搜索结果：Pocket

Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substrates. Currently, generative models dominate functional protein design but cannot model pocket-substrate interactions, which limits the generation of enzymes with precise catalytic environments. To address this issue, we propose EnzyPGM, a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates, with a particular focus on learning accurate pocket-substrate interactions. At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Function Fusion (RFF) that incorporates enzyme function priors into residue representations. Also, we curate EnzyPock, an enzyme-pocket dataset comprising 83,062 enzyme-substrate pairs across 1,036 four-level enzyme families. Extensive experiments demonstrate that EnzyPGM achieves state-of-the-art performance on EnzyPock. Notably, EnzyPGM reduces the

The modulator pocket is a cryptic site discovered in the TREK1 K2P channel that accommodates agonists capable of increasing the channel's activity. Since its discovery, equivalent sites in other K2P channels have been shown to bind various ligands, both endogenous and exogenous. In this review, we attempt to elucidate how the modulator pocket contributes to K2P channel activation. To this end, we first describe the gating mechanisms reported in the literature and rationalize their modes of action. We then highlight previous experimental and computational evidence for agonists that bind to the modulator pocket, together with mutations at this site that affect gating. Finally, we elaborate how the activation signal arising from the modulator pocket is transduced to the gates in K2P channels. In doing so, we outline a potential common modulator pocket architecture across K2P channels: a largely amphipathic structure -consistent with the expected properties of a pocket exposed at the interface between a hydrophobic membrane and the aqueous solvent- but still with some important channel-sequence-variations. This architecture and its key differences can be leveraged for the design of new

Designing ligand-binding proteins, such as enzymes and biosensors, is essential in bioengineering and protein biology. One critical step in this process involves designing protein pockets, the protein interface binding with the ligand. Current approaches to pocket generation often suffer from time-intensive physical computations or template-based methods, as well as compromised generation quality due to the overlooking of domain knowledge. To tackle these challenges, we propose PocketFlow, a generative model that incorporates protein-ligand interaction priors based on flow matching. During training, PocketFlow learns to model key types of protein-ligand interactions, such as hydrogen bonds. In the sampling, PocketFlow leverages multi-granularity guidance (overall binding affinity and interaction geometry constraints) to facilitate generating high-affinity and valid pockets. Extensive experiments show that PocketFlow outperforms baselines on multiple benchmarks, e.g., achieving an average improvement of 1.29 in Vina Score and 0.05 in scRMSD. Moreover, modeling interactions make PocketFlow a generalized generative model across multiple ligand modalities, including small molecules, pe

Upon the impact of a flat disk on a boiling liquid, i.e., a liquid that is in thermal equilibrium with its own vapor, a thin vapor layer is entrapped under the disk. Due to the tendency of vapor to undergo phase change under pressure variation upon impact, the dynamics of this entrapped vapor pocket are different from those of a non-condensable air pocket. In this work, we experimentally investigate the dynamics of the entrapped vapor pocket, more specifically its time evolution and its subsequent influence on the hydrodynamic loads at different equilibrium ambient temperatures and impact velocities. We find that the retraction of the vapor pocket at high ambient temperature and small impact velocity is slow, occurring from the disk edge, and driven by the dynamic pressure $ρ_{\text{L}}U_0^2$. In contrast, at lower ambient temperatures and large impact velocities, after a short initial stage, the vapor pocket will collapse rapidly due to condensation. This scenario is confirmed by conducting experiments where, by heating the disk, the vapor pocket collapse is observed to slow down. We attribute this to the vaporization of liquid near the three-phase contact line region that frustra

