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Beta notice: This application is a beta release. Features and outputs may change, and results may be incomplete or contain errors.
This application is a peptide analysis tool designed to support early-stage sequence evaluation and synthesis planning. It estimates aggregation propensity and SPPS synthesis difficulty, calculates key physicochemical properties, and provides structure visualization tools.
The aggregation and synthesis analysis scans the sequence to highlight regions that may aggregate on resin and reduce coupling efficiency. The assessment considers local aggregation trends, stronger impact near the C-terminus, peptide length effects, and amino acid composition. Results are intended as comparative guidance rather than a guarantee of experimental outcome.
The Properties module reports commonly used descriptors such as monoisotopic mass, ε280, GRAVY, aliphatic index, predicted pI, estimated molecular dimensions, and amino acid composition. Two-dimensional structures and SMILES are generated using RDKit.
3D conformers are generated using PyPept and displayed interactively. To keep the web app responsive, only limited optimization is performed; therefore, the resulting conformations should be treated as approximate visual models rather than validated structures.
For high-confidence structural modeling, design support, or a more accurate assessment of synthesis feasibility (including timelines and quotations), please contact us . Our team can provide a CADD service using advanced software and experienced modelers, and an experienced peptide chemist can review sequences and propose optimized synthesis strategies, including for complex or aggregation-prone peptides.
This tool is intended for research and development support and provides heuristic, model-based estimates for planning purposes. Outputs—especially synthesis difficulty assessments and 3D conformers—should be treated as indicative and should be reviewed by an experienced scientist/peptide chemist before informing experimental work.
Concept Life Sciences is a contract research organization (CRO) serving the global life sciences industry. For more than 25 years, we have provided consultative and collaborative drug discovery and development services, supported by experienced scientists and advanced laboratory capabilities.
Our peptide services help progress from biological insight to development-ready peptide candidates. By integrating custom peptide synthesis, computational peptide design, peptide ADME analysis, biology, and adapted toxicology within a coordinated workflow, we help generate reliable data, reduce iteration cycles, and streamline project progression.
The synthesis difficulty and aggregation profile estimate regions of a peptide sequence that may present challenges during solid-phase peptide synthesis (SPPS). Aggregation of resin-bound peptide chains can reduce reagent accessibility, decrease coupling efficiency, and increase the risk of truncations or deletions.
Each amino acid is assigned an aggregation tendency value derived from literature data, and the sequence is analyzed using a sliding-window approach to estimate local aggregation behavior along the chain.
Hydrophobicity-related properties, including GRAVY, are calculated using hydropathy parameters described by Kyte and Doolittle.
Predictions are heuristic estimates intended to provide comparative guidance for sequence design and planning. Experimental outcomes depend strongly on synthesis protocol, resin, solvent, and other practical factors and should be reviewed by an experienced peptide chemist.
Krchnák, V., Flegelová, Z., Vágner, J. Aggregation of resin-bound peptides during
solid-phase peptide synthesis: Prediction of difficult sequences.
Int. J. Peptide Protein Res. 42, 450–454 (1993).
Kyte, J., Doolittle, R.F. A simple method for displaying the hydropathic character of a protein.
J. Mol. Biol. 157(1), 105–132 (1982).
The Charge vs pH profile estimates the theoretical net charge of a peptide as a function of pH. The calculation considers all ionisable groups in the sequence, including the N-terminus, C-terminus, and side chains of charged amino acids.
At each pH value, the fractional protonation state of each ionisable group is calculated using the Henderson–Hasselbalch relationship and literature pKa values. The total net charge is obtained by summing the contributions of all ionisable groups across the peptide. The predicted isoelectric point (pI) is estimated as the pH at which the net charge approaches zero.
Non-natural amino acids: Where supported, non-natural residues are handled using approximate ionisation parameters. In this implementation, ornithine uses a reported side-chain pKa of 10.76 (δ-amine), and pyrrolysine (O) is approximated as lysine-like for ionisation (using the lysine ε-amine pKa 10.53). These approximations may be less accurate than predictions for standard amino acids.
These values represent theoretical estimates. Experimental charge and pI may vary depending on buffer composition, ionic strength, temperature, neighboring residues, and structural effects.
Stryer, L., Berg, J.M., Tymoczko, J.L. Biochemistry, 5th ed. W.H. Freeman.
Bjellqvist, B. et al. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their
amino acid sequences. Electrophoresis 14, 1023–1031 (1993).
Srinivasan, G., James, C.M., Krzycki, J.A. Pyrrolysine: A 22nd Amino Acid in the Genetic Code.
Science 296, 1459–1462 (2002).
Sigma-Aldrich Technical Article: L-Ornithine properties (pKa values 1.94, 8.65, 10.76).
