Figure 1 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Examples of currently approved molecular therapeutics. (A) Examples of small-molecule drugs, most of which are under 500 Da and less than 1 nm in size, shown as ball-and-stick models with transparent molecular surfaces. Clockwise from top left, Cambridge structural database (CSD) crystal structures COTZAN03, JEMJOA01, BZPENK01, and APUYOA are shown. (B) Examples of peptide macrocycle drugs, of which only 20 are approved for clinical use. Molecules are shown as ball-and-stick models (with apolar hydrogen atoms omitted for clarity) and transparent molecular surfaces. Clockwise from top left, CSD structures YICMUS and DEKSAN, and PDB structures 5L3 F (chain C) and 2VDN (chain C) are shown. (C) Examples of protein drugs, shown as ribbons and molecular surfaces. Clockwise from top, PDB structures 1 CG2 (chains A and B), 1BML (chains C and D), and 5HYS (chains C and D). Insets: small-molecule and peptide macrocycle drugs from panels A and B, respectively, shown to scale.
Source publication
Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles.
Areas Covered: This review summar...
Contexts in source publication
Context 1
... of the rest are either cellular therapies or protein therapeutics with masses over 10 kDa. Examples of small-molecule drugs are shown in Figure 1A: here, acetaminophen (paracetamol) is a common analgesic, diphenhydramine is a widely used antihistamine, penicillin G is a lactam antibiotic, and darunavir is an HIV protease inhibitor. While small molecules are easier to mass produce, more shelf-stable, easier to administer, frequently orally bioavailable, sometimes cell permeable, and often able to evade the human immune system, their small size limits the surface area that they can present for target recognition. ...
Context 2
... under 45 kDa are also rapidly cleared by the kidneys, limiting serum half-life [30]. Unlike small molecules, protein therapeutics ( Figure 1C) can present much larger surfaces for very specifically recognizing a target, resulting in high-affinity binding and excellent specificity, and producing high therapeutic indices. Shown are glucarpidase, an enzyme able to remove excess methotrexate from the blood of patients with impaired kidney function, streptokinase, which enzymatically dissolves blood clots, and the Fab fragment of omalizumab, a monoclonal antibody used to treat asthma. ...
Context 3
... they represent a small fraction of known drugs, some of the most potent and versatile therapeutics are intermediate-or mesoscale macrocycles, often built from amino acids or amino acid-like building-blocks. Figure 1B shows octreotide, a synthetic analogue of the natural hormone somatostatin which is used to treat tumors producing growth hormone, cyclosporine A, a potent immunosuppressant, polymyxin B1, an antibiotic that targets Gram-negative bacteria, and eptifibatide, a powerful anticoagulant. Currently, there are 20 peptide macrocycle drugs in use in the clinic (Table 1), most of which were discovered or invented prior to 1990 [27,. ...
Similar publications
Simultaneous targeting of different antigens by bispecific antibodies (bsAbs) is permitting synergistic binding functionalities with high therapeutic potential, but is also rendering their analysis challenging. We introduce flow-induced dispersion analysis (FIDA) for the in-depth characterization of bsAbs with diverse molecular architectures and va...
Citations
... peptides, they primarily thrive in the realm of smaller, simpler molecules, neglecting the specific challenges of peptide synthesis (Mulligan, 2020). Macrocyclic peptides are usually synthesized through the one-by-one incorporation of natural or non-natural amino acids, and are followed by reactions to cyclize peptides (Qian et al., 2015). ...
Motivation
Macrocyclic peptides hold great promise as therapeutics targeting intracellular proteins. This stems from their remarkable ability to bind flat protein surfaces with high affinity and specificity while potentially traversing the cell membrane. Research has already explored their use in developing inhibitors for intracellular proteins, such as KRAS, a well-known driver in various cancers. However, computational approaches for de novo macrocyclic peptide design remain largely unexplored.
