CycloPepper: a machine learning platform for predicting cyclization outcomes and optimizing synthesis of therapeutic cyclopeptides

Domain: neuroscience
Published: 2026-02-14
Editorial outcome: major_revision

Abstract
Cyclic peptides exhibit remarkable stability, membrane permeability, and binding affinity, positioning them as promising therapeutics. However, their synthesis, particularly on-resin head-to-tail cyclization, remains challenging, with cyclization site selection critically influencing yield. Here, we introduce a machine learning (ML) approach to predict cyclization outcomes, leveraging CycloBot, our fully automated cyclic peptide synthesis platform. Using this system, we generate a standardized dataset of 306 cyclic peptides (2–14 residues) and develop an ML model achieving an average prediction accuracy of 84%. Experimental validation with 74 random and therapeutic peptides showed an 86% prediction consistency. To facilitate practical use, we built CycloPepper, a user-friendly platform available through both web and software interfaces, enabling rapid cyclization site assessment. This tool effectively identified potential cyclization sites for disease-targeting peptides, including cancer biomarkers. Our work illustrates the potential of ML-assisted synthesis to streamline cyclic peptide synthesis and accelerate therapeutic discovery.

Introduction
Cyclic peptides represent a structurally diverse class of biomolecules, exhibiting a broad spectrum of shapes, sizes, and chemical functionalities1. Their natural abundance underscores their evolutionary significance, with many displaying potent biological activities ranging from hormonal signaling (e.g., oxytocin and vasopressin) to antimicrobial defense (e.g., vancomycin and its clinically approved derivatives telavancin, dalbavancin, and oritavancin)2–5. The constrained architecture of cyclic peptides often confers superior pharmacological properties compared to their linear counterparts, including enhanced binding affinity and specificity6, improved proteolytic stability7, and increased membrane permeability2 (Fig. 1a). Additionally, cyclization can refine selectivity and potency, unlocking peptide-based therapeutic potential while enabling differentiated pharmacological profiles8.

Among the various cyclization strategies, head-to-tail cyclization is particularly advantageous for drug development9. Unlike alternative approaches (e.g., side chain-to-side chain, head-to-side chain, or side chain-to-tail cyclization)10–12, head-to-tail closure eliminates free N- and C-termini, thereby enhancing metabolic stability2. Furthermore, this method preserves native amide bonds without requiring transition metal catalysts or special amino acids, ensuring broad compatibility with diverse peptide sequences. The simplicity of this approach also facilitates computational design and optimization13, enabling the development of cyclic peptides tailored to specific biological targets (Fig. 1b).

Despite these advantages, head-to-tail cyclization remains synthetically challenging. The entropic penalty associated with pre-cyclization conformational organization often hinders intramolecular ring closure, particularly for shorter peptides (<7 residues)9. Competing intermolecular reactions, such as dimerization or oligomerization, further complicate the process9. Additionally, the inherent preference of amide bonds for trans-configurations promotes extended linear conformations, exacerbating cyclization inefficiencies9. Solid-phase peptide synthesis (SPPS) mitigates some purification challenges but introduces steric constraints due to resin tethering, rendering cyclization highly sequence-dependent. For instance, bulky residues near the cyclization site can impede product ring closure, necessitating careful retrosynthetic disconnection planning (Fig. 1c).

Advances in machine learning (ML) have revolutionized the identification of complex structure-activity relationships, offering insights beyond traditional empirical approaches. In synthetic chemistry, ML-integrated automation has accelerated drug discovery by optimizing reaction pathways and predicting molecular properties14,15. Within peptide science, ML has been applied to forecast self-assembly16, membrane permeability17, bioactivity (e.g., antimicrobial18,19, anticancer20,21, and cell-penetrating peptides20), structure prediction and design22–24. However, its utility in cyclic peptide synthesis, particularly on-resin head-to-tail cyclization, remains underexplored. A major limitation is data heterogeneity25: literature-reported cyclization yields often derive from disparate methods, conditions, and analytical standards, complicating reproducibility and model training26. Automated synthesis platforms address this by generating standardized, high-fidelity datasets27, as exemplified by Pentelute’s high-throughput flow synthesis of small proteins28,29.

Here, we bridge this gap by combining automated flow synthesis with ML-driven predictive modeling. Leveraging a diaminonicotinic acid (DAN) linker system that enables rapid, cleavage-free on-resin cyclization, we construct a dataset of 306 cyclic peptides (2–14 residues) using the CycloBot automated platform and AutoXpert control software30. Twelve ML algorithms are evaluated, yielding an optimized model with 84% accuracy in predicting cyclization feasibility. Experimental validation confirms an 86% success rate, demonstrating good generalization within the scope of the available dataset. To democratize access, we develop CycloPepper, an open-source platform featuring standalone software and a web interface for real-time cyclization prediction. This tool streamlines cyclic peptide design for therapeutic targets, as validated by its performance on the synthesis of disease-relevant peptides. By unifying automated synthesis, ML prediction, and a user-friendly interface, this platform reduces empirical optimization burdens and accelerates the development of functional cyclic peptides (Fig. 1d).

Results
Dataset building
To establish a reliable dataset for cyclic peptide synthesis, we employed an on-resin head-to-tail cyclization strategy using DAN resin30 (Fig. 2a). Linear peptides were first synthesized via standard solid-phase peptide synthesis (SPPS) on DAN resin. Subsequent treatment with isoamyl nitrite facilitated the conversion of the DAN linker into reactive triazolopyridine intermediates, which, upon addition of N, N-diisopropylethylamine (DIEA) as a base, underwent efficient intramolecular cyclization to afford the desired cyclic peptides. To ensure rapid and reproducible dataset generation, we leveraged an automated flow synthesis platform, CycloBot30, which enabled high-temperature on-resin cyclization in a controlled and scalable manner. The system was equipped with precision switching valves, pumps, a temperature-regulated reactor, an in-line UV detector, and dedicated reagent reservoirs (see Supplementary Information Section 2.1 for more details). Reaction mixtures were collected directly from the system and analyzed by liquid chromatography-mass spectrometry (LC-MS). Successful cyclization was determined by the presence of the target molecular weight, with samples assigned a binary classification (“1” for successful cyclization, “0” for unsuccessful cyclization) (Fig. 2b). This fully automated approach not only optimized labor efficiency but also ensured high data consistency, enabling the rapid assembly of a high-quality dataset for downstream machine learning applications.

Given our focus on predicting cyclization feasibility, we established a training dataset of 306 linear peptide sequences (2–14 residues), each corresponding to a single experimentally validated cyclization outcome. Crucially, each entry represents only the specified linear precursor. For example, “FPGM” exclusively denotes cyclization of linear FPGM to cyclo-FPGM, excluding permutation variants such as PGMF. The dataset comprises 174 cyclization-viable sequences (labeled “1”) and 132 non-viable sequences (labeled “0”), ensuring balanced representation without outcome bias. Notably, shorter cyclic peptides (2- and 3-residue) exhibited distinct cyclization behaviors due to their constrained backbones, prompting us to prioritize these subsets for deeper analysis. The final stratified dataset comprised 45 two-residue, 53 three-residue, 120 four-to-six-residue, 69 seven-to-nine-residue, and 19 ten-residue and longer cyclic peptides. This stratified distribution facilitated more balanced model training and provided preliminary evidence of the model’s generalization capability.

Peptide sequences encoding and feature engineering
To enable reliable analysis of complex biological and chemical data and achieve high predictive accuracy, we established a systematic methodology for peptide sequence encoding and feature extraction. Our approach leveraged sequence-based encodings to capture essential structural and functional attributes of cyclic peptides (Fig. 3).

One-hot encoded sequences: Each peptide sequence, derived from cyclic peptides by cleavage at cyclization sites, was encoded as a fixed-length numerical vector to facilitate machine learning analysis. We employed one-hot encoding to represent each amino acid in the peptide sequence as a 20-dimensional binary vector, where each position corresponds to one of the 20 canonical amino acids. For instance, alanine (A) is encoded as [1, 0, 0, …, 0], with the “1” in the position corresponding to alanine. Given a maximum sequence length of 14 residues, each sequence was represented as a concatenation of fourteen 20-dimensional vectors. Sequences shorter than 14 residues were padded with zero vectors to maintain uniform dimensionality, resulting in a final 280-dimensional feature vector per sequence.

Amino acid composition (AAC) measures the normalized frequency of the 20 standard amino acids in a peptide sequence, producing 20-dimensional feature vectors. It is computed as:

f(a) = N(a) / L (1)

Here, f(a) represents the frequency of amino acid a, N(a) is the count of amino acid a in the peptide sequence, and L is the total length of the sequence.

Dipeptide composition (DPC) generates a 400-dimensional feature vector, calculated as:

D(r,s) = N(rs) / (L - 1), r,s ∈ {A,C,D,...,Y} (2)

In this case, N(rs) refers to the number of dipeptides composed of amino acids r and s.

Domain-specific hand-crafted features: In addition to sequence-based encodings, we designed a set of 16-dimensional hand-crafted features derived from empirical observations of peptide cyclization efficiency. These features were formulated through systematic analysis of synthetic outcomes, incorporating domain knowledge to enhance predictive performance. For example, inductive reasoning from experimental data revealed that sequences terminating in proline (P) or cysteine (C) exhibit reduced cyclization propensity, likely due to steric constraints or unfavorable conformational dynamics. Conversely, glycine (G) at the N-terminus was found to promote cyclization, presumably owing to its minimal steric hindrance and conformational flexibility. To encode these trends, binary indicators (1/0) were assigned to sequences exhibiting these features, thereby integrating empirical synthetic knowledge into the feature space.

Collectively, this multi-faceted feature engineering strategy, combining one-hot encoding, AAC/DPC descriptors, and hand-crafted experimental insights, provided a solid foundation for predictive modeling of cyclic peptide behavior. The resulting 716-dimensional feature vectors (280 from one-hot encoding, 20 from AAC, 400 from DPC, and 16 from hand-crafted features) ensured a comprehensive representation of sequence information, laying the foundation for accurate and interpretable machine learning predictions.

ML model selection and performance evaluation
Before constructing the machine learning model, we first concatenated the derived feature vector, followed by the removal of all zero dimensions. The processed dataset was then employed as input for model training and evaluation. In our systematic search for the optimal predictive algorithm, we assessed the performance of twelve distinct machine learning models, encompassing both individual classifiers and ensemble methods Fig. 3. The evaluated algorithms included Gaussian Naive Bayes (GaussianNB), k-Nearest Neighbors (KNN), Gradient Boosting Classifier (GB), Random Forest Classifier (RF), Support Vector Classifier (SVC), Logistic Regression (LR), Extreme Gradient Boosting (XGB), Adaptive Boosting Classifier (AdaBoost), and four ensemble configurations: (i) a voting classifier combining GaussianNB, RF, SVC, and XGB (Voting1), (ii) a voting classifier integrating RF and SVC (Voting2), (iii) a voting classifier incorporating GaussianNB, RF, and SVC (Voting3), and (iv) a stacked classifier composed of GaussianNB, RF, and SVC (Stacking).

