@pleaseignore1.bsky.social
Bottom-up design of calcium channels from defined selectivity filter geometry [new]
RFdiffusion bottom-up creates Ca2+ channels; filter-selectivity tested via construction.
RFdiffusion bottom-up creates Ca2+ channels; filter-selectivity tested via construction.
December 21, 2024 at 5:49 AM
Bottom-up design of calcium channels from defined selectivity filter geometry [new]
RFdiffusion bottom-up creates Ca2+ channels; filter-selectivity tested via construction.
RFdiffusion bottom-up creates Ca2+ channels; filter-selectivity tested via construction.
Guiding Generative Protein Language Models with Reinforcement Learning [new]
Fine-tuning protein language models via RL guides them towards rare, high-value sequences by maximizing custom reward signals; applied for EGFR binder design.
Fine-tuning protein language models via RL guides them towards rare, high-value sequences by maximizing custom reward signals; applied for EGFR binder design.
December 18, 2024 at 5:50 AM
Guiding Generative Protein Language Models with Reinforcement Learning [new]
Fine-tuning protein language models via RL guides them towards rare, high-value sequences by maximizing custom reward signals; applied for EGFR binder design.
Fine-tuning protein language models via RL guides them towards rare, high-value sequences by maximizing custom reward signals; applied for EGFR binder design.
December 13, 2024 at 4:20 PM
December 13, 2024 at 4:09 PM
December 13, 2024 at 4:07 PM
Generative Humanization for Therapeutic Antibodies[new]
This paper introduces a generative approach to antibody humanization, reframing it as a conditional sequence modeling task. Instead of traditional methods that yield few candidates, this approach generates diverse sets of humanized ...
This paper introduces a generative approach to antibody humanization, reframing it as a conditional sequence modeling task. Instead of traditional methods that yield few candidates, this approach generates diverse sets of humanized ...
December 9, 2024 at 2:15 PM
Generative Humanization for Therapeutic Antibodies[new]
This paper introduces a generative approach to antibody humanization, reframing it as a conditional sequence modeling task. Instead of traditional methods that yield few candidates, this approach generates diverse sets of humanized ...
This paper introduces a generative approach to antibody humanization, reframing it as a conditional sequence modeling task. Instead of traditional methods that yield few candidates, this approach generates diverse sets of humanized ...
Multi-Scale Representation Learning for Protein Fitness Prediction[new]
This paper introduces S3F, a multi-scale protein representation learning model for zero-shot fitness prediction. It integrates protein sequence, structure, and surface features, outperforming sequence-only or ...
This paper introduces S3F, a multi-scale protein representation learning model for zero-shot fitness prediction. It integrates protein sequence, structure, and surface features, outperforming sequence-only or ...
December 9, 2024 at 6:33 AM
Multi-Scale Representation Learning for Protein Fitness Prediction[new]
This paper introduces S3F, a multi-scale protein representation learning model for zero-shot fitness prediction. It integrates protein sequence, structure, and surface features, outperforming sequence-only or ...
This paper introduces S3F, a multi-scale protein representation learning model for zero-shot fitness prediction. It integrates protein sequence, structure, and surface features, outperforming sequence-only or ...
Temporal and Spatial Characterization of CUL3KLHL20-driven Targeted Degradation of BET family, BRD Proteins by the Macrocycle-based Degrader BTR2004
This paper characterizes the temporal and spatial dynamics of BTR2004, a macrocycle-based PROTAC, in degrading BRD proteins via the ...
This paper characterizes the temporal and spatial dynamics of BTR2004, a macrocycle-based PROTAC, in degrading BRD proteins via the ...
December 8, 2024 at 3:57 AM
Temporal and Spatial Characterization of CUL3KLHL20-driven Targeted Degradation of BET family, BRD Proteins by the Macrocycle-based Degrader BTR2004
This paper characterizes the temporal and spatial dynamics of BTR2004, a macrocycle-based PROTAC, in degrading BRD proteins via the ...
This paper characterizes the temporal and spatial dynamics of BTR2004, a macrocycle-based PROTAC, in degrading BRD proteins via the ...
Interaction of plant-derived metabolites and rhizobiome functions enhances drought stress tolerance
This study investigates how plant-derived metabolites shape rhizobiome function and influence drought tolerance in *Andropogon gerardii*. Using gene- and genome-centric approaches, it ...
This study investigates how plant-derived metabolites shape rhizobiome function and influence drought tolerance in *Andropogon gerardii*. Using gene- and genome-centric approaches, it ...
December 8, 2024 at 3:34 AM
Interaction of plant-derived metabolites and rhizobiome functions enhances drought stress tolerance
This study investigates how plant-derived metabolites shape rhizobiome function and influence drought tolerance in *Andropogon gerardii*. Using gene- and genome-centric approaches, it ...
This study investigates how plant-derived metabolites shape rhizobiome function and influence drought tolerance in *Andropogon gerardii*. Using gene- and genome-centric approaches, it ...
