Tootfinder

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 374 nodes and 621 edges.
Tags: Biological, Protein interactions

Learning Protein-Ligand Binding in Hyperbolic Space
Jianhui Wang, Wenyu Zhu, Bowen Gao, Xin Hong, Ya-Qin Zhang, Wei-Ying Ma, Yanyan Lan
https://arxiv.org/abs/2508.15480 https://…

Learning Protein-Ligand Binding in Hyperbolic Space
Protein-ligand binding prediction is central to virtual screening and affinity ranking, two fundamental tasks in drug discovery. While recent retrieval-based methods embed ligands and protein pockets into Euclidean space for similarity-based search, the geometry of Euclidean embeddings often fails to capture the hierarchical structure and fine-grained affinity variations intrinsic to molecular interactions. In this work, we propose HypSeek, a hyperbolic representation learning framework that em…

AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
https://arxiv.org/abs/2509.17224 https://

AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles
Advances in deep learning have opened an era of abundant and accurate predicted protein structures; however, similar progress in protein ensembles has remained elusive. This review highlights several recent research directions towards AI-based predictions of protein ensembles, including coarse-grained force fields, generative models, multiple sequence alignment perturbation methods, and modeling of ensemble descriptors. An emphasis is placed on realistic assessments of the technological maturit…

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 780 nodes and 3099 edges.
Tags: Biological, Protein interactions

Så här kan en dag i mitt liv se ut just nu.
Chokladpuddingen på kvällen tillsammans med mitt ingefära-te är absolut nödvändigt, då varvar jag ner framför något på TV och slappar. Spetsar den med lakritspulver ibland för en extra egg.
Det är viktigt att inte ta ifrån sig själv allt livets goda. Något litet gott måste man få ha, och just den här puddingen är förvånansvärt god. Särskilt med lakrits :heart_fuchsia:

Nonequilibrium protein complexes as molecular automata
Jan Kocka, Kabir Husain, Jaime Agudo-Canalejo
https://arxiv.org/abs/2508.15603 https://arxiv.org/pdf…

Nonequilibrium protein complexes as molecular automata
Biology stores information and computes at the molecular scale, yet the ways in which it does so are often distinct from human-engineered computers. Mapping biological computation onto architectures familiar to computer science remains an outstanding challenge. Here, inspired by Crick's proposal for molecular memory, we analyse a thermodynamically-consistent model of a protein complex subject to driven, nonequilibrium enzymatic reactions. In the strongly driven limit, we find that the system ma…

Barfing is their love language.♥️ A kinky male fruit fly barfs up a bit of his last meal and offers it as a nuptial meal to his intended. Discerning lady fruit flies swoon, and lap it up. #science #entomology

A single protein makes lovesick flies spill their guts
Producing a male-specific protein in digestion-related neurons may have led to the evolution of an odd “romantic” barfing behavior in one species of fruit flies.

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

Now *thats* a medical announcement worth making a big deal about!!
https://wandering.shop/@adapalmer/115248780757355899

Spicy Southwestern Breakfast Burrito #plantbased

Spicy Southwestern Breakfast Burrito
Make this plant-based, high-protein spicy southwestern breakfast burrito for a comforting and filling start to your day.

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

mist: MIST protein interaction database (2020)
The Molecular Interaction Search Tool (MIST) is a comprehensive resource of molecular interactions, assembled from severla primary sources. MIST currently supports several species, including:.
This network has 66 nodes and 65 edges.
Tags: Biological, Protein interactions, Unweighted

🤹🏻‍♂️ One billion-year-old rules of protein stability revealed; could lead to faster drug and enzyme design
https://phys.org/news/2025-07-billion-year-protein-stability-revealed.html

One billion-year-old rules of protein stability revealed; could lead to faster drug and enzyme design
Proteins are life's molecular workhorses, doing everything from turning sunlight into food to fighting viruses. They are built from 20 different types of amino acid molecules, so even a small protein made of 60 amino acids in length can, in theory, be constructed in a quinquavigintillion (1078) different ways. That's about as many atoms as there are in the entire universe.

