cs.LGMar 3, 2026

Deep learning-guided evolutionary optimization for protein design

Authors: Erik Hartman, Di Tang, Johan Malmström

Organizations: Division of Infection Medicine, Faculty of Medicine, Lund University, Sweden

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Abstract

Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of Streptococcus pneumoniae. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at GitHub.

Figures & tables

Figure 1: BoGA couples evolutionary optimization with Bayesian selection for sequence design. a Schematic of the optimization cycle. From an initial evaluated dataset D0\mathcal{D}_{0} , an elite set SkS_{k} is chosen from a leaderboard where each sequence is ranked by its fitness f(x)f(\mathbf{x}) . A proposer (genetic mutations: substitutions/insertions/deletions) generates a pool of candidates X′\mathcal{X}^{\prime} of size kproposek_{\text{propose}} . Sequences are embedded and scored by a surrogate model f^θ\hat{f}_{\theta} ; an acquisition function α\alpha selects the mselectm_{\text{select}} top candidates for explicit evaluation. The fitness function is determined by the user, and can utilize properties derived from the amino acid sequence, the folded structure, and/or interchain interactions defined by a folded complex. The newly scored sequences are added to the dataset Dt\mathcal{D}_{t} and the loop is repeated. b Conceptual search in sequence space. A standard GA (orange) evaluates many proposals, including those that decrease fitness, whereas BoGA (blue) uses the surrogate to discard low-value proposals (gray) and concentrates evaluations to proposals that are more likely to increase fitness. c Example of optimization trajectories when maximizing molecular weight, showing improved performance as kproposek_{\text{propose}} increases.
Figure 2: BoGA improves optimization efficiency for sequence- and structure-level objectives. a Optimization of β\beta -sheet fraction (fraction of E, M, A, and L residues). The left panel shows optimization trajectories for different values of kproposek_{\text{propose}} , and the right panel shows the distribution of fitness values at various stages of optimization. The inset shows the surrogate model R2R^{2} over the course of optimization. b Similar layout as in a but the objective function is the normalized hydrophobic moment (uHrel). c Structure-guided optimization using AlphaFold 2-predicted secondary structure weighted by predicted TM-score (pTM). The left panel shows optimization trajectories and violinplots show the distribution of fitness values for the last 10 generations. The right panel shows a triplot of the secondary structures of samples for the run with kpropose=500k_{\text{propose}}=500 .
Figure 3: BoGA enables efficient design of peptide binders targeting pneumolysin. a Illustration of the neutralizing monoclonal antibody and PLY complex. PLY is shown in gray and the antibody in green. The model at the bottom represents domain 4. b Optimization trajectories for the binder score across 100 generations for kpropose=10k_{\mathrm{propose}}=10 (teal) and kpropose=500k_{\mathrm{propose}}=500 (purple). The points indicate individual evaluated candidates; solid lines are running top quartile means. Right: Kernel density estimates of the binding score distribution during early (0-10), intermediate (10-50), and late (50-100) generations, showing that larger proposal pools accelerate discovery of high-scoring binders. c Relationship between predicted interface pTM (ipTM) and peptide predicted aligned error (PAE) for evaluated sequences. Larger kproposek_{\mathrm{propose}} increases sampling of high-confidence, low-PAE binders. The dashed lines show cutoffs for ipTM=0.9 and PAE=5 Å. d Outline of the post-optimization refinement approach. The top 100 BoGA sequences were subjected to three rounds of sequence recovery using ProteinMPNN and FastRelax, resulting in a total of 400 candidates, which are subsequently re-docked to the full-length PLY and scored. e pLDDT versus ipTM for refined candidates. Points are colored by Boltz-2 interface distance score. High-confidence binders cluster at high ipTM and high pLDDT (upper right quadrant, dashed lines). f Example predicted complexes (Boltz-2) of top-performing binders (cyan) bound to PLY (gray). Filtering of the top candidates using quality, solubility and structural consistency yielded a final set of 41 high-confidence binders. g Orthogonal structure prediction using AlphaFold 3 and Boltz 2 for one of the top binders. Both models support a consistent binding pose with high interface confidence (ipTM = 0.85 for AlphaFold 3; ipTM = 0.92 for Boltz 2). Estimated binding free energy ( ΔG\Delta G ) from PyRosetta [ 34 ] indicates a strongly favorable interaction.

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