
Martin Mayta
3.3K posts

Martin Mayta
@MartinMayta2
BSc Biotechnology - PhD in Biological Sciences. Teaching assistant professor at @UNRoficial & @UAPArgentina (Argentina)🇦🇷.




TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations 1. TerraBind achieves 26-fold faster inference than state-of-the-art methods like Boltz-2 while improving binding affinity prediction accuracy by approximately 20%, addressing a critical computational bottleneck in structure-based drug design. 2. The core innovation challenges the prevailing assumption that full all-atom diffusion is necessary for accurate predictions. Instead, TerraBind uses a coarse pocket-level representation with only protein Cβ atoms and ligand heavy atoms, eliminating expensive generative modeling. 3. The architecture combines frozen pretrained encoders—COATI-3 for molecular representations and ESM-2 for protein sequences—with a lean 48-layer pairformer trunk of just 27M parameters, compared to Boltz-2's 509M parameters. 4. For pose generation, TerraBind employs a diffusion-free optimization module that produces 3D coordinates in under 0.2 seconds, matching diffusion-based baselines on FoldBench, PoseBusters, and Runs N'Poses benchmarks. 5. The binding affinity module operates directly on structural pairformer representations without requiring coordinate generation, outperforming Boltz-2 on 15 of 18 proprietary drug discovery targets and achieving superior Pearson correlation on CASP16. 6. A built-in uncertainty quantification system uses pairwise distance entropy as a zero-shot confidence metric, validated to correlate with both pose accuracy and binding strength without separate training. 7. The epistemic neural network (epinet) module provides calibrated affinity uncertainty estimates, enabling a continual learning framework that achieves 6× greater affinity improvement over greedy selection strategies in simulated drug discovery cycles. 8. Structural fine-tuning on minimal proprietary crystallographic data (as few as 3-6 structures) yields 17% affinity improvement on held-out compounds, demonstrating practical adaptability for specific drug programs. 📜Paper: arxiv.org/abs/2602.07735 #TerraBind #DrugDiscovery #MachineLearning #ProteinLigand #BindingAffinity #StructurePrediction #ComputationalBiology #AIforScience

IntelliFold-2: Surpassing AlphaFold 3 via Architectural Refinement and Structural Consistency 1. The authors present IntelliFold-2, an open-source biomolecular structure prediction model that outperforms AlphaFold 3 on therapeutically relevant tasks, particularly antibody-antigen interactions and protein-ligand co-folding. 2. On antibody-antigen docking, IntelliFold-2 achieves 54.5% success rate (v2) and 58.2% (v2-Pro), substantially exceeding AlphaFold 3's 47.9% on the Foldbench benchmark. 3. For protein-ligand co-folding, the model reaches 66.7% (v2) and 67.7% (v2-Pro) success rates, compared to AlphaFold 3's 64.9%, demonstrating consistent improvements in small molecule binding prediction. 4. The architecture introduces latent space scaling in Pairformer blocks, increasing hidden dimensions to enhance representational capacity and hardware efficiency, achieving approximately 30% model FLOPs utilization in the v2-Plus variant. 5. A revised atom-attention mechanism with stochastic atomization enforces more principled multiscale structural representations, improving robustness at the atomic level while maintaining global structural coherence. 6. The authors apply Proximal Policy Optimization (PPO) to fine-tune the diffusion sampling module, framing the sampler as a stochastic policy to encourage physically plausible trajectories and reduce random sampling failures. 7. Difficulty-aware loss reweighting using a focal-loss-style approach emphasizes hard examples such as flexible loops and ambiguous side-chain configurations, leading to more stable optimization dynamics. 8. Three model variants are released: IntelliFold-2-Flash for efficient academic use and fine-tuning, IntelliFold-2 as the most accurate open-source version with 48 widened Pairformer blocks, and IntelliFold-2-Pro as the server-side flagship with exclusive PPO-enhanced sampling. 9. The training pipeline includes re-processed Protein Data Bank curation and scaled self-distillation datasets to improve generalization across complex biomolecular systems. 💻Code: github.com/IntelliGen-AI/… 📜Paper: biorxiv.org/content/10.648… #AlphaFold3 #ProteinStructurePrediction #Bioinformatics #ComputationalBiology #DeepLearning #StructuralBiology #DrugDiscovery #AntibodyDesign #OpenSource #MachineLearning

Despite progress in drug discovery, approximately 90% of druggable disease targets still lack small-molecule therapies. Although virtual screening can accelerate hit identification, traditional methods such as molecular docking remain too slow for genome-scale applications. In a new Science study, researchers introduce DrugCLIP, a contrastive learning framework that virtually screens small molecules and protein pockets, analyzing protein-ligand interactions 10 million times faster than most standard molecular docking approaches. scim.ag/49v6Xln

Visualizing pairwise genome comparisons? No need to run BLAST separately anymore! 🧬 #gbdraw has implemented #LOSAT, a WASM-powered Rust reimplementation of BLASTN/TBLASTX. ✅ No local installation ✅ No data transfer (Serverless) Try it here: gbdraw.app



Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on @proteinbase




Introducing PeptiVerse 🚀, our open-source platform for therapeutic peptide property prediction. We support WT and modified SMILES inputs, and can predict solubility💧, permeability🔬, hemolysis🩸, non-fouling👯, half-life⏱️, tox ☠️, and binding affinity🔗 -- try it out! 🤗: huggingface.co/spaces/Chatter… 📜: biorxiv.org/content/10.648… 🧵👇

