Começando a semana do jeito que tem que ser… você tendo recaída!! e com a cabeça bem estragada @RT4MastersBR2 @Alphafindom_prm @nick_gtwb @LorenzoFetiche #feetworshi̇p

4/30 7PMから4月にポストしたプレプリントやジャーナルを一枚のイラストを使って紹介します！ 是非リアルタイム視聴してくださいね✨ 【#PaperTalk #8】 バイオインフォマティクス最新研究紹介 2026年4月 AlphaFind v2 Foldablity Crypt... youtube.com/live/DGJ2ts2QvsE…

AlphaFind v2: similarity search in AlphaFold DB and TED domains across structural contexts academic.oup.com/nar/advance… AlphaFind alphafind.ics.muni.cz/

AlphaFold DBに対して高速かつ生物学的妥当性の高い構造類似性検索するAlphaFind v2が公開されました。ESM3埋め込み＋US-align精緻化の2段階検索を実装し、pLDDTフィルタ・TED単一ドメイン・マルチドメイン検索に対応することで、近似回答はFoldSeekの約18倍高速でTM-scoreも有意に向上 ソースはリプ🔽

AlphaFind v2: Similarity search in AlphaFold DB and TED domains across structural contexts 1 AlphaFind v2 is a web server for fast, structure-based similarity search at AlphaFold DB scale, combining embedding-based prefiltering with alignment-based refinement to keep results biologically interpretable (TM-score/RMSD) while staying interactive. 2 The key design idea is “search across structural contexts”: users can search full chains, restrict comparisons to high-confidence regions using AlphaFold pLDDT thresholds (70/80/90), search TED domains, or run a TED Multidomain mode that captures domain combinations rather than single-domain matches. 3 The workflow is staged for responsiveness: Phase 2 returns immediate approximate kNN results from a vector database (top k=100 by cosine similarity), while Phase 3 runs asynchronously in the background to refine rankings using US-align and report TM-score, RMSD, aligned residues, and interactive superpositions. 4 pLDDT-aware search directly addresses a common AlphaFold-era problem: low-confidence/disordered regions can dominate alignments and hide true homologs. By trimming residues below chosen pLDDT thresholds, AlphaFind v2 focuses similarity on stable structural cores. 5 Domain-level search is integrated via TED: AlphaFind v2 supports direct TED domain retrieval and alignment restricted to domain residue boundaries, enabling more fine-grained detection of shared folds when full-length proteins differ in architecture. 6 TED Multidomain mode targets proteins where function/evolution is encoded in domain composition and order. It aggregates multiple domain-to-domain matches into a single score/alignment, aiming to recover “same architecture” relationships that single-domain hits would miss. 7 A distinctive interface feature in TED Multidomain is interactive weighting: sliders adjust each matched domain pair’s contribution, updating the 3D alignment view to move between (i) inspecting one domain precisely and (ii) assessing global multi-domain arrangement. 8 Under the hood, AlphaFold DB v4 chains are embedded into 1536D vectors using an ESM3-based pipeline; additional embeddings are computed after removing low-confidence residues (pLDDT < 70/80/90). TED domains use precomputed 128D Foldclass embeddings. 9 Engineering choices focus on scalable, low-latency search: OpenSearch vector DB with HNSW (16x compression, on-disk), a Python/Flask REST API, Celery Redis for async refinement jobs, PostgreSQL for state/caching, and Kubernetes for horizontal scaling. 10 Reported benchmarks show rapid retrieval plus strong refinement quality: approximate results in ~2.4 s for chains and ~0.49 s for domains, with refinement completing in tens of seconds; evaluation indicates higher average TM-scores than AlphaFind v1, FoldSeek server (TM computed separately), and Merizo-search (domains), with statistical significance (P < 0.05). 11 Case study (PIN3 auxin carrier): full-chain search struggles due to large disordered loops, but pLDDT ≥ 90 mode finds homologs with TM-score up to 0.947, illustrating how confidence-filtered structural search can recover relationships obscured by structural “noise”. 12 Case study (NCAM1): TED Multidomain mode captures the characteristic Ig-domain fibronectin type III arrangement, helping identify proteins with similar multidomain architecture; interactive reweighting helps resolve cases where domain positions differ across predictions. 📜Paper: doi.org/10.1093/nar/gkag372 #ProteinStructure #AlphaFold #StructuralBioinformatics #ProteinDomains #SimilaritySearch #Embeddings #WebServer #TMscore #CATH #TED #ComputationalBiology