Splice-mediated drug resistance occurs in up to 40% of patients on targeted kinase inhibitors, yet state-of-the-art druggability tools operate on single structures and cannot compare across isoforms. We introduce SpliceBind, a graph neural network framework for isoform-aware druggability prediction. Beyond improving prediction accuracy (AUROC 0.703 vs. P2Rank 0.634, p = 0.026), we address a more fundamental question: when do structural methods succeed, and when must they fail? Systematic analysis of six clinically validated variants spanning five mechanism classes reveals a two-tier resistance taxonomy. Domain deletions (AR-V7, Delta = -18.39) and pocket disruptions produce structurally detectable changes, while allosteric mechanisms (BRAF-p61) remain fundamentally invisible to any pocket-centric approach -- a boundary no algorithmic improvement can cross. Notably, learned embeddings capture affinity-based resistance missed by geometry alone (ALK-L1196M: Delta_SB = -0.228 vs. Delta_P2Rank = -0.95), partially bridging the structural-biochemical gap. On 229 kinase pockets spanning 25 families, SpliceBind achieves AUROC 0.703 (p = 0.026 vs. P2Rank) with robust generalization to held-o

Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding pocket. However, existing diffusion-based SBDD methods typically couple pocket and ligand representation learning, model interactions only at the atom level, and prioritize binding affinity over other developability properties. Here, we introduce conDitar-dev, a conditional diffusion-based SBDD framework for generating ligands with strong binding affinities and favorable ADMET properties. It consists of three modules: msPRL, a pretrained multi-scale pocket representation learning module; conDitar, a pocket-conditioned diffusion model guided by msPRL representations; and paOPT, a generation-time method for optimizing ligand developability. On a newly curated benchmark of human disease targets, conDitar outperforms state-of-the-art SBDD baselines, achieving an average binding score of -8.85 kcal/mol. Across five ADMET properties, conDitar-dev improves performance by up to 73% over conDitar. To further validate t

Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating small molecule ligands that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the intrinsic flexibility of proteins and the conformational rearrangements induced by ligand binding, limiting their applicability in practical drug discovery. Here, we propose Apo2Mol, a diffusion-based generative framework for 3D molecule design that explicitly accounts for conformational flexibility in protein binding pockets. To support this, we curate a dataset of over 24,000 experimentally resolved apo-holo structure pairs from the Protein Data Bank, enabling the characterization of protein structure changes associated with ligand binding. Apo2Mol employs a full-atom hierarchical graph-based diffusion model that simultaneously generates 3D ligand molecules and their corresponding holo pocket conformations from input apo states. Empirical studies demonstrate that Apo2Mol can achieve state-of-the-art performance in generating high-affinity ligands and accurately capture realistic protein pocket conformational changes.

We investigate the feasibility and accuracy of a machine learning model to predict the dynamics of a gas pocket that is formed when a breaking wave impacts on a solid wall. The proposed ML model is based on the convolutional long short-term memory structure and is trained with experimental data. In particular, it takes as input two high-speed camera snapshots before impact and produces as output six scalars that describe the dynamics of the gas pocket. The experiments are performed in a wave flume, where we use solitons -- in combination with a bathymetry profile -- to generate wave breaking close to a solid wall which is instrumented with dynamic pressure sensors. By varying the water depth $h_\ell$ and the parameter $α= A/h_\ell$, where $A$ is the soliton wave amplitude, we are able to generate a family of unique breaking waves with different gas pocket sizes and wave kinematics. In this so-called phase space of wave generation ($h_\ell$, $α$), we perform experiments on 67 different wave states that form our dataset. Experimentally, we find that the frequency of oscillation of the gas pocket can be attributed to the initial volume of gas plus a geometric correction and that the m

We report the results of the parquet renormalization group (RG) analysis of the phase diagram of the most general 5-pocket model for Fe-based superconductors. We use as an input the orbital structure of excitations near the five pockets made out of $d_{xz}$, $d_{yz}$, and $d_{xy}$ orbitals and argue that there are 40 different interactions between low-energy fermions in the orbital basis. All interactions flow under RG, as one progressively integrates out fermions with higher energies. We find that the low-energy behavior is amazingly simple, despite the large number of interactions. Namely, at low-energies the full 5-pocket model effectively reduces either to a 3-pocket model made of one $d_{xy}$ hole pocket and two electron pockets, or a 4-pocket model made of two $d_{xz}/d_{yz}$ hole pockets and two electron pockets. The leading instability in the effective 4-pocket model is a spontaneous orbital (nematic) order, followed by $s^{+-}$ superconductivity. In the effective 3-pocket model orbital fluctuations are weaker, and the system develops either $s^{+-}$ superconductivity or stripe SDW. In the latter case, nematicity is induced by composite spin fluctuations.

Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or protein binding pocket design often fall short in capturing the dynamic and complex nature of enzyme-substrate interactions, particularly in catalytic processes. To address the challenges, we introduce EnzymeFlow, a generative model that employs flow matching with hierarchical pre-training and enzyme-reaction co-evolution to generate catalytic pockets for specific substrates and catalytic reactions. Additionally, we introduce a large-scale, curated, and validated dataset of enzyme-reaction pairs, specifically designed for the catalytic pocket generation task, comprising a total of $328,192$ pairs. By incorporating evolutionary dynamics and reaction-specific adaptations, EnzymeFlow becomes a powerful model for designing enzyme pockets, which is capable of catalyzing a wide range of biochemical reactions. Experiments on the new dataset demonstrate the model's effectiveness in designing high-quality, functional enzyme catalytic pockets, paving the way for advancements in enzyme engineering and synthetic biology.

Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for the accurate prediction of binding pockets and seamless integration with docking pipelines. On the PoseBusters benchmark, RAPID-Net-guided AutoDock Vina achieves 54.9% of Top-1 poses with RMSD < 2 A and satisfying the PoseBusters chemical-validity criterion, compared to 49.1% for DiffBindFR. On the most challenging time split of PoseBusters aiming to assess generalization ability (structures submitted after September 30, 2021), RAPID-Net-guided AutoDock Vina achieves 53.1% of Top-1 poses with RMSD < 2 A and PB-valid, versus 59.5% for AlphaFold 3. Notably, in 92.2% of cases, RAPID-Net-guided Vina samples at least one pose with RMSD < 2 A (regardless of its rank), indicating that pose ranking, rather than sampling, is the primary accuracy bottleneck. The lightweight inference, scalability, and competitive accuracy of RAPID-Net position it as a viable option for large-scale virtual screening campaigns. Across diverse benchmark datasets, RAPID-Net outperforms other

The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ generation of 3D ligand structures using the equivariant diffusion model PILOT, combining pocket conditioning with a large-scale pre-training and property guidance. Its multi-objective trajectory-based importance sampling strategy is designed to direct the model towards molecules that not only exhibit desired characteristics such as increased binding affinity for a given protein pocket but also maintains high synthetic accessibility. This ensures the practicality of sampled molecules, thus maximizing their potential for the drug discovery pipeline. PILOT significantly outperforms existing methods across various metrics on the common benchmark dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands for unseen protein pockets from the Kinodata-3D dataset, which encompasses a substantial portion of the human kinome. The generated structures exhibit predicted $IC_{50}$ values indicative of potent biological activity, which h

A common denominator for most therapy treatments for children who suffer from an anxiety disorder is daily practice routines to learn techniques needed to overcome anxiety. However, applying those techniques while experiencing anxiety can be highly challenging. This paper presents the design, implementation, and pilot study of a tactile hand-held pocket robot AffectaPocket, designed to work alongside therapy as a focus object to facilitate coping during an anxiety attack. The robot does not require daily practice to be used, has a small form factor, and has been designed for children 7 to 12 years old. The pocket robot works by sensing when it is being held and attempts to shift the child's focus by presenting them with a simple three-note rhythm-matching game. We conducted a pilot study of the pocket robot involving four children aged 7 to 10 years, and then a main study with 18 children aged 6 to 8 years; neither study involved children with anxiety. Both studies aimed to assess the reliability of the robot's sensor configuration, its design, and the effectiveness of the user tutorial. The results indicate that the morphology and sensor setup performed adequately and the tutorial

Background-oriented schlieren (BOS) is a powerful technique for flow visualization. Nevertheless, the widespread dissemination of BOS is impeded by its dependence on scientific cameras, computing hardware, and dedicated analysis software. In this work, we aim to democratize BOS by providing a smartphone based scientific tool called "Pocket Schlieren". Pocket Schlieren enables users to directly capture, process, and visualize flow phenomena on their smartphones. The underlying algorithm incorporates consecutive frame subtraction (CFS) and optical flow (OF) techniques to compute the density gradients inside a flow. It performs on both engineered and natural background patterns. Using Pocket Schlieren, we successfully visualized the flow produced from a burning candle flame, butane lighter, hot soldering iron, room heater, water immersion heating rod, and a large outdoor butane flame. Pocket Schlieren promises to serve as a frugal yet potent instrument for scientific and educational purposes. We have made it publicly available at doi: 10.5281/zenodo.10949271.

Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design. While existing geometric features and pretrained representations have demonstrated promising results, they usually treat pockets independent of ligands, neglecting the fundamental interactions between them. However, the limited pocket-ligand complex structures available in the PDB database (less than 100 thousand non-redundant pairs) hampers large-scale pretraining endeavors for interaction modeling. To address this constraint, we propose a novel pocket pretraining approach that leverages knowledge from high-resolution atomic protein structures, assisted by highly effective pretrained small molecule representations. By segmenting protein structures into drug-like fragments and their corresponding pockets, we obtain a reasonable simulation of ligand-receptor interactions, resulting in the generation of over 5 million complexes. Subsequently, the pocket encoder is trained in a contrastive manner to align with the representation of pseudo-ligand furnished by some pretrained small molecule encoders. Our method, named ProFSA,

Layered Nickelates have gained intensive attention as potential high-temperature superconductors, showing similarities and subtle differences to well-known Cuprates. This study introduces a modelling framework to analyze the tunability of electronic structures by focusing on effective orbitals and additional Fermi pockets, mimicking doping or external pressure qualitatively. It investigates the role of the $3d_{z^2}$ orbital in interlayer hybridization, which leads to the formation of a second pocket in the Fermi surface. The resulting effective model also predicts specific charge and spin susceptibility in the form of Lindhard susceptibility at wave vector $\mathbf{q_{0}} = (π, π)$, which can be tuned by doping or pressure. These results provide valuable insights into tunable orbital contributions and their influence on potential ordering and electronic instabilities in Layered Nickelates.

Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the specificity and success rate of in silico drug design by considering the structure of protein pockets. This setting posts fundamental computational challenges in sampling new chemical compounds that could satisfy multiple geometrical constraints imposed by pockets. Previous sampling algorithms either sample in the graph space or only consider the 3D coordinates of atoms while ignoring other detailed chemical structures such as bond types and functional groups. To address the challenge, we develop Pocket2Mol, an E(3)-equivariant generative network composed of two modules: 1) a new graph neural network capturing both spatial and bonding relationships between atoms of the binding pockets and 2) a new efficient algorithm which samples new drug candidates conditioned on the pocket representations from a tractable distribution without relying on MCMC. Experimental results demonstrate that molecules sampled from Pocket2Mol achieve significantly better binding affinity and other drug properties such as druglikeness

We provide thermodynamic evidence for the presence of a magnetic-field-induced small phase pocket near the antiferro-quadrupole (AFQ) phase boundary in the non-Kramers $Γ_3$ doublet system PrIr$_2$Zn$_{20}$. In particular, we measured the specific heat as functions of temperature $T$, magnetic field $B$, and field angle $φ_B$, and found a second specific-heat anomaly in a relatively wide field-angle range near $B \parallel [001]$, although fine tuning of the field strength is required. We also investigated the rotational magnetocaloric effect and evaluated an entropy change in this phase pocket. The present findings demonstrate that multipole degrees of freedom give rise to a magnetic-field-induced exotic order in PrIr$_2$Zn$_{20}$, suggesting the possibility of switching between the order parameters or emergence of a multiple-$q$ order of quadrupoles.

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual SES probe spheres obtained from the geometrical primitives generated by the NanoShaper software. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight on which geometrical (clustering) and chemical (residues) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a reliable representation that pinpoint the binding site with greater precision. Site Ferret is outstanding in its versatility, accurately predicting a wide range of binding sites, from small molecules to peptides and difficult shallow sites.