Cyclic peptide structure prediction and design using AlphaFold

NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application; as such, peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with noncanonical amino acids. By integrating multigranularity information at the residual and atomic level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide root-mean-square deviation (RMSD) for cyclic peptides being 1.640 Å. In summary, this is a novel deep learning model designed specifically for cyclic peptides with noncanonical amino acids, offering great potential for peptide drug design and advancing biomedical research.

Circular RNA discovery with emerging sequencing and deep learning technologies

Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disease pathogenesis. However, their low expression levels and high sequence similarity to linear RNAs present substantial challenges for circRNA detection and characterization. Recent advances in long-read and single-cell RNA sequencing technologies, coupled with sophisticated deep learning-based algorithms, have revolutionized the investigation of circRNAs at unprecedented resolution and scale. This Review summarizes recent breakthroughs in circRNA discovery, characterization and functional analysis algorithms. We also discuss the challenges associated with integrating large-scale circRNA sequencing data and explore the potential future development of artificial intelligence (AI)-driven algorithms to unlock the full potential of circRNA research in biomedical applications.

De Novo Design of Cyclic Peptide Binders Based on Fragment Docking and Assembling

Cyclic peptides offer distinct advantages in modulating protein-protein interactions (PPIs), including enhanced target specificity, structural stability, reduced toxicity, and minimal immunogenicity. However, most cyclic peptide therapeutics currently in clinical development are derived from natural products or the cyclization of protein loops, with few methodologies available for de novo cyclic peptide design based on target protein structures. To fill this gap, we introduce CycDockAssem, an integrative computational platform that facilitates the systematic generation of head-to-tail cyclic peptides made entirely of natural - or -amino acid residues. The cyclic peptide binders are constructed from oligopeptide fragments containing 3-5 amino acids. A fragment library comprising 15 million fragments was created from the Protein Data Bank. The assembly workflow involves dividing the targeted protein surface into two docking boxes; the updated protein-protein docking program SDOCK2.0 is then utilized to identify the best binding fragments for these boxes. The fragments binding in different boxes are concatenated into a ring using two additional peptide fragments as linkers. A ROSETTA script is employed for sequence redesign, while molecular dynamics simulations and MM-PBSA calculations assess the conformational stability and binding free energy. To enhance docking performance, cation-π interactions, backbone hydrogen bonding potential, and explicit water exclusion energy were incorporated into the docking score function of SDOCK2.0, resulting in a significantly improved performance on the updated test set. A mirror design strategy was developed for cyclic peptides composed of -amino acids, where natural amino acid cyclic peptide binders are first designed for the mirror image of the target protein and the resulting complexes are then mirrored back. CycDockAssem was experimentally validated using tumor necrosis factor α (TNFα) as the target. Surface plasmon resonance experiments demonstrated that six of the seven designed cyclic peptides bind TNFα with micromolar affinity, two of which significantly inhibit TNFα downstream gene expression. Overall, CycDockAssem provides a robust strategy for targeted de novo cyclic peptide drug discovery.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Emerging frontiers in protein structure prediction following the AlphaFold revolution

Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated in a very short space of time through a revolution in protein structure prediction driven by deep learning, led by AlphaFold. This has provided a wealth of new structural information. Interpreting these predictions is critical to determining where and when this information is useful. But proteins are not static nor do they act alone, and structures of proteins interacting with other proteins and other biomolecules are critical to a complete understanding of their biological function at the molecular level. This review focuses on the application of state-of-the-art protein structure prediction to these advanced applications. We also suggest a set of guidelines for reporting AlphaFold predictions.

Heuristic energy-based cyclic peptide design

Rational computational design is crucial to the pursuit of novel drugs and therapeutic agents. Meso-scale cyclic peptides, which consist of 7-40 amino acid residues, are of particular interest due to their conformational rigidity, binding specificity, degradation resistance, and potential cell permeability. Because there are few natural cyclic peptides, de novo design involving non-canonical amino acids is a potentially useful goal. Here, we develop an efficient pipeline (CyclicChamp) for cyclic peptide design. After converting the cyclic constraint into an error function, we employ a variant of simulated annealing to search for low-energy peptide backbones while maintaining peptide closure. Compared to the previous random sampling approach, which was capable of sampling conformations of cyclic peptides of up to 14 residues, our method both greatly accelerates the computation speed for sampling conformations of small macrocycles (ca. 7 residues), and addresses the high-dimensionality challenge that large macrocycle designs often encounter. As a result, CyclicChamp makes conformational sampling tractable for 15- to 24-residue cyclic peptides, thus permitting the design of macrocycles in this size range. Microsecond-length molecular dynamics simulations on the resulting 15, 20, and 24 amino acid cyclic designs identify designs with kinetic stability. To test their thermodynamic stability, we perform additional replica exchange molecular dynamics simulations and generate free energy surfaces. Three 15-residue designs, one 20-residue and one 24-residue design emerge as promising candidates.

