Molecular mechanism related to the binding of fluorophores to Mango-II revealed by multiple-replica molecular dynamics simulations

Accelerated molecular dynamics study of the interaction mechanism between small molecule inhibitors and phosphoglycerate mutase 1

In 2020, cancer-related deaths reached 9.96 million globally, of which China accounted for 3 million, ranking first in the world. Phosphoglycerate mutase 1 (PGAM1) is a key metabolic enzyme in glycolysis, catalysing the conversion of 3-phosphoglycerate to 2-phosphoglycerate. Based on the excellent anticancer activity of PGMI-004A and HKB99, new small molecules with an anthraquinone core were synthesised to inhibit tumour growth. Developing small molecules with an anthraquinone core targeting PGAM1 may be an effective strategy for treating cancer. In this study, accelerated molecular dynamics (aMD) simulation, dynamic cross-correlation map (DCCM) calculation, principal component analysis (PCA) and free energy landscape (FEL) analysis were used to analyse conformational changes of PGAM1 caused by binding of inhibitors 8KX, 9HU and HKB. DCCM calculations and PCA showed that inhibitor binding significantly affected the kinetic behaviour of PGAM1 and conformational rearrangement of PGAM1. The binding ability and mechanism of 8KX, 9HU and HKB to PGAM1 were studied using the molecular mechanics generalised Born surface area (MM-GBSA) method. The results showed that compared with 8KX, the binding ability of 9HU and HKB to PGAM1 was enhanced by sulphonamide reversal and aminocarboxyl trifluoromethyl substitution. There were several hydrophobic interactions between inhibitors and PGAM1, providing significant contributions for inhibitor binding. Calculation of residue-based free energy decomposition revealed that F22, R90, Y92, L95, V112, W115, R116, V121, P123, P124, R191 and M206 were key residues of the PGAM1-inhibitor interaction and could be used as effective targets for designing drugs that inhibit the activity of PGAM1.

Insights into the selectivity of a brain-penetrant CDK4/6 vs CDK1/2 inhibitor for glioblastoma used in multiple replica molecular dynamics simulations

Cyclin dependent kinases (CDKs) play an important role in cell cycle regulation and their dysfunction is associated with many cancers. That is why CDKs have been attractive targets for the treatment of cancer. Glioblastoma is a cancer caused by the aberrant expression of CDK4/6, so exploring the mechanism of the selection of CDK4/6 toward inhibitors relative to the other family members CDK1/2 is essential. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), molecular mechanics Poisson-Boltzmann/Generalized Born surface area (MM-PB/GBSA) and other methods were integrated to decipher the selectively binding mechanism of the inhibitor N1J to CDK4/6 and CDK1/2. Molecular electrostatic potential (MESP) analysis provides an explanation for the N1J selectivity. Residue-based free energy decomposition reveals that most of the hot residues are located at the same location of CDKs proteins, but the different types of residues in different proteins cause changes in binding energy, which is considered as a potential developmental direction to improve the selectivity of inhibitors to CDK4/6. These results provide insights into the source of inhibitor and CDK4/6 selectivity for the future development of more selective inhibitors.Communicated by Ramaswamy H. Sarma.

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

S-Adenosyl-l-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (S_MK) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand-nucleotide interactions not only uncovers that the π-π, cation-π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.

Binding mechanism of inhibitors to SARS-CoV-2 main protease deciphered by multiple replica molecular dynamics simulations

The outbreak caused by SARS-CoV-2 has received extensive worldwide attention. As the main protease (M^pro) in SARS-CoV-2 has no human homologues, it is feasible to reduce the possibility of targeting the host protein by accidental drugs. Thus, M^pro has been an attractive target of efficient drug design for anti-SARS-CoV-2 treatment. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), and the molecular mechanics-generalized Born surface area (MM-GBSA) method were integrated together to decipher the binding mechanism of four inhibitors masitinib, O6K, FJC and GQU to M^pro. The results indicate that the binding of four inhibitors clearly affects the structural flexibility and internal dynamics of M^pro along with dihedral angle changes of key residues. The analysis of FELs unveils that the stability in the relative orientation and geometric position of inhibitors to M^pro is favorable for inhibitor binding. Residue-based free energy decomposition reveals that the inhibitor-M^pro interaction networks involving hydrogen bonding interactions and hydrophobic interactions provide significant information for the design of potent inhibitors against M^pro. The hot spot residues including H41, M49, F140, N142, G143, C145, H163, H164, M165, E166 and Q189 identified by computational alanine scanning are considered as reliable targets of clinically available inhibitors inhibiting the activities of M^pro.

Deciphering binding mechanism of inhibitors to SARS-COV-2 main protease through multiple replica accelerated molecular dynamics simulations and free energy landscapes

The pneumonia outbreak caused by the SARS-CoV-2 virus poses a serious threat to human health and the world economy. Development of safe and highly effective antiviral drugs is of great...

Binding mechanism of inhibitors to p38α MAP kinase deciphered by using multiple replica Gaussian accelerated molecular dynamics and calculations of binding free energies

The p38α MAP Kinase has been an important target of drug design for treatment of inflammatory diseases and cancers. This work applies multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics generalized Born surface area (MM-GBSA) method to probe the binding mechanism of inhibitors L51, R24 and 1AU to p38α. Dynamics analyses show that inhibitor bindings exert significant effect on conformational changes of the active helix α2 and the conserved DFG loop. The rank of binding free energies calculated with MM-GBSA not only agrees well with that determined by the experimental IC50 values but also suggests that mutual compensation between the enthalpy and entropy changes can improve binding of inhibitors to p38α. The analyses of free energy landscapes indicate that the L51, R24 and 1AU bound p38α display a DFG-out conformation. The residue-based free energy decomposition method is used to evaluate contributions of separate residues to the inhibitor-p38α binding and the results imply that residues V30, V38, L74, L75, I84, T106, H107, L108, M109, L167, F169 and D168 can be utilized as efficient targets of potent inhibitors toward p38α.