Allosteric modulation of protein oligomerization: an emerging approach to drug design

Many disease-related proteins are in equilibrium between different oligomeric forms. The regulation of this equilibrium plays a central role in maintaining the activity of these proteins in vitro and in vivo. Modulation of the oligomerization equilibrium of proteins by molecules that bind preferentially to a specific oligomeric state is emerging as a potential therapeutic strategy that can be applied to many biological systems such as cancer and viral infections. The target proteins for such compounds are diverse in structure and sequence, and may require different approaches for shifting their oligomerization equilibrium. The discovery of such oligomerization-modulating compounds is thus achieved based on existing structural knowledge about the specific target proteins, as well as on their interactions with partner proteins or with ligands. In silico design and combinatorial tools such as peptide arrays and phage display are also used for discovering compounds that modulate protein oligomerization. The current review highlights some of the recent developments in the design of compounds aimed at modulating the oligomerization equilibrium of proteins, including the shiftides approach developed in our lab

Aptamers in oncology: a diagnostic perspective

Nucleic acid sequences can produce a wide variety of three-dimensional conformations. Some of these structural forms are able to interact with proteins and small molecules with high affinity and specificity. These sequences, comprising either double or single stranded oligonucleotides, are called 'aptamers' based on the Greek word aptus, which means 'to fit'. Using an efficient selection process, randomised oligonucleotide libraries can be rapidly screened for aptamers with the appropriate binding characteristics. This technology has spawned the development of a new class of oligonucleotide therapeutic products. However, while interest among pharmaceutical companies continues to grow with some candidates already in clinical trials and one in the market, there appears to be some reluctance to fully explore the diagnostic potential of this technology. This article will review aptamer developments in diagnostics, compare them with other oligonucleotide therapeutics and highlight both potentials and pitfalls of technological development in this area

Peptide and Protein Interaction Prediction and Intervention with Computational Methods

Proteins are the most fascinating multifaceted biomacromolecules in living systems and play various important roles such as structural, sensory, catalytic, and regulatory function. Protein and peptide interactions have emerged as an important and challenging topic inbiochemistry and medicinal chemistry. Computational methods as promising tools have been utilized to predict protein and peptide interactions in order to intervene in the biochemical processes and facilitate pharmaceutical peptide design and clarify the complications. This review will introduce the computational methods which are applicable in protein and peptide interaction prediction and summarizes the most successful examples of computational methods described in the literature.HIGHLIGHTS•Highlights the importance of peptides and proteins interactions.•Summarizes the computational methods which are applicable in peptide and protein interaction prediction.•Highlights the applications of computational methods in peptides and proteins interactions

Progress in the development and application of computational methods for probabilistic protein design

Proteins exhibit a wide range of physical and chemical properties, including highly selective molecular recognition and catalysis, and are also key components in biological metabolic, catabolic, and signaling pathways. Given that proteins are well-structured and can now be rapidly synthesized, they are excellent targets for engineering of both molecular structure and biological function. Computational analysis of the protein design problem allows scientists to explore sequence space and systematically discover novel protein molecules. Nonetheless, the complexity of proteins, the subtlety of the determinants of folding, and the exponentially large number of possible sequences impede the search for peptide sequences compatible with a desired structure and function. Directed search algorithms, which identify directly a small number of sequences, have achieved some success in identifying sequences with desired structures and functions. Alternatively, one can adopt a probabilistic approach. Instead of a finite number of sequences, such calculations result in a probabilistic description of the sequence ensemble. In particular, by casting the formalism in the language of statistical mechanics, the site-specific amino acid probabilities of sequences compatible with a target structure may be readily identified. The computational probabilities are well suited for both de novo protein design of particular sequences as well as combinatorial, library-based protein engineering. The computed site-specific amino acid profile may be converted to a nucleotide base distribution to allow assembly of a partially randomized gene library. The ability to synthesize readily such degenerate oligonucleotide sequences according to the prescribed distribution is key to constructing a biased peptide library genuinely reflective of the computational design. Herein we illustrate how a standard DNA synthesizer can be used with only a slight modification to the synthesis protocol to generate a pool of degenerate DNA sequences, which encodes a predetermined amino acid distribution with high fidelity

Computational methodologies applied to Protein-Protein Interactions for molecular insights in Medicinal Chemistry