Three-Dimensional Structural Heteromorphs of Mating-Type Proteins in Hirsutella sinensis and the Natural Cordyceps sinensis Insect-Fungal Complex

The MAT1-1-1 and MAT1-2-1 proteins are essential for the sexual reproduction of Ophiocordyceps sinensis. Although Hirsutella sinensis has been postulated to be the sole anamorph of O. sinensis and to undergo self-fertilization under homothallism or pseudohomothallism, little is known about the three-dimensional (3D) structures of the mating proteins in the natural Cordyceps sinensis insect-fungal complex, which is a valuable therapeutic agent in traditional Chinese medicine. However, the alternative splicing and differential occurrence and translation of the MAT1-1-1 and MAT1-2-1 genes have been revealed in H. sinensis, negating the self-fertilization hypothesis but rather suggesting the occurrence of self-sterility under heterothallic or hybrid outcrossing. In this study, the MAT1-1-1 and MAT1-2-1 proteins in 173 H. sinensis strains and wild-type C. sinensis isolates were clustered into six and five clades in the Bayesian clustering trees and belonged to 24 and 21 diverse AlphaFold-predicted 3D structural morphs, respectively. Over three-quarters of the strains/isolates contained either MAT1-1-1 or MAT1-2-1 proteins but not both. The diversity of the heteromorphic 3D structures of the mating proteins suggested functional alterations of the proteins and provided additional evidence supporting the self-sterility hypothesis under heterothallism and hybridization for H. sinensis, Genotype #1 of the 17 genome-independent O. sinensis genotypes. The heteromorphic stereostructures and mutations of the MAT1-1-1 and MAT1-2-1 proteins in the wild-type C. sinensis isolates and natural C. sinensis insect-fungi complex suggest that there are various sources of the mating proteins produced by two or more cooccurring heterospecific fungal species in natural C. sinensis that have been discovered in mycobiotic, molecular, metagenomic, and metatranscriptomic studies, which may inspire future studies on the biochemistry of mating and pheromone receptor proteins and the reproductive physiology of O. sinensis.

Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer

Membrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. The model demonstrated robust predictive performance, achieving determination coefficients (R 2 ) of 0.67 for PAMPA permeability prediction, and R 2 values of 0.75, 0.62, and 0.73 for Caco-2, RRCK, and MDCK cell permeability predictions, respectively. Its performance outperforms traditional machine learning methods and graph-based neural network models. In ablation experiments, we validated the effectiveness of each component in the MAT architecture. Additionally, we analyzed the impact of data pre-training and cyclic peptide conformation optimization on model performance.

CYCLICCAE: A CONFORMATIONAL AUTOENCODER FOR EFFICIENT HETEROCHIRAL MACROCYCLIC BACKBONE SAMPLING

Macrocycles are a promising therapeutic class. The incorporation of heterochiral and non-natural chemical building-blocks presents challenges for rational design, however. With no existing machine learning methods tailored for heterochiral macrocycle design, we developed a novel convolutional autoencoder model to rapidly generate energetically favorable macrocycle backbones for heterochiral design and structure prediction. Our approach surpasses the current state-of-the-art method, Generalized Kinematic loop closure (GenKIC) in the Rosetta software suite. Given the absence of large, available macrocycle datasets, we created a custom dataset in-house and in silico. Our model, CyclicCAE, produces energetically stable backbones and designable structures more rapidly than GenKIC. It enables users to perform energy minimization, generate structurally similar or diverse inputs via MCMC, and conduct inpainting with fixed anchors or motifs. We propose that this novel method will accelerate the development of stable macrocycles, speeding up macrocycle drug design pipelines.

Structural information in therapeutic peptides: Emerging applications in biomedicine

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides

Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and cis-peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temperature molecular dynamics (high-T MD) simulations on 250 CPs with random sequences and applied the point-adaptive k-nearest neighbors (PAk) method to estimate the free energies of millions of sampled conformations. Using this data set, we trained a SchNet-based deep learning model, termed CPconf_score, to predict the conformational free energies of CPs. We tested CPconf_score to identify near-native conformations from MD-sampled conformations of 50 CPs from the Cambridge Structural Database. Our method achieved accurate predictions for 41 out of 50 CPs with a backbone RMSD of less than 1.0 Å compared to crystal structures. In comparison, other advanced CP structure prediction tools, such as HighFold and Rosetta, successfully predicted 12 and 19 CPs, respectively.

Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold

Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel - CaV1.1, and human voltage-gated potassium (KV) channel - KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.

