Evidence map›Paper›PMID 40950151›Full record

ArticlebioRxiv : the preprint server for biology2025

Generating functional and multistate proteins with a multimodal diffusion transformer.

Bowen Jing, Anna Sappington, Mihir Bafna, Ravi Shah, Adrina Tang, Rohith Krishna, Adam Klivans, Daniel J Diaz, Bonnie Berger

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Bowen JingMassachusetts Institute of Technology.
Anna SappingtonMassachusetts Institute of Technology.ORCID 0000-0003-1442-0420
Mihir BafnaMassachusetts Institute of Technology.ORCID 0000-0001-8182-5888
Ravi ShahUniversity of Texas at Austin.
Adrina TangMassachusetts Institute of Technology.
Rohith KrishnaUniversity of Washington.ORCID 0009-0003-6387-9622
Adam KlivansUniversity of Texas at Austin.ORCID 0000-0001-6960-2235
Daniel J DiazUniversity of Texas at Austin.ORCID 0000-0002-7891-2128
Bonnie BergerMassachusetts Institute of Technology.ORCID 0000-0002-2724-7228

Funding

Manifold representations and active learning for 21 st century biologyR35GM141861 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2021 to 2025
$1.9M
NIGMS NIH HHS R35 GM141861
6 · The paper itself

Abstract

Generating proteins with the full diversity and complexity of functions found in nature is a grand challenge in protein design. Here, we present ProDiT, a multimodal diffusion model that unifies sequence and structure modeling paradigms to enable the design of functional proteins at scale. Trained on sequences, 3D structures, and annotations for 214M proteins across the evolutionary landscape, ProDiT generates diverse, novel proteins that preserve known active and binding site motifs and can be successfully conditioned on a wide range of molecular functions, spanning 465 Gene Ontology terms. We introduce a diffusion sampling protocol to design proteins with multiple functional states, and demonstrate this protocol by scaffolding enzymatic active sites from carbonic anhydrase and lysozyme to be allosterically deactivated by a calcium effector. Our results showcase ProDiT's unique capacity to satisfy design specifications inaccessible to existing generative models, thereby expanding the protein design toolkit.

Identifiers

PMID40950151
PMCPMC12424740

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.