SHREC 2025: Protein surface shape retrieval including electrostatic potential
Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma, Tin Barisin, Eugen Rusakov, Udo Gobel, Yuxu Peng, Shiqiang Deng, Yuki Kagaya, Joon Hong Park, Daisuke Kihara, Marco Guerra, Giorgio Palmieri, Andrea Ranieri, Ulderico Fugacci, Silvia Biasotti, Ruiwen He, Halim Benhabiles, Adnane Cabani, Karim Hammoudi, Haotian Li, Hao Huang, Chunyan Li, Alireza Tehrani, Fanwang Meng, Farnaz Heidar-Zadeh, Tuan-Anh Yang, Matt…

SHREC 2025: Protein surface shape retrieval including electrostatic potential
This SHREC 2025 track dedicated to protein surface shape retrieval involved 9 participating teams. We evaluated the performance in retrieval of 15 proposed methods on a large dataset of 11,555 protein surfaces with calculated electrostatic potential (a key molecular surface descriptor). The performance in retrieval of the proposed methods was evaluated through different metrics (Accuracy, Balanced accuracy, F1 score, Precision and Recall). The best retrieval performance was achieved by the prop…

#Protein Powders and Shakes Contain High Levels of Lead. Also cadmium and inorganic arsenic.
Researchers at Consumer Reports tested various protein powders and shakes and their conclusion was Jesus Christ that's a lot of lead.

Protein Powders and Shakes Contain High Levels of Lead
CR tests of 23 popular protein powders and shakes found that most contain high levels of lead.

Crosslisted article(s) found for math.AT. https://arxiv.org/list/math.AT/new
[1/1]:
- Topological potentials guiding protein self-assembly
Ivan Spirandelli, Dmitriy Morozov, Arnur Nigmetov, Myfanwy E. Evans

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

Crosslisted article(s) found for cs.CG. https://arxiv.org/list/cs.CG/new
[1/1]:
- Topological potentials guiding protein self-assembly
Ivan Spirandelli, Dmitriy Morozov, Arnur Nigmetov, Myfanwy E. Evans

Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models
Yuxi Lin, Yaxue Fang, Zehong Zhang, Zhouwu Liu, Siyun Zhong, Fulong Yu
https://arxiv.org/abs/2507.16801

Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models
Understanding how 5' untranslated regions (5'UTRs) regulate mRNA translation is critical for controlling protein expression and designing effective therapeutic mRNAs. While recent deep learning models have shown promise in predicting translational efficiency from 5'UTR sequences, most are constrained by fixed input lengths and limited interpretability. We introduce UTR-STCNet, a Transformer-based architecture for flexible and biologically grounded modeling of variable-length 5'UTRs. UTR-STCNet …

Random functions as data compressors for machine learning of molecular processes
Jayashrita Debnath, Gerhard Hummer
https://arxiv.org/abs/2509.17937 https://

Random functions as data compressors for machine learning of molecular processes
Machine learning (ML) is rapidly transforming the way molecular dynamics simulations are performed and analyzed, from materials modeling to studies of protein folding and function. ML algorithms are often employed to learn low-dimensional representations of conformational landscapes and to cluster trajectories into relevant metastable states. Most of these algorithms require selecting a small number of features that describe the problem of interest. Although deep neural networks can tackle larg…

6 High-Protein Sheet Pan Meals #plantbased

6 High-Protein Sheet Pan Meals
For quick and wholesome vegan dinners, make these high-protein sheet pan meals loaded with veggies, nutrients, and heaps of flavor

GridFF: Efficient Simulation of Organic Molecules on Rigid Substrates
Indranil Mal, Milan Ko\v{c}\'i, Paolo Nicolini, Prokop Hapala
https://arxiv.org/abs/2508.15522 https://…

GridFF: Efficient Simulation of Organic Molecules on Rigid Substrates
We present GridFF, an efficient method for simulating molecules on rigid substrates, derived from techniques used in protein-ligand docking in biochemistry. By projecting molecule-substrate interactions onto precomputed spatial grids with tricubic B-spline interpolation, GridFF reduces the computational cost by orders of magnitude compared to traditional pairwise atomistic models, without compromising the accuracy of forces or trajectories. The CPU implementation of GridFF in the open-source Fi…

