G2P Explorer is an open, reproducible mobile companion to the G2P portal for hypothesis generation, teaching, and on the go variant interpretation, and presents the result through six interlinked modules sharing a single observable view model.
Abstract
Genetic testing reports coding variants far faster than they can be interpreted, and placing a variant in its biophysical context, the domain it perturbs, whether its residue is buried or exposed, whether it lies near a disulfide bond or a predicted binding pocket increasingly requires projecting it onto a three dimensional protein model. The Genomics 2 Proteins (G2P) portal unifies the gene-to-structure identifier chain with a dense, residue indexed annotation table, but its visualization layer presumes a desktop browser and is awkward at the bedside, in the classroom, or in the field, where a phone or tablet is often the only device. We present G2P Explorer, a native iOS framework that consumes the public G2P REST API on device, parses its seventy one column tab separated feature tables without loss of fidelity, and presents the result through six interlinked modules sharing a single observable view model. Beyond porting, it contributes a SwiftUI Canvas multi-track sequence renderer, an on device reconstruction of the portal's unavailable isoform alignment route, a bidirectional Swift JavaScript structural bridge that absorbs the AlphaFold model file versioning scheme, and a fault tolerant ingestion layer that parses semi structured free text and distinguishes absent annotations from zero. Each searched protein is cached on device after the first fetch, so it reopens instantly and works offline, and the embedded structural view is drawn in a reduced form suited to a small screen. Across six proteins spanning 189-1{,}863 residues, the framework sustains interactive frame times (3.1-16.6\,ms) and modest memory (16-72\,MB). G2P Explorer is an open, reproducible mobile companion to the G2P portal for hypothesis generation, teaching, and on the go variant interpretation.
Protein–ligand interaction diagrams are a routine part of structural and medicinal chemistry, but the tools that produce them tend to force a choice: comprehensive detection with tabular output, publication-quality figures behind a licence, or a scripting environment that assumes expertise. PandaMap (Protein AND ligAnd interaction MAPper) is an open-source Python package that produces a 2D interaction diagram, an interactive 3D viewer, a text report, a machine-readable CSV, and a four-panel graphical summary from a single command. It reads PDB, mmCIF and PDBQT files, detects 15 interaction classes using crystallographically validated distance thresholds, and depends only on NumPy, Matplotlib, BioPython and Requests; RDKit improves the 2D ligand layout when present but is not required. Hydrogen bonds are filtered on the true D–H· · · A angle when the structure contains explicit hydrogens, matching PLIP’s 100◦ criterion on the same evidence, and on distance alone otherwise, with the provenance of each measurement recorded. We benchmarked the package on three complexes chosen for different chemistry: enolase with a phosphonate transition-state analogue (PDB 1ELS), the EGFR kinase with erlotinib (1M17), and aldose reductase with IDD594 (1US0). PandaMap recovers the contacts these structures are known for, including the EGFR hinge hydrogen bond to MET769 and the IDD594 bromine· · · THR113 halogen bond, both at distances identical to PLIP’s. All detection thresholds, scoring weights and the exact commands used are given in the Supplementary Information, and the release carries a regression suite covering each interaction class. PandaMap 4.3.0 is available on PyPI under the MIT licence.
Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.
Swethasree Bhattaram, D. Bhowmik, Ramakrishnan Kannan· Proceedings of the 32nd ACM...· 0 citations
The VizFold plugin is described, a modular framework that can be extended toward end-to-end composable pipelines and demonstrated feasibility through standardized hook-based tracing for ESMFold and Boltz-2, archive validation, and reproducible deployment on an HPC cluster using managed caches, modules, quotas, and Slurm workflows.
