The Field Flashcards

get to know the people, labs, and papers that define the field I'm in

1
Q

Bonnie Berger

A

Mathematics professor at MIT and head of the Berger Group.

Papers:
- Topsy-Turvy (PPI predictor)

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2
Q

Debora Marks

A

Computational biologist who runs the Marks Lab in the Department of Systems Biology at Harvard Medical School. Lab vision is to computationally address three critical challenges: protein conformational plasticity in health and disease, genome-wide evaluation of mutations on disease likelihood, antibiotic resistance and personal drug response, and synthetic protein design.

Papers:
- ProteinGym

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3
Q

David Baker

A

Runs the Baker Lab (Institute for Protein Design) at the University of Washington.

Papers/Contributions:
- The entire Rosetta platform

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4
Q

DSMBind

A

Jin et al., Broad Institute, 2023.

An unsupervised binding energy prediction framework that does not need experimental binding data for training. This energy-based model estimates the likelihood of a protein complex via SE(3) denoising score matching (DSM). It is for both protein-protein and protein-ligand predictions. DSMBind maximizes the log-likelihood (minimizes the energy) of crystal structures in a training set.

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5
Q

SE(3) in protein modeling tasks

A

SE(3) can be used to superimpose protein structure for comparative analysis; describe dynamics of protein molecules in simulations where they move and interact; make predictions about protein folding, interactions, and conformational changes

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6
Q

Tranception

A

Marks lab, 2022

A novel transformer architecture leveraging autoregressive predictions and retrieval of homologous sequences at inference to achieve SOTA fitness prediction performance

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7
Q

ProteinGym

A

Marks lab, 2022

An extensive set of multiplexed assays of variant effects, introduced in the Tranception paper.

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8
Q

DeepSequence

A

Marks lab, 2017

A generative, unsupervised VAE-based method for biological sequences. Given a multiple sequence alignment as input, it can be used to predict accessible mutations, extract quantitative features for supervised learning, and generate libraries of new sequences satisfying apparent constraints.

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