Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Binding Dynamics of a Stapled Peptide Targeting the Transcription Factor NF-Y

Transcription factors (TFs) play a central role in gene regulation, and their malfunction can result in a plethora of severe diseases. TFs are therefore interesting therapeutic targets, but their involvement in protein-protein interaction networks and the frequent lack of well-defined binding pockets render them challenging targets for classical small molecules. As an alternative, peptide-based scaffolds have proven useful, in particular with an α-helical active conformation. Peptide-based strategies often require extensive structural optimization efforts, which could benefit from a more detailed understanding of the dynamics in inhibitor/protein interactions. In this study, we investigate how truncated stapled α-helical peptides interact with the transcription factor Nuclear Factor-Y (NF-Y). We identified a 13-mer minimal binding core region, for which two crystal structures with an altered C-terminal peptide conformation when bound to NF-Y were obtained. Subsequent molecular dynamics simulations confirmed that the C-terminal part of the stapled peptide is indeed relatively flexible while still showing defined interactions with NF-Y. Our findings highlight the importance of flexibility in the bound state of peptides, which can contribute to overall binding affinity.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

Dissecting the geometric and hydrophobic constraints of stapled peptides

Stapled peptides are a promising class of molecules with potential as highly specific probes of protein-protein interactions and as therapeutics. Hydrocarbon stapling affects the peptide properties through the interplay of two factors: enhancing the overall hydrophobicity and constraining the conformational flexibility. By constructing a series of virtual peptides, we study the role of each factor in modulating the structural properties of a hydrocarbon-stapled peptide PM2, which has been shown to enter cells, engage its target Mouse Double Minute 2 (MDM2), and activate p53. Hamiltonian replica exchange molecular dynamics (HREMD) simulations suggest that hydrocarbon stapling favors helical populations of PM2 through a combination of the geometric constraints and the enhanced hydrophobicity of the peptide. To further understand the conformational landscape of the stapled peptides along the binding pathway, we performed HREMD simulations by restraining the peptide at different distances from MDM2. When the peptide approaches MDM2, the binding pocket undergoes dehydration which appears to be greater in the presence of the stapled peptide compared with the linear peptide. In the binding pocket, the helicity of the stapled peptide is increased due to the favorable interactions between the peptide residues as well as the staple and the microenvironment of the binding pocket, contributing to enhanced affinity. The dissection of the multifaceted mechanism of hydrocarbon stapling into individual factors not only deepens fundamental understanding of peptide stapling, but also provides guidelines for the design of new stapled peptides.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

pIChemiSt ─ Free Tool for the Calculation of Isoelectric Points of Modified Peptides

The isoelectric point (pI) is a fundamental physicochemical property of peptides and proteins. It is widely used to steer design away from low solubility and aggregation and guide peptide separation and purification. Experimental measurements of pI can be replaced by calculations knowing the ionizable groups of peptides and their corresponding pK_a values. Different pK_a sets are published in the literature for natural amino acids, however, they are insufficient to describe synthetically modified peptides, complex peptides of natural origin, and peptides conjugated with structures of other modalities. Noncanonical modifications (nCAAs) are ignored in the conventional sequence-based pI calculations, therefore producing large errors in their pI predictions. In this work, we describe a pI calculation method that uses the chemical structure as an input, automatically identifies ionizable groups of nCAAs and other fragments, and performs pK_a predictions for them. The method is validated on a curated set of experimental measures on 29 modified and 119093 natural peptides, providing an improvement of R² from 0.74 to 0.95 and 0.96 against the conventional sequence-based approach for modified peptides for the two studied pK_a prediction tools, ACDlabs and pKaMatcher, correspondingly. The method is available in the form of an open source Python library at https://github.com/AstraZeneca/peptide-tools, which can be integrated into other proprietary and free software packages. We anticipate that the pI calculation tool may facilitate optimization and purification activities across various application domains of peptides, including the development of biopharmaceuticals.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

Monoclonal antibody designed for SARS-nCoV-2 spike protein of receptor binding domain on antigenic targeted epitopes for inhibition to prevent viral entry

SARS, or severe acute respiratory syndrome, is caused by a novel coronavirus (COVID-19). This situation has compelled many pharmaceutical R&D companies and public health research sectors to focus their efforts on developing effective therapeutics. SARS-nCoV-2 was chosen as a protein spike to targeted monoclonal antibodies and therapeutics for prevention and treatment. Deep mutational scanning created a monoclonal antibody to characterize the effects of mutations in a variable antibody fragment based on its expression levels, specificity, stability, and affinity for specific antigenic conserved epitopes to the Spike-S-Receptor Binding Domain (RBD). Improved contacts between Fv light and heavy chains and the targeted antigens of RBD could result in a highly potent neutralizing antibody (NAbs) response as well as cross-protection against other SARS-nCoV-2 strains. It undergoes multipoint core mutations that combine enhancing mutations, resulting in increased binding affinity and significantly increased stability between RBD and antibody. In addition, we improved. Structures of variable fragment (Fv) complexed with the RBD of Spike protein were subjected to our established in-silico antibody-engineering platform to obtain enhanced binding affinity to SARS-nCoV-2 and develop ability profiling. We found that the size and three-dimensional shape of epitopes significantly impacted the activity of antibodies produced against the RBD of Spike protein. Overall, because of the conformational changes between RBD and hACE2, it prevents viral entry. As a result of this in-silico study, the designed antibody can be used as a promising therapeutic strategy to treat COVID-19.