Model performance was evaluated using five-fold cross-validation to ensure statistical reliability. The results indicated that the differences among the learning algorithms were not statistically significant, as all performances fell within one standard deviation. The GaussianNB classifier displayed the lowest performance (0.74±0.05). Several models, including RF (0.79±0.06), LR (0.81±0.07), and XGB (0.76±0.06), achieved moderate accuracies within the 0.70-0.80 range. Slightly higher accuracies were obtained for GB (0.82±0.04), AdaBoost (0.82±0.05), and the stacking (0.82±0.06). Notably, KNN (0.83±0.06) and SVC (0.84±0.06) delivered the highest mean accuracies. Given the close performance and comparable variance, the SVC model with the highest mean accuracy was selected for subsequent experimental validation and predictive applications.

Experimental validation of ML model
To assess the predictive performance of our ML model, we implemented a validation protocol wherein each candidate cyclic peptide sequence was systematically evaluated across all potential cyclization sites. For the 8 randomly generated sequences (Fig. 4a–h): five 7-residue sequences, cyclo-APYCGSI, cyclo-YLHIEGV, cyclo-QSVQERS, cyclo-QEYQHEK, cyclo-WMLWEPF, a 6-residue sequence, cyclo-MMVMPY, a 5-residue sequence, cyclo-HMHPW, and a 3-residue sequence, cyclo-KCL, we synthesized every linear precursor by changing cyclization sites. Specifically, cyclo-KCL yielded three linear precursors (KCL, CLK, LKC), while each 7-residue peptide generated seven precursors. Following automated solid-phase synthesis using DAN resin under standard conditions, reaction outcomes were verified via LC-MS detection of the desired cyclic products. The experimental results demonstrated overall agreement with the model’s predictions, with cyclization accuracies of 71% for both cyclo-QEYQHEK and cyclo-WMLWEPF, 86% for cyclo-QSVQERS, and 100% for cyclo-APYCGSI, cyclo-YLHIEGV, cyclo-KCL, cyclo-HMHPW, and cyclo-MMVMPY. These findings underscore the model’s reliability in identifying viable cyclization sites within the evaluated dataset.

To further validate the model’s practical utility, we extended our investigation to four biologically active cyclic peptides derived from Pseudostellariae radix31, including Heterophyllin J (5-residue), Pseudostellarins D (7-residue), Pseudostellarins A (5-residue), and Pseudostellarins H (8-residue) (Fig. 4i–l). Comparative analysis between the predicted and experimentally observed cyclization efficiencies revealed a substantial degree of consistency, with prediction accuracies of 80% for Heterophyllin J, 86% for Pseudostellarins D, 80% for Pseudostellarins A, and 75% for Pseudostellarins H.

In the entire experimental validation set, 39 sequences were cyclizable, while 35 were non-cyclizable, providing a nearly balanced dataset for evaluating model performance. After conducting 74 synthesis experiments across seven distinct cyclic peptide sequences, 64 predictions were consistent with experimental results. The aggregate predictive accuracy of the model reached 86% (Fig. 4m), supporting its reliability in guiding the selection of suitable cyclization sites for peptide synthesis within the evaluated dataset. This success rate suggests the model could serve as a useful tool for synthetic chemists, assisting in the design and optimization of cyclic peptides while minimizing experimental trial and error.

CycloPepper prediction for bioactive cyclic peptides
To address the critical challenge of translating computational predictions into practical cyclic peptide synthesis, we developed CycloPepper, a platform incorporating our optimized machine learning algorithm (Fig. 5). This versatile tool, accessible through both standalone software and web interface, enabled: (1) single-sequence analysis (e.g., GFTDFGF), (2) batch processing of multiple sequences via CSV upload (software only), and (3) real-time prediction of cyclization feasibility across all potential sites. The web interface is accessible from mobile devices, allowing researchers to easily perform predictions from their phones, enabling seamless predictions anytime and anywhere. The platform’s automated workflow was initiated upon activation of the execution module, wherein input sequences were transformed into 716-dimensional feature vectors, processed by our SVC model, and yielded results within seconds. The output interface provides immediate quality assessment, displaying “SUCCESS” for sequences containing ≥1 viable cyclization site, with detailed positional annotations (green checkmarks indicating successful sites versus red crosses for non-viable positions) to guide synthetic efforts.

We validated CycloPepper’s utility across seven therapeutically relevant protein targets: CD52, PD-L1, TNF-α, CD41, CEACAMs, HER2, and APP, leveraging known antibody complementarity-determining regions as design templates32. Systematic analysis of 561 designed cyclic peptides revealed 341 (60.8%) feasible cyclization sites, with some sequences only exhibiting 1–2 viable cyclization sites. This predictive capability offered three key advantages for peptide drug development: elimination of 39.2% non-viable sequences prior to synthesis, accelerated identification of bioactive candidates, and substantial reduction in resource expenditure, decreasing both synthetic timelines and associated costs by enabling data-driven sequence prioritization.

Methods
Rapid cyclic peptides dataset building
Comprising 306 linear peptides, the dataset was engineered to ensure comprehensive representation of all 20 native amino acids, with each type appearing in more than 20 distinct sequences to support balanced model training and reliable evaluation. The seed sequences were randomly generated using a programmatic approach to ensure the inclusion of all 20 native amino acids. From this pool, specific subsequences were selected for the directed variation strategies, including residue addition (e.g., TDDA to TDDAG), deletion, and substitution. These modifications were manually performed (Supplementary Information Section 2.5). Minimal sequence alterations revealed critical structure-cyclability relationships while maintaining broad heterogeneity without redundancy, as evidenced by TDDA (no cyclization) versus TDDAG/GTDDA (successful cyclization).

The main step of dataset construction is the synthesis of cyclic peptides. The first step is manual DAN resin synthesis. Forty milligrams of DAN resin (0.36 mmol/g loading) were used in all experiments in the dataset. The next step is automated synthesis. All cyclic peptides were synthesized on the automated instrument CycloBot along with control software AutoXpert. The following stock solutions were used for cyclic peptide synthesis: Fmoc-protected amino acids, including 20 native amino acids. Activating agent O-(7-Azabenzotriazol-1-yl)-N, N, N′, N′-tetramethyluronium hexafluorophosphate (HATU) as a 0.38 M stock solution in DMF, DIEA (10% v/v in DMF), DIEA (1% v/v in DMF), and deprotection stock solution (40% piperidine and 1% HCOOH (v/v) in DMF, which was diluted inline to 20% (v/v) and 0.5% (v/v) during synthesis). The automated process includes linear peptide synthesis at 90°C, activation at room temperature (25°C), and cyclization at 50°C. The reaction mixtures were characterized by LC-MS (for more details, see Supplementary Information Section 2).

Model training
We made a 716-dimensional vector feature depending on the cyclic peptide sequences, which consists of one-hot encoded sequences, amino acid composition (AAC), dipeptide composition (DPC), and domain-specific hand-crafted features. After removing all 0 dimensions, it ended up with a 414-dimensional vector (for more details, see Supplementary Information Section 3).

Featurization
Twelve ML model architectures were implemented in the Python programming environment, version 3.7.1. Five-fold cross-validation was used for evaluating model performance. ML algorithms were mostly imported from the module “scikit-learn”.

Similarity evaluation
Following the DOME recommendations for supervised machine learning validation in biology33, we measured sequence similarity as percent identity, quantifying the independence between training set and experimental validation set by computing global pairwise identity between each experimental amino-acid sequence and all training sequences on the linearized peptides. Since cyclic peptides can be represented with different starting positions when linearized, we performed a rotation-invariant audit for high-similarity pairs (initial identity ≥60%) (for more details, see Supplementary Information Section 4.2).

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
All the relevant data generated in this study are provided in the Supplementary Information and Source Data. The training dataset of 306 cyclic peptides, along with all prediction results, has been deposited in the GitHub repository under https://github.com/Yongboxiao/Selection-of-Cyclization-Sites. Source data are provided with this paper.

