Directing evolution: the next revolution in drug discovery?

A gradient phage-assisted continuous evolution method for screening suppressor tRNAs in Escherichia coli

Suppressor tRNAs, notable for their capability of reading through the stop codon while maintaining normal peptide synthesis, are promising in treating diseases caused by premature termination codons (PTC). However, the lack of effective engineering methods for suppressor tRNAs has curtailed their application potential. Here, we introduce a directed evolution technology that employs phage-assisted continuous evolution (PACE), combined with gradient biosensors featuring various PTCs in the M13 gene III. Utilizing this novel methodology, we have successfully evolved tRNATrp (UGG) reading through the UGA stop codon in Escherichia coli. Massively parallel sequencing revealed that these mutations predominantly occurred in the anticodon loop. Finally, two suppressor tRNATrp (UGA) mutants exhibited over fivefold increases in readthrough efficiency.

Computational peptide discovery with a genetic programming approach

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Engineered bacterial orthogonal DNA replication system for continuous evolution

Continuous evolution can generate biomolecules for synthetic biology and enable evolutionary investigation. The orthogonal DNA replication system (OrthoRep) in yeast can efficiently mutate long DNA fragments in an easy-to-operate manner. However, such a system is lacking in bacteria. Therefore, we developed a bacterial orthogonal DNA replication system (BacORep) for continuous evolution. We achieved this by harnessing the temperate phage GIL16 DNA replication machinery in Bacillus thuringiensis with an engineered error-prone orthogonal DNA polymerase. BacORep introduces all 12 types of nucleotide substitution in 15-kilobase genes on orthogonally replicating linear plasmids with a 6,700-fold higher mutation rate than that of the host genome, the mutation rate of which is unchanged. Here we demonstrate the utility of BacORep-based continuous evolution by generating strong promoters applicable to model bacteria, Bacillus subtilis and Escherichia coli, and achieving a 7.4-fold methanol assimilation increase in B. thuringiensis. BacORep is a powerful tool for continuous evolution in prokaryotic cells.

Engineering of Therapeutic and Detoxifying Enzymes

Therapeutic enzymes present excellent opportunities for the treatment of human disease, modulation of metabolic pathways and system detoxification. However, current use of enzyme therapy in the clinic is limited as naturally occurring enzymes are seldom optimal for such applications and require substantial improvement by protein engineering. Engineering strategies such as design and directed evolution that have been successfully implemented for industrial biocatalysis can significantly advance the field of therapeutic enzymes, leading to biocatalysts with new-to-nature therapeutic activities, high selectivity, and suitability for medical applications. This minireview highlights case studies of how state-of-the-art and emerging methods in protein engineering are explored for the generation of therapeutic enzymes and discusses gaps and future opportunities in the field of enzyme therapy.

Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios

Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (Mpro) of SARS-CoV-2. We show that combinations of functional groups and sites are critical to design drugs with improved binding affinity, which can be easily achieved using our framework by exploring a fraction of the available search space. Atomistic simulations and experimental validation elucidate that enhanced and prolonged interactions between functionalized drugs and Mpro residues result in their improved therapeutic value over that of the parental compound. Overall, this novel framework is extremely flexible and has the potential to rapidly design inhibitors for any protein with available crystal structures.

Instant Macrocyclizations via Multicomponent Reactions

Macrocycles fascinate chemists due to both their structure and their applications. However, we still lack efficient and sustainable synthetic methods, giving us straightforward access to them. Herein, a rapid macrocyclization utilizing a two-step, one-pot approach based on orthogonal multicomponent reaction (MCR) tactics is introduced. This synthetic protocol, which is based on Ugi and Groebke-Blackburn-Bienaymé reactions with isocyanides tethered to alkyl tosylates, yields medium sized macrocycles that are otherwise difficult to achieve. Single crystal structures reveal conformational reorganization via intramolecular hydrogen bonding, and modeling studies profile the synthesized libraries.

Construction and Applications of Mammalian Cell-Based DNA-Encoded Peptide/Protein Libraries

DNA-encoded peptide/protein libraries are the starting point for protein evolutionary modification and functional peptide/antibody selection. Different display technologies, protein directed evolution, and deep mutational scanning (DMS) experiments employ DNA-encoded libraries to provide sequence variations for downstream affinity- or function-based selections. Mammalian cells promise the inherent post-translational modification and near-to-natural conformation of exogenously expressed mammalian proteins and thus are the best platform for studying transmembrane proteins or human disease-related proteins. However, due to the current technical bottlenecks of constructing mammalian cell-based large size DNA-encoded libraries, the advantages of mammalian cells as screening platforms have not been fully exploited. In this review, we summarize the current efforts in constructing DNA-encoded libraries in mammalian cells and the existing applications of these libraries in different fields.

