Christian Dallago

The paper predicts quaternary structures of viral proteins using AlphaFold2 and AlphaFold-Multimer, identifying new insights into vaccinology and protease targets.

Reward-guided inference-time search over the Proteína-Complexa sequence-structure codesign model yields validated binders across 127 targets, kinases and a free carbohydrate, with codesigned sequences outperforming ProteinMPNN redesign.

FLIP2 adds seven protein fitness datasets and 16 splits for enzymes, light-sensitive proteins, and interactions; simple ridge and zero-shot baselines often rival fine-tuned protein language models.

The paper expands the AlphaFold Protein Structure Database to include proteome-scale quaternary structures, predicting 1.81M high-confidence protein complexes and revealing conserved structural clusters across domains of life.

The paper accelerates protein structure prediction using OpenFold with TensorRT and MMseqs2-GPU, achieving 131x faster inference than AlphaFold2 on specific hardware.

ProFam is an open-source protein family language model that improves fitness prediction and design by leveraging evolutionary context through homologous sequences.

The paper introduces a benchmark for evaluating protein ensemble generators across ordered and disordered proteins and presents a new model that improves performance on disordered regions.

PLMNovo improves de novo peptide sequencing by aligning spectrum and peptide embeddings using pre-trained protein language models.

The paper introduces GPU-accelerated homology search with MMseqs2, achieving faster sequence similarity detection than CPU methods and improving structure prediction speeds.

TMVisDB provides a database and visualization tool for transmembrane proteins, enabling large-scale analysis of predicted structures and topologies without requiring costly AlphaFold3-style predictions.

The paper introduces the Nucleotide Transformer, a foundation model for genomics, showing it can predict molecular phenotypes from DNA sequences with transfer learning across tasks and organisms.

GenSLMs use large language models to analyze SARS-CoV-2 genomes, enabling rapid identification of variants of concern and providing insights into evolutionary dynamics.

LambdaPP provides fast, accessible protein feature predictions including structure and function, using AI models and embeddings from protein Language Models.

TMvisDB provides a resource for browsing and visualizing predicted transmembrane proteins with topological annotations, using AlphaFold DB and TMbed predictions.

This paper reviews recent advances in deep learning for protein design, presents a practical pipeline for sequence-to-property analysis, and suggests a generated protein sequence for biosynthetic applications.

The paper introduces bindEmbed21, a deep learning method using protein embeddings to predict binding residues for metal ions, nucleic acids, and small molecules, achieving high accuracy without using MSAs or structural data.

The paper introduces Biofactoid, a tool that crowdsources structured biological knowledge from authors, enabling efficient human-computer interaction and expanding existing pathway databases.

This paper trains and evaluates language models on protein sequences to predict structural and localization features, achieving state-of-the-art results without evolutionary information.

The paper improves the purity of protein functional families by clustering them using sequence embeddings, doubling the number of pure clusters per family.

PredictProtein provides protein sequence analysis tools, offering structure and function predictions with updates including deep learning and DNA/RNA/protein binding predictions.

This paper proposes using deep learning embeddings to transfer GO annotations based on protein sequence proximity rather than homology, achieving performance comparable to existing methods.

The paper introduces a lightweight deep learning method using protein language model embeddings to predict protein subcellular localization, outperforming state-of-the-art methods without using multiple sequence alignments.

The paper generates 872 structural models of SARS-CoV-2 proteins and creates a structural coverage map to help researchers understand viral mechanisms and immune disruption.

AlignmentViewer provides a web-based tool for visualizing and analyzing large protein family alignments, supporting evolutionary coupling analysis and 3D structure computation.

This paper introduces an end-to-end multitask learning approach using language models to predict protein structure and function without evolutionary alignments, achieving faster predictions with moderate accuracy.

This paper introduces SeqVec, a method to represent protein sequences as continuous vectors using deep learning, achieving better prediction results than methods relying on evolutionary information.

Pathway Commons updates its resource with integrated pathway data, tools, and software for biological pathway analysis.

The paper introduces EVcouplings, a Python framework for coevolutionary sequence analysis that enables structure prediction and mutation effect assessment.