@ElePapaleo's saved articles

Structure-based prediction of T cell receptor:peptide-MHC interactions

1. Philip Bradley

• Curated by eLife

  eLife assessment

  The author customises an alpha-fold multimer neural network to predict TCR-pMHC and applies this to the problem of identifying peptides from a limited library, that might engage TCR with a known sequence from a limited list of potential peptides. This is an important structural problem and a useful step that can be further improved through better metrics, comparison to existing approaches, and consideration of the sensitivity of the recognition processes to small changes in structure.

Reviewed by eLife

RAS and PP2A activities converge on epigenetic gene regulation

1. Anna Aakula
2. Mukund Sharma
3. Francesco Tabaro
4. Reetta Nätkin
5. Jesse Kamila
6. Henrik Honkanen
7. Matthieu Schapira
8. Cheryl Arrowsmith
9. Matti Nykter
10. Jukka Westermarck

Reviewed by Review Commons

Curvature Sensing and Membrane Remodeling of the VPS37A N-terminal Domain during Autophagy

1. Yansheng Ye
2. Xinwen Liang
3. Guifang Wang
4. Maria C Bewley
5. Xiaoming Liu
6. John M. Flanagan
7. Hong-Gang Wang
8. Yoshinori Takahashi
9. Fang Tian

Reviewed by preLights

Adaptation dynamics between copy-number and point mutations

1. Isabella Tomanek
2. Călin C Guet

• Curated by eLife

  eLife assessment

  This is an important paper that proposes a novel evolutionary mechanism by which copy-number mutations can slow down the accumulation of point mutations in populations evolving in certain environments. The authors use an evolution experiment in bacteria equipped with a clever reporter system to provide solid evidence that this mechanism indeed operates. This paper will be of broad interest to readers in evolutionary biology and related fields.

Reviewed by eLife

Generative power of a protein language model trained on multiple sequence alignments

1. Damiano Sgarbossa
2. Umberto Lupo
3. Anne-Florence Bitbol

• Curated by eLife

  Evaluation Summary:

  This valuable paper proposes an innovative iterative masking approach that enables models such as the MSA Transformer to generate new protein sequence designs, which are validated using a wide-ranging set of computational experiments. A key strength of the MSA Transformer is the ability to learn and generalize across protein families, enabling impressive performance across a range of downstream tasks. However, to date, these models have not been used to generate new protein sequence designs. The approach proposed in this paper is quite novel, and a number of metrics are used to examine the resulting performance of the MSA Transformer at generating new protein sequences from specific families.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

Reviewed by eLife

Pan-cancer association of DNA repair deficiencies with whole-genome mutational patterns

1. Simon Grund Sørensen
2. Amruta Shrikhande
3. Gustav Alexander Poulsgaard
4. Mikkel Hovden Christensen
5. Johanna Bertl
6. Britt Elmedal Laursen
7. Eva R Hoffmann
8. Jakob Skou Pedersen

• Curated by eLife

  eLife assessment

  This paper will be of interest to researchers in the field of genomic medicine and cancer mutagenesis. It presents predictive models with potential clinical applications that can identify patients with specific gene dysfunction based on characteristic patterns of mutation. The key findings are well supported.

Reviewed by eLife

Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs

1. Ipsita Agarwal
2. Zachary L Fuller
3. Simon R Myers
4. Molly Przeworski

• Curated by eLife

  eLife assessment

  This paper directly estimates the fitness cost of loss-of-function mutations in almost every gene in the human genome, providing an interpretable measure of the severity of mutations. The authors then compare datasets of presumably healthy individuals and individuals affected by severe complex disorders or genetic disorders, finding enrichment of de novo loss-of-function mutations in highly constrained genes among probands alongside other illuminating results. This important study will be useful to researchers interested in interpreting and prioritizing disease-causing mutations and in the process of human evolution. Overall, the approach is elegant and the results are of high quality and compelling.

Reviewed by eLife

Rapid protein stability prediction using deep learning representations

1. Lasse M Blaabjerg
2. Maher M Kassem
3. Lydia L Good
4. Nicolas Jonsson
5. Matteo Cagiada
6. Kristoffer E Johansson
7. Wouter Boomsma
8. Amelie Stein
9. Kresten Lindorff-Larsen

• Curated by eLife

  eLife Assessment:

  Predicting the effect of mutations on protein stability is important both for protein engineering and for helping to decipher the effects of genetic and clinical mutations. The machine learning methodology introduce here is timely in view of the millions of AlphaFold model structures that are now becoming available, which could hypothetically be examined through approaches such as this one. The methodology presented is valuable, but the manuscript would benefit from a substantial amount of comparative data to provide more compelling evidence for the validity of the methods.

Reviewed by eLife

Identify Non-Mutational p53 Functional Deficiency in Human Cancers

1. Qianpeng Li
2. Yang Zhang
3. Sicheng Luo
4. Zhang Zhang
5. Ann L. Oberg
6. David E. Kozono
7. Hua Lu
8. Jann N. Sarkaria
9. Lina Ma
10. Liguo Wang

• Curated by eLife

  eLife assessment:

  This study by Li et al describes an interesting attempt to predict the functional status of the p53 tumor suppressor in tumors where no DNA mutations in p53 could be identified. To this end, the authors employed SVM models to train the algorithm for the detection of 'p53 inactivation' features contrasting normal and tumor tissues. The approach could be a valuable tool for attributing tumors with unknown p53 status. The authors provide solid evidence supporting their findings and the concept of the study is solid, but in its current formulation, some of the bioinformatic analyses are incomplete, particularly related to the selection of associated genes and the potential mechanism(s).

Reviewed by eLife

Structure-guided isoform identification for the human transcriptome

1. Markus J Sommer
2. Sooyoung Cha
3. Ales Varabyou
4. Natalia Rincon
5. Sukhwan Park
6. Ilia Minkin
7. Mihaela Pertea
8. Martin Steinegger
9. Steven L Salzberg

• Curated by eLife

  eLife assessment

  This study applies AlphaFold to the CHESS selection of transcripts with the goal of generating predicted 3D protein structures and a quality measure of folding, the pLDDT score. From these data, the authors build a database for result exploration, documented by several examples, including proteins, where the authors propose the pLDDT score as a measure of presumed superior biological functionality over other isoforms. These results will be highly relevant for anyone working with proteins that occur in different isoforms.

Reviewed by eLife