しんしあ@バイオテクコミュニティ「BioSpace」モデレーター
@BioSpace9

バイオテクノロジーコミュニティ「BioSpace」運営 | ブログ・Udemy・note で情報を発信 | トピックは、バイテク・創薬・機械学習・DAO・メタバースなど | 「BioTechnology for Citizens」を目指して活動 | 創薬研究従事 |

 http://bit.ly/4afiWmM
biospace.info
Joined August 2020
140 Photos and videos
SixPack-AbScan: a web server to discover cross-reactivity of antibodies across species biorxiv.org/content/10.64898…

SixPack-AbScan: a web server to discover cross-reactivity of antibodies across species

Availability of commercial antibodies for immunochemistry is typically limited to major model species. Researchers working on non-model organisms therefore often need either to generate novel...

biorxiv.org
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Integrating structural and biological evidence to rerank ESMFold2 protein-protein interactions biorxiv.org/content/10.64898…

Integrating structural and biological evidence to rerank ESMFold2 protein-protein interactions

Large-scale protein structure prediction enables proteome-wide protein–protein interaction (PPI) screening, but distinguishing biologically meaningful interactions from spurious interfaces remains...

biorxiv.org
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High-throughput physics-based enzyme engineering biorxiv.org/content/10.64898…

High-throughput physics-based enzyme engineering

Enzyme engineering aims to tailor natural enzymes for industrial and therapeutic applications, yet physically grounded rational design has been limited by a trade-off between accuracy and cost,...

biorxiv.org
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ProteinMPNNの後継となるか。離散フローマッチングと3トラックアーキテクチャを組み合わせた新配列設計モデルTriFlowが、500標的規模のバインダー設計ベンチマークでBindCraft/RFdiffusionパイプラインの成功率を底上げています！ biospace.info/blog/2026/09/2… #biospace

【TriFlow】離散フローマッチング×3トラックアーキテクチャでProteinMPNNを超える配列設計モデル

ProteinMPNNの後継となるか。離散フローマッチングと3トラックアーキテクチャを組み合わせた新配列設計モデルTriFlowが、500標的規模のバインダー設計ベンチマークでBindCraft/RFdiffusionパイプラインの成功率を底上げています！

biospace.info
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Interaction Profiles as a Universal Language for Generative Molecular Design with ShEPhERD–2 biorxiv.org/content/10.64898…

Interaction Profiles as a Universal Language for Generative Molecular Design with ShEPhERD–2

Three-dimensional intermolecular interactions govern molecular recognition and are fundamental to the pharmacological activity of small-molecule drugs. We propose that an interaction profile,...

biorxiv.org
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De novo design of allosterically controlled enzymes biorxiv.org/content/10.64898…

De novo design of allosterically controlled enzymes

The ability of enzymes to sense signals and respond by changing their structure and activity underlies cellular processes from signaling to metabolic control. While there have been recent advances in...

biorxiv.org
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Linear antibody epitope prediction using AlphaFold2 doi.org/10.7554/elife.98369.… 抗体のCDRをscFv形式に載せて、抗原を短い重なりペプチドに刻んでAlphaFold2で複合体予測し、線状エピトープを当てる「PAbFold」というパイプライン。

Linear antibody epitope prediction using AlphaFold2

AlphaFold2 can be used to accurately determine linear epitopes of antibodies with known heavy and light chain sequences.

elifesciences.org
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全原子拡散トランスフォーマーVilya-2が、非天然アミノ酸・マクロサイクルを含むペプチド医薬の構造予測（Riptidesベンチマーク）でBoltz-2を大差で上回るSOTA精度を達成しています！ biospace.info/blog/2026/09/2… #biospace

【Vilya-2】非天然ペプチド医薬の構造予測でco-folding勢を圧倒する全原子拡散トランスフォーマー

全原子拡散トランスフォーマーVilya-2が、非天然アミノ酸・マクロサイクルを含むペプチド医薬の構造予測（Riptidesベンチマーク）でBoltz-2を大差で上回るSOTA精度を達成しています！

biospace.info
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XL-MS and De Novo Protein Design Identified a Common Motif for TREM2 Binding doi.org/10.1021/acschemneuro…