Code availability
The Python code for the machine learning model described in this manuscript is available in a GitHub repository (https://github.com/Yongboxiao/Selection-of-Cyclization-Sites) and archived on https://doi.org/10.5281/zenodo.1815499434. Additionally, the CycloPepper platform can be accessed through its user-friendly website interface at http://www.cyclopepper.com/.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary\nThe authors present a ML approach for predicting peptide viability for head-to-tail cyclization. The authors constructed a\ndataset with 306 cyclic peptides (2-14 residues) using their own automated flow synthesis protocol. This dataset was\nstratified depending on peptide size into di-, tri-, tetra- to hexa-, hepta- to nona- and decapeptides or longer.\nStrengths\nThe webserver interface is intuitive and easy to use.\nThe purpose of this tool is interesting and would be valuable for peptide design.\nThe experimental validation, although limited in its scope, is good for demonstrating the practical utility of the tool.\nWeaknesses\n(1) In the results section there is no mention to the model performance being measured in a 70:30 train/test random split. It\ngives the impression that the results are from the five-fold cross-validation. In fact, considering that the model selection is\ndone looking at the train/test split, the five-fold cross validation seems redundant. It would have been better to not do the\ntrain/test split and instead perform model selection and report the results in the 5-fold cross validation with the associated\nstandard deviation.\n(2) Experimental validation procedure is unclear as to what the percentage of model accuracy corresponds to. I assume from\nthe figure that for each sequence, several experiments were performed in which the N- and C-termini were moved. For\nexample, for cyclo-KCL, the sequence KCL, CLK, and LKC were predicted with the model and experimentally validated. I\nwould appreciate it if the authors could clarify this point both in their response and in the manuscript.\n(3) It was not clear in the initial training dataset if the training peptides were synthesised as all possible linear starts/stops as\nillustrated in results for Fig 4. Does 306 peptides mean 306 unique peptides with all their different permutations (so more\nthan 306 sequences in the actual dataset) or 306 unique sequences (which are permutations of each other)? The larger\npeptides would be much more over-represented in training if the former is true.\n(4) Figure 4 legend should specify what the symbols in colour 1/0 means, does this mean predicted to be cyclised, but not\nobserved experimentally, or the other way around?\n(5) Was the feature selection process repeated for each cross-validation fold, or for the 70% of the data in the training set, or\nfor the whole dataset. The authors should clarify in their response and the manuscript. If the feature selection was performed\nin the whole dataset, that could introduce overfitting, it would have been more appropriate to perform it only in the training\ndata or the cross-validation folds.\n(6) The process for generating the random sequences should be specified. It is also important that the authors report how\nsimilar these sequences are to the sequences in their training data. If they are too similar, then the authors cannot claim\nrobust generalization.\n(7) The link to the code repository is not publicly available. he authors should make the code publicly available that is used\nto train the models and generate the sequences, to verify the claims made in the text.\n(8) The authors should make available the training and evaluation data in order to assess claims made.\n(9) The authors do not specify what proportion of the peptides where positive and which were not in the training dataset.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "A standard way to improve the therapeutic competence of peptides is to cyclize them. This can be done in many ways,\nincluding sidechain-to-sidechain, head-to-sidechain, sidechain-to-tail, and head-to-tail. Head-to-tail cyclization presents\nseveral advantages. It eliminates free N- and C-termini, retains sidechain stereochemistries, and is less affected by steric\nconstraints. Yet not all peptides readily cyclize, and there can be many factors that complicate head-to-tail unions. For\ninstance: entropic penalties, competitive dimerization, and limited flexibility. Just because a peptide could cyclize doesn’t\nmean it will, and solid-phase synthesis can only offset some of the synthetic challenges. Li et al aim to remove the\nguesswork of whether a peptide will cyclize or not by developing a predictive algorithm. In brief, Li et al developed an\nautomated synthesis platform to generate a library (306 members; size range 2-14mer) of cyclic head-to-tail peptides using\nDAN resin technologies. Then, they deployed this library to train a machine learning model to predict cyclization efficiencies\n(both head-to-tail and across all other sites) in linear peptides using one-hot encoding, AAC/DPC descriptors, and hand-\ncrafted experimental insights. What resulted is an SVC model with 86% cross-validation accuracy in cyclization site\nprediction and 83% success rate. Predictive accuracy was verified by modeling three randomly generated cyclic peptide\nsequences and four biologically active cyclic peptides derived from Pseufostellariae Radix. The model was made available\nvia an open-source platform known as “CycloPepper” with standalone software and a web interface.\nOverall, the CycloPepper software is a significant advance in the field, and this reviewer suggests that the manuscript be\nconsidered after revisions. The following points should be addressed.\n(1) Although the distribution in peptide size is well presented, the authors should include the amino acid diversity score of\nthe starting library. In other words, how similar (or different) are the starting sequences in terms of amino acid\nsimilarity/identity and three-dimensional structure? How were the 306 peptides selected?\n(2) In assigning the binary classification, “1” signifies that the target molecular weight was observed and “0” if it was not. It is\nunclear if the target molecular weight refers exclusively to the parent ion and what degree of precision was considered for\nsuccessful identification. The authors should provide these details. Looking at the Supporting Information, it appears that\n[M+H]+ and [M+Na]+ are being considered. The authors should also state if they attempted to validate successful\ncyclizations by checking for adducted masses or smaller fragments.\n(3) One limitation of the training model is that it only accounts for the twenty native amino acids. It does not account for non-\nproteinogenic residues, which are frequently included in peptides to improve drug-like properties as well as binding affinity\nand target selectivity. It would be good to consider peptides harboring some of the most frequently encountered non-\nproteinogenic residues in the model.\n(4) In the Supporting Information, the ‘Found: [M+H]+’ for cyclo-YLHIEGV is incorrect. What is written does not match the\nprovided MS spectra.\n(5) In the Supporting Information, some of the peptides form [2M+Na]+ in addition to [M+H]+. As outlined in the successful\ncyclization criteria, this would still be considered successful because it made some of the correct product in addition to the\ndimer. Can the model predict oligomerization based on the current training data? Aside from cyclization, the risk of\noligomerization would be an important parameter for peptide medicinal chemists designing sequences."}, "author_response": "Dear Reviewers,\nWe sincerely appreciate your rigorous evaluation and insightful critiques of our\nmanuscript (ID: NCOMMS-25-33268). Your expertise and meticulous attention to\ndetail have been instrumental in elevating the scientific quality of this work.\nIn response to your valuable recommendations, we have undertaken extensive\nrevisions with utmost diligence. Each comment has been addressed through substantive\nmodifications to the manuscript text, supplementary materials, and data repositories.\nDetailed point-by-point responses follow below, with reviewer comments in italics and\nenumerated concerns for clarity. Revised content is explicitly highlighted in blue\nthroughout the updated documents.\nReviewer #1 and Reviewer #2\nComment_1:\n“In the results section there is no mention to the model performance being\nmeasured in a 70:30 train/test random split. It gives the impression that the results are\nfrom the five-fold cross-validation. In fact, considering that the model selection is done\nlooking at the train/test split, the five-fold cross validation seems redundant. It would\nhave been better to not do the train/test split and instead perform model selection and\nreport the results in the 5-fold cross validation with the associated standard deviation.”\nOur response:\nWe acknowledge the reviewer’s perceptive comment regarding the potential\nambiguity in reporting the performance metrics. The reviewer rightly pointed out the\nneed for clarity in distinguishing between evaluation methodologies. In response, we\nhave carefully revised the manuscript to explicitly state that the reported performance\nmetrics (e.g., 86% accuracy) were obtained exclusively through 5-fold cross-validation,\nrather than a fixed train/test split. This clarification has been revised in the “Methods”\nsection of the manuscript, which addresses the previously unclear descriptions,\nensuring a more precise and unambiguous explanation of the machine learning process.\nWe thank the reviewer for bringing this important matter to our attention.\nBefore:\nFeaturization\nTwelve ML model architectures were implemented in the Python programming\nenvironment, version 3.7.1. Fivefold cross-validation was used for a random split of 70%\ntraining, and 30% held-out testing datasets. ML algorithms were mostly imported from\nthe module “scikit-learn”.\nAfter:\nFeaturization\nTwelve ML model architectures were implemented in the Python programming\nenvironment, version 3.7.1. ML algorithms were mostly imported from the module\n“scikit-learn”. Model performance was evaluated via 5-fold cross-validation.\nComment_2:\n“Experimental validation procedure is unclear as to what the percentage of model\naccuracy corresponds to. I assume from the figure that for each sequence, several\nexperiments were performed in which the N- and C-termini were moved. For example,\nfor cyclo-KCL, the sequence KCL, CLK, and LKC were predicted with the model and\nexperimentally validated. I would appreciate it if the authors could clarify this point\nboth in their response and in the manuscript.”\nOur response:\nWe warmly thank the reviewer for emphasizing procedural clarification in\nexperimental validation. Our revised manuscript now comprehensively details the\nmethodology: For each target cyclopeptide sequence of length n, we systematically\ngenerated n distinct linear precursors by rotating the N- and C-termini positions across\nall possible cyclization sites. Taking cyclo-KCL as an example, we synthesized and\ncyclized all three linear permutation variants (KCL, CLK, and LKC). Similarly, for 7-\nresidue cyclopeptide cyclo-APYCGSI, we prepared seven linear precursors (APYCGSI,\nPYCGSIA, YCGSIAP, CGSIAPY, GSIAPYC, SIAPYCG, IAPYCGS) and subjected\neach to cyclization under standardized conditions. Model accuracy was quantified as\nthe percentage of correctly predicted cyclization outcomes across all validated linear\nprecursors. Specifically, in the cyclo-APYCGSI case, the model accurately predicted\ncyclization outcomes for six precursors but failed to anticipate cyclization for\nCGSIAPY, yielding a sequence-level accuracy of 86% (6/7). Across the entire\nvalidation set comprising 7 cyclopeptides and 42 precursors, the aggregate accuracy\nreached 83% (35 correct predictions). This methodology has now been explicitly\ndetailed in “Experimental Validation of ML Model” section in the manuscript. We\nbelieve this clarification fully resolves the methodological ambiguity noted by the\nreviewer.\nBefore:\nTo assess the predictive accuracy of our ML model, we first evaluated its\nperformance on three randomly generated cyclic peptide sequences: two 7-residue\nsequences, cyclo-APYCGSI and cyclo-YLHIEGV, and a 3-residue sequence, cyclo-KCL\n(Fig. 4a–c). Each peptide was subjected to cyclization under standard conditions, and\nthe reaction mixtures were analyzed via LC-MS to verify the formation of the desired\ncyclic products. The experimental results demonstrated strong agreement with the\nmodel’s predictions, with cyclization accuracies of 86% for both cyclo-APYCGSI and\ncyclo-YLHIEGV, and 100% for cyclo-KCL. These findings underscore the model’s\nrobustness in identifying viable cyclization sites.