of a large complex, which was further confirmed by diffusion NMR experiments. Microsecond-scale atomistic molecular dynamics simulations showed that 4F interaction with Aβ(M1-42) is electrostatically driven and induces unfolding of Aβ(M1-42). Neurotoxicity profiling of Aβ(M1-42) complexed with 4F confirms a significant reduction in cell viability and neurite growth. Thus, the molecular architecture of heteromerization between 4F and Aβ(M1-42) discovered in this study provides evidence toward our understanding of the role of apolipoproteins or their truncated fragments in exacerbating AD pathology.

Molecular Docking Simulations with ArgusLab

Molecular docking is the major computational technique employed in the early stages of computer-aided drug discovery. The availability of free software to carry out docking simulations of protein-ligand systems has allowed for an increasing number of studies using this technique. Among the available free docking programs, we discuss the use of ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) for protein-ligand docking simulation. This easy-to-use computational tool makes use of a genetic algorithm as a search algorithm and a fast scoring function that allows users with minimal experience in the simulations of protein-ligand simulations to carry out docking simulations. In this chapter, we present a detailed tutorial to perform docking simulations using ArgusLab.

Recent Developments and Applications of the MMPBSA Method

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.

Conformational states of HAMP domains interacting with sensory rhodopsin membrane systems: an integrated all-atom and coarse-grained molecular dynamics simulation approach

Understanding the downstream signaling mechanism of sensory rhodopsin and its cognate transducer complex (srII-htrII) has long been a challenge in the field of photoreceptor research. Here, an integration of all-atom and coarse-grained (CG) molecular dynamics (MD) simulations in different srII-htrII complex states is carried out. It is shown that the cytoplasmic four-helix HAMP dimer gives rise to a gear-box model interaction with discrete hydrophobic packing in Natronomonas pharaonis (Np). Structural analysis in all-atom and CG-MD reveals a stable conformational state in the physiological environment (323 K and 1.15 M salt). Comparative analysis in the ground and intermediate state conformations reveals substantial inter-HAMP interactions in the intermediate state with uniform clockwise (+10° to +30°) and counterclockwise (-20° to -40°) rotations in the α1 helix and the α2 helix of the monomer, respectively. Low temperature and low salt environments (283 K and 0.15 M) significantly affect srII-htrII binding affinity in both states with unusual helix bending. The distinguished control cable, knob-into-holes packing and piston-like movements in HAMP helices are found in the intermediate state complex. The N-terminal htrII (159 residues) coupled with srII yields a binding energy (ΔG_bind) of -309.22, -436.53 and -331.11 kJ mol^-1 in the MM/PBSA calculation for the NphtrII homodimer, the NpsrII-htrII ground state conformation and the NpsrII-htrII intermediate state conformation, respectively. Only the HAMP1 domain shows a very low ΔG_bind value (-21.03 kJ mol^-1) for the ground state in comparison to that for the intermediate state (-54.68 kJ mol^-1). The structural analysis highlights the key residues that include Y199^srII, T189^srII, E43^htrII, T86^htrII, M100^htrII, E116^htrII, E126^htrII and S130^htrII for complex stabilization and signal transduction.

Structural insights into ligand binding of PGRP1 splice variants in Chinese giant salamander (Andrias davidianus) from molecular dynamics and free energy calculations

Peptidoglycan (PGN) recognition proteins (PGRPs) are important pattern recognition receptors of the innate immune system. A number of PGRP splicing variants produced by alternative splicing of PGRP genes have been reported. However, several important aspects of interactions between PGRP splice variants and their ligands are still unclear. In the present study, three dimensional models of salamander PGRP1 (adPGRP1) and its splice variant (adPGRP1a) were constructed, and their key amino acids involved in interacting with PGNs were analyzed. The results revealed that adPGRP1a has a typical PGRPs structure containing five β-sheets and four α-helices, while adPGRP1 contained five β-sheets and only one α-helix due to the lack of 51 amino acids at its C-terminus. Molecular docking revealed that van der Waals and Coulombic interactions contributed to interactions in the protein-ligand complex. Further binding energy of adPGRP-PGNs computed by the MM-PBSA method revealed that adPGRP1a and adPGRP1 might selectively bind to different PGNs; the former might selectively bind Dap-type PGNs and the latter both types of PGNs. In addition, the binding energy of each residue of adPGRP1a and adPGRP1 was also calculated, revealing that residues involved in the interaction of protein-ligand complexes were different in adPGRP1a and adPGRP1. These results provided a first insight into the potential basis for interaction between PGRPs generated by alternative splicing and PGN derivatives.