Evidence map›Paper›PMID 42875594›Full record

ArticleNature methods2026

LoL-align: sensitive and fast probabilistic protein structure alignment.

Lasse Reifenrath, Michel van Kempen, Gyuri Kim, Soo Hyun Kim, Mohammadreza Radnezhad, Cameron L M Gilchrist, Milot Mirdita, Martin Steinegger, Johannes Söding

Abstract read
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Article in Nature methods, 2026. 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.

Lasse ReifenrathQuantitative and Computational Biology Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.ORCID http://orcid.org/0009-0001-2925-7585
Michel van KempenQuantitative and Computational Biology Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Gyuri KimSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Soo Hyun KimSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Mohammadreza RadnezhadQuantitative and Computational Biology Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.ORCID http://orcid.org/0009-0005-1613-5537
Cameron L M GilchristSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Milot MirditaSchool of Biological Sciences, Seoul National University, Seoul, South Korea.ORCID http://orcid.org/0000-0001-8637-6719
Martin SteineggerSchool of Biological Sciences, Seoul National University, Seoul, South Korea. martin.steinegger@snu.ac.kr.ORCID http://orcid.org/0000-0001-8781-9753
Johannes SödingQuantitative and Computational Biology Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany. soeding@mpinat.mpg.de.ORCID http://orcid.org/0000-0001-9642-8244

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) RTG2984 EvorestKorea Basic Science Institute (KBSI) C539200National Research Foundation of Korea (NRF) RS-2020-NR049543National Research Foundation of Korea (NRF) RS-2020-NR049543, RS-2021-NR061659 and RS-2021-NR056571, RS-2024-00396026National Research Foundation of Korea (NRF) RS-2023-00250470
6 · The paper itself

Abstract

The ubiquitous availability of protein structures permits replacing sequence alignment with more accurate and sensitive structure alignment algorithms. LoL-align maximizes a local log-odds score for proteins to be homologous, given their intra-protein C

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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.