Design Of Protein-protein Interaction Inhibitors Research Articles

Structural basis for the mutation-induced dysfunction of the human IL-15/IL-15α receptor complex.

In silico strategies offer a reliable, fast, and inexpensive, way compared to the clumsy in vitro approaches to boost understanding of the effect of amino acid substitution on the structure and consequently the associated function of proteins. In the present work, we report an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the Interleukin-15 (IL-15) protein and its receptor Interleukin-15α (IL-15α), consequently, providing qualitative and quantitative details of the key molecular determinants in ligand/receptor recognition. Molecular dynamics simulations were used to investigate the dynamic behavior of the specific binding between IL-15 and IL-15α followed by estimation of the free energies via molecular mechanics/generalized Born surface area (MM/GBSA). In particular, residues Y26, E46, E53, and E89 of the IL-15 protein receptor-binding domain are identified as main hot spots, shaping and governing the stability of the assembly. These results can be used for the development of neutralizing antibodies and the effective structure-based design of protein-protein interaction inhibitors against the so-called orphan disease, vitiligo.

Computational Evaluation of Peptide-Protein Binding Affinities: Application of Potential of Mean Force Calculations to SH2 Domains.

Many biological functions are mediated by protein-protein interactions (PPIs), often involving specific structural modules, such as SH2 domains. Inhibition of PPIs is a pharmaceutical strategy of growing importance. However, a major challenge in the design of PPI inhibitors is the large interface involved in these interactions, which, in many cases, makes inhibition by small organic molecules ineffective. Peptides, which cover a wide range of dimensions and can be opportunely designed to mimic protein sequences at PPI interfaces, represent a valuable alternative to small molecules. Computational techniques able to predict the binding affinity of peptides for the target domain or protein represent a crucial stage in the workflow for the design of peptide-based drugs. This chapter describes a protocol to obtain the potential of mean force (PMF) for peptide-SH2 domain binding, starting from umbrella sampling (US) molecular dynamics (MD) simulations. The PMF profiles can be effectively used to predict the relative standard binding free energies of different peptide sequences.

Synthesis and conformation of backbone N-aminated peptides.

The chemical modification of peptides is a promising approach for the design of protein-protein interaction inhibitors and peptide-based drug candidates. Among several peptidomimetic strategies, substitution of the amide backbone maintains side-chain functionality that may be important for engagement of biological targets. Backbone amide substitution has been largely limited to N-alkylation, which can promote cis amide geometry and disrupt important H-bonding interactions. In contrast, N-amination of peptides induces distinct backbone geometries and maintains H-bond donor capacity. In this chapter we discuss the conformational characteristics of designed N-amino peptides and present a detailed protocol for their synthesis on solid support. The described methods allow for backbone N-amino scanning of biologically active parent sequences.

Water envelope has a critical impact on the design of protein-protein interaction inhibitors.

We show that a water envelope network plays a critical role in protein-protein interactions (PPI). The potency of a PPI inhibitor is modulated by orders of magnitude on manipulation of the solvent envelope alone. The structure-activity relationship of PEX14 inhibitors was analyzed as an example using in silico and X-ray data.

CHIPMUNK: A Virtual Synthesizable Small-Molecule Library for Medicinal Chemistry, Exploitable for Protein-Protein Interaction Modulators.

A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein-protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors.

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

In vivo modulation of hypoxia-inducible signaling by topographical helix mimetics.

Development of small-molecule inhibitors of protein-protein interactions is a fundamental challenge at the interface of chemistry and cancer biology. Successful methods for design of protein-protein interaction inhibitors include computational and experimental high-throughput and fragment-based screening strategies to locate small-molecule fragments that bind protein surfaces. An alternative rational design approach seeks to mimic the orientation and disposition of critical binding residues at protein interfaces. We describe the design, synthesis, biochemical, and in vivo evaluation of a small-molecule scaffold that captures the topography of α-helices. We designed mimics of a key α-helical domain at the interface of hypoxia-inducible factor 1α and p300 to develop inhibitors of hypoxia-inducible signaling. The hypoxia-inducible factor/p300 interaction regulates the transcription of key genes, whose expression contributes to angiogenesis, metastasis, and altered energy metabolism in cancer. The designed compounds target the desired protein with high affinity and in a predetermined manner, with the optimal ligand providing effective reduction of tumor burden in experimental animal models.

