sequence-to-function modeling

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.

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.

Human-specific CCG-repeat insertion in the RBFOX1 promoter implicated in autism and speech enhances EGR1-responsive activity

A human-specific CCG insertion in the RBFOX1 promoter boosts EGR1-driven reporter activity in HEK293T cells, as does an autism-linked CCG-gaining duplication in PTCHD1.

A Taxonomy-Informed Sparse DNA Foundation Model for Microbial Genomics

A sparse mixture-of-experts DNA model conditioned on hyperbolic taxonomy embeddings learns better microbial representations than its taxonomy-free twin and comes close to Evo2-7B in probing and fine-tuning at lower compute.

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.

Coevolutionary mining of prokaryotic non-coding elements with a genome language model

Minerva extracts RNA base-pairing, protein-contact and repeat maps from a genome language model without alignments, flagging unannotated structured loci and a phage ncRNA array reverse-transcribed into short cDNAs.

A Unified 3D Generative Model for Synthesizable Structure-Based Drug Design

This paper introduces LDDM, a generative model for drug design that produces synthetically accessible compounds validated experimentally for five protein targets.

A foundation model learns the sequence and functional grammar of fully human heavy-chain-only antibodies

The paper develops a foundation model trained on fully human heavy-chain-only antibodies, showing it captures sequence and functional constraints distinct from conventional antibody models.

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.

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.

CodonMamba: a foundation model for programmable mRNA coding sequence design

CodonMamba is a foundation model for mRNA coding sequence design that achieves state-of-the-art performance and enables programmable codon optimization through inference-time steering.

GFMBench-API: A Standardized Interface for Benchmarking Genomic Foundation Models

Wrap a genomic foundation model's inference methods once to score it on supervised and zero-shot GUE, VariantBenchmarks, BEND, TraitGym and ClinVar tasks; code is promised upon acceptance.

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.

Fast remote nucleotide sequence alignment with Riboseek

Riboseek is a fast and sensitive sequence alignment tool for RNA and DNA that uses di-mers for alignment, outperforming BLASTN and nhmmer in speed and sensitivity.

Breaking the Synthesis Barrier for AI-Designed DNA Libraries

This paper introduces PGLD, an AI method that designs cost-effective DNA libraries for high-throughput experiments by optimizing synthesis-aware stochastic libraries.

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.

Generative design of antigen-specific T-cell receptor sequences with a conditional diffusion model

This paper introduces TCRDiff, a conditional diffusion model for generating antigen-specific TCR sequences, validated through in vitro assays.

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.

Reinforcement learning-driven unified generative framework for multi-objective RNA codon design

RNARL is a reinforcement learning framework for RNA codon design that generates high-performance sequences across multiple species and RNA types.

GermRL: Alleviating The Germline Bias In Autoregressive Antibody Language Models Through Reinforcement Learning

This paper introduces GermRL, a reinforcement learning framework that reduces germline bias in antibody language models, improving mutation diversity while maintaining structural plausibility.

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.

A generative-AI framework for target-Specific MicroRNAs towards RNAi-based drug design

This paper introduces a generative AI framework to design target-specific siRNAs for RNAi-based drug design, showing improved specificity and reduced off-target effects compared to existing methods.

PlasmidLM: A Promptable DNA Language Model via Verifiable-Reward Post-Training

PlasmidLM is a DNA language model that generates plasmid constructs from component specifications, achieving high fidelity and viability in generated sequences.

Fast and Ultra-Capable Protein Design: Advancing the Frontier Through Atomistic SE(3)-Equivariance with Genie 3

This paper introduces Genie 3, a diffusion model that improves protein design by combining SE(3)-equivariance with hallucination, achieving state-of-the-art results in binder design and motif scaffolding.

linearPOA: A parallel, memory-efficient framework for Partial Order Alignment with linear space complexity

linearPOA introduces a memory-efficient framework for Partial Order Alignment with linear space complexity, significantly reducing memory usage for long-read sequencing.

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.

Maximally Divergent Synonymous Gene Design with SIRIUS

SIRIUS generates maximally divergent DNA sequences for a given protein, reducing shared subsequences compared to existing methods.

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species

CREsted is a software package for modeling and designing cell type-specific enhancers using deep learning, validated across tissues and species.

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.