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Debora S. Marks

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8 papers

2 author rows

JMLR Journal 2025 Journal Article

Biological Sequence Kernels with Guaranteed Flexibility

Alan N. Amin
Debora S. Marks
Eli N. Weinstein

Applying machine learning to biological sequences---DNA, RNA and protein---has enormous potential to advance human health and environmental sustainability. To support such high-stakes applications, it is important to develop models and evaluations that not only capture underlying biology, but also have theoretical guarantees of reliability and performance. In this article, we analyze kernel methods for biological sequences, including both hand-crafted kernels and deep neural network-based kernels. We show that popular biological kernels can severely fail at learning functions or distinguishing distributions. We then develop modified kernels that (1) are universal, characteristic, and metrize the space of distributions, and (2) preserve the underlying biological inductive biases and domain knowledge embedded in the original kernel. Our results rest on novel proof techniques for kernels that handle the structure of biological sequence space--discrete, variable length sequences--and biological notions of sequence similarity. We illustrate our theoretical results in simulation and on real biological data sets. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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ICML Conference 2025 Conference Paper

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction

Ruben Weitzman
Peter Mørch Groth
Lood van Niekerk
Aoi Otani
Yarin Gal
Debora S. Marks
Pascal Notin

Retrieving homologous protein sequences is essential for a broad range of protein modeling tasks such as fitness prediction, protein design, structure modeling, and protein-protein interactions. Traditional workflows have relied on a two-step process: first retrieving homologs via Multiple Sequence Alignments (MSA), then training mod- els on one or more of these alignments. However, MSA-based retrieval is computationally expensive, struggles with highly divergent sequences or complex insertions & deletions patterns, and operates independently of the downstream modeling objective. We introduce Protriever, an end-to-end differentiable framework that learns to retrieve relevant homologs while simultaneously training for the target task. When applied to protein fitness prediction, Protriever achieves state-of-the-art performance compared to sequence-based models that rely on MSA-based homolog retrieval, while being two orders of magnitude faster through efficient vector search. Protriever is both architecture and task-agnostic, and can flexibly adapt to different retrieval strategies and protein databases at inference time – offering a scalable alternative to alignment-centric approaches.

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ICML Conference 2024 Conference Paper

Kernel-Based Evaluation of Conditional Biological Sequence Models

Pierre Glaser
Steffanie Paul
Alissa M. Hummer
Charlotte M. Deane
Debora S. Marks
Alan Nawzad Amin

We propose a set of kernel-based tools to evaluate the designs and tune the hyperparameters of conditional sequence models, with a focus on problems in computational biology. The backbone of our tools is a new measure of discrepancy between the true conditional distribution and the model’s estimate, called the Augmented Conditional Maximum Mean Discrepancy (ACMMD). Provided that the model can be sampled from, the ACMMD can be estimated unbiasedly from data to quantify absolute model fit, integrated within hypothesis tests, and used to evaluate model reliability. We demonstrate the utility of our approach by analyzing a popular protein design model, ProteinMPNN. We are able to reject the hypothesis that ProteinMPNN fits its data for various protein families, and tune the model’s temperature hyperparameter to achieve a better fit.

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ICML Conference 2023 Conference Paper

A Kernelized Stein Discrepancy for Biological Sequences

Alan Nawzad Amin
Eli N. Weinstein
Debora S. Marks

Generative models of biological sequences are a powerful tool for learning from complex sequence data, predicting the effects of mutations, and designing novel biomolecules with desired properties. To evaluate generative models it is important to accurately measure differences between high-dimensional distributions. In this paper we propose the “KSD-B”, a novel divergence measure for distributions over biological sequences that is based on the kernelized Stein discrepancy (KSD). The KSD-B can be evaluated even when the normalizing constant of the model is unknown; it allows for variable length sequences and can take into account biological notions of sequence distance. Unlike previous KSDs over discrete spaces the KSD-B (a) is theoretically guaranteed to detect convergence and non-convergence of distributions over sequence space and (b) can be efficiently estimated in practice. We demonstrate the advantages of the KSD-B on problems with synthetic and real data, and apply it to measure the fit of state-of-the-art machine learning models. Overall, the KSD-B enables rigorous evaluation of generative biological sequence models, allowing the accuracy of models, sampling procedures, and library designs to be checked reliably.

