Biology subjects

Fannjiang, C.

Publications and source records attributed to Fannjiang, C..

5 recordsLinked to original sources

bioRxiv2025

Deep Evolutionary Fitness Inference for Variant Nomination from Directed Evolution

Iterative screening techniques, such as directed evolution, enable high-throughput affinity maturation to optimize binders to molecular interfaces. However, the decision problem of selecting variants from rich, evolved populations to enter low-throughput follow-up methods remains a significant bottleneck. Here, we present evolutionary fitness inference (EVFI) and DeepEVFI, two machine learning methods that model directed evolution from time-series sequencing data, and infer fitness, a variants ability to enrich under selection pressure. Our methods flexibly handle mutation mechanisms and starting populations that may be partially unknown - settings relevant to drug discovery - and achieve strong performance on a diverse set of experimental data. We conducted two experimental directed evolution campaigns, using antibodies and macrocyclic peptides libraries to identify and optimize binders to therapeutically relevant targets. EVFI and DeepEVFI identified tighter binders that were missed by human experts using conventional frequency-based approaches, including "rising stars" with low frequency. Beyond initial hit discovery, EVFI and Deep-EVFI enables labeling large-scale sequence-fitness datasets and identifying variants of initial binders with diverse properties.

bioinformatics↗

bioRxiv2021

Machine learning-based library design improves packaging and diversity of adeno-associated virus (AAV) libraries

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for clinical gene therapy, but they need improvement. AAVs with enhanced properties, such as more efficient and/or cell-type specific infection, can be engineered by creating a large, diverse starting library and screening for desired phenotypes, in some cases iteratively. Although this approach has succeeded in numerous specific cases, such as infecting cell types from the brain to the lung, the starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a general prerequisite for engineering any gene delivery goal. Herein, we develop and showcase a machine learning (ML)-based method for systematically designing more effective starting libraries -- ones that have broadly good packaging capabilities while being as diverse as possible. Such carefully designed but general libraries stand to significantly increase the chance of success in engineering any property of interest. Furthermore, we use this approach to design a clinically-relevant AAV peptide insertion library that achieves 5-fold higher packaging fitness than the state-of-the-art library, with negligible reduction in diversity. We demonstrate the general utility of this designed library on a downstream task to which our approach was agnostic: infection of primary human brain tissue. The ML-designed library had approximately 10-fold more successful variants than the current state-of-the-art library. Not only should our new library serve useful for any number of other engineering goals, but our library design approach itself can also be applied to other types of libraries for AAV and beyond.

bioengineering↗

bioRxiv2021

Combining evolutionary and assay-labelled data for protein fitness prediction

Predictive modelling of protein properties has become increasingly important to the field of machine-learning guided protein engineering. In one of the two existing approaches, evolutionarily-related sequences to a query protein drive the modelling process, without any property measurements from the laboratory. In the other, a set of protein variants of interest are assayed, and then a supervised regression model is estimated with the assay-labelled data. Although a handful of recent methods have shown promise in combining the evolutionary and supervised approaches, this hybrid problem has not been examined in depth, leaving it unclear how practitioners should proceed, and how method developers should build on existing work. Herein, we present a systematic assessment of methods for protein fitness prediction when evolutionary and assay-labelled data are available. We find that a simple baseline approach we introduce is competitive with and often outperforms more sophisticated methods. Moreover, our simple baseline is plug-and-play with a wide variety of established methods, and does not add any substantial computational burden. Our analysis highlights the importance of systematic evaluations and sufficient baselines.

synthetic biology↗

bioRxiv2019

Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens

Zooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individuals in situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studying in situ behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8 Chrysaora fuscescens in Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers on in situ rather than laboratory data is essential.\n\nSummary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scale in situ behavior of zooplankton for long durations.

animal behavior and cognition↗

bioRxiv2018

A deep learning approach to pattern recognition for short DNA sequences

MotivationInferring properties of biological sequences--such as determining the species-of-origin of a DNA sequence or the function of an amino-acid sequence--is a core task in many bioinformatics applications. These tasks are often solved using string-matching to map query sequences to labeled database sequences or via Hidden Markov Model-like pattern matching. In the current work we describe and assess an deep learning approach which trains a deep neural network (DNN) to predict database-derived labels directly from query sequences. ResultsWe demonstrate this DNN performs at state-of-the-art or above levels on a difficult, practically important problem: predicting species-of-origin from short reads of 16S ribosomal DNA. When trained on 16S sequences of over 13,000 distinct species, our DNN achieves read-level species classification accuracy within 2.0% of perfect memorization of training data, and produces more accurate genus-level assignments for reads from held-out species than k-mer, alignment, and taxonomic binning baselines. Moreover, our models exhibit greater robustness than these existing approaches to increasing noise in the query sequences. Finally, we show that these DNNs perform well on experimental 16S mock community dataset. Overall, our results constitute a first step towards our long-term goal of developing a general-purpose deep learning approach to predicting meaningful labels from short biological sequences. AvailabilityTensorFlow training code is available through GitHub (https://github.com/tensorflow/models/tree/master/research). Data in TensorFlow TFRecord format is available on Google Cloud Storage (gs://brain-genomics-public/research/seq2species/). Contactseq2species-interest@google.com Supplementary informationSupplementary data are available in a separate document.

bioinformatics↗