Exploring Quantum Annealing for Coarse-Grained Protein Folding
Timon Scheiber, Matthias Heller, Andreas Giebel
https://arxiv.org/abs/2508.10660 https://arx…

Exploring Quantum Annealing for Coarse-Grained Protein Folding
We explore the potential application of quantum annealing to address the protein structure problem. To this end, we compare several proposed ab initio protein folding models for quantum computers and analyze their scaling and performance for classical and quantum heuristics. Furthermore, we introduce a novel encoding of coordinate based models on the tetrahedral lattice, based on interleaved grids. Our findings reveal significant variations in model performance, with one model yielding unphysic…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/3]:
- Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Jantre, Wang, Park, Chopra, Jeon, Qian, Urban, Yoon

🔦 Engineered protein switches may lead to safer, smarter medicines
#biology

Engineered protein switches may lead to safer, smarter medicines
A new way to potentially control when drugs are active or inactive in the body is introduced in a study reported Sept. 24 in Nature. The research showed that, instead of controlling how tightly proteins bind to partner molecules, scientists can now directly control how long they stay bound. This advance has broad implications for developing safer medicines.

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Jantre, Wang, Park, Chopra, Jeon, Qian, Urban, Yoon

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations
Xin Gao, Michael Lanz, Rosslyn Grosely, Jonas Cremer, Joseph Puglisi, Jan M. Skotheim
https://arxiv.org/abs/2508.14997

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations
Despite growth being fundamental to all aspects of cell biology, we do not yet know its organizing principles in eukaryotic cells. Classic models derived from the bacteria E. coli posit that protein-synthesis rates are set by mass-action collisions between charged tRNAs produced by metabolic enzymes and mRNA-bound ribosomes. These models show that faster growth is achieved by simultaneously raising both ribosome content and peptide elongation speed. Here, we test if these models are valid for e…

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 431 nodes and 438 edges.
Tags: Biological, Protein interactions, Unweighted

Protein erobert die Kühlschränke vieler Konsumenten.

Adaptive Protein Design Protocols and Middleware
Aymen Alsaadi, Jonathan Ash, Mikhail Titov, Matteo Turilli, Andre Merzky, Shantenu Jha, Sagar Khare
https://arxiv.org/abs/2510.06396

Adaptive Protein Design Protocols and Middleware
Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing…

Implicitly and Differentiably Representing Protein Surfaces and Interfaces
Cory B. Scott, Charlie Rothschild, Benjamin Nye
https://arxiv.org/abs/2508.11641 https://

Implicitly and Differentiably Representing Protein Surfaces and Interfaces
We introduce a pipeline for representing a protein, or protein complex, as the union of signed distance functions (SDFs) by representing each atom as a sphere with the appropriate radius. While this idea has been used previously as a way to render images of proteins, it has not, to our knowledge, been widely adopted in a machine learning setting. Mirroring recent successful work applying SDFs to represent 3D geometry, we present a proof of concept that this representation of proteins could be u…

Information Transmission and Processing in G-Protein-Coupled-Receptor Complexes
Roger D. Jones, Achille Giacometti, Alan M. Jones
https://arxiv.org/abs/2508.11039 https://

Information Transmission and Processing in G-Protein-Coupled-Receptor Complexes
G-protein-coupled receptors (GPCRs) are central to cellular information processing, yet the physical principles governing their switching behavior remain incompletely understood. We present a first principles theoretical framework, grounded in nonequilibrium thermodynamics, to describe GPCR switching as observed in light-controlled impedance assays. The model identifies two fundamental control parameters: (1) ATP/GTP-driven chemical flux through the receptor complex, and (2) the free-energy dif…

10 High-Protein Vegan Recipes (That Use Only Whole Foods) #plantbased

10 High-Protein Vegan Recipes (That Use Only Whole Foods)
Try these high-protein vegan recipes that only use whole foods for nutritious and tasty meal options ideal for dinner

