Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Jasper Butcher, Rohith Krishna, Raktim Mitra, Rafael I. Brent + 19 more
Deep learning has accelerated protein design, but most existing methods are restricted to generating protein backbone coordinates and often neglect interactions with other biomolecules. We present RFdiffusion3 (RFD3), a diffusion model that generates protein structures in the context of ligands, nucleic acids and other…
Amelia Villegas-Morcillo, Gijs J. Admiraal, Marcel J. T. Reinders, Jana M. Weber
Advancing protein design is crucial for breakthroughs in medicine and biotechnology. Traditional approaches for protein sequence representation often rely solely on the 20 canonical amino acids, limiting the representation of non-canonical amino acids and residues that undergo post-translational modifications. This…
Lakshaditya Singh, Adwait Shelke, Divyansh Agrawal
—Building new protein structures is a major hurdle in computational biology, impacting everything from drug design to creating new enzymes. The problem with standard diffusion models is that they usually work in Cartesian coordinates. This often breaks the rules of geometry—specifically, it messes up bond lengths and…
Hannes Stark, Felix Faltings, MinGyu Choi, Yuxin Xie + 37 more
We introduce BoltzGen, an all-atom generative model for designing proteins and peptides across all modalities to bind a wide range of biomolecular targets. BoltzGen builds strong structural reasoning capabilities about target-binder interactions into its generative design process. This is achieved by unifying design…
Dylan Silke, Julie Iskander, Junqi Pan, Andrew P. Thompson + 3 more
Leveraging artificial intelligence and deep learning to generate proteins de novo (a.k.a. ‘synthetic proteins’) has unlocked new frontiers of protein design. Deep learning models trained on protein structures can generate novel protein designs that explore structural landscapes unseen by evolution. This approach…
Shaowen Zhu, Siddhant Gulati, Yuxuan Liu, Siddhi Kotnis + 2 more
Computational design of functional proteins is of both fundamental and applied interest. This study introduces a generative framework for co-designing protein sequence and structure in a unified process by modeling their joint distribution, with the goal of enabling cross-modality interactions toward coherent and…
Zhang, Odin, Zhang, Xujun + 70 more
Biomolecular interactions underpin almost all biological processes, and their rational design is central to programming new biological functions. Generative AI models have emerged as powerful tools for molecular design, yet most remain specialized for individual molecular types and lack fine-grained control over…
Yuxuan Lou, Tianhao Wu, Fan Xia, Anwen Zhao + 1 more
Artificial intelligence (AI) is poised to reshape the research paradigm of the life sciences by rapidly advancing the adoption of protein language models and their derivative tools. These technologies are increasingly being applied to protein structure prediction, function analysis, and protein design throughout the…
Kevin Michalewicz, Jin, Chen, Philip Teare + 4 more
A fundamental challenge in protein design is the trade-off between generating structural diversity while preserving motif biological function. Current state-ofthe-art methods, such as partial diffusion in RFdiffusion, often fail to resolve this trade-off: small perturbations yield motifs nearly identical to the native…
Yuxuan Li, Yeyu Su, Yanbo Jing, Tao Liu
Current protein binder design largely relies on a decoupled paradigm: generating backbones via unconditioned diffusion followed by sequence filling or refilling with inverse folding models. This separation prevents the design process from accessing the holistic validation metrics of structure predictors during…
Yeqing Lin, Minji Lee, Aakarsh Vermani, Ellena Jiang + 3 more
Despite the breakneck pace of progress in protein design methodology, frontier problems remain challenging, with leading methods struggling to design high-affinity binders, scaffold multiple functional motifs, or stabilize large multi-domain proteins. Recent research efforts have focused on two areas: improving model…
Srinivas Kashyap Chilakamarri, Sneha Reddy Kasturi, Sai Pranav Reddy Yerrabandla, Sanjana Gogte + 1 more
Designing functional peptides with specific structural and biochemical properties is critical for applications in protein engineering and therapeutic discovery. However, most peptide design approaches rely on evolutionary or local sequence optimization methods, which are limited when adapting to peptides’ shorter…
Ha Thi Ngoc Nguyen, Bao Hong Ngoc Le, Nhung Thi Hong Van, Trinh Thi Tuyet Tran + 2 more
The majority of disease-associated proteins are considered “undruggable” due to the absence of well-defined binding pockets, the presence of extended interaction surfaces, and intrinsic structural disorder, which collectively limit the effectiveness of conventional small molecules and biologics. Representative examples…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Hui-wang Ai, Frances H Arnold, Doug Barrick, Hagan Bayley + 77 more
With this status report, we aim to provide a timely snapshot of the protein engineering field as a broad and rapidly advancing discipline that integrates computational, molecular biology, structure-guided, evolutionary, and synthetic approaches to create new and improved proteins with tailored structures and useful…
Fang Wu, Zhengyuan Zhou, Shuting Jin, Xiangxiang Zeng + 2 more
Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in proteinprotein interactions (PPIs) has been underexplored. To…
Authors not listed
Fragment-based drug design (FBDD) has become a key approach in structure-based drug discovery, allowing researchers to systematically develop molecular fragments into potent ligands. Although recent generative AI models, such as diffusion-based approaches, show great potential for designing new molecules, applying them…
Hannes Junker, Clara T. Schoeder
G protein-coupled receptors (GPCRs) play an ubiquitous role in the transduction of extracellular stimuli into intracellular responses and therefore represent a major target for the development of novel peptide-based therapeutics. In fact, approximately 30% of all non-sensory GPCRs are peptide-targeted, representing a…
Bowen Jing, Mihir Bafna, Anisha Parsan, Heyuan Michael Ni + 4 more
Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative implications for many areas of biotechnology, yet lies beyond the capabilities of existing deep learning frameworks for protein design. To address…
Bin Sun, Alec Loftus, Peter Kekenes-Huskey
We review the Brownian dynamics (BD) modeling of biomolecular binding events, with an emphasis on enzyme-substrate interactions in cellular environments. We begin with theoretical foundations of BD and its applications to association and dissociation binding processes in both homogeneous and heterogeneous media. Next…
Sean M. Brown, Ashley B. Cohen, Scott N. Dean
Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standard alphabet has been identified to be non-randomly…
Authors not listed
While artificial intelligence has revolutionized the prediction of static protein structures, characterizing their dynamics and interactions with drug candidates remains a computational bottleneck. Here, we introduce FeNNix-Bio1, a foundation machine learning model designed to power accurate, reactive atomistic…
Authors not listed
Photoexcitation energy transfer between aromatic residues tryptophan, tyrosine, is the first step in triggering the intracellular responses after UV light absorption in photopro- tective organisms. At sufficiently large separations, F¨orster Resonance Energy Transfer (FRET) between aromatic residues takes place, the…
Authors not listed
We present the next generation of AMP, a neural network potential (NNP) with anisotropic message passing designed to study large biomolecular systems at DFT accuracy in the condensed phase using a multiscale approach similar to quantum-mechanics/molecular-mechanics (QM/MM) with electrostatic embedding. We trained AMPv3…