Role of protein flexibility in the discovery of new drugs

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

The Picornavirus Precursor 3CD Has Different Conformational Dynamics Compared to 3Cpro and 3Dpol in Functionally Relevant Regions

Viruses have evolved numerous strategies to maximize the use of their limited genetic material, including proteolytic cleavage of polyproteins to yield products with different functions. The poliovirus polyprotein 3CD is involved in important protein-protein, protein-RNA and protein-lipid interactions in viral replication and infection. It is a precursor to the 3C protease and 3D RNA-dependent RNA polymerase, but has different protease specificity, is not an active polymerase, and participates in other interactions differently than its processed products. These functional differences are poorly explained by the known X-ray crystal structures. It has been proposed that functional differences might be due to differences in conformational dynamics between 3C, 3D and 3CD. To address this possibility, we conducted nuclear magnetic resonance spectroscopy experiments, including multiple quantum relaxation dispersion, chemical exchange saturation transfer and methyl spin-spin relaxation, to probe conformational dynamics across multiple timescales. Indeed, these studies identified differences in conformational dynamics in functionally important regions, including enzyme active sites, and RNA and lipid binding sites. Expansion of the conformational ensemble available to 3CD may allow it to perform additional functions not observed in 3C and 3D alone despite having nearly identical lowest-energy structures.

Salt bridge impact on global rigidity and thermostability in thermophilic citrate synthase

It has been suggested that structural rigidity is connected to thermostability, e.g. in enzymes from thermophilic microorganisms. We examine the importance of correctly handling salt bridges, and interactions which we term 'strong polars', when constructing the constraint network for global rigidity analysis in these systems. Through a comparison of rigidity in citrate synthases, we clarify the relationship between rigidity and thermostability. In particular, with our corrected handling of strong polar interactions, the difference in rigidity between mesophilic and thermophilic structures is detected more clearly than in previous studies. The increase in rigidity did not detract from the functional flexibility of the active site in all systems once their respective temperature range had been reached. We then examine the distribution of salt bridges in thermophiles that were previously unaccounted for in flexibility studies. We show that in hyperthermophiles these have stabilising roles in the active site; occuring in close proximity to key residues involved in catalysis and binding of the protein.

Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

Perspectives in biological physics: the nDDB project for a neutron Dynamics Data Bank for biological macromolecules

Neutron spectroscopy provides experimental data on time-dependent trajectories, which can be directly compared to molecular dynamics simulations. Its importance in helping us to understand biological macromolecules at a molecular level is demonstrated by the results of a literature survey over the last two to three decades. Around 300 articles in refereed journals relate to neutron scattering studies of biological macromolecular dynamics, and the results of the survey are presented here. The scope of the publications ranges from the general physics of protein and solvent dynamics, to the biologically relevant dynamics-function relationships in live cells. As a result of the survey we are currently setting up a neutron Dynamics Data Bank (nDDB) with the aim to make the neutron data on biological systems widely available. This will benefit, in particular, the MD simulation community to validate and improve their force fields. The aim of the database is to expose and give easy access to a body of experimental data to the scientific community. The database will be populated with as much of the existing data as possible. In the future it will give value, as part of a bigger whole, to high throughput data, as well as more detailed studies. A range and volume of experimental data will be of interest in determining how quantitatively MD simulations can reproduce trends across a range of systems and to what extent such trends may depend on sample preparation and data reduction and analysis methods. In this context, we strongly encourage researchers in the field to deposit their data in the nDDB.

Protein flexibility in docking and surface mapping

Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein-ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80-95%. This review examines the current toolbox for flexible protein-ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.

The Fundamental Role of Flexibility on the Strength of Molecular Binding

Non-covalent molecular association underlies a diverse set of biologically and technologically relevant phenomena, including the action of drugs on their biomolecular targets and self- and supra-molecular assembly processes. Computer models employed to model binding frequently use interaction potentials with atomistic detail while neglecting the thermal molecular motions of the binding species. However, errors introduced by this simplification and, more broadly, the thermodynamic consequences of molecular flexibility on binding, are little understood. Here, we isolate the fundamental relationship of molecular flexibility to binding thermodynamics via simulations of simplified molecules with a wide range of flexibilities but the same interaction potential. Disregarding molecular motion is found to generate large errors in binding entropy, enthalpy and free energy, even for molecules that are nearly rigid. Indeed, small decreases in rigidity markedly reduce affinity for highly rigid molecules. Remarkably, precisely the opposite occurs for more flexible molecules, for which increasing flexibility leads to stronger binding affinity. We also find that differences in flexibility suffice to generate binding specificity: for example, a planar surface selectively binds rigid over flexible molecules. Intriguingly, varying molecular flexibility while keeping interaction potentials constant leads to near-linear enthalpy-entropy compensation over a wide range of flexibilities, with the unexpected twist that increasing flexibility produces opposite changes in entropy and enthalpy for molecules in the flexible versus the rigid regime. Molecular flexibility is thus a crucial determinant of binding affinity and specificity and variations in flexibility can lead to strong yet non-intuitive consequences.

Statistical mechanics and molecular dynamics in evaluating thermodynamic properties of biomolecular recognition

Molecular recognition plays a central role in biochemical processes. Although well studied, understanding the mechanisms of recognition is inherently difficult due to the range of potential interactions, the molecular rearrangement associated with binding, and the time and length scales involved. Computational methods have the potential for not only complementing experiments that have been performed, but also in guiding future ones through their predictive abilities. In this review, we discuss how molecular dynamics (MD) simulations may be used in advancing our understanding of the thermodynamics that drive biomolecular recognition. We begin with a brief review of the statistical mechanics that form a basis for these methods. This is followed by a description of some of the most commonly used methods: thermodynamic pathways employing alchemical transformations and potential of mean force calculations, along with end-point calculations for free energy differences, and harmonic and quasi-harmonic analysis for entropic calculations. Finally, a few of the fundamental findings that have resulted from these methods are discussed, such as the role of configurational entropy and solvent in intermolecular interactions, along with selected results of the model system T4 lysozyme to illustrate potential and current limitations of these methods.