\nTo further validate the model’s practical utility, we extended our investigation to\nfour biologically active cyclic peptides derived from Pseudostellariae Radix32,\nincluding Heterophyllin J (5-residue), Pseudostellarins D (7-residues),\nPseudostellarins A (5-residue), and Pseudostellarins H (8-residue) (Fig. 4d–g).\nComparative analysis between the predicted and experimentally observed cyclization\nefficiencies revealed a high degree of consistency, with prediction accuracies of 100%\nfor Heterophyllin J, 71% for Pseudostellarins D, 80% for Pseudostellarins A, and 75%\nfor Pseudostellarins H.\nAfter conducting 42 synthesis experiments across seven distinct cyclic peptide\nsequences, the aggregate predictive accuracy of the model reached 83% (Fig. 4h),\ndemonstrating its reliability in guiding the selection of optimal cyclization sites for\npeptide synthesis. This high success rate highlights the model’s potential as a valuable\ntool for synthetic chemists, streamlining the design and optimization of cyclic peptides\nwith minimal experimental trial and error.\nAfter:\nTo assess the predictive performance of our ML model, we implemented a\nvalidation protocol wherein each candidate cyclic peptide sequence was systematically\nevaluated across all potential cyclization sites. For the three randomly generated\nsequences (Fig. 4a–c), two 7-residue peptides (cyclo-APYCGSI and cyclo-YLHIEGV)\nand one 3-residue peptide (cyclo-KCL), we synthesized every linear precursor by\nchanging cyclization sites. Specifically, cyclo-KCL yielded three linear precursors\n(KCL, CLK, LKC), while each 7-residue peptide generated seven precursors.\nFollowing automated solid-phase synthesis using DAN resin under standard conditions\nwith reaction outcomes verified via LC-MS detection of the desired cyclic products.\nThe experimental results demonstrated strong agreement with the model’s predictions,\nwith cyclization accuracies of 86% for both cyclo-APYCGSI and cyclo-YLHIEGV,\nand 100% for cyclo-KCL. These findings underscore the model’s robustness in identifying\nviable cyclization sites.\nTo further validate the model’s practical utility, we extended our investigation to\nfour biologically active cyclic peptides derived from Pseudostellariae Radix32,\nincluding Heterophyllin J (5-residue), Pseudostellarins D (7-residues), Pseudostellarins\nA (5-residue), and Pseudostellarins H (8-residue) (Fig. 4d–g). Comparative analysis\nbetween the predicted and experimentally observed cyclization efficiencies revealed a\nhigh degree of consistency, with prediction accuracies of 100% for Heterophyllin J, 71%\nfor Pseudostellarins D, 80% for Pseudostellarins A, and 75% for Pseudostellarins H.\nAfter conducting 42 synthesis experiments across seven distinct cyclic peptide\nsequences, 35 predictions were consistent with experimental results. The aggregate\npredictive accuracy of the model reached 83% (Fig. 4h), demonstrating its reliability\nin guiding the selection of optimal cyclization sites for peptide synthesis. This high\nsuccess rate highlights the model’s potential as a valuable tool for synthetic chemists,\nstreamlining the design and optimization of cyclic peptides with minimal experimental\ntrial and error.\nComment_3:\n“It was not clear in the initial training dataset if the training peptides were\nsynthesised as all possible linear starts/stops as illustrated in results for Fig 4. Does\n306 peptides mean 306 unique peptides with all their different permutations (so more\nthan 306 sequences in the actual dataset) or 306 unique sequences (which are\npermutations of each other)? The larger peptides would be much more over-represented\nin training if the former is true.”\nOur response:\nWe sincerely appreciate the reviewer’s astute inquiry regarding the structure of our\ntraining dataset, which highlights a critical methodological detail requiring clarification.\nTo address this concern explicitly: The 306 entries in our training dataset exclusively\ncomprise unique linear precursor sequences and their corresponding cyclization\noutcomes, not comprehensive sets of circular permutation variants for each peptide.\nSpecifically, each entry represents a distinct linear sequence subjected to head-to-tail\ncyclization under standardized conditions. For example, the 4-residue peptide sequence\nFPGM constitutes a single dataset entry, reflecting solely the cyclization outcome of\nthe linear precursor FPGM to cyclo-FPGM. It does not encompass permutation variants\nincluding PGMF, GMFP, and MFPG. Consequently, the dataset contains 306\nindependent sequence-cyclization pairs with no inherent sequence permutation\nrelationships, ensuring balanced representation across peptide lengths (2-14 residues)\nwithout over-representation of larger peptides. This design principle is now explicitly\nstated in “Dataset Building” Section in the manuscript. We regret any ambiguity in\nthe original description and thank the reviewer for prompting this essential clarification.\nBefore:\nGiven our focus on predicting cyclization feasibility, we systematically\ninvestigated a diverse set of 306 linear peptides ranging from 2 to 14 residues in length.\nAfter:\nGiven our focus on predicting cyclization feasibility, we established a training\ndataset of 306 unique linear peptide sequences (2-14 residues), each corresponding to\na single experimentally validated cyclization outcome. Crucially, each entry represents\nonly the specified linear precursor. For example, “FPGM” exclusively denotes\ncyclization of linear FPGM to cyclo-FPGM, excluding permutation variants such as\nPGMF.\nComment_4:\n“Figure 4 legend should specify what the symbols in colour 1/0 means, does this\nmean predicted to be cyclised, but not observed experimentally, or the other way\naround?”\nOur response:\nWe commend the reviewer’s acute attention to graphical clarity, which identified\ncritical ambiguity in Figure 4’s symbology. To clarify the symbolic notation in Figure\n4, each cyclization site is annotated with a binary label “P/E”, where P denotes the\npredicted cyclization outcome (1 = feasible, 0 = infeasible) and E represents the\nexperimental observation (1 = successful cyclization detected, 0 = no cyclic product\nobserved). A concordant label (e.g., 1/1 or 0/0) indicates accurate prediction, whereas\ndiscordant pairs (e.g., 1/0 or 0/1) reflect prediction errors. Specifically, “1/0” signifies\na false positive case where cyclization was predicted feasible but not experimentally\nviable. We have revised the figure by adding more explicit labeling to enhance the\nclarity of the results presented. This modification ensures that the figure conveys the\ninformation in a clearer and more straightforward manner. Also, this annotation system\nis now explicitly defined in the revised Figure 4 legend in “Experimental Validation\nof ML Model” Section in the manuscript. We regret any confusion caused by the\noriginal omission and appreciate the reviewer’s meticulous critique.\nBefore:\nFig. 4. Predicted cyclic peptide synthesis results agree with experimental validation.\nThe amide bonds representing cyclization sites are marked in color. The corresponding\nverification results are presented in the form of ‘predicted result/experimental result’\nwithin the colored boxes. If the box is surrounded by a gray dashed line, it indicates a\ndiscrepancy between the experimental and predicted results; otherwise, it indicates that\nthe two results align, confirming the accuracy of the prediction. Cyclic peptides in a),\nb), and c) are cyclic peptides generated randomly. Cyclic peptides in d), e), f), and g)\nare cyclic peptides in Pseudostellariae Radix, which present tyrosinase inhibitory\nactivity. The accuracy of each cyclic peptide and overall accuracy are represented in\nh).\nAfter:\nFig. 4. Predicted cyclic peptide synthesis results agree with experimental\nvalidation. The amide bonds representing cyclization sites are marked in color, with\nresults presented in the form of ‘P/E’ within the colored boxes. ‘P’ indicates predicted\ncyclization (1 = feasible, 0 = infeasible), and ‘E’ indicates experimental outcome (1 =\nsuccessful, 0 = not observed). Boxes with gray dashed lines indicate a discrepancy\nbetween the experimental and predicted results; otherwise, it indicates that the two\nresults align, confirming the accuracy of the prediction. Cyclic peptides in a), b), and c)\nare cyclic peptides generated randomly. Cyclic peptides in d), e), f), and g) are cyclic\npeptides in Pseudostellariae Radix, which present tyrosinase inhibitory activity. The\naccuracy of each cyclic peptide and overall accuracy are represented in h).\nComment_5:\n“Was the feature selection process repeated for each cross-validation fold, or for\nthe 70% of the data in the training set, or for the whole dataset. The authors should\nclarify in their response and the manuscript. If the feature selection was performed in\nthe whole dataset, that could introduce overfitting, it would have been more appropriate\nto perform it only in the training data or the cross-validation folds.”\nOur response:\nThis incisive query regarding feature selection protocols is highly valued, because\nit is crucial for ensuring methodological rigor. To clarify our approach, the feature\nselection process was strictly nested within the 5-fold cross-validation framework to\nprevent data leakage and overfitting. This design principle is now clearly outlined in\nSection 3.3 of the Supplementary Information.\nBefore:\nTo identify the features that significantly impact model performance from the 414-\ndimensional vector, a forward feature selection process was conducted utilizing the SVC.\nThis process commenced with an empty feature set and iteratively added the most\nimpactful feature (defined as the one enhancing model performance the most) at each\niteration, guided by cross-validation accuracy. The feature selection process concluded\nwhen 40 features were identified, beyond which further improvements in accuracy were\nno longer observed. Overall, the model’s accuracy was enhanced from 0.71 to 0.86\n(Supplementary Fig. 7).\nAfter:\nTo identify features significantly impacting model performance from the 414-\ndimensional vector, we conducted forward feature selection using 5-fold cross-\nvalidation within the training data. This process commenced with an empty feature set\nand iteratively added the most impactful feature based on cross-validation accuracy\nimprovement. Feature selection was terminated at 40 features where accuracy gains\nplateaued, yielding a final model accuracy of 0.86 from initial 0.71 (Supplementary\nFig. 7).\nComment_6:\n“The process for generating the random sequences should be specified. It is also\nimportant that the authors report how similar these sequences are to the sequences in\ntheir training data. If they are too similar, then the authors cannot claim robust\ngeneralization.”\nOur response:\nWe recognize the reviewer's valuable emphasis on clarifying our random sequence\ngeneration methodology and assessing similarity to training data. The three validation\npeptides (cyclo-APYCGSI, cyclo-YLHIEGV, and cyclo-KCL) were generated through\na purely stochastic process: sequence lengths were randomly sampled from 2 to 14\nresidues with each amino acid independently selected from the 20 native amino acids\nusing a uniform probability distribution. Comprehensive manual comparison against\nour training dataset confirmed no significant sequence similarity, with particular\nattention to the absence of ≥3 consecutive residue matches, ensuring these represent\nnovel validation cases. The generation script is publicly available at\nhttps://github.com/Yongboxiao/Selection-of-Cyclization-Sites. This methodology\nrobustly substantiates the model’s generalization capability, and we have incorporated\nthese clarifications in Section 4 “Details of the validation experiment” of the revised\nSupplementary Information. We appreciate this constructive feedback which\nstrengthens our validation framework documentation.