Biology subjects

Pefaur, N.

Publications and source records attributed to Pefaur, N..

4 recordsLinked to original sources

bioRxiv2026

Integrating complementary biological information for multi-objective enzyme engineering

Enzyme catalysts are increasingly used for sustainable pharmaceutical manufacturing, but engineering industrial biocatalysts remains challenging as multiple catalytic and developability properties must be optimized simultaneously from limited experimental data. Here, we develop a machine learning-guided multi-objective design framework that integrates sparse functional measurements with complementary evolutionary and structural information to engineer the ketoreductase Gre2. The resulting designs achieved simultaneous improvements in catalytic performance, protein yield, and thermal stability, with selected designs retaining improved performance under process-relevant conditions. More broadly, our results demonstrate that integrating complementary biological information enables efficient multi-objective enzyme engineering from sparse experimental data, providing a general strategy for accelerating industrial biocatalyst development.

bioengineering↗

bioRxiv2026

Directed Evolution in Codon Space

Directed evolution is commonly used in protein engineering, where mature molecules are routinely improved through iterative local search of amino acid space. Here, we extend this principle to coding DNA. We developed a language-model-guided framework that iteratively refined industry-optimized coding sequences of clinical-stage therapeutics through synonymous exploration of codon space. Across 23 antibody-based therapeutics, SynCodonLM-guided refinement significantly increased recombinant expression in CHO cells for 17 molecules (74% responder rate), without significant compromise of product-quality or biophysical attributes. Moreover, changes in model likelihood predicted expression gains more effectively than heuristic statistical or mRNA-structure descriptors, despite no explicit expression objective. Codon-level likelihood also tracked temporal progression in influenza A H1N1 sequences, indicating the model captures evolutionary signal. These results show that even production-optimized sequences retain accessible fitness in synonymous codon space, establishing directed evolution as a practical strategy to improve biologic expression, a key manufacturing bottleneck, without altering protein sequence.

bioengineering↗

bioRxiv2025

Advancing Codon Language Modeling with Synonymous Codon Constrained Masking

Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on 6 of 7 benchmarks sensitive to DNA-level features, including mRNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.

bioinformatics↗

bioRxiv2022

Segmental flexibility of bispecific T-cell engagers regulates the dynamics of immune synapse formation

Bispecific T-cell engagers (TcEs) link T cell receptors to tumor-associated antigens on cancer cells, forming cytotoxic immunological synapses (IS). Close membrane-to-membrane contact ([≤]13 nm) has been proposed as a key mechanism of TcE function. To investigate this and identify potential additional mechanisms, we compared four immunoglobulin G1-based (IgG1) TcE Formats (A-D) targeting CD3{varepsilon} and Her2, designed to create varying intermembrane distances (A B=C>D. In a minimal system for IS formation on SLBs, TcE performance followed the trend A=B=C>D. Addition of close-contact requiring CD58 co-stimulation revealed phospholipase C-{gamma} activation matching cytotoxicity with A>B=C>D. Our findings suggest that, when adhesion is equivalent, TcE potency is determined by two parameters: contact distance and flexibility. Both the close/far-contact formation axis and the low/high flexibility axis significantly impact TcE potency, explaining the similar potency of Format B (close-contact/high flexibility) and C (far-contact/low flexibility). Significance statementBispecific T-cell engagers (TcEs) are immunotherapeutic drugs that trigger the destruction of cancer cells by linking T cells to cancer cell through specific surface molecules (antigens). We designed a series of TcEs with varying distances between their binding sites and flexibilities of the TcE-antigen complexes. By combining structural and functional analyses, we confirmed close-contact formation between T cells and cancer cells as a critical determinant, mediated by co-activating receptors. Furthermore, we also identified molecular flexibility of the TcE-antigen complex as a further critical parameter for TcE potency. These findings provide novel insights into TcE function and highlight the importance of both parameters for future research and the design of improved immunotherapies.

immunology↗