Results
Here, we introduce HELM-GPT, a novel method that combines the strength of the hierarchical editing language for macromolecules (HELM) representation and generative pre-trained transformer (GPT) for de novo macrocyclic peptide design. Through reinforcement learning (RL), our experiments demonstrate that HELM-GPT has the ability to generate valid macrocyclic peptides and optimize their properties. Furthermore, we introduce a contrastive preference loss during the RL process, further enhanced the optimization performance. Finally, to co-optimize peptide permeability and KRAS binding affinity, we propose a step-by-step optimization strategy, demonstrating its effectiveness in generating molecules fulfilling both criteria. In conclusion, the HELM-GPT method can be used to identify novel macrocyclic peptides to target intracellular proteins.
Availability and implementation
The code and data of HELM-GPT are freely available on GitHub (https://github.com/charlesxu90/helm-gpt).
... Although rarely mentioned in publications, computational tools have demonstrated successful applications in facilitating the macrocyclization process 16,17 . Wagner et al. 18 and Sindhikara et al. 19 utilized geometrically constrained linker database searching and linker connection strategy to generate macrocycles from acyclic ligands. ...
Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.
... Therefore, attention was turned to the binding mode of β-lactamase antibiotics to NDM-1. For example, some macrocyclic peptides have been constructed from a mixture of L-and D-amino acids (D-type structures tend to have stronger affinity) [53]. The design of another group is similar to that of Mulligan in that it uses a portion of the natural product largazole as the "anchor" for cyclic peptide design, targeting the cancer target histone deacetylase [54]. ...
In recent years, an increasing number of drug-resistant bacterial strains have been identified due to the abuse of antibiotics, which seriously threatens human and animal health. Antimicrobial peptides (AMPs) have become one of the most effective weapons to solve this problem. AMPs have little tendency to induce drug resistance and have outstanding antimicrobial effects. The study of AMPs, especially cyclic peptides, has become a hot topic. Among them, macrocyclic AMPs have received extensive attention. This mini-review discusses the structures and functions of the dominant cyclic natural and synthetic AMPs and provides a little outlook on the future direction of cyclic AMPs.
... The development of new drugs typically requires the screening of hundreds of thousands or millions of molecules spanning a large chemical space. In the last few decades, computer-aided drug design methods have attempted to accelerate the discovery of lead drug candidates [1,2]. More recently, quantum computing has emerged as a promising tool that can complement classical computational methods in the drug discovery process [3][4][5]. ...
Molecular docking, which aims to find the most stable interacting configuration of a set of molecules, is of critical importance to drug discovery. Although a considerable number of classical algorithms have been developed to carry out molecular docking, most focus on the limiting case of docking two molecules. Since the number of possible configurations of N molecules is exponential in N, those exceptions which permit docking of more than two molecules scale poorly, requiring exponential resources to find high-quality solutions. Here, we introduce a one-hot encoded quadratic unconstrained binary optimization formulation (QUBO) of the multibody molecular docking problem, which is suitable for solution by quantum annealer. Our approach involves a classical pre-computation of pairwise interactions, which scales only quadratically in the number of bodies while permitting well-vetted scoring functions like the Rosetta REF2015 energy function to be used. In a second step, we use the quantum annealer to sample low-energy docked configurations efficiently, considering all possible docked configurations simultaneously through quantum superposition. We show that we are able to minimize the time needed to find diverse low-energy docked configurations by tuning the strength of the penalty used to enforce the one-hot encoding, demonstrating a 3-4 fold improvement in solution quality and diversity over performance achieved with conventional penalty strengths. By mapping the configurational search to a form compatible with current- and future-generation quantum annealers, this work provides an alternative means of solving multibody docking problems that may prove to have performance advantages for large problems, potentially circumventing the exponential scaling of classical approaches and permitting a much more efficient solution to a problem central to drug discovery and validation pipelines.
... The energetics of this desolvation process will have an enthalpic component, due to breaking of hydrogen bonds to water, and an entropic component due to changes to the ordered solvation shell. A membrane permeating macrocycle is deemed to have "chameleonicity" 11,12 when differing conformational ensembles exist in the bulk water and membrane interior phases. The "puckering" of a molecule to shield polar/charged groups from the membrane interior is said to drive membrane permeation for flexible macrocycles ( Figure 1A). ...