\nBefore:\nThe selected Pseudostellariae Radix cyclic peptides, along with the randomly\ngenerated cyclic peptides, were processed using the “Cyclic Peptides Disconnection\nSequences Generated” section of the program available at\nhttps://github.com/Yongboxiao/Selection-of-Cyclization-Sites to disconnect them at all\ncyclization sites, thereby generating the corresponding linear peptide sequences for\nsynthesis. These sequences were subsequently input into the “Prediction” section of the\ntrained model for prediction. Experimental validation was performed to assess the\naccuracy of the model. The experimental process was consistent with the synthesis\nprocess in the dataset (see Automated cyclic peptide synthesis).\nAfter:\nThe three randomly generated cyclic peptides, cyclo-APYCGSI, cyclo-YLHIEGV,\nand cyclo-KCL, were created using “Random Cyclopeptide Generator”, our dedicated\nrandom sequence generator package. This tool employs a purely stochastic algorithm\nthat: (1) randomly samples sequence lengths from 2 to 14 residues, and (2)\nindependently selects each amino acid from the 20 native types with a uniform\nprobability distribution. The “Random Cyclopeptide Generator” package has been\nuploaded to our GitHub repository (https://github.com/Yongboxiao/Selection-of-\nCyclization-Sites).\nThese randomly generated peptides, along with the selected Pseudostellariae\nRadix-derived cyclic peptides, were processed using the “Cyclic Peptides\nDisconnection Sequences Generated” section of the program available in our GitHub\nrepository to disconnect them at all cyclization sites, thereby generating the\ncorresponding linear peptide sequences for synthesis. These sequences were\nsubsequently input into the “Prediction” section of the trained model for prediction.\nExperimental validation was performed to assess the accuracy of the model. The\nexperimental process was consistent with the synthesis process in the dataset (see\nAutomated cyclic peptide synthesis).\nComment_7:\n“The link to the code repository is not publicly available. The authors should make\nthe code publicly available that is used to train the models and generate the sequences,\nto verify the claims made in the text.”\nOur response:\nThe reviewer’s advocacy for open science is profoundly respected. Our GitHub\nrepository (https://github.com/Yongboxiao/Selection-of-Cyclization-Sites) has\nprovided full public access since submission, containing all code and data resources for\nthis study, encompassing seven core components: (1) the “Random Cyclopeptide\nGenerator” algorithm for stochastic sequence generation for experimental validation,\n(2) the “Cyclic peptides disconnection sequences generated” module generating linear\nprecursors, (3) complete code for model training (4) Prediction implementation scripts,\n(5) the dataset of 306 peptides for model training, (6) Sequence data from experimental\nvalidation studies, and (7) Sequence data of therapeutic cyclic peptides targeting\nvarious diseases. The repository’s comprehensive README.md provides detailed\nexecution protocols and dependency specifications.\nComment_8:\n“The authors should make available the training and evaluation data in order to\nassess claims made.”\nOur response:\nWe enthusiastically embrace the reviewer’s emphasis on data accessibility. All\ncritical datasets are now fully accessible through three complementary channels: (1)\nThe complete training dataset of 306 peptides and validation experimental results are\nprovided in the Supplementary Information; (2) Machine-readable versions of all data\nare archived in our GitHub repository (https://github.com/Yongboxiao/Selection-of-\nCyclization-Sites), including source CSV files for model training/evaluation.\nComment_9:\n“The authors do not specify what proportion of the peptides where positive and\nwhich were not in the training dataset.”\nOur response:\nThe reviewer’s focus on class balance is analytically appreciated. Our dataset\ncomprises exactly 174 cyclization-viable sequences (labeled “1”) and 132 non-viable\nsequences (labeled “0”), yielding a near-balanced distribution of 56.9% positive and\n43.1% negative cases, indicating no significant bias toward either class. We thank the\nreviewer for prompting this important clarification, which underscores the statistical\nrobustness of our training data for model generalization. The relevant content has been\nadded and revised in the “Dataset Building” section of the manuscript.\nBefore:\nNotably, shorter cyclic peptides (2- and 3-residue) exhibited distinct cyclization\nbehaviors due to their constrained backbones, prompting us to prioritize these subsets\nfor deeper analysis. The final stratified dataset comprised 45 two-residue, 53 three-\nresidue, 120 four-to-six-residue, 69 seven-to-nine-residue, and 19 ten-residue and\nlonger cyclic peptides. This stratified distribution facilitated robust model training and\ngeneralization.\nAfter:\nThe dataset comprises 174 cyclization-viable sequences (labeled “1”) and 132 non-\nviable sequences (labeled “0”), ensuring balanced representation without outcome bias.\nNotably, shorter cyclic peptides (2- and 3-residue) exhibited distinct cyclization\nbehaviors due to their constrained backbones, prompting prioritized analysis of these\nsubsets. Final stratification included 45 two-residue, 53 three-residue, 120 four-to-six-\nresidue, 69 seven-to-nine-residue, and 19 ten-to-fourteen-residue peptides, enabling\nrobust model generalization across diverse peptide architectures.\nReviewer #3\nComment_1:\n“Although the distribution in peptide size is well presented, the authors should\ninclude the amino acid diversity score of the starting library. In other words, how\nsimilar (or different) are the starting sequences in terms of amino acid\nsimilarity/identity and three-dimensional structure? How were the 306 peptides\nselected?”\nOur response:\nWe commend the reviewer’s discerning analysis regarding our dataset’s sequence\ndiversity design. The 306-peptide dataset was meticulously engineered to ensure\ncomprehensive representation of all 20 native amino acids, with each type appearing in\n≥20 distinct sequences to guarantee robust generalization capability. To maximize\npattern discernment, we employed a directed variation strategy: seed sequences\nunderwent residue addition, deletion, or substitution—as exemplified by TDDA (no\ncyclization) yielding successfully cyclized variants TDDAG and GTDDA. These\nminimal sequence alterations revealed critical structure-cyclability relationships more\neffectively than random sampling, while maintaining broad heterogeneity across the\ndataset. This approach achieved optimal generalization capacity without significant\nredundancy. The relevant content has been added and revised in the “Methods” section\nin the manuscript.\nBefore:\nRapid cyclic peptides dataset building\nThe main step of dataset construction is the synthesis of cyclic peptides. The first\nstep is manual DAN resin synthesis. Forty milligrams of DAN resin (0.36 mmol/g\nloading) were used in all experiments in the dataset. The next step is automated\nsynthesis. All cyclic peptides were synthesized on the automated instrument iChemAFS\nalong with control software AutoXpert. The following stock solutions were used for\ncyclic peptide synthesis: Fmoc-protected amino acids including 20 native amino acids.\nActivating agent O-(7-Azabenzotriazol-1-yl)-N, N, N’, N’-tetramethyluronium\nhexafluorophosphate (HATU) as a 0.38 M stock solution in DMF, DIEA (10% v/v in\nDMF), DIEA (1% v/v in DMF) and deprotection stock solution (40% piperidine and 1%\nHCOOH (v/v) in DMF). The automated process includes linear peptide synthesis at\n90 °C, activation at room temperature (25 °C), and cyclization at 50 °C. The reaction\nmixtures were characterized by LC-MS (for more details, see Supplementary\nInformation).\nAfter:\nRapid cyclic peptides dataset building\nComprising 306 unique linear peptides, the dataset was engineered to ensure\ncomprehensive representation of all 20 native amino acids, with each type appearing in\nmore than 20 distinct sequences for robust generalization. Through directed variation\nstrategies, including residue addition (e.g., TDDA to TDDAG), deletion, and\nsubstitution, minimal sequence alterations revealed critical structure-cyclability\nrelationships while maintaining broad heterogeneity without redundancy, as evidenced\nby TDDA (no cyclization) versus TDDAG/GTDDA (successful cyclization).\nThe main step of dataset construction is the synthesis of cyclic peptides. The first\nstep is manual DAN resin synthesis. Forty milligrams of DAN resin (0.36 mmol/g\nloading) were used in all experiments in the dataset. The next step is automated\nsynthesis. All cyclic peptides were synthesized on the automated instrument iChemAFS\nalong with control software AutoXpert. The following stock solutions were used for\ncyclic peptide synthesis: Fmoc-protected amino acids including 20 native amino acids.\nActivating agent O-(7-Azabenzotriazol-1-yl)-N, N, N’, N’-tetramethyluronium\nhexafluorophosphate (HATU) as a 0.38 M stock solution in DMF, DIEA (10% v/v in\nDMF), DIEA (1% v/v in DMF) and deprotection stock solution (40% piperidine and 1%\nHCOOH (v/v) in DMF). The automated process includes linear peptide synthesis at\n90 °C, activation at room temperature (25 °C), and cyclization at 50 °C. The reaction\nmixtures were characterized by LC-MS (for more details, see Supplementary\nInformation).\nComment_2:\n“In assigning the binary classification, “1” signifies that the target molecular\nweight was observed and “0” if it was not. It is unclear if the target molecular\nweight refers exclusively to the parent ion and what degree of precision was considered for\nsuccessful identification. The authors should provide these details. Looking at the\nSupplementary Information, it appears that [M+H]+ and [M+Na]+ are being\nconsidered. The authors should also state if they attempted to validate successful\ncyclizations by checking for adducted masses or smaller fragments.”\nOur response:\nWe gratefully acknowledge the reviewer’s rigorous assessment of our cyclization\nclassification criteria. The binary labeling system requires definitive detection of\n[M+H]⁺ or [M+Na]⁺ parent ions via LC-MS for a “1” designation (successful\ncyclization), while “0” indicates absence of both target monomeric species. Although\nadducted masses (e.g., [2M+H]⁺) and fragment ions were documented during analysis,\nthese were exclusively treated as supplementary diagnostic indicators—never as\nprimary evidence for cyclization success. This stringent classification protocol ensures\nunambiguous interpretation of experimental outcomes. We have updated Section 2.5 of\nthe Supplementary Information accordingly.\nBefore:\nThe dataset was established through two primary processes: synthesis and\ndetection of cyclic peptides. Synthesis was efficiently conducted using iChemAFS on a\n40 mg scale of DAN resin (0.36 mmol/g loading) following standard protocols (refer to\nAutomated cyclic peptide synthesis). For cyclic peptide detection, the mixture obtained\nafter automated synthesis was used directly for LC-MS analysis without additional\nprocessing, with a sample injection volume of 1 µL.\nAfter:\nThe dataset was established through two primary processes: synthesis and\ndetection of cyclic peptides. Synthesis utilized iChemAFS with 40 mg DAN resin (0.36\nmmol/g loading) following standard protocols (refer to Automated Cyclic Peptide\nSynthesis). Post-synthesis mixtures underwent direct LC-MS analysis without\nprocessing (1 µL injection volume). Critical cyclization success criteria mandated\ndetection of [M+H]⁺ or [M+Na]⁺ parent ions for “1” classification, while absence of\nboth monomeric species yielded “0” designation. Adducted masses (e.g., [2M+H]⁺) and\nfragment ions were served as supplementary validation indicators instead of primary\nevidence for cyclization success.\nComment_3:\n“One limitation of the training model is that it only accounts for the twenty native\namino acids. It does not account for non-proteinogenic residues, which are frequently\nincluded in peptides to improve drug-like properties as well as binding affinity and\ntarget selectivity. It would be good to consider peptides harboring some of the most\nfrequently encountered non-proteinogenic residues in the model.”\nOur response:\nWe are grateful for the reviewer’s thoughtful insight regarding the model’s amino\nacid scope. The current framework intentionally focuses on the 20 native residues given\ntheir prevalence in naturally occurring therapeutic cyclopeptides. While non-\nproteinogenic residues offer valuable functional enhancements, their structural\nheterogeneity introduces computational complexities requiring specialized\nmethodologies beyond this study's scope. Incorporating these residues constitutes a\nplanned research initiative for next-phase development, necessitating expanded feature\nengineering and training paradigms. This work establishes a foundational framework\nfor that forthcoming advancement, and we value the reviewer’s perspective in\nhighlighting this critical progression pathway.