The structural and functional impacts of rationally designed cyclic peptides on self-assembly-mediated functionality

Compared with their linear counterparts, cyclic peptides, characterized by their unique topologies, offer superior stability and enhanced functionality. In this review article, the rational design of cyclic peptide primary structures and their significant influence on self-assembly processes and functional capabilities are comprehensively reviewed. We emphasize how strategically modifying amino acid sequences and ring sizes critically dictate the formation and properties of peptide nanotubes (PNTs) and complex assemblies, such as rotaxanes. Adjusting the number of amino acid residues and side chains allows researchers to tailor the diameter, surface properties, and functions of PNTs precisely. In addition, we discuss the complex host-guest chemistry of cyclic peptides and their ability to form rotaxanes, highlighting their potential in the development of mechanically interlocked structures with novel functionalities. Moreover, the critical role of computational methods for accurately predicting the solution structures of cyclic peptides is also highlighted, as it enables the design of novel peptides with tailored properties for a range of applications. These insights set the stage for groundbreaking advances in nanotechnology, drug delivery, and materials science, driven by the strategic design of cyclic peptide primary structures.

cyclicpeptide: a Python package for cyclic peptide drug design

The unique cyclic structure of cyclic peptides grants them remarkable stability and bioactivity, making them powerful candidates for treating various diseases. However, the lack of standardized tools for cyclic peptide data has hindered their potential in today's artificial intelligence-driven efficient drug design landscape. To bridge this gap, here we introduce a Python package named cyclicpeptide specifically for cyclic peptide drug design. This package provides standardized tools such as Structure2Sequence, Sequence2Structure, and format transformation to process, convert, and standardize cyclic peptide structure and sequence data. Additionally, it includes GraphAlignment for cyclic peptide-specific alignment and search and PropertyAnalysis to enhance the understanding of their drug-like properties and potential applications. This comprehensive suite of tools aims to streamline the integration of cyclic peptides into modern drug discovery pipelines, accelerating the development of cyclic peptide-based therapeutics.

Accurate de novo design of high-affinity protein binding macrocycles using deep learning

The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest. We test 20 or fewer designed macrocycles against each of four diverse proteins and obtain medium to high-affinity binders against all selected targets. Designs against MCL1 and MDM2 demonstrate KD between 1-10 μM, and the best anti-GABARAP macrocycle binds with a KD of 6 nM and a sub-nanomolar IC50 in vitro. For one of the targets, RbtA, we obtain a high-affinity binder with KD < 10 nM despite starting from the target sequence alone due to the lack of an experimentally determined target structure. X-ray structures determined for macrocycle-bound MCL1, GABARAP, and RbtA complexes match very closely with the computational design models, with three out of the four structures demonstrating Ca RMSD of less than 1.5 Å to the design models. In contrast to library screening approaches for which determining binding mode can be a major bottleneck, the binding modes of RFpeptides-generated macrocycles are known by design, which should greatly facilitate downstream optimization. RFpeptides thus provides a powerful framework for rapid and custom design of macrocyclic peptides for diagnostic and therapeutic applications.

Exploring a Potential Optimization Route for Peptide Ligands of the Sam Domain from the Lipid Phosphatase Ship2

The Sam (Sterile alpha motif) domain of the lipid phosphatase Ship2 (Ship2-Sam) is engaged by the Sam domain of the receptor tyrosine kinase EphA2 (EphA2-Sam) and, this interaction is principally linked to procancer effects. Peptides able to hinder the formation of the EphA2-Sam/Ship2-Sam complex could possess therapeutic potential. Herein, by employing the FoldX software suite, we set up an in silico approach to improve the peptide targeting of the so-called Mid Loop interface of Ship2-Sam, representing the EphA2-Sam binding site. Starting from a formerly identified peptide antagonist of the EphA2-Sam/Ship2-Sam association, first, the most stabilizing mutations that could be inserted in each peptide position were predicted. Then, they were combined, producing a list of potentially enhanced Ship2-Sam ligands. A few of the in silico generated peptides were experimentally evaluated. Interaction assays with Ship2-Sam were performed using NMR and BLI (BioLayer Interferometry). In vitro assays were conducted as well to check for cytotoxic effects against both cancerous and healthy cells, and also to assess the capacity to regulate EphA2 degradation. This study undoubtedly enlarges our knowledge on how to properly target EphA2-Sam/Ship2-Sam associations with peptide-based tools and provides a promising strategy that can be used to target any protein-protein interaction.