Development of a Cyclic, Cell Penetrating Peptide Compatible with In Vitro Selection Strategies

A key limitation for the development of peptides as therapeutics is their lack of cell permeability. Recent work has shown that short, arginine-rich macrocyclic peptides containing hydrophobic amino acids are able to penetrate cells and reach the cytosol. Here, we have developed a new strategy for developing cyclic cell penetrating peptides (CPPs) that shifts some of the hydrophobic character to the peptide cyclization linker, allowing us to do a linker screen to find cyclic CPPs with improved cellular uptake. We demonstrate that both hydrophobicity and position of the alkylation points on the linker affect uptake of macrocyclic cell penetrating peptides (CPPs). Our best peptide, 4i, is on par with or better than prototypical CPPs Arg9 (R9) and CPP12 under assays measuring total cellular uptake and cytosolic delivery. 4i was also able to carry a peptide previously discovered from an in vitro selection, 8.6, and a cytotoxic peptide into the cytosol. A bicyclic variant of 4i showed even better cytosolic entry than 4i, highlighting the plasticity of this class of peptides toward modifications. Since our CPPs are cyclized via their side chains (as opposed to head-to-tail cyclization), they are compatible with powerful technologies for peptide ligand discovery including phage display and mRNA display. Access to diverse libraries with inherent cell permeability will afford the ability to find cell permeable hits to many challenging intracellular targets.

Translational aspect in peptide drug discovery and development: An emerging therapeutic candidate

In the last two decades, protein-protein interactions (PPIs) have been used as the main target for drug development. However, with larger or superficial binding sites, it has been extremely difficult to disrupt PPIs with small molecules. On the other hand, intracellular PPIs cannot be targeted by antibodies that cannot penetrate the cell membrane. Peptides that have a combination of conformational rigidity and flexibility can be used to target difficult binding interfaces with appropriate binding affinity and specificity. Since the introduction of insulin nearly a century ago, more than 80 peptide drugs have been approved to treat a variety of diseases. These include deadly diseases such as cancer and human immunodeficiency virus infection. It is also useful against diabetes, chronic pain, and osteoporosis. Today, more research is being done on these drugs as lessons learned from earlier approaches, which are still valid today, complement newer approaches such as peptide display libraries. At the same time, integrated genomics and peptide display libraries are new strategies that open new avenues for peptide drug discovery. The purpose of this review is to examine the problems in elucidating the peptide-protein recognition mechanism. This is important to develop peptide-based interventions that interfere with endogenous protein interactions. New approaches are being developed to improve the binding affinity and specificity of existing approaches and to develop peptide agents as potentially useful drugs. We also highlight the key challenges that must be overcome in peptide drug development to realize their potential and provide an overview of recent trends in peptide drug development. In addition, we take an in-depth look at early efforts in human hormone discovery, smart medicinal chemistry and design, natural peptide drugs, and breakthrough advances in molecular biology and peptide chemistry.

Accurate Prediction for Protein-Peptide Binding Based on High-Temperature Molecular Dynamics Simulations

The structural characterization of protein-peptide interactions is fundamental to elucidating biological processes and designing peptide drugs. Molecular dynamics (MD) simulations are extensively used to study biomolecular systems. However, simulating the protein-peptide binding process is usually quite expensive. Based on our previous studies, herein, we propose a simple and effective method to predict the binding site and pose of the peptide simultaneously using high-temperature (high-T) MD simulations with the RSFF2C force field. Thousands of binding events (nonspecific or specific) can be sampled during microseconds of high-T MD. From density-based clustering analysis, the structures of all of the 12 complexes (nine with linear peptides and three with cyclic peptides) can be successfully predicted with root-mean-square deviation (RMSD) < 2.5 Å. By directly simulating the process of the ligand binding onto the receptor, our method approaches experimental precision for the first time, significantly surpassing previous protein-peptide docking methods in terms of accuracy.

Think Biologically, Act Chemically

A drug is a sophisticated molecule, purposely evolved, resulting from the accumulation of knowledge learned and exploited from simpler molecules over time. Advanced molecules with increased sophistication and capability are derived from simpler, less sophisticated structures with less capabilities. Medicinal chemists do not find, stumble upon, accidentally discover, screen for, or construct drugs. We purposefully evolve molecules through the use of feedback cycles; we emphasize efficiency and simplicity in pursuit of multiproperty homeostasis; and we design and learn from molecular outliers. This Miniperspective illustrates inspirational themes from nature including evolution, feedback cycles, homeostasis, efficiency, and mutation. These biological themes are then exemplified in modern medicinal chemistry practices, such as design-make-test-analyze cycles (feedback), balancing molecular properties (homeostasis), defining the minimum pharmacophore (simplicity, efficiency), understanding molecular outliers (mutants), and the unifying concept of molecular evolution.

Optimization of the antimicrobial peptide Bac7 by deep mutational scanning

Background

Intracellularly active antimicrobial peptides are promising candidates for the development of antibiotics for human applications. However, drug development using peptides is challenging as, owing to their large size, an enormous sequence space is spanned. We built a high-throughput platform that incorporates rapid investigation of the sequence-activity relationship of peptides and enables rational optimization of their antimicrobial activity. The platform is based on deep mutational scanning of DNA-encoded peptides and employs highly parallelized bacterial self-screening coupled to next-generation sequencing as a readout for their antimicrobial activity. As a target, we used Bac7_1-23, a 23 amino acid residues long variant of bactenecin-7, a potent translational inhibitor and one of the best researched proline-rich antimicrobial peptides.