\nComment_4:\n“In the Supplementary Information, the ‘Found: [M+H]+’ for cyclo-YLHIEGV is\nincorrect. What is written does not match the provided MS spectra.”\nOur response:\nWe profoundly thank the reviewer for this meticulous observation and apologize\nfor the oversight. Upon re-examining the raw mass spectra, we confirm the reported\n[M+H]⁺ value for cyclo-YLHIEGV in the Supplementary Information was incorrect in\nthe annotation. The value has been corrected from “1066.60” to “1166.60” to accurately\nreflect the observed LC-MS data. To ensure data accuracy, we conducted a thorough re-\nexamination of all LC-MS data in the SI. The revised Supplementary Information\nhighlights this correction in yellow. We are grateful for the reviewer's rigorous scrutiny,\nwhich has significantly enhanced the reliability of our dataset.\nBefore:\nLC-MS condition: Method B. MS (ESI) exact mass calcd. for C H N O ,\n66 87 9 10\n[M+H]+: 1166.67; [M+Na]+: 1188.65. Found: [M+H]+: 1066.60.\nAfter:\nLC-MS condition: Method B. MS (ESI) exact mass calcd. for C H N O ,\n66 87 9 10\n[M+H]+: 1166.67; [M+Na]+: 1188.65. Found: [M+H]+: 1166.60.\nComment_5:\n“In the Supplementary Information, some of the peptides form [2M+Na]+ in\naddition to [M+H]+. As outlined in the successful cyclization criteria, this would still\nbe considered successful because it made some of the correct product in addition to the\ndimer. Can the model predict oligomerization based on the current training data? Aside\nfrom cyclization, the risk of oligomerization would be an important parameter for\npeptide medicinal chemists designing sequences.”\nOur response:\nWe are grateful for the reviewer's perceptive comment regarding the observation\nof oligomeric species like [2M+Na]⁺ alongside monomeric ions in the Supplementary\nInformation and the relevance of oligomerization prediction for peptide design. Our\nmodel successfully identifies viable cyclization, including cases where [2M+Na]⁺\ncoexists with the monomeric product, consistent with our established cyclization\ncriteria. However, explicit prediction of oligomerization propensity falls beyond the\nmodel's current capabilities due to fundamental limitations in the training data design.\nEmpirical studies using DAN-resin-based automated synthesis indicate oligomerization\noccurs predominantly in 3-4 residue peptides and exhibits significant, yet poorly\ncharacterized, sequence-dependent behavior (ACS Comb. Sci. 2013, 15, 120–129;\nAmino Acids 2015, 47, 1495–1505). This issue pertains to reaction pathways that\ncompete with cyclization, the mechanisms of which cannot be deduced from sequence\ninformation alone. The development of more efficient methodologies may represent a\npromising approach to address this challenge. We fully concur with the reviewer’s\ninsightful perspective on the critical importance of oligomerization prediction for drug\ndevelopment. With the accumulation of additional relevant data in the future, we aim\nto build upon our current model framework to further develop predictive capabilities\nfor such outcomes. Once again, we sincerely appreciate the reviewer’s forward-\nthinking and highly valuable suggestions.\nWe have carefully enhanced the manuscript based on your constructive suggestions.\nAll modifications in the revised manuscript and supplementary information are marked\nwith yellow highlights and strengthen the work while preserving its content or structure.\nWe sincerely thank you for your time and expertise in reviewing our work, and hope\nour responses and revisions meet your approval.\nSincerely,\nChengxi Li, Ph.D.\nAssistant Professor,\nCollege of Chemical and Biological Engineering, ZJU\nZJU-Hangzhou Global Scientific and Technological Innovation Center, ZJU"}, {"round": 2, "reviewer_comments": {"reviewer_1": "We appreciate the effort the authors have made to address the concerns from the reviewers and improve the paper.\nMain issue\nHowever, there remains one main problem with the paper: in its current state the experimental validation is insufficient (only\n7 peptides are used as a hold out test set). Moreover, given the enrichment in cyclization sites in the evaluation set (there are\n32 positive cyclization sites against 10 negative ones), the expected model accuracy for a model that always predicts “1”\nwould be 76.2% with CycloPepper showing an accuracy of 83%, is only 8% above. It would be more appropriate to report\nbinary classification metrics robust to class imbalance (like Matthew’s correlation coefficient). Alternatively, the discussion of\nthe results should clearly highlight that the expected baseline accuracy for a classifier on this dataset is 76.2% to properly\ncontextualize the results.\nOther issues arising from further inspection\nAfter a closer examination of Figure 3 and the code, the model seems to be always predicting peptide cyclization sites\nwhere the N belongs to Pro to be negative, even in some cases where it is positive. I would recommend an ablation study to\nsee whether the combination of features used in the study is necessary or positive, or whether the one-hot encoding is\nsufficient to make the predictions.\nReading Comment_9, the proportion of labels in the training dataset is 56.9%, which is our closest estimate to the expected\nprobability of a randomly sampled peptide to be cyclizable. However, the expected probability looking only at the evaluation\nset is 76.2%. This highlights the problem with the sample size in the evaluation set, which does not allow for even getting\nappropriate estimators for the expected probability of cyclization sites.\nFinally, after reviewing the code, there is an additional problem with the approach, the MinMaxScaler that is applied to\nX_train in the code block that starts with “#Concatentation” should not be fitted to the whole training dataset, it should be\napplied to each training fold individually as it is part of model building and feature selection.\nWe clarify below some minor points that are still unsolved. These issues are fixable and would only require minor\ncomments.\nRegarding Comment_1:\nThe clarification of the method of evaluation is clear. We continue recommending that the standard deviation or standard\nerror of the mean across the 5 folds is included when reporting model performance, not only the average. The purpose of this\nrecommendation is to give an intuition to readers of how significant are the differences between different approaches\nconsidered in the study. For example, this would affect lines 172, 196, or 199. Similarly, figure 3 should also display error\nbars representing that deviation.\nThis is the bare minimum for Machine learning model reporting (see [1] for a discussion on more robust statistics methods for\ncomparing machine learning models).\nRegarding Comment_6:\nThe similarity between the sequences in training and testing should be reported (see [2]) to give a sense of how\ngeneralizable the approach really is. This includes both random sequences and the sequences from Pseduostellariae radix\n(incidentally, in Figure 4, there is a typo in the species name which should be italicised and the second name in lower case).\nRegarding Comment_1 to Reviewer #3:\nAuthors should include further details on what the seed sequences are. Additionally, if the directed variation strategies were\nperformed according to an algorithm it should be clearly stated and the text should direct to the algorithm in the\nsupplementary; alternatively, it should be clearly stated that this process was manually performed.\nReferences:\n1. Ash JR, Wognum C, Rodríguez-Pérez R, Aldeghi M, Cheng AC, Clevert D-A, et al. Practically significant method\ncomparison protocols for machine learning in small molecule drug discovery. ChemRxiv. 2024; doi:10.26434/chemrxiv-\n2024-6dbwv-v2 This content is a preprint and has not been peer-reviewed.\n2. Walsh I, Fishman D, Garcia-Gasulla D, Titma T, Pollastri G, Harrow J, Psomopoulos FE, Tosatto SC. DOME:\nrecommendations for supervised machine learning validation in biology. Nature methods. 2021 Oct;18(10):1122-7.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "This reviewer thanks the authors for fully addressing the prior critiques. The manuscript is now acceptable for publication."}, "author_response": "Response to Reviewers’ Comments\nDear Reviewers,\nWe sincerely thank you for your rigorous evaluation and insightful critiques of our\nmanuscript (ID: NCOMMS-25-33268A). Your expertise and meticulous attention to\ndetail have significantly contributed to enhancing the scientific quality of our work.\nIn accordance with your valuable recommendations, we have diligently undertaken\nextensive revisions to the manuscript. Each of your comments has been addressed\nthrough substantive modifications in the main text and supplementary materials. Below,\nwe provide a point-by-point response to each of your comments. For ease of reference,\nall reviewer comments are reproduced in italics, and revised content has been explicitly\nhighlighted in blue within the updated documents.\nReviewer #1\nComment_1:\n“in its current state the experimental validation is insufficient (only 7 peptides are\nused as a hold out test set). Moreover, given the enrichment in cyclization sites in the\nevaluation set (there are 32 positive cyclization sites against 10 negative ones), the\nexpected model accuracy for a model that always predicts “1” would be 76.2% with\nCycloPepper showing an accuracy of 83%, is only 8% above.”\nOur response:\nWe are grateful to the reviewer for highlighting these important issues. Some of the\nsequences used in the experimental validation were randomly generated by our program, and\nwe had not initially noticed the imbalance in the validation dataset. To expand the validation\nset and achieve a balanced ratio of positive and negative cyclization sites, we randomly\ngenerated an additional batch of sequences using the same program and manually selected some\nsequences with a higher proportion of negatives for experimental validation. The experimental\nvalidation dataset now consists of 12 peptides with 74 sequences, including 39 positive\ncyclization sites, which account for 24.2% of the training set. We have updated the\ncorresponding content in “Experimental Validation of ML Model” section of the manuscript\nand revised the associated figure accordingly.\nBefore:\nTo assess the predictive performance of our ML model, we implemented a\nvalidation protocol wherein each candidate cyclic peptide sequence was systematically\nevaluated across all potential cyclization sites. For the three randomly generated\nsequences (Fig. 4a–c)—two 7-residue peptides (cyclo-APYCGSI and cyclo-YLHIEGV)\nand one 3-residue peptide (cyclo-KCL)—we synthesized every linear precursor by\nchanging cyclization sites. Specifically, cyclo-KCL yielded three linear precursors\n(KCL, CLK, LKC), while each 7-residue peptide generated seven precursors. Following\nautomated solid-phase synthesis using DAN resin under standard conditions with\nreaction outcomes verified via LC-MS detection of the desired cyclic products. The\nexperimental results demonstrated strong agreement with the model’s predictions, with\ncyclization accuracies of 86% for both cyclo-APYCGSI and cyclo-YLHIEGV, and 100%\nfor cyclo-KCL. These findings underscore the model’s robustness in identifying viable\ncyclization sites.\nAfter:\nTo assess the predictive performance of our ML model, we implemented a\nvalidation protocol wherein each candidate cyclic peptide sequence was systematically\nevaluated across all potential cyclization sites. For the 8 randomly generated sequences\n(Fig. 4a–h): five 7-residue sequences, cyclo-APYCGSI, cyclo-YLHIEGV, cyclo-\nQSVQERS, cyclo- QEYQHEK, cyclo-WMLWEPF, a 6-residue sequnce, cyclo-\nMMVMPY, a 5-residue sequence, cyclo-HMHPW, and a 3-residue sequence, cyclo-\nKCL, we synthesized every linear precursor by changing cyclization sites. Specifically,\ncyclo-KCL yielded three linear precursors (KCL, CLK, LKC), while each 7-residue\npeptide generated seven precursors. Following automated solid-phase synthesis using\nDAN resin under standard conditions with reaction outcomes verified via LC-MS\ndetection of the desired cyclic products. The experimental results demonstrated strong\nagreement with the model’s predictions, with cyclization accuracies of 71% for both\ncyclo-QEYQHEK and cyclo-WMLWEPF, 86% for cyclo-QSVQERS, and 100% for\ncyclo-APYCGSI, cyclo-YLHIEGV, cyclo-KCL, cyclo-HMHPW and cyclo-MMVMPY.