Incorporating protein flexibility in structure-based drug discovery: using HIV-1 protease as a test case.

The use of structure in drug design has become widespread, mainly thanks to recent advances in crystallography. Nevertheless, biological macromolecules are intrinsically flexible and it is increasingly evident that their function depends critically on both their structure and dynamics. In this review the authors discuss the implications of protein flexibility for drug design and review recent progress in incorporating protein flexibility into docking and structure-based drug design.

Developing methods to incorporate protein flexibility into structure-based drug design is an important challenge. Our approach uses multiple protein structures (MPS) to create a receptor-based pharmacophore model of the desired target. We have previously demonstrated the success of the method by applying it to human immunodeficiency virus-1 protease (HIV-1p). Our models, based on an apo structure, discriminated known HIV-1p inhibitors from druglike inactive compounds and also accurately identified bound conformations of known inhibitors. Here, we test the method by applying it to all three unbound crystal structures of HIV-1p. We have also improved our method with denser probe mapping of the binding site and refined our selection criteria for pharmacophore elements. Our improved protocol has led to the development of a consistent 8-site pharmacophore model for HIV-1p, which is independent of starting structure, and a robust MPS pharmacophore method that is more amenable to automation.

We explored the Protein Data Bank (PDB) to collect protein–ssDNA structures and create a multi‐conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence‐identity groups identified as bound–unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions and compared it to the supposedly binding‐induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software. This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA–protein interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.

Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein-ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein-ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.

HIV protease has served as a model protein for understanding protein structure, enzyme kinetics, structure-based drug design, and protein evolution. Inhibitors of HIV protease are also an essential part of effective HIV/AIDS treatment and have provided great societal benefits. The broad applications for HIV protease and its inhibitors make it a perfect framework for integrating foundational topics in biochemistry around a big picture scientific and societal issue. Herein, I describe a series of classroom exercises that integrate foundational topics in biochemistry around the structure, biology, and therapeutic inhibition of HIV protease. These exercises center on foundational topics in biochemistry including thermodynamics, acid/base properties, protein structure, ligand binding, and enzymatic catalysis. The exercises also incorporate regular student practice of scientific skills including analysis of primary literature, evaluation of scientific data, and presentation of technical scientific arguments. Through the exercises, students also gain experience accessing computational biochemical resources such as the protein data bank, Proteopedia, and protein visualization software. As these HIV centered exercises cover foundational topics common to all first semester biochemistry courses, these exercises should appeal to a broad audience of undergraduate students and should be readily integrated into a variety of teaching styles and classroom sizes.

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.

Louis Sullivan (1856 - 1924) revolutionized architecture by designing the first skyscraper and he became famous by proclaiming that “form follows function”. When x-ray crystallographers visualized the structures of proteins for the first time, the structural biology field embraced the view that “function follows form” as the 3D-architecture of proteins could unveil various aspects of their function. Despite the original “1 gene - 1 protein structure - 1 function” relationship, nowadays a far more complicated picture emerges where the flexibility and dynamics of a protein can play a central role in a multitude of functions. The ultimate form(s) that a protein adopt when interacting with (a) partner molecule(s) are the most biologically relevant and in this context Sullivan’s quote is still appropriate: the conformation that the protein adopts follows from the function of that protein. Despite the fact that many well-characterized proteins have a well-folded structure, there is a growing interest in the conformational flexibility within proteins. This flexibility is also a balanced phenomenon: excess of flexibility can be detrimental for protein behaviour, as well as the lack thereof. Notwithstanding its importance, studying intrinsically disordered protein regions or conformational rearrangements can be a very challenging. Therefore, flexibility can be perceived as a friend or a foe, depending on the context. This e-book showcases the impact of the study of protein flexibility on the structural biology field and presents protein flexibility in the context of disease as well as its benign aspects. As detailed knowledge of the structural aspects of polypeptides remains essential to comprehend protein function, one of the future challenges for structural biology also lies with large macromolecular protein complexes. Also there the dynamics and flexibility are essential for proper functioning and molecular movement, which is an important aspect of living matter. This challenge stimulated the development of advanced techniques to study protein flexibility and the use of those techniques to address fundamental biological and biomedical problems. Those innovations should help us to unravel the intimate link between protein function and flexibility and explore new horizons.

