Biology subjects

Kiefer, J. R.

Publications and source records attributed to Kiefer, J. R..

3 recordsLinked to original sources

bioRxiv2026

Rapid generation of antibody conformational ensembles using machine learning

We introduce Cadense, a generative framework for antibody ensemble prediction that produces diverse Fv structures from sequence, informed by conformational dynamics. While a valuable resource, static structure prediction provides only a single snapshot of dynamic proteins, whose motions influence their binding and other properties. This is particularly relevant for antibodies, an important class of molecules for drug discovery, where flexible complementarity determining region (CDR) loops are difficult to model with static approaches. Cadense addresses this gap by generating ensembles that match conformational and biophysical property distributions from molecular dynamics (MD), while reducing the computational burden from days of simulation to seconds of generation. We show Cadense ensembles can recover both apo and holo conformations of the same antibody and serve as effective input to physics-based docking, capturing the conformational flexibility important to properly model antibody antigen interactions. Together, these results establish Cadense as a useful tool for antibody conformational ensemble generation with the promise to enable faster, dynamics-aware antibody design.

bioinformatics↗

bioRxiv2025

Lab-in-the-loop therapeutic antibody design with deep learning

Therapeutic antibody design is a complex multi-property optimization problem with substantial promise for improvement with the application of machine-learning methods. Towards realizing that promise, we introduce "Lab-in-the-loop," a new approach that orchestrates state-of-the-art repertoire mining methods, generative machine learning models, multi-task property predictors, active learning ranking and selection, and in vitro experimentation in a semi-autonomous, iterative optimization loop. By automating the design of antibody variants, property prediction, ranking and selection of designs to assay in the lab, and ingestion of in vitro data, we enable an end-to-end approach to developing computationally-informed therapeutic antibody design pipelines. We apply lab-in-the-loop to eleven seed antibodies obtained via animal immunization with four clinically relevant antigen targets: EGFR, IL-6, HER2, and OSM. Over 1,800 unique antibody variants are tested throughout four rounds of iterative optimization identifying 3-100x better binding variants for all targets and 10/11 seeds, with the best binders exceeding 100 pM affinity, demonstrating a process by which end-to-end machine learning can be developed for therapeutic antibody development.

bioengineering↗

bioRxiv2025

DyAb: sequence-based antibody design and property prediction in a low-data regime

Protein therapeutic design and property prediction are frequently hampered by data scarcity. Here we propose a new model, DyAb, that addresses these issues by leveraging a pair-wise representation to predict differences in protein properties, rather than absolute values. DyAb is built on top of a pre-trained protein language model and achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across molecules targeting three different antigens (EGFR, IL-6, and an internal target), given as few as 100 training data. We employ DyAb in two design contexts: as a ranking model to score combinations of known mutations, and combined with a genetic algorithm to generate new sequences. Our method consistently generates novel antibody candidates with high binding rates, including designs that improve on the binding affinity of the lead molecule by more than ten-fold. DyAb represents a powerful tool for engineering therapeutic protein properties in low data regimes common in early-stage drug development.

bioengineering↗