Biology subjects

Pecoraro, R.

Publications and source records attributed to Pecoraro, R..

6 recordsLinked to original sources

bioRxiv2026

Generative design of sequence specific DNA binding proteins

De novo protein design has advanced rapidly in recent years, yet the programmable recognition of specific DNA sequences remains a longstanding challenge. Here we describe a deep learning based approach for designing sequence selective DNA binding proteins. Our method combines structure generation using RFdiffusion3 with explicit screening against off-target interactions using AlphaFold3. We test this approach by generating 96 designs for each of 15 diverse DNA targets and identify specific binders for 7 targets, representing a ~100-fold improvement in success rates over previous approaches. We further characterize the binding landscape using variant competition assays and randomized library screening, revealing robust sequence discrimination across diverse targets. Together, these results represent a significant step forward in de novo sequence specific DNA binder design.

synthetic biology↗

bioRxiv2026

Designing AI-programmable therapeutics with the EDEN family of foundation models

The ability to interpret, modify, and design DNA has driven many of the most significant advances in modern medicine, from diagnostics, biologics, and vaccines to cell and gene therapies. However, the inherent complexity of biological systems means that most modern medicines are still engineered using bespoke, labor-intensive processes. To address the need for a generalisable and programmable approach to therapeutic design, we introduce the EDEN (environmentally-derived evolutionary network) family of metagenomic foundation models, including a 28 billion parameter model trained on 9.7 trillion nucleotide tokens from BaseData1. This dataset, at the time of training, contained more than 10 billion novel genes from over 1 million new species, and is intentionally enriched for environmental and host-associated metagenomes, phage sequences, and mobile genetic elements, enabling the model to learn from diverse and novel cross-species evolutionary mechanisms and apply them to key challenges in human health. EDEN achieves state-of-the-art performance across a series of predictive and generative genomic and protein benchmarks. To demonstrate the models broad applicability across biology, we evaluate EDENs capacity for programmable therapeutic design by challenging a single architecture to design biological novelty across three distinct therapeutic modalities, disease areas and biological scales: (i) large gene insertion, (ii) antibiotic peptide design, and (iii) microbiome design. First, we demonstrate AI-programmable Gene Insertion (aiPGI), in which EDEN designs de novo large serine recombinases (LSRs) capable of inserting large pieces of DNA at desired target sites in the human genome when prompted only on 30 nucleotides of DNA sequence from the desired target site. In low-N experimental validation, EDEN generated multiple active recombinases for all tested disease-associated genomic loci (ATM, DMD, F9, FANCC, GALC, IDS, P4HA1, PHEX, RYR2, USH2A) and 4 potential safe harbor sites in the human genome. EDEN achieves an overall functional hit rate of 63.2% across diverse DNA prompts when prompted on only 30bp of DNA from outside the training data. 50% of EDEN-generated LSRs were active in human cells, achieving therapeutically relevant levels of CAR insertion in primary human T cells. We also show that EDEN can generate active bridge recombinases when prompted on the associated guide RNA alone, with sequence identities to training and public data as low as 65%. These results pave the way for a new generation of cell and gene therapies by opening the door to rapid, programmable and site-specific integration of large genetic payloads without double-strand breaks. This offers an alternative to the safety, efficiency and payload limitations inherent in viral or nuclease-based editing at thousands of currently intractable human therapeutic targets. Second, we use the same model to generate a focused low-N library of novel antimicrobial peptides where 97% showed activity, with top candidates achieving single-digit micromolar potency against critical-priority multidrug-resistant pathogens. Third, to demonstrate that EDEN captures inter-genomic features, we design a gigabase-scale microbiome with over 94,000 synthetic metagenomic assemblies, including prophage genomes and correct cross-species metabolic pathway completions. The EDEN-generated synthetic microbiome covers 9,067 species with a biome-specific taxonomic accuracy of 99%. Over 1,500 of the generated species were outside the fine-tuning dataset while retaining the correct microecological properties and biome association, thus significantly expanding genetic and taxonomic diversity. Together, these results establish a new strategic direction for AI-programmable therapeutics, in which a single foundation model architecture designs candidate therapeutics across diverse modalities and disease areas. This suggests that the combination of billions of years of evolutionary data with specific therapeutic records offers a clear, scaling-driven path to making therapeutic design a predictable engineering discipline. O_FIG O_LINKSMALLFIG WIDTH=141 HEIGHT=200 SRC="FIGDIR/small/699009v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@68c20borg.highwire.dtl.DTLVardef@19b8e8corg.highwire.dtl.DTLVardef@1ab9362org.highwire.dtl.DTLVardef@1592cb7_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

