sequence-to-function modeling

Physiological mapping and prediction reveal stress-responsive translation initiation landscapes

This study introduces TISCA2 and IncepTIS to map and predict stress-responsive translation initiation sites, revealing tissue-specific patterns and enabling engineered mRNA 5' leaders.

Massively parallel quantification and prediction of monomeric protein yield in Escherichia coli

The paper develops a scalable assay and a protein language model to predict monomeric yield of proteins in E. coli, showing it can be learned and designed.

AlphaFold 3 Prediction Accuracy and Reliability across RNA Structures and Protein-RNA Interfaces

This study evaluates AlphaFold 3's accuracy and reliability in predicting RNA structures and protein-RNA interfaces, finding that confidence scores do not always correlate with correct predictions.

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.

PepPr: De Novo peptide sequencing with peptide priors

PepPr improves peptide sequencing by integrating peptide priors, enhancing reference and variant peptide identification while maintaining discovery flexibility.

RiboDFM: Robust RNA sequence design using backbone-conditioned discrete flow matching

RiboDFM designs RNA sequences that fold into target 3D structures using a backbone-conditioned discrete flow-matching framework, achieving high sequence recovery and tertiary-structure consistency.

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

OpenKinetics Predictor unifies thirteen enzyme kinetic predictors behind one interface and API; on E. coli iML1515, eight kcat predictors correlate pairwise at r 0.08 to 0.67.

Optimized Multiple Circular Sequence Alignment for Cyclic Peptide Motif Discovery

The paper optimizes multiple circular sequence alignment for cyclic peptides, achieving faster performance while maintaining alignment quality.

Sequence Generation and Phylogenetic Inference with Generative Flow Networks

This paper introduces AncestorGFN, a method using Generative Flow Networks for alignment-free sequence generation and phylogenetic inference, showing it can recover ancestral relationships and generate novel sequences.

OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation

OmniTCR is a foundation model that predicts TCR recognition and generates TCR sequences, outperforming existing methods on multiple tasks.

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.

Multiplexed sequence-resolved screening of transient DNA hybridization for programmable nanotechnology

The paper introduces SPARXS-Hyb, a method for multiplexed screening of DNA hybridization kinetics, identifying sequences with improved performance in DNA-PAINT.

Benchmarking Spectral Library Prediction Platforms for Neuropeptidomics Applications

This study benchmarks four spectral prediction platforms for neuropeptidomics, finding no single model captures neuropeptide fragmentation well and highlighting the importance of neutral loss fragments and hybrid libraries.

Distribution-Constrained Optimization for Reliable ML-Guided 5'UTR Sequence Design

This paper introduces a constrained optimization method for designing 5'UTR sequences using deep learning models, ensuring candidates stay within a trust region defined by an OOD score based on KNN distance.

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.

Beyond the Static Caliper: Dynamical Translocases and the Mathematical Imperative for Single-Molecule Proteomics

This paper proposes dynamical translocases and Physics-Informed Machine Learning to overcome biophysical limitations in single-molecule proteomics, achieving >90% accurate amino acid classification.

Amplicon/Protein Bead Display enables quantitative in vitro biochemistry at scale

The paper introduces APB-Display, a method for high-throughput in vitro protein library production and analysis, quantifying binding affinities and expression levels of thousands of variants.

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.

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

RetroMol converts natural product structures and biosynthetic gene clusters into shared primary sequences to enable biosynthetic similarity comparisons.

Deep Proteoform Sequencing with Top-Down Direct Mass Technology

This paper introduces a top-down mass spectrometry workflow with DMTm technology and a new software platform to achieve deeper proteoform sequencing and improved sequence coverage.

Functional Profiling of Thousands of Sequence-Diverse Protease Homologs with GROQ-seq

The paper uses GROQ-seq to profile function across a diverse protease library, showing robust activity across sequence divergence.

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.

DefensePredictor: A Machine Learning Model to Discover Novel Prokaryotic Immune Systems

DefensePredictor combines ESM2 protein language model embeddings with genomic context features in a gradient boosting classifier to identify prokaryotic anti-phage defense genes, validating 45 previously unknown Escherichia coli systems.

Assessment of Generative De Novo Peptide Design Methods for G Protein-Coupled Receptors

This paper benchmarks deep learning-based peptide design methods for GPCRs, finding that validation fails to distinguish valid from invalid designs, highlighting scoring challenges.

STRUCT2SEQ: RNA INVERSE FOLDING WITH DEEP Q-LEARNING

Struct2SeQ uses deep Q-learning to design RNA sequences that fold into target structures and match SHAPE reactivity constraints, outperforming humans and existing methods.

BiRNA-BERT Allows Efficient RNA Language Modeling with Adaptive Tokenization

BiRNA-BERT is a Transformer-based model pretrained on RNA sequences using adaptive tokenization, achieving state-of-the-art results in long-sequence tasks and efficient performance on short sequences.

RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation

RNAtranslator generates RNA sequences that bind to specific proteins by treating RNA design as a sequence-to-sequence translation task, producing novel and high-affinity sequences.

RNA sequence design and protein-DNA specificity prediction with NA-MPNN

This paper introduces NA-MPNN, a unified deep learning model for nucleic acid inverse folding, which outperforms existing methods in RNA sequence design and protein-DNA specificity prediction.

Split inteins for generating combinatorial non-ribosomal peptide libraries

This paper introduces split inteins to enable modular NRPS engineering, generating 324 hybrid NRPS and over 200 novel peptides through combinatorial transformation.

NucleicBERT: Deciphering the language of nucleic acids

NucleicBERT is a self-supervised masked-language model for RNA that learns contextual representations without alignments or evolutionary data, outperforming existing models in structural and functional tasks.