Cooperation and competition of basepairing and electrostatic interactions in mixtures of DNA nanostars and polylysine
Gabrielle R. Abraham, Tianhao Li, Anna Nguyen, William M. Jacobs, Omar A. Saleh
https://arxiv.org/abs/2507.16179

Cooperation and competition of basepairing and electrostatic interactions in mixtures of DNA nanostars and polylysine
Phase separation in biomolecular mixtures can result from multiple physical interactions, which may act either complementarily or antagonistically. In the case of protein--nucleic acid mixtures, charge plays a key role but can have contrasting effects on phase behavior. Attractive electrostatic interactions between oppositely charged macromolecules are screened by added salt, reducing the driving force for coacervation. By contrast, base-pairing interactions between nucleic acids are diminished…

Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation
Brian Shing-Hei Wong, Joshua Mincheol Kim, Sin-Hang Fung, Qing Xiong, Kelvin Fu-Kiu Ao, Junkang Wei, Ran Wang, Dan Michelle Wang, Jingying Zhou, Bo Feng, Alfred Sze-Lok Cheng, Kevin Y. Yip, Stephen Kwok-Wing Tsui, Qin Cao
https://arxiv.o…

Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation
Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. Th…

Fast and Interpretable Protein Substructure Alignment via Optimal Transport
Zhiyu Wang, Bingxin Zhou, Jing Wang, Yang Tan, Weishu Zhao, Pietro Li\`o, Liang Hong
https://arxiv.org/abs/2510.11752

Fast and Interpretable Protein Substructure Alignment via Optimal Transport
Proteins are essential biological macromolecules that execute life functions. Local motifs within protein structures, such as active sites, are the most critical components for linking structure to function and are key to understanding protein evolution and enabling protein engineering. Existing computational methods struggle to identify and compare these local structures, which leaves a significant gap in understanding protein structures and harnessing their functions. This study presents PLAS…

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 1178 nodes and 7623 edges.
Tags: Biological, Protein interactions

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Folding-unfolding transition of active polymer on the reconfiguration of bidirectional tangential active force
Arindam Panda, Sunil P Singh
https://arxiv.org/abs/2509.17824 http…

Folding-unfolding transition of active polymer on the reconfiguration of bidirectional tangential active force
The role of active stress on the conformational dynamics of a polymer has drawn significant interest due to its potential applications in understanding the energy landscape of protein structures, buckling of biopolymers, genomic spatial organization, and their large scale coherent dynamics. We present a model of bidirectional active force that acts along the polymer's tangent, with its direction stochastically reversing between head to tail and tail to head orientations. The active polymer show…

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Protein folding classes -- High-dimensional geometry of amino acid composition space revisited
Boryeu Mao

🪭 Previously unknown protein 'folding factories' discovered
#biology

Previously unknown protein 'folding factories' discovered
In order to fulfill their many functions, proteins must be folded into the correct shape. Researchers at the University of Basel have now discovered tiny "folding factories" in cells that enable efficient and accurate protein folding. A lack of these structures can lead to diseases such as diabetes and neurodegenerative disorders.

Virtuous Machines: Towards Artificial General Science
Gabrielle Wehr, Reuben Rideaux, Amaya J. Fox, David R. Lightfoot, Jason Tangen, Jason B. Mattingley, Shane E. Ehrhardt
https://arxiv.org/abs/2508.13421

Virtuous Machines: Towards Artificial General Science
Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human oversight. The exponential growth of scientific literature and increasing domain specialisation constrain researchers' capacity to synthesise knowledge across disciplines and develop unifying theories, motivating exploration of more general-purpose AI systems for sci…

Energy-Based Models for Predicting Mutational Effects on Proteins
Patrick Soga, Zhenyu Lei, Yinhan He, Camille Bilodeau, Jundong Li
https://arxiv.org/abs/2508.10629 https://

Energy-Based Models for Predicting Mutational Effects on Proteins
Predicting changes in binding free energy ($ΔΔG$) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between $ΔΔG$ and entropy, using probabilities of biologically important objects such as side chain angles and residue identities to estimate $ΔΔG$. However, estimating the full conformational distribution of a protein complex is generally considered intractable. In this work, we propo…

