A molecular dynamics study of the complete binding process of meropenem to New Delhi metallo-β-lactamase 1

Temperature Accelerated Sliced Sampling to Probe Ligand Dissociation from Protein

Modeling ligand unbinding in proteins to estimate the free energy of binding and probing the mechanism presents several challenges. They primarily pertain to the entropic bottlenecks resulting from protein and solvent conformations. While exploring the unbinding processes using enhanced sampling techniques, very long simulations are required to sample all of the conformational states as the system gets trapped in local free energy minima along transverse coordinates. Here, we demonstrate that temperature accelerated sliced sampling (TASS) is an ideal approach to overcome some of the difficulties faced by conventional sampling methods in studying ligand unbinding. Using TASS, we study the unbinding of avibactam inhibitor molecules from the Class C β-lactamase (CBL) active site. Extracting CBL-avibactam unbinding free energetics, unbinding pathways, and identifying critical interactions from the TASS simulations are demonstrated.

A modified bonded model approach for molecular dynamics simulations of New Delhi Metallo-β-lactamase

Modelling metalloproteins using the classical force fields is challenging. Several methods have been devised to model metalloproteins in force fields. Of these methods, the bonded model, combined with Restrained Electrostatic Potential (RESP) charge fitting, proved its superiority. The latter method was facilitated by the development of the python-based Metal Centre Parameter Builder (MCPB.py) AmberTool. However, the standard bonded model method offered by the MCPB.py tool may not be appropriate for validating and refining the binding modes predicted by docking when crystal structures are lacking. That is because the representation of coordination interactions between any bound ligand and metal ions by covalent bonds can hinder the flexibility of the ligand. Therefore, a new modification to the standard bonded model approach is proposed here. Molecular dynamics (MD) simulations based on the new modified bonded model (MBM) approach avoid the bias caused by coordination bonds and, unlike hybrid QM/MM MD, allow for sufficient sampling of the binding mode given the currently available computational power. The MBM MD approach reproduced the studied crystal structure conformations of New Delhi Metallo-β-lactamase 1 (NDM-1). Furthermore, the MBM approach described the binding interactions of intact β-lactams with NDM-1 reasonably, and predicted a non-productive binding mode for the poor NDM-1 substrate aztreonam whilst predicting productive binding modes for known good substrates. This study presents a useful MD method for metallo-β-lactamases and provides better understanding of β-lactam substrates recognition by NDM-1. The proposed MBM approach might also be useful in the investigation of other metal-containing protein targets.

HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study

Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.

MEPSAnd: minimum energy path surface analysis over n-dimensional surfaces

SUMMARY

n-dimensional energy surfaces are becoming computationally accessible, yet interpreting their information is not straightforward. We present minimum energy path surface analysis over n-dimensional surfaces (MEPSAnd), an open source GUI-based program that natively calculates minimum energy paths across energy surfaces of any number of dimensions. Among other features, MEPSAnd can compute the path through lowest barriers and automatically provide a set of alternative paths. MEPSAnd offers distinct plotting solutions as well as direct python scripting.

AVAILABILITY AND IMPLEMENTATION

MEPSAnd is freely available (under GPLv3 license) at: http://bioweb.cbm.uam.es/software/MEPSAnd/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Can multiscale simulations unravel the function of metallo-enzymes to improve knowledge-based drug discovery?

Metallo-enzymes are a large class of biomolecules promoting specialized chemical reactions. Quantum-classical quantum mechanics/molecular mechanics molecular dynamics, describing the metal site at quantum mechanics level, while accounting for the rest of system at molecular mechanics level, has an accessible time-scale limited by its computational cost. Hence, it must be integrated with classical molecular dynamics and enhanced sampling simulations to disentangle the functions of metallo-enzymes. In this review, we provide an overview of these computational methods and their capabilities. In particular, we will focus on some systems such as CYP19A1 a Fe-dependent enzyme involved in estrogen biosynthesis, and on Mg2+-dependent DNA/RNA processing enzymes/ribozymes and the spliceosome, a protein-directed ribozyme. This information may guide the discovery of drug-like molecules and genetic manipulation tools.