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This module reports several calculated descriptors derived directly from the peptide sequence:
Non-natural amino acids: Where supported, non-natural or modified residues are handled using approximate parameters. In particular, GRAVY values for non-natural amino acids are estimated based on similarity to natural amino acids or representative literature values and may therefore be less accurate than predictions for standard residues.
Aggregation Potential [Pa]: Krchnák, V., Flegelová, Z., Vágner, J.
Int. J. Peptide Protein Res. 42, 450–454 (1993).
Kyte, J., Doolittle, R.F. A simple method for displaying the hydropathic character of a protein.
J. Mol. Biol. 157(1), 105–132 (1982).
This module provides sequence-to-structure utilities for rapid visualization. For accurate, project-grade structural modeling and design support, please contact us — we offer CADD services delivered by experienced computational chemists using cutting-edge software (e.g., Schrödinger Maestro).
3D coordinates are generated using a fast embedding workflow designed for web responsiveness, relying primarily on RDKit’s ETKDGv3 (Experimental Torsion Knowledge Distance Geometry) method. Only limited optimization is performed; therefore, the resulting conformations should be treated as approximate visual models rather than validated structures.
Known issues. Very aromatic-rich sequences (approximately more than 20 aromatic residues) may fail during RDKit sanitization or kekulization. These validation checks are intentionally retained to ensure chemical consistency and reliability of generated structures, and we aim to address this limitation in future updates. Protonation states and covalent connectivity are assigned using chemical rules. However, because no full energy minimization or solvent refinement is performed in this web tool, local geometries— especially for flexible side chains, protonated groups (e.g., NH₃⁺/iminium), and hydrogen-bond orientations—may not correspond to an energetically preferred arrangement. For physically refined structures, export the model and run an external minimization/solvent refinement workflow. For cyclic peptides, conformer generation may sometimes yield cis amide geometries due to perceived ring constraints during embedding. Experimentally, trans amide geometries are often preferred in cyclic peptides, so such structures should be interpreted with care and used primarily for visualization.
Ochoa, R., Brown, J.B., Fox, T.
pyPept: a python library to generate atomistic 2D and 3D representations of peptides.
Journal of Cheminformatics (2023).
pyPept (Boehringer Ingelheim) — GitHub repository:
https://github.com/Boehringer-Ingelheim/pyPept
This software is provided for research and development support, sequence evaluation, and planning purposes only. The calculations, models, and visualizations generated are based on literature data, empirical relationships, and computational heuristics and should be interpreted as indicative estimates rather than definitive experimental predictions.
Predicted values—including aggregation propensity, synthesis difficulty, physicochemical properties, charge profiles, and structural conformations—may differ from experimental results. Actual behavior depends on many factors, including synthesis protocol, resin and solvent system, concentration, temperature, buffer composition, neighboring residues, and other experimental conditions.
Three-dimensional structures generated within this application are intended for visualization and qualitative interpretation only. Limited conformational sampling and optimization are performed to maintain web performance, and the resulting conformations should not be considered validated structural models or used as the sole basis for structural or design decisions.
Predictions related to synthesis feasibility should be reviewed by an experienced peptide chemist before being used to guide experimental work. Structural outputs intended for modeling, docking, or quantitative analysis should be refined using specialized computational methods.
Where non-natural amino acids, modifications, or approximated parameters are used, prediction accuracy may be reduced.
For accurate synthesis planning, structural modeling, or project-grade computational analysis, specialist services and expert review are recommended.
Concept Life Sciences makes no warranty or representation, express or implied, that the results generated by this software are complete, accurate, or suitable for any specific research purpose.
To the maximum extent permitted by applicable law, Concept Life Sciences shall not be liable for any direct, indirect, incidental, or consequential damages, including but not limited to experimental failure, loss of research materials, loss of data, project delays, business interruption, or loss of profits arising from the use of this software or reliance on its outputs.
This application is provided by Concept Life Sciences. In addition to the disclaimers and limitations set out herein, use of this application is also subject to the Concept Life Sciences website disclaimer and applicable terms of use. The full website disclaimer, which applies in conjunction with this disclaimer, is available at: https://www.conceptlifesciences.com/policies/website-disclaimer .
This application uses third-party open-source software. Licenses and attributions are provided below in accordance with the respective license terms.
Chemical informatics toolkit used for structure generation and SMILES handling.
License: BSD 3-Clause
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Library used to generate peptide 2D/3D structures from BILN representations.
License: MIT
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Interactive molecular visualization library for displaying 3D peptide structures.
License: MIT
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License: MIT
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License: MIT
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License: MIT
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JavaScript library used for DOM manipulation and event handling.
License: MIT
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Collection of Python tools for computational biology and bioinformatics.
License: BSD-style
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