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ICML Conference 2022 Conference Paper

Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval

Pascal Notin
Mafalda Dias
Jonathan Frazer
Javier Marchena-Hurtado
Aidan N. Gomez
Debora S. Marks
Yarin Gal

The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence alignments have been the most successful approaches so far to address these tasks. The performance of these methods is however contingent on the availability of sufficiently deep and diverse alignments for reliable training. Their potential scope is thus limited by the fact many protein families are hard, if not impossible, to align. Large language models trained on massive quantities of non-aligned protein sequences from diverse families address these problems and show potential to eventually bridge the performance gap. We introduce Tranception, a novel transformer architecture leveraging autoregressive predictions and retrieval of homologous sequences at inference to achieve state-of-the-art fitness prediction performance. Given its markedly higher performance on multiple mutants, robustness to shallow alignments and ability to score indels, our approach offers significant gain of scope over existing approaches. To enable more rigorous model testing across a broader range of protein families, we develop ProteinGym – an extensive set of multiplexed assays of variant effects, substantially increasing both the number and diversity of assays compared to existing benchmarks.

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ICML Conference 2021 Conference Paper

A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting

Eli N. Weinstein
Debora S. Marks

Generative probabilistic modeling of biological sequences has widespread existing and potential application across biology and biomedicine, from evolutionary biology to epidemiology to protein design. Many standard sequence analysis methods preprocess data using a multiple sequence alignment (MSA) algorithm, one of the most widely used computational methods in all of science. However, as we show in this article, training generative probabilistic models with MSA preprocessing leads to statistical pathologies in the context of sequence prediction and forecasting. To address these problems, we propose a principled drop-in alternative to MSA preprocessing in the form of a structured observation distribution (the "MuE" distribution). We prove theoretically that the MuE distribution comprehensively generalizes popular methods for inferring biological sequence alignments, and provide a precise characterization of how such biological models have differed from natural language latent alignment models. We show empirically that models that use the MuE as an observation distribution outperform comparable methods across a variety of datasets, and apply MuE models to a novel problem for generative probabilistic sequence models: forecasting pathogen evolution.

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ICLR Conference 2019 Conference Paper

Learning Protein Structure with a Differentiable Simulator

John Ingraham
Adam J. Riesselman
Chris Sander
Debora S. Marks

The Boltzmann distribution is a natural model for many systems, from brains to materials and biomolecules, but is often of limited utility for fitting data because Monte Carlo algorithms are unable to simulate it in available time. This gap between the expressive capabilities and sampling practicalities of energy-based models is exemplified by the protein folding problem, since energy landscapes underlie contemporary knowledge of protein biophysics but computer simulations are challenged to fold all but the smallest proteins from first principles. In this work we aim to bridge the gap between the expressive capacity of energy functions and the practical capabilities of their simulators by using an unrolled Monte Carlo simulation as a model for data. We compose a neural energy function with a novel and efficient simulator based on Langevin dynamics to build an end-to-end-differentiable model of atomic protein structure given amino acid sequence information. We introduce techniques for stabilizing backpropagation under long roll-outs and demonstrate the model's capacity to make multimodal predictions and to, in some cases, generalize to unobserved protein fold types when trained on a large corpus of protein structures.

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ICML Conference 2017 Conference Paper

Variational Inference for Sparse and Undirected Models

John Ingraham
Debora S. Marks

Undirected graphical models are applied in genomics, protein structure prediction, and neuroscience to identify sparse interactions that underlie discrete data. Although Bayesian methods for inference would be favorable in these contexts, they are rarely used because they require doubly intractable Monte Carlo sampling. Here, we develop a framework for scalable Bayesian inference of discrete undirected models based on two new methods. The first is Persistent VI, an algorithm for variational inference of discrete undirected models that avoids doubly intractable MCMC and approximations of the partition function. The second is Fadeout, a reparameterization approach for variational inference under sparsity-inducing priors that captures a posteriori correlations between parameters and hyperparameters with noncentered parameterizations. We find that, together, these methods for variational inference substantially improve learning of sparse undirected graphical models in simulated and real problems from physics and biology.

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