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 2724 nodes and 14322 edges.
Tags: Biological, Protein interactions, Unweighted…

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 543 nodes and 1491 edges.
Tags: Biological, Protein interactions

Crosslisted article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 971 nodes and 4170 edges.
Tags: Biological, Protein interactions

Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations
Murat Isik, Mandeep Kaur Saggi, Humaira Gowher, Sabre Kais
https://arxiv.org/abs/2508.14844

Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations
Accurately predicting enzyme functionality remains one of the major challenges in computational biology, particularly for enzymes with limited structural annotations or sequence homology. We present a novel multimodal Quantum Machine Learning (QML) framework that enhances Enzyme Commission (EC) classification by integrating four complementary biochemical modalities: protein sequence embeddings, quantum-derived electronic descriptors, molecular graph structures, and 2D molecular image representa…

A Guide To Making Your Own ‘Malloumi’ (Vegan Halloumi) #plantbased

A Guide To Making Your Own 'Malloumi' (Vegan Halloumi)
Make malloumi, a vegan high-protein cheese that grills, fries, or sautés without melting, for a tasty plant-based alternative to halloumi.

Understanding protein function with a multimodal retrieval-augmented foundation model
Timothy Fei Truong Jr, Tristan Bepler
https://arxiv.org/abs/2508.04724 https://

Understanding protein function with a multimodal retrieval-augmented foundation model
Protein language models (PLMs) learn probability distributions over natural protein sequences. By learning from hundreds of millions of natural protein sequences, protein understanding and design capabilities emerge. Recent works have shown that scaling these models improves structure prediction, but does not seem to improve mutation understanding and representation quality for protein function prediction. We introduce PoET-2, a multimodal, retrieval-augmented protein foundation model that inco…

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

Synthetic Protein-Ligand Complex Generation for Deep Molecular Docking
Sofiene Khiari, Matthew R. Masters, Amr H. Mahmoud, Markus A. Lill
https://arxiv.org/abs/2509.12915 https:…

Synthetic Protein-Ligand Complex Generation for Deep Molecular Docking
The scarcity of experimental protein-ligand complexes poses a significant challenge for training robust deep learning models for molecular docking. Given the prohibitive cost and time constraints associated with experimental structure determination, scalable generation of realistic protein-ligand complexes is needed to expand available datasets for model development. In this study, we introduce a novel workflow for the procedural generation and validation of synthetic protein-ligand complexes, …

PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations
Sazan Mahbub, Souvik Kundu, Eric P. Xing
https://arxiv.org/abs/2510.11750

PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations
Designing protein sequences that fold into a target three-dimensional structure, known as the inverse folding problem, is central to protein engineering but remains challenging due to the vast sequence space and the importance of local structural constraints. Existing deep learning approaches achieve strong recovery rates, yet they lack explicit mechanisms to reuse fine-grained structure-sequence patterns that are conserved across natural proteins. We present PRISM, a multimodal retrieval-augme…

Applied causality to infer protein dynamics and kinetics
Akashnathan Aranganathan, Eric R. Beyerle
https://arxiv.org/abs/2508.12060 https://arxiv.org/pdf/2…

Applied causality to infer protein dynamics and kinetics
The use of generative machine learning models, trained on the experimentally resolved structures deposited in the protein data bank, is an attractive approach to sampling conformational ensembles of proteins. Unfortunately, since the machine-learned model utilized to generate these ensembles is not tied to an equation of motion, such as a molecular dynamics integrator or other causal generator of the dynamics, there is no timescale or causal information encoded in them. As such, with this work,…

A warning: y’all know I like #startups and I like #foodscience and I’m an adventurous eater and no complainer, but Wilde Protein Chips are one of the worst products I’ve ever tried — I actually bit into the included silica gel packet and it wasn’t too different from the chips.