... The LPE is a function of the decadienewater distribution coefficient at pH 7.4 (logD 7.4 ) , the calculated octanol-water partition coefficient (clogP), and scaling factors to standardize the LPE across different chemical scaffolds and clogP metrics. Naylor et al. proposed that the LPE is more useful for modeling permeability, especially for compounds that are "chameleonic" 11,12 in nature. ...
... In contrast to PSA_w, Es_w, ALOGP, and nHBD, the latter three features T (N...N), T(N...O), and piPC02 are novel features for inclusion in a permeability model. These structural features encode compound flexibility and, likely, capture the ability of the macrocycle to adopt multiple environment-dependent conformational states while transitioning reversibly from water to the membrane (Figure 1), i.e., to behave in a 'chameleonic' 11,12 manner. We would suggest that while PSA_w, Es_w, ALOGP, and nHBD encode the enthalpic contributions to the free energy of membrane permeation, T (N...N), T(N...O), and piPC02 likely model (partially) the entropic contributions to membrane permeation. ...
The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.
... Hit refinement to produce leads for preclinical, animal, and clinical evaluation can also be guided by computation to further increase the likelihood of leads succeeding. The ultimate effect is to shift failures to earlier, less expensive stages of the pipeline and to allow more successes in later stages, allowing more drugs to be brought to the clinic with less cost in time, effort, or resources recently, computational tools have been developed to facilitate the rational design of peptide macrocycles able to bind to target proteins of therapeutic interest (Mulligan 2020). ...
... Compared to small molecules, they present large surfaces for specific target recognition, potentially permitting higher target affinity and fewer off-target effects. Compared to proteins, peptides offer greater potential for penetrating barriers such as the intestinal epithelium, the blood-brain barrier, or even the plasma membrane of cells (Mulligan 2020). Since peptides can be synthesized from thousands of noncanonical amino acid building blocks, they can access chemical functionalities beyond those accessible to genetically encodable proteins, which must be built from the 20 canonical amino acids. ...
Recent advances in the field of peptide therapeutics have led to the design and synthesis of many promising peptides. However, research and development to provide safe, stable, efficacious, and patient compliant formulation plays a vital role in bringing peptide therapeutics to market. The conventional parenteral route has made scientific advances to overcome multiple barriers, leading to the approval of many peptide-based products via parenteral route of administration in recent years. In parallel, oral, pulmonary, transdermal, and other delivery routes have been extensively investigated to deliver peptides with improved patient compliance. This includes the use of novel strategies for developing delivery systems that can offer various advantages over conventional dosage forms. This chapter focuses on fundamentals, formulations, and recent advances for successful peptide delivery.KeywordsPeptidesPharmaceutical marketFormulation developmentDelivery systemsParenteralOralPulmonaryTransdermalControlled release parenteralCarrier systems
... target effects. Compared to proteins, peptides offer greater potential for penetrating barriers such as the intestinal epithelium, the blood-brain barrier, or even the plasma membrane of cells (Mulligan 2020). Since peptides can be synthesized from thousands of non-canonical amino acid building-blocks, they can access chemical functionalities beyond those accessible to genetically-encodable proteins, which must be built from the 20 canonical amino acids. ...
... Computational design tools enable the design of peptide sequences that both present maximally favourable binding interactions with a target protein (predominantly optimizing the enthalpy of binding given a folded peptide, Δ H binding|folded , and thereby the overall change in enthalpy, Δ H binding ), while simultaneously promoting rigidly-folded peptides that uniquely favour the binding-competent conformation (predominantly minimizing the entropic cost of folding, Δ S folding , and thereby the overall entropic cost of binding, Δ S binding ) (Mulligan 2020). Indeed, it is possible to design peptides for which Δ G folding is negative -i.e., for which the binding-competent, folded state of the peptide is predominantly populated even when the peptide is unbound. ...
