Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
Resurrecting recurrent neural networks for long sequences
Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …
AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences
M Varadi, D Bertoni, P Magana, U Paramval… - Nucleic acids …, 2024 - academic.oup.com
Abstract The AlphaFold Database Protein Structure Database (AlphaFold DB,
https://alphafold. ebi. ac. uk) has significantly impacted structural biology by amassing over …
https://alphafold. ebi. ac. uk) has significantly impacted structural biology by amassing over …
Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations
Engineering stabilized proteins is a fundamental challenge in the development of industrial
and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph …
and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph …
Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
We present the results for CAPRI Round 54, the 5th joint CASP‐CAPRI protein assembly
prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo …
prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo …
Machine learning-aided design and screening of an emergent protein function in synthetic cells
Abstract Recently, utilization of Machine Learning (ML) has led to astonishing progress in
computational protein design, bringing into reach the targeted engineering of proteins for …
computational protein design, bringing into reach the targeted engineering of proteins for …
State-of-the-art in the drug discovery pathway for Chagas disease: A framework for drug development and target validation
JC Gabaldón-Figueira… - … and Reports in …, 2023 - Taylor & Francis
Chagas disease is the most important protozoan infection in the Americas, and constitutes a
significant public health concern throughout the world. Development of new medications …
significant public health concern throughout the world. Development of new medications …
Random, de novo, and conserved proteins: how structure and disorder predictors perform differently
Understanding the emergence and structural characteristics of de novo and random proteins
is crucial for unraveling protein evolution and designing novel enzymes. However …
is crucial for unraveling protein evolution and designing novel enzymes. However …
AI for organic and polymer synthesis
Recent years have witnessed the transformative impact from the integration of artificial
intelligence with organic and polymer synthesis. This synergy offers innovative and …
intelligence with organic and polymer synthesis. This synergy offers innovative and …