Results

Using the platform, we simultaneously determined the antimicrobial activity of >600,000 Bac7_1-23 variants and explored their sequence-activity relationship. This dataset guided the design of a focused library of ~160,000 variants and the identification of a lead candidate Bac7PS. Bac7PS showed high activity against multidrug-resistant clinical isolates of E. coli, and its activity was less dependent on SbmA, a transporter commonly used by proline-rich antimicrobial peptides to reach the cytosol and then inhibit translation. Furthermore, Bac7PS displayed strong ribosomal inhibition and low toxicity against eukaryotic cells and demonstrated good efficacy in a murine septicemia model induced by E. coli.

Conclusion

We demonstrated that the presented platform can be used to establish the sequence-activity relationship of antimicrobial peptides, and showed its usefulness for hit-to-lead identification and optimization of antimicrobial drug candidates.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01304-4.

Spatial confinement toward creating artificial living systems

Lifeforms are regulated by many physicochemical factors, and these factors could be controlled to play a role in the construction of artificial living systems. Among these factors, spatial confinement is an important one, which mediates biological behaviors at multiscale levels and participates in the biomanufacturing processes accordingly. This review describes how spatial confinement, as a fundamental biological phenomenon, provides cues for the construction of artificial living systems. Current knowledge about the role of spatial confinement in mediating individual cell behavior, collective cellular behavior, and tissue-level behavior are categorized. Endeavors on the synthesis of biomacromolecules, artificial cells, engineered tissues, and organoids in spatially confined bioreactors are then emphasized. After that, we discuss the cutting-edge applications of spatially confined artificial living systems in biomedical fields. Finally, we conclude by assessing the remaining challenges and future trends in the context of fundamental science, technical improvement, and practical applications.

In silico design and in vitro assessment of anti-Helicobacter pylori compounds as potential small-molecule arginase inhibitors

Related to a variety of gastrointestinal disorders ranging from gastric ulcer to gastric adenocarcinoma, the infection caused by the gram-negative bacteria Helicobacter pylori (H. pylori) poses as a great threat to human health; hence, the search for new treatments is a global priority. The H. pylori arginase (HPA) protein has been widely studied as one of the main virulence factors of this bacterium, being involved in the prevention of nitric oxide-mediated bacterial cell death, which is a central component of innate immunity. Given the growing need for the development of new drugs capable of combating the infection by H. pylori, the present work describes the search for new HPA inhibitors, using virtual screening techniques based on molecular docking followed by the evaluation of the proposed modes of interaction at the HPA active site. In vitro studies of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), followed by cytotoxicity activity in gastric adenocarcinoma and non-cancer cells, were performed. The results highlighted compounds 6, 11, and 13 as potential inhibitors of HPA; within these compounds, the results indicated 13 presented an improved activity toward H. pylori killing, with MIC and MBC both at 64 µg/mL. Moreover, compound 13 also presented a selectivity index of 8.3, thus being more selective for gastric adenocarcinoma cells compared to the commercial drug cisplatin. Overall, the present work demonstrates the search strategy based on in silico and in vitro techniques is able to support the rational design of new anti-H. pylori drugs.

Protein Engineering in the Design of Protein-Protein Interactions: SARS-CoV-2 Inhibitors as a Test Case

The formation of specific protein-protein interactions (PPIs) drive most biological processes. Malfunction of such interactions is the molecular driver of many diseases. Our ability to engineer existing PPIs or create new ones has become a vital research tool. In addition, engineered proteins with new or altered interactions are among the most critical drugs that have been developed in recent years. These include antibodies, cytokines, inhibitors, and others. Here, we provide a perspective on the current status of the methods used to engineer new or altered PPIs. The emergence of the COVID-19 pandemic, which resulted in a worldwide quest to develop specific PPI inhibitors as drugs, provided an up-to-date and state-of-the-art status report on the methodologies for engineering PPIs targeting the interaction of the viral spike protein with its cellular target, ACE2. Multiple, very high affinity binders were generated within a few months using in vitro evolution by itself, or in combination with computational design. The different experimental and computational methods used to block this interaction provide a road map for the future of PPI engineering.