1 This time, we updated the AlphaFind to allow quick structural search over AlphaFoldDB - this time with domains (TED and multi) and structure quality filtering alphafind.ics.muni.cz Try it out! academic.oup.com/nar/advance…

AlphaFind v2: Similarity Search in AlphaFold DB and TED Domains across Structural Contexts 1 AlphaFind v2 is a web application designed for fast structure-based similarity search in the AlphaFold Database of predicted protein structures, addressing the computational challenges of large-scale 3D structure comparison. 2 It combines fast pre-filtering using protein embeddings that preserve structural information with refinement via US-align, balancing search speed and biological relevance. 3 The tool offers six complementary search modes, including full-protein chain search, pLDDT-filtered searches at 70%, 80%, and 90% thresholds, TED domain search, and TED multidomain search. 4 It supports optional filtering by organism, taxonomy ID, or CATH label, and links search results to corresponding experimental protein structures. 5 The approximate search phase delivers results in seconds, with full structural refinement completed in under a minute on average, outperforming tools like FoldSeek Server and Merizo-search in both speed and average TM-Score. 6 Key applications include identifying homologous proteins in disordered regions via pLDDT filtering and detecting conserved multidomain architectures as demonstrated in case studies of PIN3 and NCAM1 proteins. 7 The web server is built with a Python backend, Flask REST API, Celery asynchronous tasks, OpenSearch vector database, and Kubernetes deployment for scalable performance. 8 AlphaFind v2 uses AlphaFold DB version 4 and precomputed embeddings, with all functionality freely accessible to users without login requirements. 📜Paper: biorxiv.org/content/10.64898… #AlphaFind #ProteinStructure #StructuralBiology #AlphaFold #Bioinformatics #StructuralSimilarity

AlphaFind v2: Similarity Search in AlphaFold DB and TED Domains across Structural Contexts biorxiv.org/content/10.64898… #biorxiv_bioinfo

Count us in. We’re thrilled to scale digital literacy and student companionship this year. 2026 is for the tech-savvy leaders. #DigitalSkills #AlphaFind #UTQ"

We couldn’t have said it better. Ready to guide the next generation of students to their dream schools this year. 2026, let’s go! #TertiaryGuide #AlphaFind #StudentJourney

New Year, Same Mission: Empowering the next generation. Happy 2026 from AlphaFind. We’re excited to continue providing innovative solutions and companionship to students across their education journey. #AlphaFind #FutureOfEducation #2026Goals

This NOVEMBER UTQ is heating up! 🔥 Get ready to test your tech skills - more details coming soon! #UTQ #Alphafind #TechQuiz #Africa #Students #Innovation #Technology

We are thrilled to announce the Ultimate Tech Quiz (UTQ) @UTechQuiz, a competition for tech-savvy students across Africa! Get ready to test your coding, typing, and problem-solving skills, and connect with other tech enthusiasts! Stay tuned for more details. #AlphaFind #UTQ

This initiative is powered by: @TertiaryGuide1 : Your one-stop platform for all things university! @keldorkglobal #UTQ #Alphafind #NodeEight #TertiaryGuide #Keldork #TechQuiz #Africa #Students #Innovation #Technology

Just stumbled across @BWEDonSOL on my TL, and wow, they’ve got something BIG in the oven! 👀 If you’re not following them, you might want to fix that ASAP. 🔔 Turn on those notification you won’t want to miss this one! #AlphaFind #StayAhead #BWEDonSOL

AlphaFind: discover structure similarity across the proteome in AlphaFold DB academic.oup.com/nar/article…

AlphaFind: discover structure similarity across the proteome in AlphaFold DB academic.oup.com/nar/advance… --- #proteomics #prot-paper

AlphaFind: discover structure similarity across the proteome in AlphaFold DB academic.oup.com/nar/advance…

We just stumbled across this new tool: AlphaFind A web-based search engine that allows for structure-based search of the entire AlphaFold Protein Structure Database. Developed at @MasarykUni, free & open. Anyone tried it yet? What are your thoughts?  shorturl.at/zCDS7