Interaction Mechanism of the Germination Stimulants Karrikins and Their Receptor ShKAI2iB

Investigation on the interaction mechanism of different SARS-CoV-2 spike variants with hACE2: insights from molecular dynamics simulations

Since the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), SARS-CoV-2 has evolved by acquiring genomic mutations, resulting in the recent emergence of several SARS-CoV-2 variants with improved transmissibility and infectivity relative to the original strain. An underlying mechanism may be the increased ability of the mutants to bind the receptor proteins and infect the host cell. In this work, we implemented all-atom molecular dynamics (MD) simulations to study the binding and interaction of the receptor binding domain (RBD) of the SARS-CoV-2 spike protein singly (D614G), doubly (D614G + L452R and D614G + N501Y), triply (D614G + N501Y + E484K), and quadruply (D614G + N501Y + E484K + K417T) mutated variants with the human angiotensin-converting enzyme 2 (hACE2) receptor protein in the host cell. A combination of multiple analysis approaches elucidated the effects of mutations and the extent of molecular divergence from multiple perspectives, including the dynamic correlated motions, interaction patterns, dominant motions, free energy landscape, and charge distribution on the electrostatic potential surface between the hACE2 and all RBD variants. Moreover, free energy calculations using the MM/PBSA method evaluated the binding affinity between these RBD variants and hACE2. The results showed that the D614G + N501Y + E484K variant possessed the lowest free energy value (highest affinity) compared to the D614G + N501Y + E484K + K417T, D614G + L452R, D614G + N501Y, and D614G mutants. The residue-based energy decomposition also indicated that the energy contribution of residues at the mutation site to the total binding energy was highly variable. The interaction mechanisms between the different RBD variants and hACE2 elucidated in this study will provide some insights into the development of drugs targeting the new SARS-CoV-2 variants.

Computational Design of Miniprotein Inhibitors Targeting SARS-CoV-2 Spike Protein

The ongoing pandemic of COVID-19 caused by SARS-CoV-2 has become a global health problem. There is an urgent need to develop therapeutic drugs, effective therapies, and vaccines to prevent the spread of the virus. The virus first enters the host cell through the interaction between the receptor binding domain (RBD) of spike protein and the peptidase domain (PD) of the angiotensin-converting enzyme 2 (ACE2). Therefore, blocking the binding of RBD and ACE2 is a promising strategy to inhibit the invasion and infection of the virus in the host cell. In the study, we designed several miniprotein inhibitors against SARS-CoV-2 by single/double/triple-point mutant, based on the initial inhibitor LCB3. Molecular dynamics (MD) simulations and trajectory analysis were performed for an in-depth analysis of the structural stability, essential protein motions, and per-residue energy decomposition involved in the interaction of inhibitors with the RBD. The results showed that the inhibitors have adapted the protein RBD in the binding interface, thereby forming stable complexes. These inhibitors display low binding free energy in the MM/PBSA calculations, substantiating their strong interaction with RBD. Moreover, the binding affinity of the best miniprotein inhibitor, H6Y-M7L-L17F mutant, to RBD was ∼45 980 times (ΔG = RT ln Ki) higher than that of the initial inhibitor LCB3. Following H6Y-M7L-L17F mutant, the inhibitors with strong binding activity are successively H6Y-L17F, L17F, H6Y, and F30Y mutants. Our research proves that the miniprotein inhibitors can maintain their secondary structure and have a highly stable blocking (binding) effect on SARS-CoV-2. This study proposes novel miniprotein mutant inhibitors with enhanced binding to spike protein and provides potential guidance for the rational design of new SARS-CoV-2 spike protein inhibitors.

Molecular Dynamics Simulation Investigation of the Binding and Interaction of the EphA6-Odin Protein Complex

Protein-protein interaction plays an important role in the development of almost all cells. Elucidating the dynamic binding and affinity of a protein-protein complex is essential for understanding the biological functions of proteins. EphA6 and Odin proteins are members of the Eph (erythropoietin-producing hepatocyte) receptor family and the Anks (ankyrin repeat and sterile α motif domain-containing) family, respectively. Odin significantly functions in regulating endocytosis, degradation, and stability of EphA receptors. In this work, the key residues of the interaction interface were determined through a hydrogen bond, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and dynamic correlation analysis of the conventional molecular dynamics (MD) simulations. The calculated standard binding free energy, -7.92 kcal/mol, between EphA6 and Odin is quite consistent with the experimental measurement value, -8.73 kcal/mol. By the combination of several MD simulation techniques, our investigation of the binding process reveals the detailed representative characteristics of the entire binding pathway at the molecular level. Based on the obtained potential of the mean force (PMF) curve, the analysis of the simulation trajectories shows that the residue Arg1013 in the receptor EphA6 is responsible for capturing Asp739 and Asp740 in the ligand Odin during the initial stage of binding. In the later stage of binding, the hydrogen bonds and salt bridges between a series of residues Lys973, Leu1007, Gly1009, His1010, and Arg1012 in the receptor and residues Leu735, Asn736, Asp739, Asp740, and Asp753 in the ligand mainly contribute to the stability of the protein complex. In addition, the specific change process of the receptor-ligand-binding mode is also clarified during the binding process. Our present simulation will promote a deep understanding of the protein-protein interaction, and the identified key interresidue interaction will be theoretical guidance for the design of protein drugs.

Integration of machine learning with computational structural biology of plants

Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.

In Silico Investigation on KAR Signaling Reveals the Significant Dynamic Change of Its Receptor's Structure

Karrikins (KARs) have been identified as a class of smoke-derived plant growth regulators widely functioning among angiosperms. However, little is known about the mechanism by which these molecules trigger the relevant signal transduction. In this research, conventional molecular dynamics simulations were used to investigate the dynamical behavior of the apo- and holo-forms of the KAR receptor KAI2. The results show that the dynamic binding conformation of KAR₁ in the active site is not completely consistent with that in the static crystal and is largely affected by the residue segment of the receptor, Tyr150-Asn180. The binding of the ligand with KAI2 changes the distribution of the electrostatic potential near the active site and drives the conformational transition of the Tyr150-Asn180 segment with strong internal positive correlation. A "dual induction" signaling mechanism is proposed in view of the present calculations. Our work paves way for in-depth understanding of the KAR signal transduction mechanism and sheds light on further experimental and theoretical exploration.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Molecular dynamics investigation on the interaction of human angiotensin-converting enzyme with tetrapeptide inhibitors

Angiotensin-converting enzyme (ACE) is a well-known zinc metalloenzyme whose physiological functions are vital to blood pressure regulation and management of hypertension. The development of more efficient peptide inhibitors is of great significance for the prevention and treatment of hypertension. In this research, molecular dynamics (MD) simulations were implemented to study the specific binding mechanism and interaction between human ACE (hACE) and tetrapeptides, YIHP, YKHP, YLVR, and YRHP. The calculation of relative binding free energy on the one hand verified that YLVR, an experimentally identified inhibitor, has a stronger inhibitory effect and, on the other hand, indicated that YRHP is the "best" inhibitor with the strongest binding affinity. Inspection of atomic interactions discriminated the specific binding mode of each tetrapeptide inhibitor with hACE and explained the difference of their affinity. Moreover, in-depth analysis of the MD production trajectories, including clustering, principal component analysis, and dynamic network analysis, determined the dynamic correlation between tetrapeptides and hACE and obtained the communities' distribution of a protein-ligand complex. The present study provides essential insights into the binding mode and interaction mechanism of the hACE-peptide complex, which paves a path for designing effective anti-hypertensive peptides.