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David McCandlish

David McCandlish

Associate Professor
Cancer Center Member

Ph.D., Duke University, 2012

mccandlish@cshl.edu | 516-367-5286

Faculty Profile

Some mutations are harmful but others are benign. How can we predict the effects of mutations, both singly and in combination? Using data from experiments that simultaneously measure the effects of thousands of mutations, I develop computational tools to predict the functional impact of mutations and apply these tools to problems in protein design, molecular evolution, and cancer.

The McCandlish lab develops computational and mathematical tools to analyze and exploit data from high-throughput functional assays. The current focus of the lab is on analyzing data from so-called “deep mutational scanning” experiments. These experiments simultaneously determine, for a single protein, the functional effects of thousands of mutations. By aggregating information across the proteins assayed using this technique, we seek to develop data-driven insights into basic protein biology, improved models of molecular evolution, and more accurate methods for predicting the functional effects of mutations in human genome sequences.

Critically, these data also show that the functional effects of mutations often depend on which other mutations are present in the sequence. We are developing new techniques in statistics and machine learning to infer and interpret the complex patterns of genetic interaction observed in these experiments. Our ultimate goal is to be able to model these sequence-function relationships with sufficient accuracy to guide the construction of a new generation of designed enzymes and drugs, and to be able to predict the evolution of drug resistance phenotypes in both populations of cancer cells and rapidly evolving microbial pathogens.

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All Publications

Density estimation for ordinal biological sequences and its applications

30 Oct 2024 | Physical Review E | 110(4)
Chen, Wei-Chia;  Zhou, Juannan;  McCandlish, David;  

A fitness distribution law for amino-acid replacements

15 Oct 2024 | bioRxiv
Sun, Mengyi;  Stoltzfus, Arlin;  McCandlish, David;  

A side-by-side comparison of variant function measurements using deep mutational scanning and base editing

26 Sep 2024 | bioRxiv
Sokirniy, Ivan;  Inam, Haider;  Tomaszkiewicz, Marta;  Reynolds, Joshua;  McCandlish, David;  Pritchard, Justin;  

Multiple distinct evolutionary mechanisms govern the dynamics of selfish mitochondrial genomes in Caenorhabditis elegans

19 Sep 2024 | Nature Communications | 15(1):8237
Gitschlag, Bryan;  Pereira, Claudia;  Held, James;  McCandlish, David;  Patel, Maulik;  

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models

Jun 2024 | Nature Machine Intelligence | 6(6):701-713
Seitz, E;  McCandlish, D;  Kinney, J;  Koo, P;  

Robust genetic codes enhance protein evolvability

16 May 2024 | PLoS Biology | 22(5):e3002594
Rozhoňová, Hana;  Martí-Gómez, Carlos;  McCandlish, David;  Payne, Joshua;  Agashe, Deepa;  

Symmetry, gauge freedoms, and the interpretability of sequence-function relationships

13 May 2024 | bioRxiv
Posfai, Anna;  McCandlish, David;  Kinney, Justin;  

Gauge fixing for sequence-function relationships

13 May 2024 | bioRxiv
Posfai, Anna;  Zhou, Juannan;  McCandlish, David;  Kinney, Justin;  

Guidelines for releasing a variant effect predictor

16 Apr 2024 | ArXiv
Livesey, Benjamin;  Badonyi, Mihaly;  Dias, Mafalda;  Frazer, Jonathan;  Kumar, Sushant;  Lindorff-Larsen, Kresten;  McCandlish, David;  Orenbuch, Rose;  Shearer, Courtney;  Muffley, Lara;  Foreman, Julia;  Glazer, Andrew;  Lehner, Ben;  Marks, Debora;  Roth, Frederick;  Rubin, Alan;  Starita, Lea;  Marsh, Joseph;  

Specificity, synergy, and mechanisms of splice-modifying drugs

29 Feb 2024 | Nature Communications | 15(1):1880
Ishigami, Yuma;  Wong, Mandy;  Martí-Gómez, Carlos;  Ayaz, Andalus;  Kooshkbaghi, Mahdi;  Hanson, Sonya;  McCandlish, David;  Krainer, Adrian;  Kinney, Justin;  

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