In living systems, proteins usually team up into \u201cmolecular machinery\u201d implementing several protein-to-protein physical contacts \u2013 or protein-protein interactions (PPIs) \u2013 to exert biological effects at both cellular and systems levels. Deregulations of protein-protein contacts have been associated with a huge number of diseases in a wide range of medical areas, such as oncology, cancer immunotherapy, infectious diseases, neurological disorders, heart failure, inflammation and oxidative stress. PPIs are very complex and usually characterised by specific shape, size and complementarity. The protein interfaces are generally large, broad and shallow, and frequently protein-protein contacts are established between non-continuous epitopes, that conversely are dislocated across the protein interfaces. For this reason, in the past two decades, PPIs were thought to be \u201cundruggable\u201d targets by the scientific research community with scarce or no chance of success. However, in recent years the Medicinal Chemistry frontiers have been changing and PPIs have gained popularity amongst the research groups due to their key roles in such a huge number of diseases. Until recently, PPIs were determined by experimental evidence through techniques specifically developed to target a small group of interactions. However, these methods present several limitations in terms of high costs and labour- and time-wasting. Nowadays, a large number of computational methods have been successfully applied to evaluate, validate, and deeply analyse the experimentally determined protein interactomes. In this context, a high number of computational tools and techniques have been developed, such as methods designed to construct interaction databases, quantum mechanics and molecular mechanics (QM/MM) to study the electronic properties, simulate chemical reactions, and calculate spectra, and all-atom molecular dynamics simulations to simulate temporal and spatial scales of inter- and intramolecular interactions. These techniques have allowed to explore PPI networks and predict the related functional features. In this PhD work, an extensive use of computational techniques has been reported as valuable tool to explore protein-protein interfaces, identify their hot spot residues, select small molecules and design peptides with the aim of inhibiting six different studied PPIs. Indeed, in this thesis, a success story of in silico approaches to PPI study has been described, where MD simulations, docking and pharmacophore screenings led to the identification of a set of PPI modulators. Among these, two molecules, RIM430 and RIM442, registered good inhibitory activity with IC50 values even within the nanomolar range against the interaction between MUC1 and CIN85 proteins in cancer disease. Furthermore, computational alanine scanning, all-atom molecular dynamics simulations, docking and pharmacophore screening were exploited to (1) rationally predict three potential interaction models of NLRP3PYD-ASCPYD complex involved in inflammatory and autoimmune diseases; (2) identify a potentially druggable region on the surface of SARS-CoV-2 Spike protein interface and select putative inhibitors of the interaction between Spike protein and the host ACE2 receptor against COVID-19 (CoronaVIrus Disease 2019); (3) investigate intramolecular modifications as a consequence of a point mutation on C3b protein (R102G) associated with the age-related macular degeneration (AMD) disease; (4) design non-standard peptides to inhibit transcriptional events associated with HOX-PBX complex involved in cancer diseases; and (5) to optimise a patented peptide sequence by designing helix-shaped peptides embedded with the hydrogen bond surrogate approach to tackle cocaine abuse relapses associated with Ras-RasGRF1 interaction. Although all the herein exploited techniques are based on predictive calculations and need experimental evidence to confirm the findings, the results and molecular insights retrieved and collected show the potential of the computer-aided drug design applied to the Medicinal Chemistry, guaranteeing labour- and time-saving to the research groups. On the other hand, computing ability, improved algorithms and fast-growing data sets are rapidly fostering advances in multiscale molecular modelling, providing a powerful emerging paradigm for drug discovery. It means that more and more research efforts will be done to invest in novel and more precise computational techniques and fine-tune the currently employed methodologies

Peptide Ligands for Pro-survival Protein Bfl-1 from Computationally Guided Library Screening

Pro-survival members of the Bcl-2 protein family inhibit cell death by binding short helical BH3 motifs in pro-apoptotic proteins. Mammalian pro-survival proteins Bcl-x[subscript L], Bcl-2, Bcl-w, Mcl-1, and Bfl-1 bind with varying affinities and specificities to native BH3 motifs, engineered peptides, and small molecules. Biophysical studies have determined interaction patterns for these proteins, particularly for the most-studied family members Bcl-x[subscript L] and Mcl-1. Bfl-1 is a pro-survival protein implicated in preventing apoptosis in leukemia, lymphoma, and melanoma. Although Bfl-1 is a promising therapeutic target, relatively little is known about its binding preferences. We explored the binding of Bfl-1 to BH3-like peptides by screening a peptide library that was designed to sample a high degree of relevant sequence diversity. Screening using yeast-surface display led to several novel high-affinity Bfl-1 binders and to thousands of putative binders identified through deep sequencing. Further screening for specificity led to identification of a peptide that bound to Bfl-1 with K[subscript d] < 1 nM and very slow dissociation from Bfl-1 compared to other pro-survival Bcl-2 family members. A point mutation in this sequence gave a peptide with ~50 nM affinity for Bfl-1 that was selective for Bfl-1 in equilibrium binding assays. Analysis of engineered Bfl-1 binders deepens our understanding of how the binding profiles of pro-survival proteins differ and may guide the development of targeted Bfl-1 inhibitors.National Institute of General Medical Sciences (U.S.) (Award GM084181)National Institute of General Medical Sciences (U.S.) (Award P50-GM68762

