Daniel Berenberg

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

PlasmoFP uses deep learning to predict protein function in Plasmodium species, reducing unannotated proteins from 15-59% to 3-28% and increasing fully annotated proteins from 7-42% to 36-68%.

This study compares the genomics of seven Trichomonas species, revealing convergent genome size expansions in human-infecting lineages and genes involved in host-switching from birds to humans.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

The paper trains a new version of AlphaFold2, OpenFold, and evaluates its generalization ability and learning mechanisms.

TM-Vec predicts structural similarity from sequence pairs to detect remote homologs, outperforming sequence alignment methods and matching structure-based alignment performance.

The paper predicts structures and annotates functions for diverse microbial protein sequences, identifying novel folds and highlighting the saturation of structural space.

This paper introduces a deep learning method for protein design that uses a sequence denoising autoencoder and function predictor to guide sampling towards functional sequences.

This paper introduces a Graph Convolutional Network for predicting protein function and identifying important residues, showing improved accuracy over sequence-based methods.

The paper introduces DeepBLAST, a method using protein language model embeddings to generate explicit sequence alignments, improving speed and accuracy for structurally similar proteins.