Pharmaceutical therapeutics for articular regeneration and restoration: state-of-the-art technology for screening small molecular drugs

Articular cartilage damage caused by sports injury or osteoarthritis (OA) has gained increased attention as a worldwide health burden. Pharmaceutical treatments are considered cost-effective means of promoting cartilage regeneration, but are limited by their inability to generate sufficient functional chondrocytes and modify disease progression. Small molecular chemical compounds are an abundant source of new pharmaceutical therapeutics for cartilage regeneration, as they have advantages in design, fabrication, and application, and, when used in combination, act as powerful tools for manipulating cellular fate. In this review, we present current achievements in the development of small molecular drugs for cartilage regeneration, particularly in the fields of chondrocyte generation and reversion of chondrocyte degenerative phenotypes. Several clinically or preclinically available small molecules, which have been shown to facilitate chondrogenesis, chondrocyte dedifferentiation, and cellular reprogramming, and subsequently ameliorate cartilage degeneration by targeting inflammation, matrix degradation, metabolism, and epigenetics, are summarized. Notably, this review introduces essential parameters for high-throughput screening strategies, including models of different chondrogenic cell sources, phenotype readout methodologies, and transferable advanced systems from other fields. Overall, this review provides new insights into future pharmaceutical therapies for cartilage regeneration.

Selection for constrained peptides that bind to a single target protein

Peptide secondary metabolites are common in nature and have diverse pharmacologically-relevant functions, from antibiotics to cross-kingdom signaling. Here, we present a method to design large libraries of modified peptides in Escherichia coli and screen them in vivo to identify those that bind to a single target-of-interest. Constrained peptide scaffolds were produced using modified enzymes gleaned from microbial RiPP (ribosomally synthesized and post-translationally modified peptide) pathways and diversified to build large libraries. The binding of a RiPP to a protein target leads to the intein-catalyzed release of an RNA polymerase σ factor, which drives the expression of selectable markers. As a proof-of-concept, a selection was performed for binding to the SARS-CoV-2 Spike receptor binding domain. A 1625 Da constrained peptide (AMK-1057) was found that binds with similar affinity (990 ± 5 nM) as an ACE2-derived peptide. This demonstrates a generalizable method to identify constrained peptides that adhere to a single protein target, as a step towards "molecular glues" for therapeutics and diagnostics.

Development of Genetically Encoded Biosensors for Reporting the Methyltransferase-Dependent Biosynthesis of Semisynthetic Macrolide Antibiotics

Clarithromycin is an improved semisynthetic analogue of the naturally occurring macrolide, erythromycin. The subtle modification of a methyl group on the C-6 hydroxyl group endows the molecule with improved acid stability and results in a clinically useful antibiotic. Here, we show that the effector specificity of the biosensor protein, MphR, can be evolved to selectively recognize clarithromycin and therefore report on the production of this molecule in vivo. In addition, a crystal structure of the evolved variant reveals the molecular basis for selectivity and provides a guide for the evolution of a new metabolic function using this biosensor.

Rational Redesign of Enzyme via the Combination of Quantum Mechanics/Molecular Mechanics, Molecular Dynamics, and Structural Biology Study

Increasing demands for efficient and versatile chemical reactions have prompted innovations in enzyme engineering. A major challenge in engineering α-ketoglutarate-dependent oxygenases is to develop a rational strategy which can be widely used for directly evolving the desired mutant to generate new products. Herein, we report a strategy for rational redesign of a model enzyme, 4-hydroxyphenylpyruvate dioxygenase (HPPD), based on quantum mechanics/molecular mechanics (QM/MM) calculation and molecular dynamic simulations. This strategy enriched our understanding of the HPPD catalytic reaction pathway and led to the discovery of a series of HPPD mutants producing hydroxyphenylacetate (HPA) as the alternative product other than the native product homogentisate. The predicted HPPD-Fe(IV)═O-HPA intermediate was further confirmed by the crystal structure of Arabidopsis thaliana HPPD/S267W complexed with HPA. These findings not only provide a good understanding of the structure-function relationship of HPPD but also demonstrate a generally applicable platform for the development of biocatalysts.

A System for the Evolution of Protein-Protein Interaction Inducers

Molecules that induce interactions between proteins, often referred to as "molecular glues", are increasingly recognized as important therapeutic modalities and as entry points for rewiring cellular signaling networks. Here, we report a new PACE-based method to rapidly select and evolve molecules that mediate interactions between otherwise noninteracting proteins: rapid evolution of protein-protein interaction glues (rePPI-G). By leveraging proximity-dependent split RNA polymerase-based biosensors, we developed E. coli-based detection and selection systems that drive gene expression outputs only when interactions between target proteins are induced. We then validated the system using engineered bivalent molecular glues, showing that rePPI-G robustly selects for molecules that induce the target interaction. Proof-of-concept evolutions demonstrated that rePPI-G reduces the "hook effect" of the engineered molecular glues, due at least in part to tuning the interaction affinities of each individual component of the bifunctional molecule. Altogether, this work validates rePPI-G as a continuous, phage-based evolutionary technology for optimizing molecular glues, providing a strategy for developing molecules that reprogram protein-protein interactions.