A ML Framework for Genetic Sequence Identification using 2D Electrical Conductance Probability Distributions from Mixed Data Sets
This paper presents a machine learning framework for genetic sequence identification using 2D electrical conductance probability distributions from mixed ...
This paper presents a machine learning framework for genetic sequence identification using 2D electrical conductance probability distributions from mixed ...
December 8, 2024 at 2:51 AM
A ML Framework for Genetic Sequence Identification using 2D Electrical Conductance Probability Distributions from Mixed Data Sets
This paper presents a machine learning framework for genetic sequence identification using 2D electrical conductance probability distributions from mixed ...
This paper presents a machine learning framework for genetic sequence identification using 2D electrical conductance probability distributions from mixed ...
Prediction of peptide structural conformations with AlphaFold2
This paper investigates AlphaFold2's (AF2) ability to predict multiple peptide conformations. It uses a benchmark dataset of 557 peptides (10-40 residues) with NMR-determined conformations. The study assesses AF2's accuracy in ...
This paper investigates AlphaFold2's (AF2) ability to predict multiple peptide conformations. It uses a benchmark dataset of 557 peptides (10-40 residues) with NMR-determined conformations. The study assesses AF2's accuracy in ...
December 8, 2024 at 2:49 AM
Prediction of peptide structural conformations with AlphaFold2
This paper investigates AlphaFold2's (AF2) ability to predict multiple peptide conformations. It uses a benchmark dataset of 557 peptides (10-40 residues) with NMR-determined conformations. The study assesses AF2's accuracy in ...
This paper investigates AlphaFold2's (AF2) ability to predict multiple peptide conformations. It uses a benchmark dataset of 557 peptides (10-40 residues) with NMR-determined conformations. The study assesses AF2's accuracy in ...
At-RS31 orchestrates hierarchical cross-regulation of splicing factors and integrates alternative splicing with TOR-ABA pathways
This paper investigates At-RS31, a plant-specific splicing factor, using iCLIP, RNAcompete, and transcriptomic analyses. It identifies At-RS31's targets and ...
This paper investigates At-RS31, a plant-specific splicing factor, using iCLIP, RNAcompete, and transcriptomic analyses. It identifies At-RS31's targets and ...
December 8, 2024 at 12:22 AM
At-RS31 orchestrates hierarchical cross-regulation of splicing factors and integrates alternative splicing with TOR-ABA pathways
This paper investigates At-RS31, a plant-specific splicing factor, using iCLIP, RNAcompete, and transcriptomic analyses. It identifies At-RS31's targets and ...
This paper investigates At-RS31, a plant-specific splicing factor, using iCLIP, RNAcompete, and transcriptomic analyses. It identifies At-RS31's targets and ...
BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions
BindingGYM is a large-scale mutational dataset for protein-protein interactions (PPIs). It addresses the challenge of accurately predicting PPI strength by leveraging Deep Mutational Scanning (DMS)
BindingGYM is a large-scale mutational dataset for protein-protein interactions (PPIs). It addresses the challenge of accurately predicting PPI strength by leveraging Deep Mutational Scanning (DMS)
December 8, 2024 at 12:00 AM
BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions
BindingGYM is a large-scale mutational dataset for protein-protein interactions (PPIs). It addresses the challenge of accurately predicting PPI strength by leveraging Deep Mutational Scanning (DMS)
BindingGYM is a large-scale mutational dataset for protein-protein interactions (PPIs). It addresses the challenge of accurately predicting PPI strength by leveraging Deep Mutational Scanning (DMS)
Protein2Text: Providing Rich Descriptions for Protein Sequences
This paper introduces BetaDescribe, a collection of deep learning models that generate rich textual descriptions of protein sequences. It leverages LLAMA2, further training it on biological data to enhance its ability to ...
This paper introduces BetaDescribe, a collection of deep learning models that generate rich textual descriptions of protein sequences. It leverages LLAMA2, further training it on biological data to enhance its ability to ...
December 7, 2024 at 11:57 PM
Protein2Text: Providing Rich Descriptions for Protein Sequences
This paper introduces BetaDescribe, a collection of deep learning models that generate rich textual descriptions of protein sequences. It leverages LLAMA2, further training it on biological data to enhance its ability to ...
This paper introduces BetaDescribe, a collection of deep learning models that generate rich textual descriptions of protein sequences. It leverages LLAMA2, further training it on biological data to enhance its ability to ...
Mechanism of Dimer Selectivity and Binding Cooperativity of BRAF inhibitors
This paper investigates the mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors using molecular dynamics simulations. It reveals unprecedented details of allostery in BRAFV600E dimerization ...
This paper investigates the mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors using molecular dynamics simulations. It reveals unprecedented details of allostery in BRAFV600E dimerization ...
December 7, 2024 at 1:50 PM
Mechanism of Dimer Selectivity and Binding Cooperativity of BRAF inhibitors
This paper investigates the mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors using molecular dynamics simulations. It reveals unprecedented details of allostery in BRAFV600E dimerization ...
