Evidence map›Paper›PMID 41105726›Full record

ArticlePLoS computational biology2025

Memorization bias impacts modeling of alternative conformational states of solute carrier membrane proteins with methods from deep learning.

G V T Swapna, Namita Dube, Monica J Roth, Gaetano T Montelione

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Modeling Alternative Conformational States in CASP16.bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

G V T SwapnaDepartment of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
Namita DubeDepartment of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
Monica J RothDepartment of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway New Jersey, United States of America.
Gaetano T MontelioneDepartment of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, United States of America.ORCID 0000-0002-9440-3059

Funding

Targeting retroviral and virus-like particles for gene and protein deliveryR35GM122518 · NIGMS · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI Monica J. Roth · 2017 to 2026
$5.5M
Hybrid Methods for Dynamic Structure Analysis of Proteins from Pathogenic MicroorganismsR35GM141818 · NIGMS · RENSSELAER POLYTECHNIC INSTITUTE · PI MONTELIONE, GAETANO T · 2021 to 2025
$3.3M
Regional 800 MHz NMR System UpgradeS10OD030482 · OD · RENSSELAER POLYTECHNIC INSTITUTE · PI MONTELIONE, GAETANO T · 2021 to 2021
$1.3M
NIGMS NIH HHS R35 GM122518NIGMS NIH HHS R35 GM141818NIH HHS S10 OD030482
6 · The paper itself

Abstract

The Solute Carrier (SLC) superfamily of integral membrane proteins transport a wide array of small molecules across plasma and organelle membranes, and function as important drug transporters and as viral receptors. They populate different conformational states during the solute transport process, including outward-open, intermediate (occluded), and inward-open conformational states. For some SLC proteins this structural "flipping" corresponds to swapping between conformations of their N-terminal and C-terminal symmetry-related sub-structures. Conventional AlphaFold2, AlphaFold3, or Evolutionary Scale Modeling methods typically generate models for only one of these multiple conformational states of SLC proteins. While several AI-based protocols for modeling multiple conformational states of proteins have been described recently, these methods are often impacted by "memorization" of one of the alternative conformational states, and do not always provide both the inward- and outward-open conformations of SLC proteins. Here we assess the impact of memorization in modeling SLC proteins with AlphaFold2/3, and describe a combined ESM - template-based-modeling process, based on a previously described template-based modeling method that relies on the internal pseudo-symmetry of many SLC proteins, to consistently model the alternate conformational states of SLC proteins. We also demonstrate how the resulting multi-state models can be validated by comparison with sequence-based evolutionary co-variance data (ECs) that encode information about contacts present in the various conformational states adopted by the protein. This simple, rapid, and robust approach for modeling conformational landscapes of pseudo-symmetric SLC proteins is demonstrated for several integral membrane protein transporters, including SLC35F2 the receptor of a feline leukemia virus envelope protein required for viral entry into eukaryotic cells.

Indexed as

Deep LearningMembrane Transport ProteinsSolute Carrier ProteinsComputational BiologyModels, MolecularProtein ConformationMembrane Transport ProteinsSolute Carrier Proteins

Identifiers

PMID41105726
PMCPMC12551959

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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.