Bioinformatics

Deep Learning Reveals Endogenous Sterols as Allosteric Modulators of the GPCR-Gα Interface

1. Sanjay Kumar Mohanty
2. Aayushi Mittal
3. Namra
4. Aakash Gaur
5. Subhadeep Duari
6. Saveena Solanki
7. Anmol Kumar Sharma
8. Sakshi Arora
9. Suvendu Kumar
10. Vishakha Gautam
11. Nilesh Kumar Dixit
12. Karthika Subramanian
13. Tarini Shankar Ghosh
14. Debarka Sengupta
15. Shashi Kumar Gupta
16. Natarajan Arul Murugan
17. Deepak Sharma
18. Gaurav Ahuja

• Curated by eLife

  eLife Assessment

  The authors present a computational pipeline for the identification of endogenous allosteric modulators of GPCRs, with experimental validation performed in a yeast system. This approach is valuable for a broad audience, including GPCR structural biologists, molecular pharmacologists, and computational biophysicists. However, the rigor of the computational methods needs to be strengthened to provide stronger evidence for the study's conclusions, which is currently incomplete. The authors should justify their methodological choices and provide greater detail and clarity regarding each computational layer of the pipeline.

Reviewed by eLife

Biophysics-based protein language models for protein engineering

1. Sam Gelman
2. Bryce Johnson
3. Chase Freschlin
4. Arnav Sharma
5. Sameer D’Costa
6. John Peters
7. Anthony Gitter
8. Philip A. Romero

Reviewed by Arcadia Science

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

1. Roozbeh Dehghannasiri
2. George Henderson
3. Rob Bierman
4. Tavor Baharav
5. Kaitlin Chaung
6. Peter Wang
7. Julia Salzman

• Curated by eLife

  eLife Assessment

  This study presents a valuable advance for the analysis of gene expression variation at the level of individual cells by introducing a novel reference-free framework that can detect splicing, fusion, editing, immune-receptor diversity and repeated elements in sequencing data. The evidence supporting these claims is solid, with rigorous validation on simulated datasets and extensive analysis of full-length single-cell sequencing data demonstrating improved performance over existing methods. This work will be of particular interest to researchers developing methods for high-resolution transcriptome analysis and to those studying cellular heterogeneity in health and disease.

Reviewed by eLife, Arcadia Science

Pathway activation model for personalized prediction of drug synergy

1. Quang Thinh Trac
2. Yue Huang
3. Tom Erkers
4. Päivi Östling
5. Anna Bohlin
6. Albin Osterroos
7. Mattias Vesterlund
8. Rozbeh Jafari
9. Ioannis Siavelis
10. Helena Backvall
11. Santeri Kiviluoto
12. Lukas Orre
13. Mattias Rantalainen
14. Janne Lehtiö
15. Soren Lehmann
16. Olli Kallioniemi
17. Yudi Pawitan
18. Trung Nghia Vu

• Curated by eLife

  eLife Assessment

  This valuable study presents a deep learning framework for predicting synergistic drug combinations for cancer treatment in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset. The level of evidence seems solid, although performance on some datasets seems unconvincing and further validation would be required to demonstrate the generalizability of the model and, in turn, its clinical relevance. The reported tool, DIPx, could be of use for personalized drug synergy prediction and exploring the activated pathways related to the effects of drug combinations.

Reviewed by eLife

MorphoNet 2.0: An innovative approach for qualitative assessment and segmentation curation of large-scale 3D time-lapse imaging datasets

1. Benjamin Gallean
2. Tao Laurent
3. Kilian Biasuz
4. Ange Clement
5. Noura Faraj
6. Patrick Lemaire
7. Emmanuel Faure

• Curated by eLife

  eLife Assessment

  This work presents an important technical advancement with the release of MorphoNet 2.0, a user-friendly, standalone platform for 3D+T segmentation and analysis in biological imaging. The authors provide convincing evidence of the tool's capabilities through illustrative use cases, though broader validation against current state-of-the-art tools would strengthen its position. The software's accessibility and versatility make it a resource that will be of value for the bioimaging community, particularly in specialized subfields.

Reviewed by eLife

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

1. Daniel Osorio
2. Parastoo Shahrouzi
3. Xavier Tekpli
4. Vessela N Kristensen
5. Marieke L Kuijjer

• Curated by eLife

  eLife Assessment

  The study conducted by Hurtado et al. offers important insights and solid evidence regarding the prediction of drug combinations for cancer treatment. By leveraging disease-specific drug response profiles and single-cell transcriptional signatures, this research not only demonstrates a novel and effective approach to identifying potential drug synergies but it also enhances our understanding of the underlying mechanisms of drug response prediction.

Reviewed by eLife

A comprehensive antigen-antibody complex database unlocking insights into interaction interface

1. Yuwei Zhou
2. Wenwen Liu
3. Ziru Huang
4. Yushu Gou
5. Siqi Liu
6. Lixu Jiang
7. Yue Yang
8. Jian Huang

• Curated by eLife

  eLife Assessment

  This useful manuscript provides a newly curated database (termed AACDB) of antibody-antigens structural information, alongside annotations that are either taken and from the PDB, or added de-novo. Sequences, structures, and annotations can be easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. The methodology presented for this data curation is solid. The curated dataset will be of broad interest and value to researchers interested in antibody-antigen interactions.

Reviewed by eLife

Identification and classification of ion-channels across the tree of life: Insights into understudied CALHM channels

1. Rahil Taujale
2. Sung Jin Park
3. Nathan Gravel
4. Saber Soleymani
5. Rayna Carter
6. Kennady Boyd
7. Sarah Keuning
8. Zheng Ruan
9. Wei Lü
10. Natarajan Kannan

• Curated by eLife

  eLife Assessment

  The first part of this manuscript describes an interdisciplinary approach to mine the human channelome and discover further ion channel orthologues across diverse organisms. Although the findings and data curation enabled by the new approach are valuable to the ion channel community, as well as to those interested in improved methods for mining sequence space for their protein of interest, this part of the work is incomplete because critical methodological information is missing. Further validation of the improvements this approach shows over others is needed. The second part of the manuscript utilizes the approach described in the first part to delineate co-conserved amino acid patterns in CALHM channels, but the evidence provided to support the role of the identified residues in channel gating is currently inadequate.

Reviewed by eLife

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

1. David Kainer
2. Matthew Lane
3. Kyle A Sullivan
4. J Izaak Miller
5. Mikaela Cashman
6. Mallory Morgan
7. Ashley Cliff
8. Jonathon Romero
9. Angelica Walker
10. D Dakota Blair
11. Hari Chhetri
12. Yongqin Wang
13. Mirko Pavicic
14. Anna Furches
15. Jaclyn Noshay
16. Meghan Drake
17. A J Ireland
18. Ali Missaoui
19. Yun Kang
20. John C Sedbrook
21. Paramvir Dehal
22. Shane Canon
23. Daniel Jacobson

Reviewed by GigaScience

Defining the limits of plant chemical space: challenges and estimations

1. Chloe Engler Hart
2. Yojana Gadiya
3. Tobias Kind
4. Christoph A. Krettler
5. Matthew Gaetz
6. Biswapriya B. Misra
7. David Healey
8. August Allen
9. Viswa Colluru
10. Daniel Domingo-Fernández

Reviewed by GigaScience