Using AI and multiplexed library assays to estimate TCR affinity at scale
Introducing ARLO, a deep learning model for TCR-pMHC binding affinity estimation
We are entering the era of generative biology. At Synteny, we rationally explore biological design space in order to develop programmable protein therapeutics. We move beyond inefficient screening processes, making TCR-based therapeutics scalable and accessible. This is unlocking a new generation of immune therapies to engage targets previously thought undruggable. Central to this mission is our ability to measure and optimise TCR–pMHC affinity at an unprecedented scale.
Effective TCR bispecific therapies depend on high-affinity interactions between a T cell receptor (TCR) and a target peptide–major histocompatibility complex (pMHC) [1]. A fundamental challenge in protein engineering is to generate a TCR sequence with a desired binding affinity (pKd) to a target [2]. Quantifying the affinity of an interaction is therefore crucial.
The gold standard of affinity measurement is surface plasmon resonance (SPR) [3]. This is slow and very expensive to perform (~£700 per interaction), which is impractical for large-scale TCR bispecific therapy discovery. In this post we introduce ARLO, a deep learning model which is able to estimate affinity without collecting SPR measurements. ARLO enables estimation of binding affinities at a fraction of the cost and time.
ARLO is powered by data from MYRIAD, our next-generation experimental assay that captures millions of TCR–pMHC interactions in their native mammalian context. In addition to in vitro binding readouts multiplexed across multiple pMHC targets, MYRIAD generates evidence of functional activation bridging the gap between high-throughput screening and real immune response. We defer further details of MYRIAD to the end of this post, but the germane points are
It is ~105 times cheaper to generate data per interaction than SPR (<£0.01).
It produces sequencing counts data for each interaction.
The counts data are highly noisy.
Each individual datapoint generated by MYRIAD is too noisy to be directly useful for affinity quantification. One solution might be to measure each observation in replicate, but this is expensive and time-consuming. Instead, we adopt a deep learning approach, leveraging the ability of modern models to handle noisy data just like this. Our deep learning model, ARLO, is fine-tuned directly on the output of MYRIAD.
Figure 1 (left) shows that counts, in isolation, convey little about affinity. And yet, after we fine-tune ARLO on the counts data, a close correlation between ARLO scores and SPR affinity measurements materialises (right).
Figure 1. (left) Before fine-tuning ARLO, there is no significant correlation between MYRIAD counts and affinity. Counts per TCR are normalised. (right) After fine-tuning ARLO, scores are predictive of affinity (r = 0.78). Values of pKd are measured using SPR in both plots. All predictions and measurements involve the same pMHC target. (NB: the right plot has more points as it contains some TCRs that were not screened in MYRIAD, including some higher affinity TCRs).
How can ARLO be used to optimise TCRs for affinity?
We would like to use ARLO to guide perturbations to TCR sequences that steer the affinity to a desired level. A well-known limitation of protein foundation models is their lack of sensitivity to single amino acid mutations [4]. This is critical for making rational perturbations, so it is particularly important that ARLO is able to learn this effect. Each point in Figure 2 represents a pair of TCRs that differ by a single mutation. The predictions and measurements are identical to those in Figure 1, except that we are considering only single amino acid mutations. We see that the change in ARLO is able to estimate the changes in pKd.
Figure 2. ARLO is robust to the effects of single amino acid mutations on affinity (r = 0.80).
Having established that ARLO predicts the effects of even minimal changes, we took a candidate TCR and introduced a handful of mutations to increase the ARLO score. Through ARLO-guided sequence perturbations (up to 14 amino acid edits from the candidate sequence), we were able to increase the true affinity of the interaction by a factor of over 104, moving the TCR sequence further away from the candidate TCR with each alteration. Figure 3 shows the results of this process.
Figure 3. ARLO is able to increase the binding affinity of a TCR to a target pMHC through an iterative process.
How much data is required to fine-tune ARLO around a target?
In order to fine-tune ARLO around a given target, we typically screen several million TCRs in MYRIAD. Figure 4 demonstrates that the predictive power of ARLO scales with the number of TCRs screened. We note a log-linear relationship between the scale of data and correlation. Of course, this trend cannot continue indefinitely, but we see that as we continue to scale up our data, we are yet to hit a ceiling in performance.
Figure 4. The predictive power of ARLO is closely correlated to the number of TCRs screened with MYRIAD (r = 0.96).
How is ARLO trained?
MYRIAD involves delivering high diversity TCR libraries into a model cell line, where they can be expressed on the cell surface in their native environment. Each cell expresses on average just one single receptor. pMHC-like reagents couple our targets of interest to a fluorescent dye and bind to TCRs on the cell surface. This enables us to specifically isolate the cells carrying TCRs that bind to those targets and identify their sequences using deep sequencing approaches, which generate sequencing read counts for each interaction. Those counts are used as input for ARLO.
In order to train ARLO, we made a key assumption that for any pair of counts within the same sample, a higher affinity interaction is likely to receive more counts. Based on this, we trained ARLO with a ranking loss objective [5] between pairs of TCRs within the same sample. For each pair of TCRs, if ARLO assigned a higher score to the TCR with a higher count, then there was no contribution to the loss. If it assigned a lower score, the loss was proportional to the score difference. ARLO does not have to draw arbitrary cutoffs to determine binding and non-binding interactions.
ARLO also benefited from large-scale pretraining on other sources of data, before successive rounds of fine-tuning on this objective on counts data for a specific target. The ARLO architecture consists of a modern transformer-based stack [6], similar to frontier protein language models [7].
What does ARLO mean for the future of TCR discovery?
ARLO demonstrates how deep learning can bridge the gap between noisy, high-throughput experimental data and precise biophysical quantities like affinity. By removing the need for costly assays, it enables scalable and rational optimisation of TCR sequences directly from inexpensive experimental screens. We expect ARLO to continue to play a crucial role in our internal drug-discovery programmes.
References
[1] Stone, Jennifer D., and David M. Kranz. “Role of T cell receptor affinity in the efficacy and specificity of adoptive T cell therapies.” Frontiers in immunology 4 (2013): 244.
[2] Riley, Timothy P., and Brian M. Baker. “The intersection of affinity and specificity in the development and optimization of T cell receptor based therapeutics.” Seminars in cell & developmental biology. Vol. 84. Academic Press, 2018.
[3] Schuck, Peter. “Use of surface plasmon resonance to probe the equilibrium and dynamic aspects of interactions between biological macromolecules.” Annual review of biophysics and biomolecular structure 26.1 (1997): 541-566.
[4] Pak, Marina A., et al. “Using AlphaFold to predict the impact of single mutations on protein stability and function.” Plos one 18.3 (2023): e0282689.
[5] Chen, Wei, et al. “Ranking measures and loss functions in learning to rank.” Advances in Neural Information Processing Systems 22 (2009).
[6] Vaswani, Ashish, et al. “Attention is all you need.” Advances in neural information processing systems 30 (2017).
[7] Hayes, Thomas, et al. “Simulating 500 million years of evolution with a language model.” Science 387.6736 (2025): 850-858.
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