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

RNA sequence design and protein-DNA binding specificity prediction can both be framed as nucleic acid inverse-folding problems: finding the most likely nucleic acid sequences given a fixed three-dimensional structure of a nucleic acid or nucleic acid-protein complex. While task-specific tools have been developed, no unified deep learning model for nucleic acid inverse folding has been described; a single model would have larger and more diverse datasets available for training and a considerably greater range of applicability. Here we introduce Nucleic Acid MPNN (NA-MPNN), a message-passing neural network that treats proteins, DNA, and RNA within a unified biopolymer graph representation. NA-MPNN outperforms previous methods on RNA sequence design and fixed-dock protein-DNA specificity prediction, and should be broadly useful for de novo RNA structure design and prediction of DNA-binding specificity.

biochemistry↗

bioRxiv2025

Breaking Through Biology's Data Wall: Expanding the Known Tree of Life by Over 10x using a Global Biodiscovery Pipeline

Advancements in the life sciences have always been built upon our collective understanding of life on Earth. Now, the rise of generative biology - the use of AI foundation models to design, generate, and annotate proteins, pathways and therapeutics - is creating unprecedented demand for large, diverse biological sequence datasets. While a limited subset of such data can be generated in clinical or laboratory settings, the vast majority of the training data for unsupervised models must be sourced from the natural world - the product of nearly four billion years of evolutionary history. However, the public databases that currently supply this data, while foundational to research, were established to aggregate results from academic experiments, not as training datasets for machine learning. Their human-centric data structure limits model performance due to redundancy, taxonomic and geographic bias, limited biological context, and inconsistent provenance. With 68% of all sequence data in the SRA database coming from just 5 species, this is one of the most severe class imbalance problems ever encountered in AI. Legal and infrastructural constraints further exacerbate this bottleneck. To address these limitations and support scalable model training, we introduce BaseData: the largest and fastest-growing biological sequence database ever built, and the first purpose-built for training foundation models. As of late 2024, BaseData contained 9.8 billion novel genes, representing more than a 10-fold expansion in known protein diversity after accounting for redundancy. BaseData also contains more than 1 million species not represented in other genomic databases. Its partnership-driven data supply chain across 26 countries and autonomous regions enables growth of up to 2 billion novel genes per month, far exceeding public repositories. All data is collected under benefit sharing agreements using standardized protocols and structured using graph-based, ontology-rich metadata that preserves evolutionary context. BaseData represents a new, ethically-grounded infrastructure for training biological foundation models, complementing public efforts and enabling the next era of generative biology. Short AbstractProgress of AI in biology is now being limited by the availability of high-quality biological sequence data from nature. To get past this data wall, we introduce BaseData, a new biological database built on top of a global, scalable biodiscovery pipeline. As of 2024, BaseData had already expanded the known protein universe by over 10x.

genomics↗

bioRxiv2023

Atomic context-conditioned protein sequence design using LigandMPNN

Protein sequence design in the context of small molecules, nucleotides, and metals is critical to enzyme and small molecule binder and sensor design, but current state-of-the-art deep learning-based sequence design methods are unable to model non-protein atoms and molecules. Here, we describe a deep learning-based protein sequence design method called LigandMPNN that explicitly models all non-protein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% vs. 50.4% & 50.5%), nucleotides (50.5% vs. 35.2% & 34.0%), and metals (77.5% vs. 36.0% & 40.6%). LigandMPNN generates not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. Experimental characterization demonstrates that LigandMPNN can generate small molecule and DNA-binding proteins with high affinity and specificity. One-sentence summaryWe present a deep learning-based protein sequence design method that allows explicit modeling of small molecule, nucleotide, metal, and other atomic contexts.

biochemistry↗

bioRxiv2023

Computational design of sequence-specific DNA-binding proteins

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

synthetic biology↗