bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/673897

Combining statistical and neural network approaches to derive energy functions for completely flexible protein backbone design

Huang, B.·Xu, Y.·Liu, H.
View original source ↗Export metadata ↓

Abstract

A designable protein backbone is one for which amino acid sequences that stably fold into it exist. To design such backbones, a general method is much needed for continuous sampling and optimization in the backbone conformational space without specific amino acid sequence information. The energy functions driving such sampling and optimization must faithfully recapitulate the characteristically coupled distributions of multiplexes of local and non-local conformational variables in designable backbones. It is also desired that the energy surfaces are continuous and smooth, with easily computable gradients. We combine statistical and neural network (NN) approaches to derive a model named SCUBA, standing for Side-Chain-Unspecialized-Backbone-Arrangement. In this approach, high-dimensional statistical energy surfaces learned from known protein structures are analytically represented as NNs. SCUBA is composed as a sum of NN terms describing local and non-local conformational energies, each NN term derived by first estimating the statistical energies in the corresponding multi-variable space via neighbor-counting (NC) with adaptive cutoffs, and then training the NN with the NC-estimated energies. To determine the relative weights of different energy terms, SCUBA-driven stochastic dynamics (SD) simulations of natural proteins are considered. As initial computational tests of SCUBA, we apply SD simulated annealing to automatically optimize artificially constructed polypeptide backbones of different fold classes. For a majority of the resulting backbones, structurally matching native backbones can be found with Dali Z-scores above 6 and less than 2 [A] displacements of main chain atoms in aligned secondary structures. The results suggest that SCUBA-driven sampling and optimization can be a general tool for protein backbone design with complete conformational flexibility. In addition, the NC-NN approach can be generally applied to develop continuous, noise-filtered multi-variable statistical models from structural data.\n\nLinux executables to setup and run SCUBA SD simulations are publicly available (http://biocomp.ustc.edu.cn/servers/download_scuba.php). Interested readers may contact the authors for source code availability.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Huang, B., Xu, Y., Liu, H.. 2019-06-18. Combining statistical and neural network approaches to derive energy functions for completely flexible protein backbone design. https://doi.org/10.1101/673897

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

PHATE-Derived Ancestry Coordinates Capture Continuous Population Structure in Human Genomic Data

Population genetic data are increasingly visualized using low-dimensional embeddings as ancestry coordinates, yet how these methods represent population structure can depend strongly on sampling and construction choices. Principal component analysis (PCA), when visualized using only leading components, can obscure fine-scale structure, while nonlinear neighbor-based methods such as uniform manifold approximation and projection (UMAP) often fragment geographic and admixture gradients into discrete clusters. Here, we introduce a framework for constructing ancestry coordinates using potential of heat-diffusion for affinity-based trajectory embedding (PHATE), a diffusion-based method that preserves relationships across scales. Across synthetic benchmarks and large genomic datasets, we show that appropriate scale tuning and population balancing allow PHATE to recover geographic organization and admixture gradients. We show that incorrectly tuned neighborhood scale and population imbalance can distort the resulting representations of population structure. These results therefore provide a methodological framework and practical guidance for constructing ancestry visualizations that better reflect continuous human genetic diversity.

bioinformatics↗
bioRxiv2026

FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction

Linear B-cell epitopes are antigenic regions recognised by antibodies that play crucial roles in vaccine development, antibody production, and other therapeutic applications.Their prediction can support peptide-based vaccine candidate screening and diagnostic assay development, but the experimental data required for model development may be distributed across institutions that cannot freely pool their records. We present FedEpitope, a federated framework that adapts the ESM-2 protein language model for peptide-level linear B-cell epitope prediction using Low-Rank Adaptation (LoRA), keeping client-partitioned data local and restricting the communicated trainable model state to approximately 1.11 MB. Simulated benchmarks of FedAvg and FedProx algorithms, evaluated using homology-aware data splits of five biologically heterogeneous, organism-partitioned clients, achieve promising global test performance without centralising training data, with FedAvg outperforming all single-client models in mean AP, F1, and MCC. Additional experiments characterise post-federation personalisation, record-level differential privacy, sensitivity to controlled client heterogeneity, LoRA rank, and generalisation to the independent BCPreds and NetBCE benchmarks, demonstrating the feasibility of parameter-efficient federated protein language model adaptation for data-localised B-cell epitope prediction and the associated trade-offs among predictive performance, personalisation, privacy, communication efficiency, and distribution shift.

bioinformatics↗
bioRxiv2026

Parameter-Efficient Adaptation of Protein Language Models for Dual-Target Antimicrobial Peptide Discovery Under Strict Homology Constraints

The worldwide escalation of antimicrobial resistance (AMR) poses a severe challenge to modern public health, necessitating novel classes of therapeutic agents. Antimicrobial peptides (AMPs) represent highly promising alternatives to small-molecule antibiotics due to their co-evolutionary resilience and rapid membrane disruption dynamics. However, translating computational AMP leads into clinical drug candidates remains bottlenecked by off-target cytotoxicity, predominantly hemolytic activity against mammalian erythrocytes. Although pretrained foundational protein language models (PLMs) have rapidly advanced molecular property prediction, current architectures frequently isolate antibacterial potency and cytotoxicity into disconnected single-task pipelines or succumb to catastrophic performance drops under strict homology-controlled evaluations. Recently, the Quantitative Mapping of Antimicrobial Peptides (QMAP) benchmark proposed that frozen PLM embeddings show weak linearly accessible features for hemolytic activity (HC50) resulting in poor rank correlation ({rho} < 0.25) on out-of-distribution sequence splits. In this work, we introduce an end-to-end multi-task parameter-efficient transfer learning framework designed to simultaneously optimize antibacterial potency and minimize mammalian cytotoxicity without sequence leakage. Built upon ESM-2 (150M), our architecture incorporates Low-Rank Adaptation (LoRA) into self-attention projections, fine-tuning approx 0.70% of model weights. To restore gradient interference originating from disparate membrane lysis mechanisms, we implement an adaptive multi-task loss guided by Kendall homoscedastic uncertainty weighting. Evaluated across predefined QMAP benchmark splits enforcing a 60% maximal sequence identity threshold, our joint architecture remarkably outperforms conventional linear probes, tree-based regressors, and frozen foundational models. The framework achieves an E. coli potency rank correlation of {rho} = 0.6132 (r = 0.6816) and successfully elevates hemolytic toxicity correlation to {rho} = 0.5374 (r = 0.5541). To establish real world utility, we deploy the optimized joint network to screen a curated in silico library of 749 de-novo designed peptides, successfully isolating selective cationic-amphipathic candidates possessing Therapeutic Indices (TI = HC50/MIC) up to 144.03 while effectively deprioritizing broadly lytic motifs (TI < 2.0). These findings demonstrate that parameter-efficient adaptation unlocks fine-grained bioactive structural semantics in foundational PLMs, providing an accurate, scalable framework for therapeutic peptide engineering.

bioinformatics↗