Combining structural and coevolution information to unveil allosteric sites

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks

The Beta-lactamase protein family is vital in countering Beta-lactam antibiotics, a widely used antimicrobial. To enhance our understanding of this family, we adopted a novel approach employing a multiplex network representation of its multiple sequence alignment. Each network layer, derived from the physiochemical properties of amino acids, unveils distinct insights into the intricate interactions among nodes, thereby enabling the identification of key motifs. Nodes with identical property signs tend to aggregate, providing evidence of the presence of consequential functional and evolutionary constraints shaping the Beta-lactamase family. We further investigate the distribution of evolutionary links across various layers. We observe that polarity manifests the highest number of unique links at lower thresholds, followed by hydrophobicity and polarizability, wherein hydrophobicity exerts dominance at higher thresholds. Further, the combinations of polarizability and volume, exhibit multiple simultaneous connections at all thresholds. The combination of hydrophobicity, polarizability, and volume uncovers shared links exclusive to these layers, implying substantial evolutionary impacts that may have functional or structural implications. By assessing the multi-degree of nodes, we unveil the hierarchical influence of properties at each position, identifying crucial properties responsible for the protein's functionality and providing valuable insights into potential targets for modulating enzymatic activity.