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Distilled Protein Backbone Generation
Liyang Xie, Haoran Zhang, Zhendong Wang, Wesley Tansey, Mingyuan Zhou
https://arxiv.org/abs/2510.03095 https://arxiv.…

Distilled Protein Backbone Generation
Diffusion- and flow-based generative models have recently demonstrated strong performance in protein backbone generation tasks, offering unprecedented capabilities for de novo protein design. However, while achieving notable performance in generation quality, these models are limited by their generating speed, often requiring hundreds of iterative steps in the reverse-diffusion process. This computational bottleneck limits their practical utility in large-scale protein discovery, where thousand…

interactome_figeys: Figeys human interactome (2007)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred using a mass spectrometry‐based approach.
This network has 2239 nodes and 6452 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

Evolutionary Profiles for Protein Fitness Prediction
Jigang Fan, Xiaoran Jiao, Shengdong Lin, Zhanming Liang, Weian Mao, Chenchen Jing, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.07286

Evolutionary Profiles for Protein Fitness Prediction
Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs) trained with masked language modeling (MLM) exhibit strong zero-shot fitness prediction; we provide a unifying view by interpreting natural evolution as implicit reward maximization and MLM as inverse reinforcement learning (IRL), in which extant sequences act as expert demonstrations and pLM log-odds serve as fitne…

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

genetic_multiplex: Multiplex genetic interactions (2014)
Multiplex networks representing different types of genetic interactions, for different organisms. Layers represent (i) physical, (ii) association, (iii) co-localization, (iv) direct, and (v) suppressive, (vi) additive or synthetic genetic interaction. Edge direction (i,j) indicates gene i interacting with gene j.
This network has 18222 nodes and 170899 edges.
Tags: Biological, Gene regulation, Protein interactions, Un…

Deep Learning-based QSAR Model for Therapeutic Strategies Targeting SmTGR Protein's Immune Modulating Role in Host-Parasite Interaction
Belaguppa Manjunath Ashwin Desai, Belaguppa Manjunath Anirudh, Kalyani S Biju, Vondhana Ramesh, Pronama Biswas
https://arxiv.org/abs/2508.12653

Deep Learning-based QSAR Model for Therapeutic Strategies Targeting SmTGR Protein's Immune Modulating Role in Host-Parasite Interaction
Schistosomiasis, a neglected tropical disease caused by Schistosoma parasites, remains a major global health challenge. The Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) is essential for parasite redox balance and immune evasion, making it a key therapeutic target. This study employs predictive Quantitative Structure-Activity Relationship (QSAR) modeling to identify potential SmTGR inhibitors. Using deep learning, a robust QSAR model was developed and validated, achieving high p…

ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
Samiha Afaf Neha, Abir Ahammed Bhuiyan, Md. Ishrak Khan
https://arxiv.org/abs/2508.07345

ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
\textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine learning, have emerged for annotating phage protein sequences from high-throughput sequencing. However, effective annotation requires specialized sequence encodings. Our paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing …

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

collins_yeast: Collins yeast interactome (2007)
Network of protein-protein interactions in Saccharomyces cerevisiae (budding yeast), measured by co-complex associations identified by high-throughput affinity purification and mass spectrometry (AP/MS).
This network has 1622 nodes and 9070 edges.
Tags: Biological, Protein interactions, Unweighted

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Dong Xu, Zhangfan Yang, Jenna Xinyi Yao, Shuangbao Song, Zexuan Zhu, Junkai Ji
https://arxiv.org/abs/2508.10775

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Three-dimensional generative models increasingly drive structure-based drug discovery, yet it remains constrained by the scarce publicly available protein-ligand complexes. Under such data scarcity, almost all existing pipelines struggle to learn transferable geometric priors and consequently overfit to training-set biases. As such, we present IBEX, an Information-Bottleneck-EXplored coarse-to-fine pipeline to tackle the chronic shortage of protein-ligand complex data in structure-based drug de…

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 813 nodes and 2611 edges.
Tags: Biological, Protein interactions

reactome: Joshi-Tope human protein interactome (2005)
A network of human proteins and their binding interactions, extracted from Reactome project. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 6327 nodes and 147547 edges.
Tags: Biological, Protein interactions, Unweighted
https:/…

