Author

Robert Blackwell

Simons FoundationORCID 0000-0002-9450-9240Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Robert Blackwell1 paper · The Alan Turing Institute

Biology Disciplines

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Bioinformatics· 2 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:benchmarking· 2 Assay:protein sequence· 2 Research Focus:sequence-to-function modeling· 2 Assay:cryo-EM· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Charlie E. M. StraussiD· 2 shared Daniel BerenbergiD· 2 shared James T. MortoniD· 2 shared Richard BonneauiD· 2 shared Vladimir GligorijevićiD· 2 shared David Silva-SáncheziD· 1 shared Julia Koehler LemaniD· 1 shared Kyunghyun ChoiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Charlie E. M. StraussORCID

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3 papers

Recent·Most cited

Heterogeneity in ligand-bound TRPV1: A comparison of methods in cryo-EM and molecular dynamics simulation

Miro A. Astore, David Silva-Sánchez, Robert Blackwell, Pilar Cossio, Sonya M. Hanson

The paper compares methods for analyzing heterogeneity in TRPV1 cryo-EM data and finds that different methods detect different types of heterogeneity.

Nature Communications · Dec 29, 2025Peer-reviewedBiophysics

TM-Vec: template modeling vectors for fast homology detection and alignment

Tymor Hamamsy, James T. Morton, Daniel Berenberg, Nicholas John Carriero, Vladimir Gligorijević, Robert Blackwell, Charlie E. M. Strauss, Julia Koehler Leman, Kyunghyun Cho, Richard Bonneau

TM-Vec predicts structural similarity from sequence pairs to detect remote homologs, outperforming sequence alignment methods and matching structure-based alignment performance.

bioRxiv · Jul 25, 2022 · PreprintBioinformatics

Protein Structural Alignments From Sequence

James T. Morton, Charlie E. M. Strauss, Robert Blackwell, Daniel Berenberg, Vladimir Gligorijević, Richard Bonneau

The paper introduces DeepBLAST, a method using protein language model embeddings to generate explicit sequence alignments, improving speed and accuracy for structurally similar proteins.

bioRxiv · Nov 3, 2020 · PreprintBioinformatics