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.
Abstract
Biomolecular structure is commonly predicted from sequence as a single structural model, yet many molecules function through conformational ensembles that reorganize with their surroundings. Whether such structural responsiveness can be learned jointly from sequence and environmental context remains unresolved. Here, we use short peptides as an experimentally tractable system to test this principle. We assembled a large experimental multi-environment dataset for peptides’ secondary structure, comprising more than 1,500 peptides and more than 5,500 peptide–environment observations across aqueous, co-solvent, and membrane-mimicking conditions. We developed 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. In two later-collected panels excluded from model development, ApexFold captured the direction and magnitude of environment-induced structural redistribution and the peptide-specific degree of plasticity, while outperforming solvent-agnostic and composition-based baselines. Static structural references, which return a single conformation, cannot represent these condition-dependent response profiles. These results establish structural responsiveness as a learnable property of sequence and environment, extending biomolecular prediction beyond static structure toward predicting—and ultimately designing—how molecules respond to the contexts in which they function.
Cyclic peptides are a promising therapeutic modality, offering the potential to target challenging intracellular protein-protein interactions involved in cancer and other diseases. However, their clinical utility is frequently restricted by poor membrane permeability. While deep learning offers new methodologies to predict permeability, current models are limited by a reliance on 2D molecular representations that fail to capture the conformational flexibility inherent to macrocycles. Existing 3D resources also lack physics-based sampling of conformational dynamics across solvent environments that are critical for membrane permeability. To bridge this gap, we present CycPeptMPDB-4D, a comprehensive dataset comprising atomistic molecular dynamics trajectories for 5,160 structurally diverse cyclic peptides, including unnatural, N-methylated, and D-residues in circle and lariat topologies. Each peptide was simulated using the AMBER14SB force field in both explicit water and hexane environments for 50 nanoseconds to generate conformational ensembles in aqueous and membrane-mimicking phases. The trajectories capture the “chameleon-like” property, evidenced by markedly reduced conformational flexibility and polar surface area in the hydrophobic phase. Technical validation demonstrates that the simulated ensembles are in high agreement with experimental NMR data, covering NMR conformers within an RMSD of 1.6 Å. The dataset provides clustered ensembles, representative structures, and specialized descriptors such as desolvation free energy. 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, Nguyen Hung Pham, Chandra S. Verma et al.· Scientific Data· 0 citations
It is demonstrated that permeability emerges from ensemble reorganization rather than a single conformational transition, establishing a generalizable framework for cyclic peptide design and ADMET prediction.
Sicheng Wen, Yang Wang, Yue Qian· Journal of Chemical Informat...· 0 citations
Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states, provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.
Hassan Nadeem, D. Kleiman, Yuming Zhou et al.· bioRxiv· 0 citations
This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
Wengan He, Yongsheng Luo, Lihong Jiang et al.· 0 citations
Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.
Vilya Research Pascal Sturmfels, Naozumi Hiranuma, M. Salem et al.· 0 citations
Together, these insights position conformational dynamics at the center of understanding and engineering the evolutionary logic of protein function, opening the door to study how proteins are tuned to operate under the nonequilibrium conditions of living cells.
Sixto M. Herrera, Elías Manríquez-Benítez, Exequiel Medina· Current Opinion in Structura...· 0 citations
