The optimised MCSA tool, validated on a library of 1,000 H2T cyclized peptides of length 18, enables library-scale cyclic peptide sequence alignment and is publicly available at https://github.com/IVB-Generative-Biology/mars-turbo.
Abstract
Head-to-tail (H2T) cyclized peptides are an increasingly important modality in drug discovery, combining high target affinity and selectivity with metabolic stability. Because their underlying chemistry is still that of a linear amino-acid chain, their linear sequence representation is the native input format of main-stream sequence generative models now driving de novo peptide design (Slough et al., 2018; Rettie et al., 2025a;b). Discovering the conserved motifs responsible for a family’s function across a library of such candidates requires a multiple sequence alignment (MSA). Because a cyclic peptide can be linearised at any residue, the alignment must additionally solve for the unknown rotation of each sequence, which is the multiple circular sequence alignment (MCSA) problem. However, leading MCSA heuristics (e.g. Ayad & Pissis, 2017) were tuned for the genomic regime (a few tens of long sequences) and become prohibitively slow on the cyclic peptide library regime (hundreds to thousands of shorter sequences). We close this gap by identifying quality-preserving optimisation opportunities, notably the library-scale preset tailored to short-sequence inputs (algorithmic details in Appendix A), and by adding an orthogonal multi-core and SIMD backend for further performance tuning, which gives near-linear thread scaling on the pairwise-comparison stage. We validate the pipeline on a library of 1,000 H2T cyclized peptides of length 18 targeting the oncoprotein Mouse double minute 2 human homolog (MDM2) produced by an internal peptide-design engine. In this practical setup, the optimised MCSA recovers the underlying positional motif of MDM2 binders at the same fidelity as the original MCSA implementation while running over 650× faster. Our optimised MCSA tool thus enables library-scale cyclic peptide sequence alignment and is publicly available at https://github.com/IVB-Generative-Biology/mars-turbo.
HighMorph is presented, an interaction-guided framework that combines protein–protein interaction information with artificial intelligence for rational cyclic peptide design and provides insights for developing therapeutics targeting challenging protein interfaces.
M. Lan, Chengyun Zhang, Wentong Wang et al.· Journal of Medicinal Chemist...· 0 citations
This review systematically examines the key methodological innovations, including peptide representation learning, multi-modal fusion strategies, multi-label learning paradigms, and emerging predictive frameworks empowered by deep neural architectures and ProtLM-based embeddings, and summarizes the practical applications of these models in peptide database mining, functional mechanism interpretation, and mutation effect prediction.
ApexFold, an environment-conditioned AI framework that combines sequence representations with physicochemical descriptors of the surrounding medium to predict circular-dichroism-derived fractions of α-helical, β-like, and unstructured conformations is developed.
M. D. Torres, Hanqun Cao, César de la Fuente-Núñez· bioRxiv· 0 citations
This resource is designed to facilitate the development of deep learning models that incorporate 3D or 4D (trajectory- or ensemble-based) information to improve the prediction of cyclic peptide membrane permeability.
Wei Liu, Pham Hung Nguyen, Chandra S. Verma et al.· Scientific Data· 0 citations
Peptides are attractive molecular recognition elements because of their compact size, ease of modification, and structural tunability. The design of high-affinity peptides for small molecules lags far behind that of antibodies and aptamers due to the lack of general and effective discovery and optimization strategies. Computational design offers a promising avenue to overcome this bottleneck. Herein, we report a computational design pipeline, termed resampling-iteration-evolution (RIE), for the rapid discovery of high-affinity molecular recognition peptides (MRPs) from large structural and sequence databases. RIE mimics a top-down evolutionary pocket-narrowing process by resampling native three-dimensional binding architectures, preserving pocket cooperativity, and introducing rational mutations to generate affinity-enhanced variants. Using this strategy, we designed MRPs for folic acid (FA), triiodothyronine (T3), and cholic acid (CHD), highlighting its applicability to structurally diverse and clinically important small molecules. Despite being derived from 130-270-residue protein receptors, the resulting 18-mer MRPs exhibited micromolar-level apparent Kd values of 8.0-12.7 μM in DMSO-based systems. By exploiting the small size and facile functionalization of MRPs, highly sensitive sensors for the rapid detection of small molecules were developed through flexible fluorophore modification. These results establish RIE as a rapid and effective in silico platform for the development of peptide binders for small-molecule targets.
Li-Hong Yu, Yun Ma, Yue-Hong Pang et al.· Analytical Chemistry· 0 citations
Antimicrobial peptide (AMP) design is commonly guided by sequence-level descriptors such as cationicity, hydrophobicity, and amphipathicity. However, this design paradigm often relies on empirical intervention or machine-learning prediction, lacks direct support from rigorous quantitative thermodynamic data, and remains difficult to apply to the optimization of known AMP sequences. Here, using the recently proposed WTM-λABF method, we develop an alchemical free-energy framework that incorporates peptide-membrane affinity calculations into sequence reoptimization. Owing to the enhanced sampling efficiency of WTM-λABF, this framework can efficiently handle densely discretized alchemical pathways required for cooperative multiresidue mutations, thereby enabling simultaneous multiresidue substitutions to be evaluated within a single alchemical transformation in each environment, without decomposition into separate single-residue transformations. Following the empirical proposal of candidate mutations, mutation-induced ΔΔG values are used as a membrane-affinity thermodynamic criterion for their interpretation, classification, and prioritization prior to experimental validation. We first validate the framework using a literature-reported membrane-active peptide series, where the calculated ΔΔG values are interpreted together with experimental activity trends and physicochemical descriptors. We then apply the framework to the iterative reoptimization of AMP sequences identified in our previous work. Stepwise multiresidue mutations are first proposed based on empirical design principles and subsequently evaluated using WTM-λABF, thereby identifying mutations that provide favorable contributions to relative membrane affinity and yielding new candidate AMP sequences for subsequent experimental screening. By extending the previously developed WTM-λABF method to mutation-dependent membrane-insertion thermodynamics in medium-length AMP-membrane systems, this work establishes a physically grounded framework that integrates empirical AMP redesign with free-energy-based thermodynamic evaluation of multiresidue reoptimization.
Chun-Suo Tian, Meng-Chen Zhou, Hai-Peng Wang et al.· Journal of Chemical Theory a...· 0 citations
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