A structural perspective on the design of decoy immune modulators

Therapeutic mAbs have dominated the class of immunotherapeutics in general and immune checkpoint inhibitors in particular. The high specificity of mAbs to the target molecule as well as their extended half-life and (or) the effector functions raised by the Fc part are some of the important aspects that contribute to the success of this class of therapeutics. Equally potential candidates are decoys and their fusions that can address some of the inherent limitations of mAbs, like immunogenicity, resistance development, low bio-availability and so on, besides maintaining the advantages of mAbs. The decoys are molecules that trap the ligands and prevent them from interacting with the signaling receptors. Although a few FDA-approved decoy immune modulators are very successful, the potential of this class of drugs is yet to be fully realized. Here, we review various strategies employed in fusion protein therapeutics with a focus on the design of decoy immunomodulators from the structural perspective and discuss how the information on protein structure and function can strategically guide the development of next-generation immune modulators.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Diverse Polycyclic Polyprenylated Acylphloroglucinol Congeners with Anti-Nonalcoholic Steatohepatitis Activity from Hypericum forrestii

The new polycyclic polyprenylated acylphloroglucinols, hyperforcinols A-J (1-10), were isolated from the fruits of Hypericum forrestii, together with 30 biogenetic congeners of known structures. The structures of hyperforcinols A-J were determined by HRESIMS and 1D/2D NMR spectroscopic analysis, and their absolute configurations were determined by a combination of the electronic circular dichroism (ECD) exciton chirality method, ECD calculations, and X-ray diffraction analysis. A selection of 25 isolates, possessing seven types of carbon skeletons, were assessed for their in vitro effects against nonalcoholic steatohepatitis (NASH) using a free fatty acid-induced L02 cell model. Compounds 20 and 40 significantly decreased intracellular lipid accumulation. QRT-PCR analyses revealed that compounds 20 and 40 regulate the expression of lipid metabolism-related genes, including CD36, FASN, PPARα, and ACOX1.

A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities

The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.

Trends in peptide drug discovery

Since the introduction of insulin almost a century ago, more than 80 peptide drugs have reached the market for a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection and chronic pain. In this Perspective, we summarize key trends in peptide drug discovery and development, covering the early efforts focused on human hormones, elegant medicinal chemistry and rational design strategies, peptide drugs derived from nature, and major breakthroughs in molecular biology and peptide chemistry that continue to advance the field. We emphasize lessons from earlier approaches that are still relevant today as well as emerging strategies such as integrated venomics and peptide-display libraries that create new avenues for peptide drug discovery. We also discuss the pharmaceutical landscape in which peptide drugs could be particularly valuable and analyse the challenges that need to be addressed for them to reach their full potential.

Deep diversification of an AAV capsid protein by machine learning

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics.

In vivo diversification of target genomic sites using processive base deaminase fusions blocked by dCas9

In vivo mutagenesis systems accelerate directed protein evolution but often show restricted capabilities and deleterious off-site mutations on cells. To overcome these limitations, here we report an in vivo platform to diversify specific DNA segments based on protein fusions between various base deaminases (BD) and the T7 RNA polymerase (T7RNAP) that recognizes a cognate promoter oriented towards the target sequence. Transcriptional elongation of these fusions generates transitions C to T or A to G on both DNA strands and in long DNA segments. To delimit the boundaries of the diversified DNA, the catalytically dead Cas9 (dCas9) is tethered with custom-designed crRNAs as a "roadblock" for BD-T7RNAP elongation. Using this T7-targeted dCas9-limited in vivo mutagenesis (T7-DIVA) system, rapid molecular evolution of the antibiotic resistance gene TEM-1 is achieved. While the efficiency is demonstrated in E. coli, the system can be adapted to a variety of bacterial and eukaryotic hosts.

Proteomimetic surface fragments distinguish targets by function

The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein-protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces.

Affinity-Selected Bicyclic Peptide G-Quadruplex Ligands Mimic a Protein-like Binding Mechanism

The study of G-quadruplexes (G4s) in a cellular context has demonstrated links between these nucleic acid secondary structures, gene expression, and DNA replication. Ligands that bind to the G4 structure therefore present an excellent opportunity for influencing gene expression through the targeting of a nucleic acid structure rather than sequence. Here, we explore cyclic peptides as an alternative class of G4 ligands. Specifically, we describe the development of de novo G4-binding bicyclic peptides selected by phage display. Selected bicyclic peptides display submicromolar affinity to G4 structures and high selectivity over double helix DNA. Molecular simulations of the bicyclic peptide-G4 complexes corroborate the experimental binding strengths and reveal molecular insights into G4 recognition by bicyclic peptides via the precise positioning of amino acid side chains, a binding mechanism reminiscent of endogenous G4-binding proteins. Overall, our results demonstrate that selection of (bi)cyclic peptides unlocks a valuable chemical space for targeting nucleic acid structures.

