Evidence map›Paper›PMID 37786693›Full record

ArticlebioRxiv : the preprint server for biology2023

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

Wen Jun Xie, Dangliang Liu, Xiaoya Wang, Aoxuan Zhang, Qijia Wei, Ashim Nandi, Suwei Dong, Arieh Warshel

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Wen Jun XieDepartment of Chemistry, University of Southern California, Los Angeles, CA, USA.
Dangliang LiuState Key Laboratory of Natural and Biomimetic Drugs, Chemical Biology Center, and School of Pharmaceutical Sciences, Peking University, Beijing, China.
Xiaoya WangState Key Laboratory of Natural and Biomimetic Drugs, Chemical Biology Center, and School of Pharmaceutical Sciences, Peking University, Beijing, China.
Aoxuan ZhangDepartment of Chemistry, University of Southern California, Los Angeles, CA, USA.
Qijia WeiState Key Laboratory of Natural and Biomimetic Drugs, Chemical Biology Center, and School of Pharmaceutical Sciences, Peking University, Beijing, China.
Ashim NandiDepartment of Chemistry, University of Southern California, Los Angeles, CA, USA.
Suwei DongState Key Laboratory of Natural and Biomimetic Drugs, Chemical Biology Center, and School of Pharmaceutical Sciences, Peking University, Beijing, China.
Arieh WarshelDepartment of Chemistry, University of Southern California, Los Angeles, CA, USA.
Peking University · CNUniversity of Southern California · US

Funding

Multiscale Simulations of Biological Systems and ProcessesR35GM122472 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ARIEH WARSHEL · 2017 to 2026
$5.0M
NIGMS NIH HHS R35 GM122472
6 · The paper itself

Abstract

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze

Identifiers

PMID37786693
PMCPMC10541610
OpenAlexW4386948514

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.