Jayanth Vennamreddy, Arish Virani, Kevin Yin et al.· Practice and Experience in A...· 0 citations
Understanding protein mechanisms in health and disease requires characterizing the functional roles of individual amino acid residues. To explore the role of residues and their mutations, we have developed Atlantis, a database that integrates structural and functional information at the human proteome residue level. A graph database enables complex queries and the retrieval of integrated information for multiple functional analysis of protein systems. A Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research. Atlantis annotates over 11M residues across 20k human proteins, identifying hundreds thousands intra- and inter-protein contacts in PDB as well as AlphaFoldDB structures. We also provide the possibility to analyze and integrate predicted 3D complexes inputted by the user, and we showcased these features on hundreds of AlphaFold-multimer complexes of GPCRs and LRRK2 interaction networks. The tool is freely accessible at https://atlantis.bioinfolab.sns.it/. GRAPHICAL ABSTRACT
Natalia De Oliveira Rosa, Piergiorgio Ferronato, M. Varisco et al.· bioRxiv· 0 citations
Motivation Molecular visualization is dominated by two families of software. Desktop programs such as PyMOL, VMD and UCSF ChimeraX are powerful but heavy to install and operate. Web viewers such as Jmol, 3Dmol.js, NGL, Mol* and iCn3D are light and installation-free, but typically stop at rendering, lacking deeper functionality needed for even basic protein sequence, structural analyses and design tasks. Tools building upon these often lack comprehensive sequence/structure manipulation, surface, interface and antibody-specific analytics functionality or require an external backend. There is a need for a tool that is as frictionless as a web viewer yet carries the analytical depth normally reserved for the desktop or the command line software. Results We present Protein Design Viz (PDV), a self-contained molecular visualization studio delivered as a single offline HTML file that runs entirely in the browser. Built as an extensive modification of 3Dmol.js, PDV combines a full visualization workflow: multi-object scenes, representations, coloring palette, linked sequence track, publication-quality outline rendering and portable sessions. Additionally we re-implemented four commonly used macromolecular analyses from scratch in client-side JavaScript: a Shrake–Rupley solvent-accessible surface area (SASA) engine, an antibody numbering and germline-assignment engine, a non-covalent interaction detector, and a developability-liability scanner. Each engine is validated against its established reference. PDV’s SASA reproduces FreeSASA at Pearson r ≈ 0.997–0.998 across 2,582 structures spanning proteins, nucleic acids and ligands; its numbering reproduces RIOT for over 99.88% of 1.3 million residue positions across 16,996 sequences and four schemes; its interaction detector reproduces PLIP at macro-F1 0.82, matching Arpeggio as closely as PLIP itself does. PDV brings validated, quantitative structural analysis into a no-install, simple to use tool. Availability and implementation PDV is a single HTML file, free for noncommercial use under the PolyForm Noncommercial License 1.0.0, available from pdv.naturalantibody.com. It requires only a WebGL-capable browser and runs fully offline. Contact konrad@naturalantibody.com
ABSTRACT The rapid expansion of bacterial genome databases presents significant opportunities for functional discovery, as a large fraction of genes and protein domains remain uncharacterized. Analyzing genomic context and domain architecture is a powerful approach for functional inference, but existing tools often lack the scalability and integrated workflow required for high-throughput analysis. To address this, we developed Pandoomain, a Snakemake pipeline that automates the acquisition of genomes from the National Center for Biotechnology Information, identifies proteins of interest using hidden Markov models (HMMs), and performs systematic domain annotation and gene neighborhood analysis. We demonstrate the utility of Pandoomain through a comprehensive analysis of the poorly characterized pre-toxin TG (PT-TG) domain across 347,289 bacterial genomes. Our analysis revealed 10,226 PT-TG-containing proteins organized into 312 unique domain architectures, highlighting their association with diverse interbacterial antagonistic systems, including the Type VI secretion, Type VII secretion, and contact-dependent inhibition systems. By leveraging genomic context, we identified a novel variant of the WXG trafficking domain, termed W10XG, and subsequently discovered 24 new families of associated toxin domains. We experimentally validated six of these toxins, confirming that all six are neutralized by their cognate immunity proteins. Pandoomain is an accessible tool that enables systematic, large-scale exploration of protein domains, and our analysis of the PT-TG domain provides a rich resource for future investigations into the mechanisms and evolution of bacterial antagonism. IMPORTANCE The rapid growth of bacterial genomic data presents a major hurdle for scientists seeking to understand the functions of newly discovered genes and proteins. To address this issue, we created Pandoomain, a powerful, accessible software tool that automates large-scale analysis of genetic information across hundreds of thousands of genomes. Using Pandoomain, we investigated a poorly understood family of proteins involved in bacterial competition, revealing novel protein domain architectural diversity. This led to the discovery of 24 new families of toxins predicted to be used by bacteria to attack their competitors, and we experimentally confirmed the toxic activity of six of them. Our work provides the scientific community with a robust tool to accelerate functional discovery and offers new insights into the evolution of bacterial conflicts, which may provide insights into the compositional dynamics of microbial communities and support methods to engineer their composition. The rapid growth of bacterial genomic data presents a major hurdle for scientists seeking to understand the functions of newly discovered genes and proteins. To address this issue, we created Pandoomain, a powerful, accessible software tool that automates large-scale analysis of genetic information across hundreds of thousands of genomes. Using Pandoomain, we investigated a poorly understood family of proteins involved in bacterial competition, revealing novel protein domain architectural diversity. This led to the discovery of 24 new families of toxins predicted to be used by bacteria to attack their competitors, and we experimentally confirmed the toxic activity of six of them. Our work provides the scientific community with a robust tool to accelerate functional discovery and offers new insights into the evolution of bacterial conflicts, which may provide insights into the compositional dynamics of microbial communities and support methods to engineer their composition.
E. Soto, Adam Oliver, Marcos H. de Moraes· mSystems· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