The science of puromycin: From studies of ribosome function to applications in biotechnology

Puromycin is a naturally occurring aminonucleoside antibiotic that inhibits protein synthesis by ribosome-catalyzed incorporation into the C-terminus of elongating nascent chains, blocking further extension and resulting in premature termination of translation. It is most commonly known as a selection marker for cell lines genetically engineered to express a resistance transgene, but its additional uses as a probe for protein synthesis have proven invaluable across a wide variety of model systems, ranging from purified ribosomes and cell-free translation to intact cultured cells and whole animals. Puromycin is comprised of a nucleoside covalently bound to an amino acid, mimicking the 3′ end of aminoacylated tRNAs that participate in delivery of amino acids to elongating ribosomes. Both moieties can tolerate some chemical substitutions and modifications without significant loss of activity, generating a diverse toolbox of puromycin-based reagents with added functionality, such as biotin for affinity purification or fluorophores for fluorescent microscopy detection. These reagents, as well as anti-puromycin antibodies, have played a pivotal role in advancing our understanding of the regulation and dysregulation of protein synthesis in normal and pathological processes, including immune response and neurological function. This manuscript reviews the current state of puromycin-based research, including structure and mechanism of action, relevant derivatives, use in advanced methodologies and some of the major insights generated using such techniques both in the lab and the clinic.

High throughput sequencing of in vitro selections of mRNA-displayed peptides: data analysis and applications

In vitro selection using mRNA display is currently a widely used method to isolate functional peptides with desired properties. The analysis of high throughput sequencing (HTS) data from in vitro evolution experiments has proven to be a powerful technique but only recently has it been applied to mRNA display selections. In this Perspective, we introduce aspects of mRNA display and HTS that may be of interest to physical chemists. We highlight the potential of HTS to analyze in vitro selections of peptides and review recent advances in the application of HTS analysis to mRNA display experiments. We discuss some possible issues involved with HTS analysis and summarize some strategies to alleviate them. Finally, the potential for future impact of advancing HTS analysis on mRNA display experiments is discussed.

Advances in ultrahigh-throughput screening for directed enzyme evolution

Enzymes are versatile catalysts and their synthetic potential has been recognized for a long time. In order to exploit their full potential, enzymes often need to be re-engineered or optimized for a given application. (Semi-) rational design has emerged as a powerful means to engineer proteins, but requires detailed knowledge about structure function relationships. In turn, directed evolution methodologies, which consist of iterative rounds of diversity generation and screening, can improve an enzyme's properties with virtually no structural knowledge. Current diversity generation methods grant us access to a vast sequence space (libraries of >1012 enzyme variants) that may hide yet unexplored catalytic activities and selectivity. However, the time investment for conventional agar plate or microtiter plate-based screening assays represents a major bottleneck in directed evolution and limits the improvements that are obtainable in reasonable time. Ultrahigh-throughput screening (uHTS) methods dramatically increase the number of screening events per time, which is crucial to speed up biocatalyst design, and to widen our knowledge about sequence function relationships. In this review, we summarize recent advances in uHTS for directed enzyme evolution. We shed light on the importance of compartmentalization to preserve the essential link between genotype and phenotype and discuss how cells and biomimetic compartments can be applied to serve this function. Finally, we discuss how uHTS can inspire novel functional metagenomics approaches to identify natural biocatalysts for novel chemical transformations.

Evolving Antibiotics against Resistance: a Potential Platform for Natural Product Development?

To avoid an antibiotic resistance crisis, we need to develop antibiotics at a pace that matches the rate of evolution of resistance. However, the complex functions performed by antibiotics-combining, e.g., penetration of membranes, counteraction of resistance mechanisms, and interaction with molecular targets-have proven hard to achieve with current methods for drug development, including target-based screening and rational design. Here, we argue that we can meet the evolution of resistance in the clinic with evolution of antibiotics in the laboratory. On the basis of the results of experimental evolution studies of microbes in general and antibiotic production in Actinobacteria in particular, we propose methodology for evolving antibiotics to circumvent mechanisms of resistance. This exploits the ability of evolution to find solutions to complex problems without a need for design. We review evolutionary theory critical to this approach and argue that it is feasible and has important advantages over current methods for antibiotic discovery.

Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules

Designing highly specific modulators of protein-protein interactions (PPIs) is especially challenging in the context of multiple paralogs and conserved interaction surfaces. In this case, direct generation of selective and competitive inhibitors is hindered by high similarity within the evolutionary-related protein interfaces. We report here a strategy that uses a semi-rational approach to separate the modulator design into two functional parts. We first achieve specificity toward a region outside of the interface by using phage display selection coupled with molecular and cellular validation. Highly selective competition is then generated by appending the more degenerate interaction peptide to contact the target interface. We apply this approach to specifically bind a single PDZ domain within the postsynaptic protein PSD-95 over highly similar PDZ domains in PSD-93, SAP-97 and SAP-102. Our work provides a paralog-selective and domain specific inhibitor of PSD-95, and describes a method to efficiently target other conserved PPI modules.

Developing inhibitors that target specific protein-protein interactions (PPIs) is challenging. Here, the authors show that target selectivity and PPI blocking can be achieved simultaneously with PPI inhibitors that contain two functional modules, and create a paralog-selective PSD-95 inhibitor as proof-of-concept.

