Bioinformatics

Integrative Analysis of Neuroimaging and Microbiome Data Predicts Cognitive Decline in Parkinson’s Disease

1. Büşranur Delice
2. Özkan Ufuk Nalbantoğlu
3. Süleyman Yıldırım

Reviewed by PREreview

Predicting the effect of CRISPR-Cas9-based epigenome editing

1. Sanjit Singh Batra
2. Alan Cabrera
3. Jeffrey P Spence
4. Jacob Goell
5. Selvalakshmi S Anand
6. Isaac B Hilton
7. Yun S Song

• Curated by eLife

  eLife Assessment

  This study presents an advance in efforts to use histone post-translational modification (PTM) data to model gene expression and to predict epigenetic editing activity. Such models are broadly useful to the research community, especially ones that can model and predict epigenetic editing activity, which is novel; additionally, the authors have nicely integrated datasets across cell types into their model. The work is mostly solid, but it would be strengthened by performing further comparisons to existing methods that predict gene expression from PTM data and from more comprehensive functional validation of model-predicted epigenome editing outcomes beyond dCas9-p300 based perturbations. This work will be of interest to the epigenetics and computational modeling communities.

Reviewed by eLife

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 Österroos
7. Mattias Vesterlund
8. Rozbeh Jafari
9. Ioannis Siavelis
10. Helena Bäckvall
11. Santeri Kiviluoto
12. Lukas M Orre
13. Mattias Rantalainen
14. Janne Lehtiö
15. Sören 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

Have protein-ligand co-folding methods moved beyond memorisation?

1. Peter Škrinjar
2. Jérôme Eberhardt
3. Janani Durairaj
4. Torsten Schwede

Reviewed by Arcadia Science

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