Bioinformatics

Structure-aware protein sequence alignment using contrastive learning

1. Ronghui You
2. Yuhao Yi
3. Shanfeng Zhu

Reviewed by Arcadia Science

Protein Set Transformer: A protein-based genome language model to power high diversity viromics

1. Cody Martin
2. Anthony Gitter
3. Karthik Anantharaman

Reviewed by Arcadia Science

Autoprot: Processing, Analysis and Visualization of Proteomics Data in Python

1. Julian Bender
2. Wignand W. D. Mühlhäuser
3. Johannes P. Zimmerman
4. Friedel Drepper
5. Bettina Warscheid

Reviewed by Arcadia Science

GenomeDelta: detecting recent transposable element invasions without repeat library

1. Riccardo Pianezza
2. Anna Haider
3. Robert Kofler

Reviewed by Arcadia Science

Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins

1. Valérie de Crécy-Lagard
2. Raquel Dias
3. Nick Sexson
4. Iddo Friedberg
5. Yifeng Yuan
6. Manal A Swairjo

Reviewed by Arcadia Science

Exploring the accuracy of ab initio prediction methods for viral pseudoknotted RNA structures

1. Vasco Medeiros
2. Jennifer M. Pearl
3. Mia Carboni
4. Ece Er
5. Stamatia Zafeiri

Reviewed by PREreview

Design of linear and cyclic peptide binders of different lengths from protein sequence information

1. Qiuzhen Li
2. Efstathios Nikolaos Vlachos
3. Patrick Bryant

Reviewed by PREreview

Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity

1. Nuwan Bandara
2. Dasun Premathilaka
3. Sachini Chandanayake
4. Sahan Hettiarachchi
5. Vithurshan Varenthirarajah
6. Aravinda Munasinghe
7. Kaushalya Madhawa
8. Subodha Charles

Reviewed by PREreview

FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking

1. Sophia Vincoff
2. Shrey Goel
3. Kseniia Kholina
4. Rishab Pulugurta
5. Pranay Vure
6. Pranam Chatterjee

Reviewed by PREreview

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

1. Victoria Mischley
2. Johannes Maier
3. Jesse Chen
4. John Karanicolas

• Curated by eLife

  eLife assessment

  This study explores simple machine learning frameworks to distinguish between interacting and non-interacting protein pairs, offering solid computational results despite some concerns about dataset generation. The authors demonstrate a modest improvement in AlphaFold-multimers' ability to differentiate these pairs. Using a simple yet sound approach, this work is a valuable contribution to the challenging problem of reconstructing protein-protein interaction networks.

Reviewed by eLife