In this chapter we describe recent developments in the practice of structure-based drug design (SBDD) and the computational methods that can be applied across a diversity of target classes. Recent technological advances are discussed that have enabled breakthroughs in membrane crystallography in particular in the field of G protein-coupled receptors. This review describes recent examples of SBDD applied to four specific target classes, kinases, proteases, protein–protein interactions and GPCRs. Overall, this is a large and rapidly moving field that is significantly impacting on drug development pipelines and the future success of the pharmaceutical industry. It is likely that a significant proportion of drugs in the future will be derived from structure- and fragment-based methods that can be used to complement other strategies in drug discovery.

Computational approaches are widely used to help discover and develop new drugs, in particular, to understand how these molecules interact with their biomolecular targets. There are well-optimised and validated force field parameters available to describe interactions of common biomolecules, such as proteins, lipids and nucleotides. However, these force fields are not designed to represent heteromolecular ligands such as substrates, inhibitors, co-factors and potential drug molecules. Errors in parameters may result in incorrect predictions of ligand structure, orientation and conformation, which in turn can lead to the failure of computational drug design efforts. With over 25% of all the structures in the Protein Data Bank (PDB) containing ligands and over a million ligand molecules of potential interest in drug design in other databases, ligand validation and parameterisation represents a significant scientific challenge. While several automated parameterisation protocols have been proposed to generate the parameters, none of the current procedures are properly validated. As a response to the high demand for interaction parameters for ligands compatible with the GROMOS force field, a web accessible Automated force field Topology Builder (ATB; http://atb.uq.edu.au/) and Repository was developed. The ATB and Repository is intended to facilitate the development of molecular force fields and generates parameters that can be used in X-ray refinement, structure-based drug design and study of biomolecule-ligand complexes. In this thesis, molecular dynamics (MD) simulations were used to calculate the thermodynamic properties for the validation and refinement of ATB force fields parameters. A fully automated validation protocol of the ATB force field parameters for small organic molecules based on thermodynamic and structural information was developed and incorporated into the ATB. A novel integration and convergence protocol which increases the efficiency of the TI method for free energy calculations was also proposed. The validation has shown good overall agreement between the experimental and computed values and indicated problematic functional groups with parameters to be refined. The generation of parameters for novel molecules that are compatible with a given biomolecular force field can be tedious, time-consuming and error-prone. Here, a novel method to refine parameters in classical force fields in an automated manner that can be extended to the parameterisation of all other atom types is also presented. Single-step perturbation protocols were developed for small halogenated molecules to establish alternative van der Waals parameters which describe the interactions of these molecules with high precision. Parameters were successfully refined against experimental hydration free energies, densities and heats of vaporisation. As a result, alternative van der Waals interactions parameters for chlorine and bromine were proposed. Properties calculated with these proposed parameters closely matched the experiment data. The quality of the ATB force field parameters in validating the X-ray structures and investigating binding of ligands to the protein endothiapepsin was also investigated. Analysis of the MD trajectories showed that the protein structure remained relatively stable after 10 ns of simulations. The results for the validation of the ligands were mixed. While in some cases the simulations reproduced the binding mode and the interactions between the protein and the ligand with high accuracy suggesting the ATB parameters performed well, in other cases the complexes were unstable. This was despite the fact that the electron density for all ligands was well defined and the parameters for the ligand were generated using the same procedure. Endothiapepsin contains a catalytic dyad consisting of two aspartates that are generally assumed to share a proton. We demonstrated that the protonation of both the ligand and the residues with the binding site were critical to the ability to reproduce the crystal complex. To conclude, the work presented in this thesis provided insight into the development and validation of the force field parameters for drug-like molecules and their applications in structure-based drug design.

Success in high-resolution protein-protein docking requires accurate modeling of side-chain conformations at the interface. Most current methods either leave side chains fixed in the conformations observed in the unbound protein structures or allow the side chains to sample a set of discrete rotamer conformations. Here we describe a rapid and efficient method for sampling off-rotamer side-chain conformations by torsion space minimization during protein-protein docking starting from discrete rotamer libraries supplemented with side-chain conformations taken from the unbound structures, and show that the new method improves side-chain modeling and increases the energetic discrimination between good and bad models. Analysis of the distribution of side-chain interaction energies within and between the two protein partners shows that the new method leads to more native-like distributions of interaction energies and that the neglect of side-chain entropy produces a small but measurable increase in the number of residues whose interaction energy cannot compensate for the entropic cost of side-chain freezing at the interface. The power of the method is highlighted by a number of predictions of unprecedented accuracy in the recent CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein-protein docking methods.

Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

Chapter 5 - Homology modeling: Developing 3D structures of target proteins missing in databases

Protein adsorption at oil-water interfaces interfacial structure and function of proteins in emulsions

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.