Bacterial production and direct functional screening of expanded molecular libraries for discovering inhibitors of protein aggregation

Protein misfolding and aggregation are associated with a many human disorders, including Alzheimer's and Parkinson's diseases. Toward increasing the effectiveness of early-stage drug discovery for these conditions, we report a bacterial platform that enables the biosynthesis of molecular libraries with expanded diversities and their direct functional screening for discovering protein aggregation inhibitors. We illustrate this approach by performing, what is to our knowledge, the largest functional screen of small-size molecular entities described to date. We generated a combinatorial library of ~200 million drug-like, cyclic peptides and rapidly screened it for aggregation inhibitors against the amyloid-β peptide (Aβ42), linked to Alzheimer's disease. Through this procedure, we identified more than 400 macrocyclic compounds that efficiently reduce Aβ42 aggregation and toxicity in vitro and in vivo. Finally, we applied a combination of deep sequencing and mutagenesis analyses to demonstrate how this system can rapidly determine structure-activity relationships and define consensus motifs required for bioactivity.

Biocatalysis and Pharmaceuticals: A Smart Tool for Sustainable Development

Biocatalysis is the term used to describe the application of any type of biocatalyst (enzymes, as isolated preparations of wild-type or genetically modified variants, or whole cells, either as native cells or as recombinant expressed proteins inside host cells) in a given synthetic schedule [...]

BA-12 Inhibits Angiogenesis via Glutathione Metabolism Activation

There is a need for an efficient and low-cost leading compound discovery mode. However, drug development remains slow, expensive, and risky. Here, this manuscript proposes a leading compound discovery strategy based on a combination of traditional Chinese medicine (TCM) formulae and pharmacochemistry, using a ligustrazine-betulinic acid derivative (BA-12) in the treatment of angiogenesis as an example. Blocking angiogenesis to inhibit the growth and metastasis of solid tumors is currently one recognized therapy for cancer in the clinic. Firstly, based on a traditional Prunella vulgaris plaster, BA-12 was synthesized according to our previous study, as it exhibited better antitumor activities than other derivatives on human bladder carcinoma cells (T24); it was then uploaded for target prediction. Secondly, the efficacy and biotoxicity of BA-12 on angiogenesis were evaluated using human umbilical vein endothelial cells (HUVECs), a quail chick chorioallantoic membrane, and Caenorhabditis elegans. According to the prediction results, the main mechanisms of BA-12 were metabolic pathways. Thus, multiple metabolomics approaches were applied to reveal the mechanisms of BA-12. Finally, the predictive mechanisms of BA-12 on glutathione metabolism and glycerophospholipid metabolism activation were validated using targeted metabolomics and pharmacological assays. This strategy may provide a reference for highly efficient drug discovery, with the aim of sharing TCM wisdom for unmet clinical needs.

P450 Monooxygenases Enable Rapid Late-Stage Diversification of Natural Products via C-H Bond Activation

The biological potency of natural products has been exploited for decades. Their inherent structural complexity and natural diversity might hold the key to efficiently address the urgent need for the development of novel pharmaceuticals. At the same time, it is that very complexity, which impedes necessary chemical modifications such as structural diversification, to improve the effectiveness of the drug. For this purpose, Cytochrome P450 enzymes, which possess unique abilities to activate inert sp3-hybridised C-H bonds in a late-stage fashion, offer an attractive synthetic tool. In this review the potential of cytochrome P450 enzymes in chemoenzymatic lead diversification is illustrated discussing studies reporting late-stage functionalisations of natural products and other high-value compounds. These enzymes were proven to extend the synthetic toolbox significantly by adding to the flexibility and efficacy of synthetic strategies of natural product chemists, and scientists of other related disciplines.

Evolution of C-Terminal Modification Tolerance in Full-Length and Split T7 RNA Polymerase Biosensors

T7 RNA polymerase (RNAP) is a powerful protein scaffold for the construction of synthetic biology tools and biosensors. However, both T7 RNAP and its split variants are intolerant to C-terminal modifications or fusions, thus placing a key limitation on their engineering and deployment. Here, we use rapid continuous-evolution approaches to evolve both full-length and split T7 RNAP variants that tolerate modified C termini and fusions to entire other proteins. Moreover, we show that the evolved split C-terminal RNAP variants can function as small-molecule biosensors, even in the context of large C-terminal fusions. This work provides a panel of modified RNAP variants with robust activity and tolerance to C-terminal fusions, and provides insights into the biophysical requirements of the C-terminal carboxylic acid functional group of T7 RNAP.

Functional-genetic approaches to understanding drug response and resistance

Drug development remains a slow and expensive process, while the effective use of established therapeutics is widely hampered by our limited understanding of response and resistance mechanisms. Functional-genetic tools such as CRISPR/Cas9, advanced RNAi methods, and targeted protein degradation, together with other emerging technologies such as time-resolved and single-cell transcriptomics, fundamentally change the way we can search for candidate therapeutic targets and evaluate them before drug development. In addition, for already available therapeutics these tools open vast opportunities for probing response mechanisms and predictive biomarkers, and thereby guide the development of personalized therapies. Here, we review promising applications and remaining limitations of recently established functional-genetic tools for high-throughput screening and the in-depth analysis of candidate targets and established drugs.