AI-assisted protein design to rapidly convert antibody sequences to intrabodies targeting diverse peptides and histone modifications @ScienceAdvances 1. A new AI-driven pipeline has been developed to convert antibody sequences into functional intrabodies, significantly improving the success rate of intrabody design. This approach leverages AlphaFold2, ProteinMPNN, and live-cell screening to optimize antibody frameworks while preserving epitope-binding regions. 2. The study successfully converted 19 out of 26 antibody sequences into functional single-chain variable fragment intrabodies, including those targeting histone modifications for real-time imaging of chromatin dynamics. Notably, 18 of these sequences had previously failed using standard methods. 3. The pipeline integrates advanced AI tools to predict and optimize the folding and stability of intrabodies in the intracellular environment. This addresses key challenges such as misfolding and aggregation that often hinder intrabody functionality. 4. The method was applied to create intrabodies targeting histone modifications, tripling the number of available intrabodies for this purpose. This advancement enables more detailed studies of chromatin dynamics and gene regulation in living cells. 5. The authors provide open-source code for their pipeline, along with useful metrics and tables to predict which designs will retain functionality inside cells. This resource will facilitate further research and development in intrabody design. 6. As antibody sequence databases continue to expand, this AI-driven approach is expected to accelerate intrabody design, making it easier, more cost-effective, and broadly accessible for biological research. 💻Code: github.com/jbderoo/scFv_P… 📜Paper: science.org/doi/10.1126/sc… #AIProteinDesign #IntrabodyEngineering #HistoneModifications #LiveCellImaging #Bioengineering #ComputationalBiology



PXDesign is now Open-Source (Apache 2.0)! SOTA hit rates. Efficient binder generation. 🔹 Updated Wet-lab data: up to 82% SR 🔹 Positive feedback from Web Server users 🔗 Code: github.com/bytedance/PXDe… 🔗 Server: protenix-server.com #ProteinDesign #OpenSource #GenerativeAI

AI-designed DNA switches that turn genes on in exactly one cell type Every cell in your body carries the same genome, yet a liver cell behaves nothing like a neuron. The difference lies in regulatory DNA—short sequences that act as switches, controlling which genes are active in which tissues. These enhancers can drive strong expression in one cell type while staying completely silent in another. Designing synthetic versions of these switches—sequences that reliably activate a target gene in diseased cells while leaving healthy tissue untouched—has remained a central challenge for gene therapy. And they need to be compact: the viral vectors used to deliver therapeutic genes have strict size limits. Lucas Ferreira DaSilva and coauthors tackle this with DNA-Diffusion, a generative AI framework that applies diffusion models—the same architecture behind image generators like DALL-E—to DNA sequence space. The model trains on regions of open, accessible chromatin from three human cell lines (B-lymphocytes, leukemia cells, and liver cancer cells), learning what sequence patterns correspond to regulatory activity in each cell type. The generated sequences aren't copies of training data—only 4.7% share even a 20 base pair (bp) match—yet they contain the right binding sites for cell-type-specific transcription factors (the proteins that read these switches). A tunable parameter lets users dial between sequences resembling natural enhancers and sequences optimized for maximum activation. Validation spans three levels: computational predictions of chromatin accessibility and gene expression; a library of 5,850 synthetic sequences tested for enhancer activity across all three cell lines; and critically, modulation of an actual gene in its native chromosomal location. The team targeted AXIN2, a gene that protects against leukemia progression but is often silenced in malignant B cells. A naturally occurring mutation upstream of AXIN2 modestly reactivates it and correlates with better patient survival. Multiple AI-designed sequences surpassed this protective variant's activation levels. The message: by combining generative AI with functional genomics, it's now possible to design compact 200 bp regulatory elements—small enough for standard gene therapy delivery—that achieve cell-type-specific control exceeding what evolution has produced, opening a path toward therapies where synthetic switches activate genes only where needed. Paper: nature.com/articles/s4158…



AI-designed proteins that survive 150 °C and nanonewton forces Proteins are usually fragile machines. Heat them, pull on them, or send them through a high-temperature sterilization step (like those used in hospitals), and most will unfold and aggregate, losing their function. Yet many natural systems—like muscle titin or spider silk—hint that if you organize β-sheet hydrogen bonds in the right way, you can get remarkable mechanical strength and thermal resilience. Bin Zheng and coauthors take that idea and push it to the extreme. Starting from the titin I27 domain, they use an AI+MD pipeline—RFdiffusion for backbone generation, ProteinMPNN for sequence design, ESMFold/AlphaFold2 for structure prediction, and steered/annealing MD for screening—to systematically elongate the force-bearing β strands and maximize backbone hydrogen bonds in a shearing geometry. Across multiple design rounds, they grow the network from 4 to 33 backbone H-bonds, creating a “SuperMyo” series of proteins with unfolding forces above 1,000 pN—roughly 4× stronger than I27 under the same pulling conditions. Remarkably, these proteins not only refold after force, but also retain structure and function after exposure to 150 °C and repeated high-temperature sterilization cycles, and can be used as crosslinkers to make hydrogels that survive those treatments intact. The message is powerful: by combining generative protein design with physics-based simulations, it’s now possible to turn a simple principle—pack as many shear-mode hydrogen bonds as possible into β sheets—into synthetic proteins and materials that rival or surpass nature’s own mechanostable systems, enabling protein-based hydrogels and biomaterials that remain functional under conditions that would normally destroy conventional proteins. Paper: nature.com/articles/s4155…