sequence-to-function modeling

Mapping the sequence preference of the generalist class II lanthipeptide synthetase ProcM by mRNA display

This study maps the sequence preferences of the generalist class II lanthipeptide synthetase ProcM using mRNA display and machine learning, revealing moderate predictive accuracy compared to specialized enzymes.

Sequence design for three-dimensional genome folding using Akita Semifreddo

This paper introduces Akita Semifreddo, a framework for designing DNA sequences that produce specific 3D genome folding patterns, demonstrating the ability to create boundaries, dots, and flames with controlled strengths.

RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization

RNASTOP predicts and optimizes mRNA chemical stability using deep learning, improving accuracy by 13% and reducing free energy in a vaccine sequence.

AmyloDeep: pLM-based ensemble model for predicting amyloid propensity from the amino acid sequence

AmyloDeep predicts amyloid propensity from amino acid sequences using an ensemble of deep learning models, achieving high accuracy and robustness.

A Structure-Aware Generative Framework for Exploring Protein Sequence and Function Space

This paper introduces a structure-aware generative framework that integrates geometric encoding and coevolutionary constraints to map, cluster, and design protein sequences, improving homology detection and structure-guided sequence generation.

Ancestral Sequences Cannot be Accurately Reconstructed via Interpolation in a Variational Autoencoder's Latent Space

This study tests using a variational autoencoder to reconstruct ancestral sequences by interpolating in its latent space, finding it less accurate than standard methods despite containing evolutionary signal.

Functional alignment of protein language models via reinforcement learning

This paper introduces RLXF, a framework that aligns protein language models with experimental functional goals, improving the generation of high-functioning protein variants.

OligoSeq: Rapid nanopore-sequencing of single-stranded oligonucleotides

OligoSeq enables accurate sequencing of single-stranded oligonucleotides using nanopore technology, offering a cost-effective alternative for quality control in oligonucleotide synthesis.

Sequence-free landscape inference for directed evolution

This paper introduces SLIDE, a method to estimate landscape ruggedness in directed evolution using population-level phenotypic data and mutation rate, improving selection strategies for protein design.

Gradient-based Optimization for mRNA Sequence Design

The paper introduces ID3, a framework for optimizing mRNA sequences using gradient-based methods, balancing multiple properties while preserving amino acid sequences.

A membrane insertion code for intrinsically disordered proteins

The paper identifies sequence determinants for aromatic residue insertion into membranes and develops a predictor model with high accuracy.

An Artificial Intelligence Model for Translating Natural Language into Functional de Novo Proteins

This paper introduces MP4, a transformer-based model that translates natural language into functional de novo proteins, achieving high performance in sequence realism, fold quality, and function alignment.

Accelerating Inference in Genomic Foundation Models via Speculative Decoding

This paper introduces speculative decoding to accelerate genomic and proteomic sequence generation using foundation models without compromising prediction quality.

Steering Sequence Generation in Protein Language Models through Iterative Lookback Monte Carlo Sampling

This paper introduces ILMC, a sampling strategy for protein language models that improves diversity while steering generation toward desired properties.

Theseus: Fast and Optimal Affine-Gap Sequence-to-Graph Alignment

Theseus provides a fast and optimal affine-gap sequence-to-graph alignment algorithm, outperforming existing methods in speed while maintaining optimality.

Context-aware sequence-to-function model of human gene regulation

The paper introduces Corgi, a context-aware sequence-to-function model that predicts gene expression and epigenetic signals across unseen cell types using DNA sequence and trans-regulator expression.

Understanding Language Model Scaling on Protein Fitness Prediction

This study shows that larger protein language models can overestimate sequence likelihood, leading to poor fitness prediction, and recommends moderate model sizes for better performance.

Computational design of blue melanin with peptide motif scaffolding

This paper designs blue melanin using peptide motifs and computational methods, achieving stable blue color with specific absorption wavelengths and thermal/photostability.

Predicting Antibody Self-Association with Sequence Structure Fusion Models: The Central Role of CSI-BLI in Early Developability Screening

The paper develops a sequence-structure fusion model to predict antibody self-association using CSI-BLI data, achieving high performance in distinguishing high and low self-interacting clones.

Beyond native sequence recovery: Improved modeling of thesequence-energy landscape of protein structures

This paper introduces PottsMPNN, a model trained to generate Potts energy functions for protein design, showing improved sequence generation and energy prediction over native sequence recovery.

How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning

This paper introduces EnzPlacer, a contrastive learning method that predicts enzyme function from sequence by placing it in known function space, even when the precise function is unknown.

Topological Entanglement in Intrinsically Disordered Proteins: Sequence, Structural, and Functional Determinants

The paper uses entanglement measures from knot theory to analyze sequence and structural features of intrinsically disordered proteins and identifies functionally enriched regions in entanglement space.

Replaying germinal center evolution on a quantified affinity landscape

Across 119 monoclonal germinal centers, B cell affinity rises reliably despite divergent phylogenies. Selection is noisy but persistent; mutability limits exploration. A DMS-based landscape shows saturating fitness and survivorship biases.

SSPSPredictor: A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins

SSPSPredictor predicts phase-separating proteins using sequence and structure data, showing higher LLPS propensity in intrinsically disordered proteins and pathogenic variants.

Fine-tuning sequence-to-expression models onpersonal genome and transcriptome data

This study fine-tunes sequence-to-expression models on personal genome and transcriptome data, showing improved performance on seen genes but not on unseen genes.

Ankh-score produces better sequence alignments than AlphaFold3

Ankh-score improves sequence alignment over AlphaFold3 structural alignments and traditional BLOSUM methods on benchmark datasets.

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing

The paper establishes the relationship between Gaussian process priors and regularized regression in sequence-to-function mapping, showing how to construct regularizers that correspond to explicit Gaussian process priors and characterize implicit function space priors.

Refining sequence-to-expression modelling with chromatin accessibility

The paper improves sequence-to-expression models by incorporating chromatin accessibility, showing better performance in predicting gene expression and cell type-specific patterns.

AlphaFind v2: Similarity Search in AlphaFold DB and TED Domains across Structural Contexts

AlphaFind v2 enables fast, structure-based search across protein structures and domains, using embeddings and US-align for refinement and supporting organism and CATH-based filtering.

PARAS: high-accuracy machine-learning of substrate specificities in nonribosomal peptide synthetases

Random forest predictors PARAS and PARASECT, trained on 3,653 curated adenylation domains, outperform earlier NRPS substrate predictors and correctly assigned L-Trp to an unusual tryptopeptin synthetase domain.