Computer-aided design and discovery of protein–protein interaction inhibitors as agents for anti-HIV therapy

Protein–protein interactions (PPI) are involved in most of the essential processes that occur in organisms. In recent years, PPI have become the object of increasing attention in drug discovery, particularly for anti-HIV drugs. Although the use of combinations of existing drugs, termed highly active antiretroviral therapy (HAART), has revolutionized the treatment of HIV/AIDS, problems with these agents, such as the rapid emergence of drug-resistant HIV-1 mutants and serious adverse effects, have highlighted the need for further discovery of new drugs and new targets. Numerous investigations have shown that PPI play a key role in the virus’s life cycle and that blocking or modulating them has a significant therapeutic potential. Here we summarize the recent progress in computer-aided design of PPI inhibitors, mainly focusing on the selection of the drug targets (HIV enzymes and virus entry machinery) and the utilization of peptides and small molecules to prevent a variety of protein–protein interactions (viral–viral or viral–host) that play a vital role in the progression of HIV infection.

Electrostatic Similarities between Protein and Small Molecule Ligands Facilitate the Design of Protein-Protein Interaction Inhibitors

One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs). We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research) SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs.

More than a rigid framework: molecular design using secondary structure element information

The structure-based design of small molecule modulators of protein-ligand binding and protein-protein interaction is a key component in drug discovery. The underlying protein interactions can be regarded based on structural similarity of the involved secondary structure elements [1]. The most prominent example is the protein fold of a protein domain that is more conserved than the amino acid sequence and proteins with similar fold but dissimilar sequence and function can bind similar ligands. Similar ligand binding can also occur between different proteins that have a similar spatial arrangement of secondary structure elements around the ligand binding site (ligand-sensing cores) independent from the overall fold [2]. Analogous to this, similar protein-protein interfaces can occur within different protein folds showing different function, since the structural space of protein interfaces is degenerated and is represented by roughly 1000 distinct interfaces [3]. These similarities in otherwise unrelated proteins can be useful in the design of protein function modulators. The successful applications in molecular design described in literature using predicted polypharmacology in protein-ligand binding will be shown and the analogy in the design of protein-protein interaction inhibitors and the potential of polypharmacology prediction will be discussed.

1P044 Electrostatic similarities between protein and small molecules facilitate the rational design of protein-protein interaction inhibitors(01B. Protein:Structure & Function,Poster)

1P044 Electrostatic similarities between protein and small molecules facilitate the rational design of protein-protein interaction inhibitors(01B. Protein:Structure & Function,Poster)

Blocking protein-protein interactions: the identification of repetitive turn structures as basis for inhibitor building blocks

Mimetics of secondary structure elements are one promising approach in the design of protein-protein interaction inhibitors, since secondary structure elements are very important recognition motifs in protein-protein interfaces. In helices and turns, the protein backbone provides a scaffold to present the sidechains in the correct orientation for the three-dimensional interaction motif. For both, scaffolds are known that resemble these backbone conformations and can be decorated with sidechains in the right position for mimicking the interaction motif [1]. Benzodiazepines are one example for a successful mimetic of b-turn structures. However, identifying small chemical scaffolds that mimic turn structures is rather complicated. Turns are irregular structures with a wider variety of possible backbone conformations [2] and for each group of conformations a different scaffold is needed. Furthermore, turn structures are generally not included in analysis of protein-protein interfaces. Due to a lack of information in publicly available databases, regions of the protein chains that are outside helices and b-sheets are generally considered as non-regular structural elements. These non-regular structural elements in proteins are by now almost completely classified as turn structures and available via Secbase for data mining approaches [3]. The results of an exhaustive analysis of turn structures involved in protein-protein interfaces will be presented and the impact on the design of secondary structure element mimetics will be discussed. This is of particular interest since the secondary structure space of proteinprotein interfaces is limited and similar interfaces with respect to secondary structure elements exists within proteins showing different overall folds and function [4]. The identification of repetitive turn structures is therefore a valuable approach to predict polypharmacology or identify backbone conformations that could easily be replaced by mimetic building blocks [4]. The decoration of these building blocks with the needed functional sidechain is a good starting point for protein-protein interface inhibitors.

Computer applications for prediction of protein–protein interactions and rational drug design

In recent years, protein-protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein-protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein-protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein-protein interactions of therapeutic significance. While rational design of protein-protein interaction inhibitors is at its very early stage, the first results are promising.