Directed Evolution of Stabilized Peptides with Bacterial Display

Interactions between proteins govern cellular and the body’s states, including aberrant interactions found in diseases such as in cancers and infections. Small molecule drugs are not ideal in targeting these interactions as their size generally prevents efficient blocking of contacts over large surface areas. Antibodies and related biologics have seen clinical success in the past few decades and can block large surfaces but are typically limited to extracellular targets. Intermediate-size peptides have the potential to bridge this gap, with the ability to target large surface areas inside the cell. Peptide stapling, by chemically linking two or more amino acid residues, can confer affinity improvements, resistance to degradation, and better biological transport properties. As such, stapled peptides show promise as next-generation therapeutics. Unfortunately, existing methods to screen sequence and stapling locations suffer from numerous disadvantages including limited search space, lack of real-time monitoring of selections, and difficulty in incorporating the non-canonical amino acids used for amino acid stapling. In this dissertation, I describe my research on stapled peptide discovery with bacterial incorporation of non-canonical amino acids. To screen stapled peptides of the type desired, we incorporated azidohomoalanine (AHA) into surface displayed peptides, enabling an in situ ‘click’ chemistry reaction to bridge two turns of an alpha helical (i, i+7) amino acid library for directed evolution. Using the p53-MDM2 interaction as a model target, we developed peptides that block MDM2 degradation of the tumor suppressor protein p53, an interaction that is dysregulated in a sizeable fraction of cancers. We generated and displayed a stapled peptide library on the bacterial cell surface with fixed residues for stabilization and binding requirements, while randomizing the remaining amino acids. After multiple rounds of selection, clones were sequenced and characterized. The dissociation constants of the peptide-MDM2 interaction were measured on both the bacterial cell surface by flow cytometry and in solution by bio-layer interferometry. The highest affinity variant, named SPD-M6-V1 with sequence VCDFXCYWNDLXGY (dissociation constant = 1.8 nM; X = azidohomoalanine) was selected for structural characterization by NMR spectroscopy, revealing a bicyclic disulfide and double click-constrained peptide. Sequencing showed that peptides with two cysteines were highly enriched, further suggesting that the MDM2-binding conformation was enforced with a disulfide bond. In addition, SPD-M6-V1 was the most protease-resistant peptide from the library that we tested. Next, we stapled the displayed peptide library with chemically distinct linkers and screened each library separately. We performed deep sequencing to better understand the relationship between amino acid sequence and linker identity in contributing to high affinity MDM2 binding. We found that both linker-specific and linker-agnostic (i.e. MDM2-specific) mutations were enhanced. Finally, we developed a dual-channel, sequential labeling selection strategy to discriminate between high-display, low-affinity peptides and low-display, high-affinity peptides, two categories that would ordinarily overlap in a typical one-color screen in the absence of an independent display marker. In summary, this thesis develops the chemical tools to screen libraries of stabilized peptides on the bacterial cell surface and applies these techniques to select stabilized alpha helices that disrupt the p53-MDM2 interaction.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163094/1/tejasn_1.pd

Expanding the Number of ‘Druggable’ Targets: Non‐Enzymes and Protein–Protein Interactions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95062/1/cbdd.12066.pd

Identification and Rational Redesign of Peptide Ligands to CRIP1, A Novel Biomarker for Cancers

Cysteine-rich intestinal protein 1 (CRIP1) has been identified as a novel marker for early detection of cancers. Here we report on the use of phage display in combination with molecular modeling to identify a high-affinity ligand for CRIP1. Panning experiments using a circularized C7C phage library yielded several consensus sequences with modest binding affinities to purified CRIP1. Two sequence motifs, A1 and B5, having the highest affinities for CRIP1, were chosen for further study. With peptide structure information and the NMR structure of CRIP1, the higher-affinity A1 peptide was computationally redesigned, yielding a novel peptide, A1M, whose affinity was predicted to be much improved. Synthesis of the peptide and saturation and competitive binding studies demonstrated approximately a 10–28-fold improvement in the affinity of A1M compared to that of either A1 or B5 peptide. These techniques have broad application to the design of novel ligand peptides