Monitoring Protein-Ligand Interactions in Human Cells by Real-Time Quantitative In-Cell NMR using a High Cell Density Bioreactor

In-cell NMR is a unique approach to observe the structural and dynamic properties of biological macromolecules at atomic resolution directly in living cells. Protein folding, chemical modifications, and conformational changes induced by ligand binding can be observed. Therefore, this method has great potential in the context of drug development. However, the short lifetime of human cells confined in the NMR spectrometer limits the application range of in-cell NMR. To overcome this issue, NMR bioreactors are employed that can greatly improve the cell sample stability over time and, importantly, enable the real-time recording of in-cell NMR spectra. In this way, the evolution of processes such as ligand penetration and binding to the intracellular protein target can be monitored in real time. Bioreactors are often limited by low cell viability at high cell numbers, which results in a trade-off between the overall sensitivity of the experiment and cell viability. We recently reported an NMR bioreactor that maintains a high number of human cells metabolically active for extended periods of time, up to 72 h. This setup was applied to monitor protein-ligand interactions and protein chemical modification. We also introduced a workflow for quantitative analysis of the real-time NMR data, based on multivariate curve resolution. The method provides concentration profiles of the chemical species present in the cells as a function of time, which can be further analyzed to obtain relevant kinetic parameters. Here we provide a detailed description of the NMR bioreactor setup and its application to monitoring protein-ligand interactions in human cells

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation

Visual analysis of protein-ligand interactions

The analysis of protein-ligand interactions is complex because of the many factors at play. Most current methods for visual analysis provide this information in the form of simple 2D plots, which, besides being quite space hungry, often encode a low number of different properties. In this paper we present a system for compact 2D visualization of molecular simulations. It purposely omits most spatial information and presents physical information associated to single molecular components and their pairwise interactions through a set of 2D InfoVis tools with coordinated views, suitable interaction, and focus+context techniques to analyze large amounts of data. The system provides a wide range of motifs for elements such as protein secondary structures or hydrogen bond networks, and a set of tools for their interactive inspection, both for a single simulation and for comparing two different simulations. As a result, the analysis of protein-ligand interactions of Molecular Simulation trajectories is greatly facilitated.Peer ReviewedPostprint (author's final draft

MoMA-LigPath: A web server to simulate protein-ligand unbinding

Protein-ligand interactions taking place far away from the active site, during ligand binding or release, may determine molecular specificity and activity. However, obtaining information about these interactions with experimental or computational methods remains difficult. The computational tool presented in this paper, MoMA-LigPath, is based on a mechanistic representation of the molecular system, considering partial flexibility, and on the application of a robotics-inspired algorithm to explore the conformational space. Such a purely geometric approach, together with the efficiency of the exploration algorithm, enables the simulation of ligand unbinding within very short computing time. Ligand unbinding pathways generated by MoMA-LigPath are a first approximation that can provide very useful information about protein-ligand interactions. When needed, this approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods. MoMA-LigPath is available at http://moma.laas.fr. The web server is free and open to all users, with no login requirement

Exploitation of proteomics strategies in protein structure-function studies

Mass spectrometry plays a central role in structural proteomics, particularly in highly intensive structural genomics projects. This review paper reports some examples taken from recent work from the authors' laboratory and is aimed at showing that modem proteomics strategies are instrumental in the integration of structural genomic projects in fields such as: (i) protein-protein interactions, (ii) protein-DNA interactions, (iii) protein-ligand interactions, and (iv) protein-folding intermediates

Screening Protein Ligand Interactions Using Microelectrode Arrays

G proteins comprising of α subunit and βγ dimer are signaling proteins that play essential roles in various pathological conditions. Direct modulation of these G proteins (specifically Gα subunit) using small chemical probes to elucidate their acute function is of great value. YM-254890 is a small molecule which is the first selective inhibitor of a class of G proteins, Gαq. However, despite its biological importance, this molecule is not available to researchers. In addition, the complex core structure of this cyclic depsipeptide has thwarted efforts to obtain a series of analogs by total synthesis. Moeller lab sought to overcome this obstacle by synthesizing simplified YM analogs that retain the ability to specifically inhibit Gαq. Thereby, this effort requires not only the synthesis of the YM analogs, but also the availability of both the purified G proteins and a rapid, cost effective method for screening newly synthesized analogs in real-time for their potential activity toward G proteins. This dissertation focuses on the 1) isolation and characterization of G proteins necessary to test the potency of simplified analogs and 2) development of a rapid screening method by utilizing the power of microelectrode arrays. In Chapter 1 of this dissertation we discuss the potential utility of directly targeting G proteins and why it is essential to develop G protein modulators. In Chapter 2, we provide details on how three different G proteins (Gαq (wild type and mutant), Gαi1 and Gαo) were isolated. While expression of recombinant proteins from insect cells is widely used, we applied the Titerless Infected-cells Preservation and Scale up method to express Gαq. A number of approaches were explored to optimize the biochemical assay that exhibits the activity of Gαq. Eventually a receptor-assisted nucleotide exchange assay was developed that could test the activity of purified Gαq. In Chapter 3, the activity of other G proteins was examined by a fluorescent nucleotide exchange assay. In addition, we introduce the first simplified analog of YM, WU-07047 and its potency towards Gαq and other G proteins was analyzed. Even though the receptor-assisted nucleotide exchange assay is a reliable way for testing the simplified analogs, it requires radio-labeled ligand and a number of accessory proteins. Hence, efforts were moved towards development of a rapid screening method utilizing the power of microelectrode arrays. The idea was to monitor binding interactions between immobilized small molecules and purified G proteins via electrochemical methods. Chapter 4 investigates an approach to modify the array surface via the use of PEG-polymer as a means to reduce non-specific binding. In addition to the reduction of non-specific binding, the ability to incorporate PEG onto the array surface provides an opportunity to utilize PEG-polymers as a linker. These linkers move the immobilized molecule away from the array surface. In Chapter 5, we tested the compatibility of G proteins to the electroanalytical methods applied on microelectrode arrays. Moreover, we study a known binding interaction between a G protein and a short peptide on the arrays. Based on preliminary results, we can see specific binding interaction between them over non-specific background binding

Examination of Molecular Recognition in Protein-Ligand Interactions

This dissertation is a compilation of two main projects that were investigated during my thesis research. The first project was a prospective study which identified and characterized drug-like inhibitors of a prototype of bacterial two-component signal transduction response regulator using computational and experimental methods. The second project was the development and validation of a scoring function, PHOENIX, derived using high-resolution structures and calorimetry measurements to predict binding affinities of protein-ligand interactions. Collectively, my thesis research aimed to better understand the underlying driving forces and principles which govern molecular recognition and molecular design. A prospective study coupled computational predictions with experimental validation resulted in the discovery of first-in-class inhibitors targeting a signal transduction module important for bacterial virulence. Development and validation of the PHOENIX scoring function for binding affinity prediction derived using high-resolution structures and calorimetry measurements should guide future molecular recognition studies and endeavors in computer-aided molecular design. To request for an electronic copy of this dissertation, please email the author: yattang at gmail dot com)