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 1501 nodes and 6848 edges.
Tags: Biological, Protein interactions

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

tree-of-life: Protein interactomes across the tree of life (2019)
Protein-protein iteraction networks ("interactome") for 1,840 species. An interactome captures all physical protein-protein interactions within one species, from direct biophysical protein-protein interactions to regulatory protein-DNA and metabolic interactions. .
This network has 277 nodes and 291 edges.
Tags: Biological, Protein interactions

collins_yeast: Collins yeast interactome (2007)
Network of protein-protein interactions in Saccharomyces cerevisiae (budding yeast), measured by co-complex associations identified by high-throughput affinity purification and mass spectrometry (AP/MS).
This network has 1622 nodes and 9070 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

reactome: Joshi-Tope human protein interactome (2005)
A network of human proteins and their binding interactions, extracted from Reactome project. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 6327 nodes and 147547 edges.
Tags: Biological, Protein interactions, Unweighted
https:/…

collins_yeast: Collins yeast interactome (2007)
Network of protein-protein interactions in Saccharomyces cerevisiae (budding yeast), measured by co-complex associations identified by high-throughput affinity purification and mass spectrometry (AP/MS).
This network has 1622 nodes and 9070 edges.
Tags: Biological, Protein interactions, Unweighted

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 2724 nodes and 14322 edges.
Tags: Biological, Protein interactions, Unweighted…

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 2436 nodes and 136930 edges.
Tags: Biological, Protein interactions, Unweighte…

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 1496 nodes and 1816 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

drosophila_flybi: Fruit fly protein interactions (Drosophila melanogaster)
Binary protein-protein interactions (PPIs) for Drosophila melanogaster, containing 8723 PPIs among 2939 proteins. The iteractions were identified using a yeast two-hybrid (Y2H) analysis.
This network has 2939 nodes and 8723 edges.
Tags: Biological, Protein interactions, Unweighted

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 912 nodes and 22738 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

reactome: Joshi-Tope human protein interactome (2005)
A network of human proteins and their binding interactions, extracted from Reactome project. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 6327 nodes and 147547 edges.
Tags: Biological, Protein interactions, Unweighted
https:/…

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 759 nodes and 1593 edges.
Tags: Biological, Protein interactions, Unweighted…

interactome_figeys: Figeys human interactome (2007)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred using a mass spectrometry‐based approach.
This network has 2239 nodes and 6452 edges.
Tags: Biological, Protein interactions, Unweighted

mist: MIST protein interaction database (2020)
The Molecular Interaction Search Tool (MIST) is a comprehensive resource of molecular interactions, assembled from severla primary sources. MIST currently supports several species, including:.
This network has 27105 nodes and 334495 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

mist: MIST protein interaction database (2020)
The Molecular Interaction Search Tool (MIST) is a comprehensive resource of molecular interactions, assembled from severla primary sources. MIST currently supports several species, including:.
This network has 46404 nodes and 643319 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

collins_yeast: Collins yeast interactome (2007)
Network of protein-protein interactions in Saccharomyces cerevisiae (budding yeast), measured by co-complex associations identified by high-throughput affinity purification and mass spectrometry (AP/MS).
This network has 1622 nodes and 9070 edges.
Tags: Biological, Protein interactions, Unweighted

interactome_yeast: Coulomb yeast interactome (2005)
A network of protein-protein binding interactions among yeast proteins. Nodes represent proteins found in yeast (Saccharomyces cerevisiae) and an edge represents a binding interaction between two proteins.
This network has 1870 nodes and 2277 edges.
Tags: Biological, Protein interactions, Unweighted

celegans_interactomes: C. elegans interactomes (2009)
Ten networks of protein-protein interactions in Caenorhabditis elegans (nematode), from yeast two-hybrid experiments, biological process maps, literature curation, orthologous interactions, and genetic interactions. The WI8 network combines WI2004, WI2007 and BPmaps, while the Integrated Network combines data from all sources.
This network has 2436 nodes and 136930 edges.
Tags: Biological, Protein interactions, Unweighte…