DNA-encoded libraries - an efficient small molecule discovery technology for the biomedical sciences

DNA-encoded compound libraries are a highly attractive technology for the discovery of small molecule protein ligands. These compound collections consist of small molecules covalently connected to individual DNA sequences carrying readable information about the compound structure. DNA-tagging allows for efficient synthesis, handling and interrogation of vast numbers of chemically synthesized, drug-like compounds. They are screened on proteins by an efficient, generic assay based on Darwinian principles of selection. To date, selection of DNA-encoded libraries allowed for the identification of numerous bioactive compounds. Some of these compounds uncovered hitherto unknown allosteric binding sites on target proteins; several compounds proved their value as chemical biology probes unraveling complex biology; and the first examples of clinical candidates that trace their ancestry to a DNA-encoded library were reported. Thus, DNA-encoded libraries proved their value for the biomedical sciences as a generic technology for the identification of bioactive drug-like molecules numerous times. However, large scale experiments showed that even the selection of billions of compounds failed to deliver bioactive compounds for the majority of proteins in an unbiased panel of target proteins. This raises the question of compound library design.

Emerging drug development technologies targeting ubiquitination for cancer therapeutics

Development of effective cancer therapeutic strategies relies on our ability to interfere with cellular processes that are dysregulated in tumors. Given the essential role of the ubiquitin proteasome system (UPS) in regulating a myriad of cellular processes, it is not surprising that malfunction of UPS components is implicated in numerous human diseases, including many types of cancer. The clinical success of proteasome inhibitors in treating multiple myeloma has further stimulated enthusiasm for targeting UPS proteins for pharmacological intervention in cancer treatment, particularly in the precision medicine era. Unfortunately, despite tremendous efforts, the paucity of potent and selective UPS inhibitors has severely hampered attempts to exploit the UPS for therapeutic benefits. To tackle this problem, many groups have been working on technology advancement to rapidly and effectively screen for potent and specific UPS modulators as intracellular probes or early-phase therapeutic agents. Here, we review several emerging technologies for developing chemical- and protein-based molecules to manipulate UPS enzymatic activity, with the aim of providing an overview of strategies available to target ubiquitination for cancer therapy.

Design of indole- and MCR-based macrocycles as p53-MDM2 antagonists

Macrocycles were designed to antagonize the protein–protein interaction p53-MDM2 based on the three-finger pharmacophore F¹⁹W²³L²⁵. The synthesis was accomplished by a rapid, one-pot synthesis of indole-based macrocycles based on Ugi macrocyclization. The reaction of 12 different α,ω-amino acids and different indole-3-carboxaldehyde derivatives afforded a unique library of macrocycles otherwise difficult to access. Screening of the library for p53-MDM2 inhibition by fluorescence polarization and ¹H,¹⁵N HSQC NMR measurements confirm MDM2 binding.

Strategies to Enhance Metabolic Stabilities

Macrocyclic peptides are a unique class of molecules that display a relatively constrained peptidic backbone as compared to their linear counterparts leading to the defined 3-D orientation of the constituent amino acids (pharmacophore). Although they are attractive candidates for lead discovery owing to the unique conformational features, their peptidic backbone is susceptible to proteolytic cleavage in various biological fluids that compromise their efficacy. In this chapter we review the various classical and contemporary chemical and biological approaches that have been utilized to combat the metabolic instability of macrocyclic peptides. We note that any chemical modification that helps in providing either local or global conformational rigidity to these macrocyclic peptides aids in improving their metabolic stability typically by slowing the cleavage kinetics by the proteases.

Second generation DNA-encoded dynamic combinatorial chemical libraries

A novel DNA-encoded chemical library architecture can mimic the mechanisms of immunity to evolve binders through recombination, dynamics and adaption.

Scalable, Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds

Directed evolution is a powerful approach for engineering biomolecules and understanding adaptation. However, experimental strategies for directed evolution are notoriously labor intensive and low throughput, limiting access to demanding functions, multiple functions in parallel, and the study of molecular evolution in replicate. We report OrthoRep, an orthogonal DNA polymerase-plasmid pair in yeast that stably mutates ∼100,000-fold faster than the host genome in vivo, exceeding the error threshold of genomic replication that causes single-generation extinction. User-defined genes in OrthoRep continuously and rapidly evolve through serial passaging, a highly straightforward and scalable process. Using OrthoRep, we evolved drug-resistant malarial dihydrofolate reductases (DHFRs) in 90 independent replicates. We uncovered a more complex fitness landscape than previously realized, including common adaptive trajectories constrained by epistasis, rare outcomes that avoid a frequent early adaptive mutation, and a suboptimal fitness peak that occasionally traps evolving populations. OrthoRep enables a new paradigm of routine, high-throughput evolution of biomolecular and cellular function.