XL-MS and De Novo Protein Design Identified a Common Motif for TREM2 Binding

Abstract. Apolipoprotein E (APOE) and triggering receptor expressed on myeloid cells 2 (TREM2) are the two strongest genetic risk factors of late-onset Alz

pubs.acs.org
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Bispecificity is a structural descriptor, not a logic descriptor. "Bispecific antibody" tells you what a molecule is built from. It does not tell you what the molecule does. Same format, same Gate (Direct), four different functional paths.
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We mapped published technologies onto this vocabulary, from conventional antibodies to de novo switches. Each assignment has an evidence status and a confidence level. Where evidence is thin, it says "uncertain" rather than inferring from marketing terms.
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The manuscript and the mapping table (Table S1) are open under CC BY 4.0. Not peer reviewed. Disagreement with an assignment is the most useful feedback. Please open an issue. DOI: doi.org/10.5281/zenodo.22868… GitHub: github.com/sakakicr/protein-…

Protein Engineering Grammar: From Molecular Formats to Programmable Medicines

Protein therapeutics are conventionally classified by structural format. This record contains the manuscript proposing the Protein Engineering Grammar, a complementary vocabulary that describes an...

zenodo.org
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TorchCraft: Unified binder design by inverting an all-atom structure predictor arxiv.org/abs/2609.19770v1 AlphaFold 3の学習済み重みをそのまま固定して、配列ロジットを勾配で最適化する形でバインダーを設計するフレームワーク「TorchCraft」。ミニバインダーやフレームワーク指定のVHHまで、同じ枠組みで扱えるとのこと。

TorchCraft: Unified binder design by inverting an all-atom...

All-atom structure predictors model diverse molecular interactions, but using their learned structural priors for binder design remains challenging. Here we present TorchCraft, a unified...

arxiv.org
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抗体のCDR（相補性決定領域）、特に最も柔軟で難しいCDR-H3をデザインする拡散モデルに対して、生成済みモデルをさらに鍛える「後処理学習」の手法ABOPDを紹介します！ biospace.info/blog/2026/09/2… #biospace

【ABOPD】拡散モデルに”自分の癖”を教え込む、抗体CDR-H3デザインの後処理学習

抗体のCDR（相補性決定領域）、特に最も柔軟で難しいCDR-H3をデザインする拡散モデルに対して、生成済みモデルをさらに鍛える「後処理学習」の手法ABOPDを紹介します！

biospace.info
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Sequence and structural determinants of efficacious de novo chimaeric antigen receptors nature.com/articles/s41551-0…

Sequence and structural determinants of efficacious de novo chimaeric antigen receptors

Nature Biomedical Engineering - A generative protein design workflow addresses important challenges with de novo protein engineering of chimaeric antigen receptors (CARs) to improve targeting of...

nature.com
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構造情報を利用するESM-IFにPreference Learning（SimPO）を組み合わせ、少ないパラメータ更新で抗体の結合親和性向上を目指したSimBinder-IFを紹介します！ biospace.info/blog/2026/09/1… #biospace

SimBinder-IF：構造情報とPreference Learningを組み合わせて抗体の結合親和性を効率よく最適化する手法

構造情報を利用するESM-IFにPreference Learning（SimPO）を組み合わせ、少ないパラメータ更新で抗体の結合親和性向上を目指したSimBinder-IFを紹介します！

biospace.info
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Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions biorxiv.org/content/10.64898…
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RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction biorxiv.org/content/10.64898… Baker labから、RFdiffusionで作ったバインダーを追加学習なしで最適化する新フレームワーク「RFO」が公開。構造予測モデルの勾配情報とMPNNベースの配列再設計をサイクルさせることで、数分オーダーでin silico成功率を底上げできるとのこと。 バインダー設計だけじゃなく、リガンド結合バイオセンサーや環状ペプチド、酵素活性部位設計にも汎用的に効くそうです。

RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction

Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic...

biorxiv.org
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