Accurate Structure Prediction for Cyclic Peptides Containing Proline Residues with High-Temperature Molecular Dynamics

Cyclic peptides (CPs) are emerging as promising drug candidates. Numerous natural CPs and their analogs are effective therapeutics against various diseases. Notably, many of them contain peptidyl cis-prolyl bonds. Due to the high rotational barrier of peptide bonds, conventional molecular dynamics simulations struggle to effectively sample the cis/trans-isomerization of peptide bonds. Previous studies have highlighted the high accuracy of the residue-specific force field (RSFF) and the high sampling efficiency of high-temperature molecular dynamics (high-T MD). Herein, we propose a protocol that combines high-T MD with RSFF2C and a recently developed reweighting method based on probability densities for accurate structure prediction of proline-containing CPs. Our method successfully predicted 19 out of 23 CPs with the backbone rmsd < 1.0 Å compared to X-ray structures. Furthermore, we performed high-T MD and density reweighting on the sunflower trypsin inhibitor (SFTI-1)/trypsin complex to demonstrate its applicability in studying CP-complexes containing cis-prolines. Our results show that the conformation of SFTI-1 in aqueous solution is consistent with its bound conformation, potentially facilitating its binding.

Integrated Anchored Stapling and Hierarchical Dynamics: MSICDA-Driven CREBBP Bromodomain Inhibition

Despite recent success in the computational approaches of cyclic peptide design, current studies face challenges in modeling noncanonical amino acids and nonstandard cyclizations due to limited data. To address this challenge, we developed an integrated framework for the tailored design of stapled peptides (SPs) targeting the bromodomain of CREBBP (CREBBP-BrD). We introduce a powerful combination of anchored stapling and hierarchical molecular dynamics to design and optimize SPs by employing the MultiScale integrative conformational dynamics assessment (MSICDA) strategy, which involves an initial virtual screening of over 1.5 million SPs, followed by comprehensive simulations amounting to 154.54 μs across 5418 of instances. The MSICDA method provides a detailed and holistic stability view of peptide-protein interactions, systematically isolated optimized peptides and identified two leading candidates, DA#430 and DA#99409, characterized by their enhanced stability, optimized binding, and high affinity toward the CREBBP-BrD. In cell-free assays, DA#430 and DA#99409 exhibited 2- to 12-fold greater potency than inhibitor SGC-CBP30. Cell studies revealed higher peptide selectivity for cancerous versus normal cells over small molecules. DA#430 combined with (+)-JQ-1 showed promising synergistic effects. Our approach enables the identification of peptides with optimized binding, high affinity, and enhanced stability, leading to more precise and effective cyclic peptide design, thereby establishing MSICDA as a generalizable and transformative tool for uncovering novel targeted drug development in various therapeutic areas.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Expansive discovery of chemically diverse structured macrocyclic oligoamides

Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.

Gaussian accelerated molecular dynamics simulations facilitate prediction of the permeability of cyclic peptides

Despite their widespread use as therapeutics, clinical development of small molecule drugs remains challenging. Among the many parameters that undergo optimization during the drug development process, increasing passive cell permeability (i.e., log(P)) can have some of the largest impact on potency. Cyclic peptides (CPs) have emerged as a viable alternative to small molecules, as they retain many of the advantages of small molecules (oral availability, target specificity) while being highly effective at traversing the plasma membrane. However, the relationship between the dominant conformations that typify CPs in an aqueous versus a membrane environment and cell permeability remain poorly characterized. In this study, we have used Gaussian accelerated molecular dynamics (GaMD) simulations to characterize the effect of solvent on the free energy landscape of lariat peptides, a subset of CPs that have recently shown potential for drug development (Kelly et al., JACS 2021). Differences in the free energy of lariat peptides as a function of solvent can be used to predict permeability of these molecules, and our results show that permeability is most greatly influenced by N-methylation and exposure to solvent. Our approach lays the groundwork for using GaMD as a way to virtually screen large libraries of CPs and drive forward development of CP-based therapeutics.

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Insights into the Adsorption Mechanisms of the Antimicrobial Peptide CIDEM-501 on Membrane Models

CIDEM-501 is a hybrid antimicrobial peptide rationally designed based on the structure of panusin and panulirin template peptides. The new peptide exhibits significant antibacterial activity against multidrug-resistant pathogens (MIC = 2-4 μM) while conserving no toxicity in human cell lines. We conducted molecular dynamics (MD) simulations using the CHARMM-36 force field to explore the CIDEM-501 adsorption mechanism with different membrane compositions. Several parameters that characterize these interactions were analyzed to elucidate individual residues' structural and thermodynamic contributions. The membrane models were constructed using CHARMM-GUI, mimicking the bacterial and eukaryotic phospholipid compositions. Molecular dynamics simulations were conducted over 500 ns, showing rapid and highly stable peptide adsorption to bacterial lipids components rather than the zwitterionic eucaryotic model membrane. A predominant peptide orientation was observed in all models dominated by an electric dipole. The peptide remained parallel to the membrane surface with the center loop oriented to the lipids. Our findings shed light on the antibacterial activity of CIDEM-501 on bacterial membranes and yield insights valuable for designing potent antimicrobial peptides targeting multi- and extreme drug-resistant bacteria.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Structure prediction of linear and cyclic peptides using CABS-flex

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.