COVID-19

intDesc-AbMut: Describing and understanding how antibody mutations impact their environmental interactions1

Previously, we proposed a double-point mutation (DPM) strategy involving the simultaneous substitution of two amino acids to optimize antibodies. By selecting mutants based on the criterion that favorable interactions between mutated residues and their local environments are preserved or enhanced, improvements in antibody affinity were achieved. Nonetheless, manual extraction of these interactions...

Computational redesign of a thermostable T7 RNA polymerase

T7 RNA polymerase (T7 RNAP) is a foundational enzyme for biotechnology, but its utility for many potential applications is limited by low thermal stability of 43-44°C. While stabilized variants exist, the most stable commercial version has a proprietary sequence. In this work we developed a highly stable T7 RNAP using structure-based computational design. We combined mutations from previous stabil...

Lab-in-the-loop therapeutic antibody design with deep learning

Therapeutic antibody design is a complex multi-property optimization problem with substantial promise for improvement with the application of machine-...

H3BERTa: A CDR-H3 specific language model for antibody repertoire analysis

Antibodies are central to immune defense and therapeutic design, yet predicting which sequences confer functional activity remains challenging. Deep l...

Integrative transcriptomic analysis identifies miR-642a-5p as a regulator of POFUT1 expression in colon cancer

Colorectal cancer (CRC) is one of the most common and deadly cancers worldwide, underscoring the urgent need for novel biomarkers and therapeutic targ...

Analyzing the Performance of Deep Learning Splice Prediction Algorithms

SpliceAI has become the leading computational tool for predicting splice-altering variants, but restrictive licensing has limited its adoption by comm...

Iterative immunogen optimization to focus immune responses on a conserved, subdominant viral epitope

Designing effective vaccination strategies against genetically diverse viruses, such as HIV or influenza, is hindered by the ability of these pathogen...

An integrated platform for high-throughput phenospace learning of 3D multilineage organoid systems

Complex multilineage organoid systems lack quantitative phenotyping methods preserving spatial architecture at high throughput. Current approaches com...

Ultrahigh throughput screening to train generative protein models for engineering specificity into unspecific peroxygenases

Enzyme engineering is central to developing biocatalysts with improved activity and specificity, yet traditional approaches are often limited by the s...

Clair-Mosaic: A deep-learning method for long-read mosaic small variant calling

Mosaic variants, defined as postzygotic mutations occurring during an organism’s development from zygote to adult, play critical roles in developmenta...

VariantFormer: A hierarchical transformer integrating DNA sequences with genetic variations and regulatory landscapes for personalized gene expression prediction

Accurately predicting gene expression from DNA sequence remains a central challenge in human genetics. Current sequence-based models overlook natural ...

Machine Learning Identifies Common Risk Variants and Implicates Abnormal Vision Physiology in ASD

Genomic technology advancements have facilitated associations between genetic variants and disease risk. Rare deleterious variants can independently i...

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular protei...

Autonomous Liquid-handling Robotics Scripting for Accessible and Responsible Protein Engineering

Laboratory automation enhances experimental throughput and reproducibility, yet widespread adoption is constrained by the expertise required for robot...

ProAffinity++

Proteins are essential biological macromolecules that play a crucial role in living organisms. Protein-protein interactions, which govern various biol...

HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody Discovery

Antibody discovery remains constrained by resource-intensive experimental screening approaches that offer limited control over critical properties. He...

Rubisco is slow across the tree of life

Rubisco is the main gateway through which inorganic carbon enters the biosphere, catalyzing the vast majority of carbon fixation on Earth. This pivota...

Predicting and Designing Red Fluorescent Protein Variants Using Sequence-to-Function Machine Learning Models

Fluorescent proteins (FPs) are widely used reporters for visualizing cellular structures and processes. Traditional wet-lab strategies for FP engineer...

Sequence and structural determinants of efficacious de novo chimeric antigen receptors

Advances in generative protein design using artificial intelligence (AI) have enabled the rapid development of binders against heterogeneous targets, ...

Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties

A prominent application of machine learning in therapeutic antibody design is the development of models that can generate or screen antibody candidate...