\nThese findings underscore the model’s robustness in identifying viable cyclization sites.\nComment_2:\n“the model seems to be always predicting peptide cyclization sites where the N\nbelongs to Pro to be negative, even in some cases where it is positive. I would\nrecommend an ablation study to see whether the combination of features used in the\nstudy is necessary or positive, or whether the one-hot encoding is sufficient to make the\npredictions.”\nOur response:\nWe sincerely thank the reviewer for the valuable suggestion. Following the\nrecommendation, we have conducted an ablation study on all selected features to further assess\ntheir contribution. In this study, each feature was removed from the selected feature set in turn,\nand five-fold cross-validation was performed. The removal of any single feature resulted in a\ndecrease in accuracy, with the removal of the N-Pro feature leading to a 2.5% drop in accuracy,\nindicating that this feature contributes positively to the model’s predictive performance. The\ncorresponding description and figure have been added to Section 3.3 in Supporting\nInformation.\nBefore:\nThere is no relevant content in the Supplementary Information.\nAfter:\nThe following is the newly added content:\nTo further evaluate the contribution of the selected features, an ablation study was\nperformed. In this procedure, each feature was removed individually from the final feature set,\nand the models were retrained and evaluated using 5-fold cross-validation. Model performance\nwas again measured by accuracy. The results showed that the removal of any single feature led\nto a decrease in cross-validation accuracy, indicating that all selected features contributed\npositively to the predictive performance of the models (Supplementary Fig. 7).\n0.84\ny 0.83\nc\na 0.82\nr u 0.81\nc 0.8\nc 0.79\nA\n0.78\n0.77\n0.76\n0.75\nenilesab 82 23 0 42 12 81 22 661 33 523 97 613 93 583 693 74 71 712 504 75 77 121 783 081 304 87 241 441 303 713 143\nFeatures\nSupplementary Fig. 7. Cross-validation Accuracy in Feature Ablation Study for SVC.\nComment_3:\n“Reading Comment_9, the proportion of labels in the training dataset is 56.9%,\nwhich is our closest estimate to the expected probability of a randomly sampled peptide\nto be cyclizable. However, the expected probability looking only at the evaluation set\nis 76.2%. This highlights the problem with the sample size in the evaluation set, which\ndoes not allow for even getting appropriate estimators for the expected probability of\ncyclization sites.”\nOur response:\nWe sincerely thank the reviewer for the constructive comments. In light of the issues raised,\nwe have supplemented the experimental validation dataset and adjusted the probability\naccordingly, as described in Comment 1. Now the expected probability of a randomly sampled\npeptide to be cyclizable in the evaluation set is 52.7% (39 feasible cyclization-sites), which is\nclose to the proportion in the training dataset. We have updated the corresponding content in\nthe “Experimental Validation of ML Model” section of the manuscript.\nBefore:\nAfter conducting 42 synthesis experiments across seven distinct cyclic peptide sequences,\n35 predictions were consistent with experimental results.\nAfter:\nIn the entire experimental validation set, 39 sequences were cyclizable, while 35 were non-\ncyclizable, providing a nearly balanced dataset for evaluating model performance. After\nconducting 74 synthesis experiments across seven distinct cyclic peptide sequences, 64\npredictions were consistent with experimental results.\nComment_4:\n“Finally, after reviewing the code, there is an additional problem with the approach,\nthe MinMaxScaler that is applied to X_train in the code block that starts with\n“#Concatentation” should not be fitted to the whole training dataset, it should be\napplied to each training fold individually as it is part of model building and feature\nselection.”\nOur response:\nWe thank the reviewer for pointing out this critical issue. Proper use of the MinMaxScaler\nis indeed essential, as applying it to the entire dataset may cause data leakage and impair\ngeneralization. We have corrected this error, re-run model selection, training, and prediction, .\nAfter this correction, we also slightly adjusted the model selection strategy by applying feature\nselection uniformly across all candidate models before performance comparison. This led to\nminor changes in the results, which have likewise been updated in both the Manuscript and\nthe Supplementary Information.\nBefore:\nThe original 716-dimensional vector was normalized using the MinMaxScaler\nfunction, resulting in the elimination of all zero dimensions and a final dimensionality\nof 414. This 414-dimensional vector was then utilized as input for twelve different\nmachine learning models. Through five-fold cross-validation, the Support Vector\nClassifier (SVC) displayed the highest average accuracy, achieving a score of 0.71\n(Supplementary Fig. 6).\n0.75\n0.71\ny\nc\na 0.7\nr\nu\nc 0.65\nc\nA\n0.6\ne\ng\na\n0.55\nr\ne\nv\nA\nSupplementary Fig. 6. Average accuracy of 12 models using 414-dimensional features.\nTo identify features significantly impacting model performance from the 414-\ndimensional vector, we conducted forward feature selection using 5-fold cross-\nvalidation within the training data. This process commenced with an empty feature set\nand iteratively added the most impactful feature based on cross-validation accuracy\nimprovement. Feature selection was terminated at 40 features where accuracy gains\nplateaued, yielding a final model accuracy of 0.86 from initial 0.71 (Supplementary\nFig. 7).\n0.9\n0.86\n0.85\ny\nc\na 0.8\nr\nu\nc 0.75\nc\nA\ne 0.7\ng\na\nr\ne 0.65\nv\nA\n0.6\n0.55\n1 6 11 16 21 26 31 36 41 46 51 56\nEpochs\nSupplementary Fig. 7. Average accuracy during epochs in forward feature selection.\nAfter:\nThe original 716-dimensional vector undergoes zero-dimensional elimination,\nresulting in a final dimension of 414. This 414-dimensional vector was then utilized as\ninput for twelve different machine learning models.\nTo identify features significantly impacting model performance from the 414-\ndimensional vector, we conducted forward feature selection using 5-fold cross-\nvalidation within the training data. This process commenced with an empty feature set\nand iteratively added the most impactful feature based on cross-validation accuracy\nimprovement (including MinMaxScalar in each fold). If no improvement in accuracy\nwas observed for five consecutive rounds, the selection process was terminated.\nComparing the five-fold cross-validation results of 12 models after feature selection,\nthe Support Vector Classifier (SVC) displayed the highest average accuracy, achieving\na score of 0.84. The feature selection process concluded when 31 features were\nidentified. Overall, the model’s accuracy was enhanced from 0.68 to 0.84\n(Supplementary Fig. 6).\n0.9\n0.84\n0.85\ny\nc 0.8\na\nr\nu\nc 0.75\nc\nA\ne 0.7\ng\na\nr\ne 0.65\nv\nA\n0.6\n0.55\n1 6 11 16 21 26 31 36\nEpochs\nSupplementary Fig. 6. Average accuracy during epochs in forward feature selection\nfor SVC.\nComment_5:\n“We continue recommending that the standard deviation or standard error of the\nmean across the 5 folds is included when reporting model performance, not only the\naverage.”\nOur response:\nWe express our sincere appreciation for the suggestions provided by the reviewer. We have\nreported the model standard deviations in the “ML Model Selection and Performance\nEvaluation” section in the manuscript.\nBefore:\nModel performance was evaluated using five-fold cross-validation to ensure statistical\nreliability. The GaussianNB classifier exhibited the lowest average accuracy (0.61), while seven\nmodels yielded accuracies within the range of 0.6–0.7. Three models demonstrated improved\nperformance, achieving accuracies between 0.7 and 0.8. Notably, two models surpassed an\naccuracy threshold of 0.8, with the SVC emerging as the top-performing algorithm, delivering\na mean accuracy of 0.86 (Fig. 3). This superior predictive capability highlights the effectiveness\nof SVC in capturing the underlying structure-property relationships within our dataset. Given\nits robust performance, the SVC model was selected for subsequent experimental validation\nand predictive applications.\nAfter:\nModel performance was evaluated using five-fold cross-validation to ensure statistical\nreliability. The GaussianNB classifier displayed the lowest performance (0.74 ± 0.05). Several\nmodels, including RF (0.79 ± 0.06), LR (0.81 ± 0.07), and XGB (0.76 ± 0.06), achieved\nmoderate accuracies within the 0.70-0.80 range. Enhanced performance with relatively low\nvariance was observed for GB (0.82 ± 0.04), Adaboost (0.82 ± 0.05), and the stacking (0.82 ±\n0.06). Notably, KNN (0.83 ± 0.06) and SVC (0.84 ± 0.06) delivered the highest mean accuracies,\nwith SVC providing the optimal balance between accuracy and stability (Fig. 3). Given its\nreliable performance, the SVC model was selected for subsequent experimental validation and\npredictive applications.\nComment_6:\n“Similarly, figure 3 should also display error bars representing that deviation.”\nOur response:\nWe sincerely thank the reviewer for the valuable suggestion. We have added error bars to\nFigure 3 in the manuscript.\nBefore:\nFig.3. Overview of machine learning models. Input features include one-hot encoding sequences, AAC,\nDPC, and domain-specific hand-crafted features. The principle of each encoding is explained in the\nfigure. Model selection was according to the average accuracy values of the models. The GaussianNB\nmodel presented the lowest value: 0.61. The SVC model presented the highest value: 0.86.\nAfter:\nFig.3. Overview of machine learning models. Input features include one-hot encoding sequences, AAC,\nDPC, and domain-specific hand-crafted features. The principle of each encoding is explained in the\nfigure. Model selection was according to the average accuracy values of the models. The GaussianNB\nmodel presented the lowest value: 0.74. The SVC model presented the highest value: 0.84.\nComment_7:\n“The similarity between the sequences in training and testing should be reported\n(see [2]) to give a sense of how generalizable the approach really is. This includes both\nrandom sequences and the sequences from Pseduostellariae radix.”\nOur response:\nWe sincerely thank the reviewer for the constructive comments and insightful suggestions.\nIn accordance with the DOME guidelines, we have calculated the percent identity as the\nsequence similarity between the training set and all sequences used for experimental validation,\nincluding both random sequences and those derived from Pseudostellariae radix. The\ncorresponding results have been added to the “Similarity Evaluation” section of the\nmanuscript and Section 4.2 of the Supporting Information.\nBefore:\nFeaturization\nTwelve ML model architectures were implemented in the Python programming\nenvironment, version 3.7.1. Fivefold cross-validation was used for a random split of 70%\ntraining and 30% held-out testing datasets. ML algorithms were mostly imported from the\nmodule “scikit-learn”.\nAfter:\nFeaturization\nTwelve ML model architectures were implemented in the Python programming\nenvironment, version 3.7.1. Fivefold cross-validation was used for a random split of 70%\ntraining and 30% held-out testing datasets. ML algorithms were mostly imported from the\nmodule “scikit-learn”.\nSimilarity Evaluation\nFollowing the DOME recommendations for supervised machine learning validation in\nbiology, we measured sequence similarity as percent identity, quantifying the independence\nbetween the training set and experimental validation set by computing global pairwise identity\nbetween each experimental amino-acid sequence and all training sequences on the linearized\npeptides. Since cyclic peptides can be represented with different starting positions when\nlinearized, we performed a rotation-invariant audit for high-similarity pairs (initial identity ≥\n60%) (for more details, see Supporting Information Section 4.2).\nComment_8:\n“In Figure 4, there is a typo in the species name which should be italicised and the\nsecond name in lower case”\nOur response:\nWe sincerely thank the reviewer for pointing out this error. The typographical mistake in\nFigure 4, as well as in the manuscript and Supporting Information, has now been corrected.