This paper investigates the mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors using molecular dynamics simulations. It reveals unprecedented details of allostery in BRAFV600E dimerization ...
Interpretable Protein-DNA Interactions Captured by Structure-Sequence Optimization
This paper introduces IDEA, an interpretable biophysical model for predicting protein-DNA binding sites and affinities. It leverages protein-DNA structures and sequences to learn a residue-level energy ...
This paper introduces IDEA, an interpretable biophysical model for predicting protein-DNA binding sites and affinities. It leverages protein-DNA structures and sequences to learn a residue-level energy ...
December 7, 2024 at 1:08 PM
Interpretable Protein-DNA Interactions Captured by Structure-Sequence Optimization
This paper introduces IDEA, an interpretable biophysical model for predicting protein-DNA binding sites and affinities. It leverages protein-DNA structures and sequences to learn a residue-level energy ...
This paper introduces IDEA, an interpretable biophysical model for predicting protein-DNA binding sites and affinities. It leverages protein-DNA structures and sequences to learn a residue-level energy ...
Assessing target genes for homing suppression gene drive
This study investigates nine female fertility genes in *Drosophila melanogaster* using CRISPR-based homing gene drives. A multiplexed gRNA approach aimed to maintain high drive conversion efficiency with low fitness costs. Drive ...
This study investigates nine female fertility genes in *Drosophila melanogaster* using CRISPR-based homing gene drives. A multiplexed gRNA approach aimed to maintain high drive conversion efficiency with low fitness costs. Drive ...
December 7, 2024 at 5:48 AM
Assessing target genes for homing suppression gene drive
This study investigates nine female fertility genes in *Drosophila melanogaster* using CRISPR-based homing gene drives. A multiplexed gRNA approach aimed to maintain high drive conversion efficiency with low fitness costs. Drive ...
This study investigates nine female fertility genes in *Drosophila melanogaster* using CRISPR-based homing gene drives. A multiplexed gRNA approach aimed to maintain high drive conversion efficiency with low fitness costs. Drive ...
Predictable modulation of plant root development using engineered cytokinin regulators
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
December 7, 2024 at 5:45 AM
Predictable modulation of plant root development using engineered cytokinin regulators
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
Predictable modulation of plant root development using engineered cytokinin regulators
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
December 7, 2024 at 5:45 AM
Predictable modulation of plant root development using engineered cytokinin regulators
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
This paper details the engineering of Type-B Arabidopsis response regulators (ARRs) to predictably modulate cytokinin signaling. By fusing activation/repression domains, they achieved tunable regulatory ...
Tokenvizz: GraphRAG-Inspired Tokenization Tool for Genomic Data Discovery and Visualization
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
December 7, 2024 at 5:42 AM
Tokenvizz: GraphRAG-Inspired Tokenization Tool for Genomic Data Discovery and Visualization
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
Tokenvizz: GraphRAG-Inspired Tokenization Tool for Genomic Data Discovery and Visualization
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
December 7, 2024 at 5:42 AM
Tokenvizz: GraphRAG-Inspired Tokenization Tool for Genomic Data Discovery and Visualization
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
Tokenvizz is a novel genomic analysis tool that uses GraphRAG-inspired tokenization and graph-based modeling to enhance data discovery and visualization. It represents genomic sequences as graphs, ...
Scalable emulation of protein equilibrium ensembles with generative deep learning
BioEmu, a new generative deep learning model, emulates protein equilibrium ensembles, generating thousands of statistically independent samples per hour on a single GPU (pg.1-3). Trained on protein ...
BioEmu, a new generative deep learning model, emulates protein equilibrium ensembles, generating thousands of statistically independent samples per hour on a single GPU (pg.1-3). Trained on protein ...
December 7, 2024 at 5:29 AM
Scalable emulation of protein equilibrium ensembles with generative deep learning
BioEmu, a new generative deep learning model, emulates protein equilibrium ensembles, generating thousands of statistically independent samples per hour on a single GPU (pg.1-3). Trained on protein ...
BioEmu, a new generative deep learning model, emulates protein equilibrium ensembles, generating thousands of statistically independent samples per hour on a single GPU (pg.1-3). Trained on protein ...
Spatial single cell transcriptomic analysis of a novel DICER1 Syndrome GEMM informs the cellular origin and developmental hierarchy of associated sarcomas
This paper investigates the cellular origin and developmental hierarchy of DICER1 syndrome-associated sarcomas using a ...
This paper investigates the cellular origin and developmental hierarchy of DICER1 syndrome-associated sarcomas using a ...
December 5, 2024 at 6:18 PM
Spatial single cell transcriptomic analysis of a novel DICER1 Syndrome GEMM informs the cellular origin and developmental hierarchy of associated sarcomas
This paper investigates the cellular origin and developmental hierarchy of DICER1 syndrome-associated sarcomas using a ...
This paper investigates the cellular origin and developmental hierarchy of DICER1 syndrome-associated sarcomas using a ...