Peptide macrocycles represent a promising class of therapeutics, albeit one that is under-represented amongst existing drugs. One disadvantage of macrocycles, however, is that they can be more conformationally heterogeneous than small molecules or large, well-folded proteins. This flexibility can impede high-affinity binding. In recent years, the development of new computational tools has made possible the structure-based design of macrocycles that are able to fold into rigid structures compatible with binding to target proteins of therapeutic interest. This chapter is intended to introduce biologists, chemists, and drug developers to current computational methods for peptide macrocycle drug design. It introduces computational concepts such as parallelism and algorithmic complexity and outlines general algorithmic approaches such as Monte Carlo and simulated annealing methods. It also describes the thermodynamics of a flexible macrocycle binding to a target protein and explores molecular dynamics and Monte Carlo methods for sampling backbone conformations, deterministic and heuristic methods for designing amino acid sequences, and large-scale sampling-based methods for computationally validating and ranking designs to prioritize the likeliest candidates for chemical synthesis and experimental validation. Particular focus is given to methods implemented in the Rosetta software suite, with detailed examination of a Rosetta design protocol that was previously used to create peptide macrocycle inhibitors of an antibiotic resistance factor, the New Delhi metallo-β-lactamase 1 (NDM-1). Finally, this chapter describes new and emerging technologies that promise to enhance computational peptide macrocycle drug design, such as deep learning and quantum computing.KeywordsPeptide macrocyclesRational drug designStructure-guided designRosettaMolecular dynamicsSimulationMachine learningAlgorithmsHeuristicsQuantum computing
... Cyclic peptides have been widely used as potential therapeutics [38][39][40] and scaffolded antigens, [41][42][43] since they are in the "beyond-rule-of-five" chemical space 44 and have many protein characteristics. 45 Computational methods have been developed to design well-structured cyclic peptides that preferentially populate a single conformation, [45][46][47][48][49][50] which have led to several applications. ...
Cyclic peptides naturally occur as antibiotics, fungicides, and immunosuppressants, and have been adapted for use as potential therapeutics. Scaffolded cyclic peptide antigens have many protein characteristics such as reduced toxicity, increased stability over linear peptides, and conformational selectivity, but with fewer amino acids than whole proteins. The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy, and is represented by the ensemble of its sampled conformations. Although some algorithms excel in creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations, and they often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS-4.6.7, and benchmarked on three small cyclic peptide model systems: a cyclized segment of Aβ (cyclo-(CGHHQKLVG)), a cyclized furin cleavage site of SARS-CoV-2 spike (cyclo-(CGPRRARSG)), and oxytocin (disulfide bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on Alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. Compared to REMD, Res-REMD significantly accelerated the ensemble generation of cyclo-(CGHHQKLVG), but not cyclo-(CGPRRARSG) or oxytocin. This difference is due to the longer auto-correlation time of torsional angles in cyclo-(CGHHQKLVG) vs. the latter two cyclic peptide systems; The randomly seeded reservoir in Res-REMD thus accelerates sampling and convergence. The auto-correlation time of the torsional angles can thus be used to determine whether Res-REMD is preferable to REMD for cyclic peptides. We provide a github page with modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD
... The main focus of this chapter is small peptides (those with fewer than 15 amino acids) that are cyclic; i.e. their N-and C-terminal residues are connected via an amide bond, disulfide bond, thioether bond, or other covalent connection ( Figure 1B). These constrained peptides are a promising class of therapeutics, complementary to antibodies and small molecules, for binding currently undruggable protein targets (9)(10)(11)(12)(13). ...
... This section describes the overall workflow for design of peptides using Rosetta, with some details about each step (Figure 3). For detailed instructions on how to run these methods, the readers are referred to references cited (13,32,44,57,58,86,102). It should be noted that while this section is focused on using Rosetta for peptide design, many of the steps are similar in other computational pipelines, and the differences are often in the implementations and in algorithmic details. ...
... However, studies of miniproteins designed through this method, in particular their synthesis, can be more high-cost and time-consuming than those designed through the first approach. 12,106 These methodologies could be in some cases combined and/or supported by rational/ manual selection of modifications. ...
Miniproteins exhibit great potential as scaffolds for drug candidates due to their well-defined structure and good synthetic availability. Because of recently described methodologies for their de novo design, the field of miniproteins is emerging and can provide molecules that effectively bind to problematic targets, i.e., those that have been previously considered to be undruggable. This review describes methodologies for the development of miniprotein scaffolds as well as for the construction of biologically active miniproteins.