\nBefore:\nFig. 4. Predicted cyclic peptide synthesis results agree with experimental validation. The amide bonds\nrepresenting cyclization sites are marked in color, with results presented in the form of ‘P/E’ within the\ncolored boxes. ‘P’ indicates predicted cyclization (1 = feasible, 0 = infeasible), and ‘E’ indicates\nexperimental outcome (1 = successful, 0 = not observed). Boxes with gray dashed lines indicate a\ndiscrepancy between the experimental and predicted results; otherwise, it indicates that the two results\nalign, confirming the accuracy of the prediction. Cyclic peptides in a), b), and c) are cyclic peptides\ngenerated randomly. Cyclic peptides in d), e), f), and g) are cyclic peptides in Pseudostellariae Radix,\nwhich present tyrosinase inhibitory activity. The accuracy of each cyclic peptide and overall accuracy\nare represented in h).\nAfter:\nFig. 4. Predicted cyclic peptide synthesis results agree with experimental validation. The amide\nbonds representing cyclization sites are marked in color, with results presented in the form of ‘P/E’ within\nthe colored boxes. ‘P’ indicates predicted cyclization (1 = feasible, 0 = infeasible), and ‘E’ indicates\nexperimental outcome (1 = successful, 0 = not observed). The corresponding verification results are\npresented in the form of ‘predicted result/experimental result’ within the colored boxes. If the box is\nsurrounded by a gray dashed line, it indicates a discrepancy between the experimental and predicted\nresults; otherwise, it indicates that the two results align, confirming the accuracy of the prediction. Cyclic\npeptides in a) to h) are cyclic peptides generated randomly. Cyclic peptides in i), j), k), and l) are cyclic\npeptides in Pseudostellariae radix, which present tyrosinase inhibitory activity. The accuracy of each\ncyclic peptide and the overall accuracy are represented in m).\nComment_9:\n“Authors should include further details on what the seed sequences are.\nAdditionally, if the directed variation strategies were performed according to an\nalgorithm it should be clearly stated and the text should direct to the algorithm in the\nsupplementary; alternatively, it should be clearly stated that this process was manually\nperformed.”\nOur response:\nWe appreciate the reviewer’s suggestion for further clarification. A total of 22 seed\nsequences (SFEGMPN, MSFEGMP, YNSFPGM, NGTKGDY, DGAFQAY, LDPGTFI,\nCGDYGTK, HTLGTID, FEGMPNS, RGFACAY, WRDGGTDDA, QVEDGTHYK,\nRHDADGGTD, SEAVDGTAK, MKWPFGPTL, NSGGTDDAD, CMILDPGTF,\nKRDPGTFIL, HVIGPSYFG, HCAYRGFAA, TYRGFAACA, ICWPFGPTL) were initially\ngenerated by a programmatic approach to ensure representation of all 20 proteinogenic amino\nacids. From this pool, subsequences were manually selected to maximize diversity in amino\nacid composition while retaining compatibility with the directed variation strategies, ultimately\nyielding a library of 306 sequences (Supplementary Table 2). This design ensured that the\nresulting variants captured the full range of structural diversity and cyclization potential,\nthereby rendering the dataset comprehensive and robust. The “Methods” section of the\nmanuscript and Supporting Information Section 2.5 have been updated accordingly.\nBefore:\nRapid cyclic peptides dataset building\nComprising 306 unique linear peptides, the dataset was engineered to ensure\ncomprehensive representation of all 20 native amino acids, with each type appearing in more\nthan 20 distinct sequences for robust generalization. Through directed variation strategies,\nincluding residue addition (e.g., TDDA to TDDAG), deletion, and substitution, minimal\nsequence alterations revealed critical structure-cyclability relationships while maintaining\nbroad heterogeneity without redundancy, as evidenced by TDDA (no cyclization) versus\nTDDAG/GTDDA (successful cyclization).\nAfter:\nRapid cyclic peptides dataset building\nComprising 306 unique linear peptides, the dataset was engineered to ensure\ncomprehensive representation of all 20 native amino acids, with each type appearing in more\nthan 20 distinct sequences for robust generalization. The seed sequences were randomly\ngenerated using a programmatic approach to ensure the inclusion of all 20 native amino acids.\nFrom this pool, specific subsequences were selected for the directed variation strategies,\nincluding residue addition (e.g., TDDA to TDDAG), deletion, and substitution. These\nmodifications were manually performed (Supporting Information section 2.5). Minimal\nsequence alterations revealed critical structure-cyclability relationships while maintaining\nbroad heterogeneity without redundancy, as evidenced by TDDA (no cyclization) versus\nTDDAG/GTDDA (successful cyclization).\nWe have endeavored to enhance the manuscript and have implemented several\nmodifications in the revised paper, which are marked with highlights. These changes\ndo not alter the content or structure of the paper. We sincerely appreciate the diligent\nefforts of the Editors/Reviewers and hope that the corrections meet with your approval.\nWe trust that the revised manuscript is now suitable for publication in Nature\nCommunications.\nI look forward to hearing from you.\nSincerely,\nChengxi Li, Ph.D.\nAssistant Professor,\nCollege of Chemical and Biological Engineering, ZJU\nZJU-Hangzhou Global Scientific and Technological Innovation Center, ZJU"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have addressed most of our concerns. There is still the issue of the dataset and evaluation sets being too small for the strong claim of a \"robust generalization\", we do not see anything in the paper to justify that claim. Otherwise, we think the paper can be accepted, provided that the following two issues are addressed:\n- Comment 5: We appreciate the inclusion of standard deviation to the results. With regards to the analysis, it is clear from the standard deviation that there is no significant effect of the choice of learning algorithm (all of them are within 1 std), the way in which the results are expressed seems to suggest the opposite. It is reasonable to choose the model with the highest average performance.\n- Comment 7: Fivefold cross validation cannot be used with a random split of 70% training and 30% test; fivefold means there are five equally sized folds each of them with 20% of the data (100/5=20), training is performed in 4 folds and testing on the fifth, in that way all folds are used for testing once. Did you perform fivefold cross-validation or repeated the experiment five times with 70/30 random split? Both things are mutually exclusive.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "This reviewer thanks the authors for fully addressing the prior critiques. The manuscript is now acceptable for publication."}, "author_response": "Dear Reviewers,\nWe sincerely thank you for your valuable comments on our revised manuscript (ID:\nNCOMMS-25-33268B). We are also grateful for your positive evaluation of the\nrevisions made in the previous round. In this response, we have addressed each of the\nremaining comments point by point. All corresponding changes have been highlighted\nin the revised Manuscript and Supporting Information for your convenience.\nReviewer #1\nComment_1:\n“There is still the issue of the dataset and evaluation sets being too small for the\nstrong claim of a \"robust generalization\", we do not see anything in the paper to justify\nthat claim.”\nOur response:\nWe thank the reviewer for this valuable comment. We fully acknowledge that the limited\nsize of our dataset constrains the strength of any claim regarding the model’s generalization\ncapability. Accordingly, we have revised the manuscript to avoid using strong assertions such\nas “robust generalization” and have appropriately toned down related statements throughout\nboth the main text and the Supporting Information. All corresponding modifications have\nbeen highlighted in the revised version.\nComment_2:\n“With regards to the analysis, it is clear from the standard deviation that there is\nno significant effect of the choice of learning algorithm (all of them are within 1 std),\nthe way in which the results are expressed seems to suggest the opposite. It is reasonable\nto choose the model with the highest average performance.”\nOur response:\nWe sincerely thank the reviewer for this insightful comment regarding the interpretation\nof model performance. We fully agree that the differences among the learning algorithms are\nnot statistically significant, as all results lie within one standard deviation. Following this\nsuggestion, we have revised the relevant descriptions in both the main text and the\nSupplementary Information to eliminate any potential ambiguity. The text now clearly states\nthat, while the performance differences are minor, the model with the highest average\nperformance was selected as the final choice.\nBefore:\n“Model performance was evaluated using five-fold cross-validation to ensure statistical\nreliability. The GaussianNB classifier displayed the lowest performance (0.74 ± 0.05). Several\nmodels, including RF (0.79 ± 0.06), LR (0.81 ± 0.07), and XGB (0.76 ± 0.06), achieved\nmoderate accuracies within the 0.70-0.80 range. Enhanced performance with relatively low\nvariance was observed for GB (0.82 ± 0.04), Adaboost (0.82 ± 0.05), and the stacking (0.82 ±\n0.06). Notably, KNN (0.83 ± 0.06) and SVC (0.84 ± 0.06) delivered the highest mean accuracies,\nwith SVC providing the optimal balance between accuracy and stability (Fig. 3). Given its\nrobust performance, the SVC model was selected for subsequent experimental validation and\npredictive applications.”\nAfter:\n“Model performance was evaluated using five-fold cross-validation to ensure statistical\nreliability. The results indicated that the differences among the learning algorithms were not\nstatistically significant, as all performances fell within one standard deviation. The\nGaussianNB classifier displayed the lowest performance (0.74 ± 0.05), while several models,\nincluding RF (0.79 ± 0.06), LR (0.81 ± 0.07), and XGB (0.76 ± 0.06), achieved moderate\naccuracies in the 0.70–0.80 range. Slightly higher accuracies were obtained for GB (0.82 ±\n0.04), AdaBoost (0.82 ± 0.05), and the stacking model (0.82 ± 0.06). Notably, KNN (0.83 ± 0.06)\nand SVC (0.84 ± 0.06) achieved the highest mean accuracies. Given the close performance and\ncomparable variance, the SVC model with the highest mean accuracy was selected for\nsubsequent experimental validation and predictive applications (Fig. 3).”\nComment_3:\n“Fivefold cross validation cannot be used with a random split of 70% training and\n30% test; fivefold means there are five equally sized folds each of them with 20% of the\ndata (100/5=20), training is performed in 4 folds and testing on the fifth, in that way\nall folds are used for testing once. Did you perform fivefold cross-validation or repeated\nthe experiment five times with 70/30 random split? Both things are mutually exclusive.”\nOur response:\nWe sincerely thank the reviewer for this careful observation. It is absolutely correct that\nfivefold cross-validation and a 70/30 random split are mutually exclusive procedures. In model\nselection part of our study, we performed standard fivefold cross-validation, where the dataset\nwas divided into five equal parts, each used once for testing while the remaining four parts were\nused for training. The statement in the manuscript was an inadvertent mistake in wording. We\nhave corrected the description in the manuscript to accurately reflect this procedure.\nBefore:\nFeaturization\nTwelve ML model architectures were implemented in the Python programming\nenvironment, version 3.7.1. Fivefold cross-validation was used for a random split of 70%\ntraining, and 30% held-out testing datasets. ML algorithms were mostly imported from the\nmodule “scikit-learn”.\nAfter:\nFeaturization\n“Twelve ML model architectures were implemented in the Python programming\nenvironment, version 3.7.1. Fivefold cross-validation was used for evaluating model\nperformance. ML algorithms were mostly imported from the module “scikit-learn”.”\nWe have endeavored to improve the manuscript and have implemented several\nrevisions in the revised version, which are highlighted for clarity. These changes do not\naffect the content or structure of the paper. We sincerely appreciate the constructive\nefforts of the Reviewers and hope that the corrections meet with your approval. We trust\nthat the revised manuscript is now suitable for publication in Nature Communications.\nWe look forward to your response.\nSincerely,\nChengxi Li, Ph.D.\nAssistant Professor,\nCollege of Chemical and Biological Engineering, ZJU\nZJU-Hangzhou Global Scientific and Technological Innovation Center, ZJU"}]

Decision letter