The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules

Chronic hepatitis B virus (HBV), a potentially life-threatening liver disease, makes people vulnerable to serious diseases such as cancer. T lymphocytes play a crucial role in clearing HBV virus, while the pathway depends on the strong binding of T cell epitope peptide and HLA. However, the experimental identification of HLA-restricted HBV antigenic peptides is extremely time-consuming. In this study, we provide a novel prediction strategy based on structure to assess the affinity between the HBV antigenic peptide and HLA molecule. We used residue scanning, peptide docking and molecular dynamics methods to obtain the molecular docking model of HBV peptide and HLA, and then adopted the MM-GBSA method to calculate the binding affinity of the HBV peptide–HLA complex. Overall, we collected 59 structures of HLA-A from Protein Data Bank, and finally obtained 352 numerical affinity results to figure out the optimal bind choice between the HLA-A molecules and 45 HBV T cell epitope peptides. The results were highly consistent with the qualitative affinity level determined by the competitive peptide binding assay, which confirmed that our affinity prediction process based on an HLA structure is accurate and also proved that the homologous modeling strategy for HLA-A molecules in this study was reliable. Hence, our work highlights an effective way by which to predict and screen for HLA-peptide binding that would improve the treatment of HBV infection.

Single-chain tandem macrocyclic peptides as a scaffold for growth factor and cytokine mimetics

Mimetics of growth factors and cytokines are promising tools for culturing large numbers of cells and manufacturing regenerative medicine products. In this study, we report single-chain tandem macrocyclic peptides (STaMPtides) as mimetics in a new multivalent peptide format. STaMPtides, which contain two or more macrocyclic peptides with a disulfide-closed backbone and peptide linkers, are successfully secreted into the supernatant by Corynebacterium glutamicum-based secretion technology. Without post-secretion modification steps, such as macrocyclization or enzymatic treatment, bacterially secreted STaMPtides form disulfide bonds, as designed; are biologically active; and show agonistic activities against respective target receptors. We also demonstrate, by cell-based assays, the potential of STaMPtides, which mimic growth factors and cytokines, in cell culture. The STaMPtide technology can be applied to the design, screening, and production of growth factor and cytokine mimetics.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Free energy perturbation guided Synthesis with Biological Evaluation of Substituted Quinoline derivatives as small molecule L858R/T790M/C797S mutant EGFR inhibitors targeting resistance in Non-Small Cell Lung Cancer (NSCLC)

Two different schemes of novel substituted quinoline derivatives were designed and synthesized via simple reaction steps and conditions. A comparative molecular docking study was carried out on two different types of EGFR enzymes which include wild-type (PDB: 4I23) and T790M mutated (PDB: 2JIV) respectively. Compounds were also validated upon T790M/C797S mutated (PDB ID: 5D41) EGFR enzyme at the allosteric binding site. Free energy perturbations were carried out to determine the absolute binding free energy of a protein-ligand complex in the form of ΔG_binding, which in turn provided 4ab and 5ad as the most potential contenders through the structural enhancement in the determined initial scaffolds. Anticancer activity of the synthesized derivatives was examined against HCC827, H1975 (L858R/T790M), A549, and HT-29 cell lines by standard MTT assay. Compound 4ad (6-chloro-2-(isoindolin-2-yl)-4-methylquinoline) has shown excellent inhibitory activities against mutant EGFR kinase with IC₅₀ value 0.91 µM. The potency of compounds 4ab, 4ad and 5adwas compared throughan insilicoADMET study.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC₅₀ values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Peptide-based inhibitors of protein-protein interactions: biophysical, structural and cellular consequences of introducing a constraint

Protein-protein interactions (PPIs) are implicated in the majority of cellular processes by enabling and regulating the function of individual proteins. Thus, PPIs represent high-value, but challenging targets for therapeutic intervention. The development of constrained peptides represents an emerging strategy to generate peptide-based PPI inhibitors, typically mediated by α-helices. The approach can confer significant benefits including enhanced affinity, stability and cellular penetration and is ingrained in the premise that pre-organization simultaneously pays the entropic cost of binding, prevents a peptide from adopting a protease compliant β-strand conformation and shields the hydrophilic amides from the hydrophobic membrane. This conceptual blueprint for the empirical design of peptide-based PPI inhibitors is an exciting and potentially lucrative way to effect successful PPI inhibitor drug-discovery. However, a plethora of more subtle effects may arise from the introduction of a constraint that include changes to binding dynamics, the mode of recognition and molecular properties. In this review, we summarise the influence of inserting constraints on biophysical, conformational, structural and cellular behaviour across a range of constraining chemistries and targets, to highlight the tremendous success that has been achieved with constrained peptides alongside emerging design opportunities and challenges.

Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.

Stapled Peptides as HIF-1α/p300 Inhibitors: Helicity Enhancement in the Bound State Increases Inhibitory Potency

Protein-protein interactions (PPIs) control virtually all cellular processes and have thus emerged as potential targets for development of molecular therapeutics. Peptide-based inhibitors of PPIs are attractive given that they offer recognition potency and selectivity features that are ideal for function, yet, they do not predominantly populate the bioactive conformation, frequently suffer from poor cellular uptake and are easily degraded, for example, by proteases. The constraint of peptides in a bioactive conformation has emerged as a promising strategy to mitigate against these liabilities. In this work, using peptides derived from hypoxia-inducible factor 1 (HIF-1α) together with dibromomaleimide stapling, we identify constrained peptide inhibitors of the HIF-1α/p300 interaction that are more potent than their unconstrained sequences. Contrary to expectation, the increased potency does not correlate with an increased population of an α-helical conformation in the unbound state as demonstrated by experimental circular dichroism analysis. Rather, the ability of the peptide to adopt a bioactive α-helical conformation in the p300 bound state is better supported in the constrained variant as demonstrated by molecular dynamics simulations and circular dichroism difference spectra.