Accurate protein-protein docking with rapid calculationMasahito Ohue
In PRIB2012 Talk (http://prib2012.org)
<reference>
Masahito Ohue, Yuri Matsuzaki, Takashi Ishida, Yutaka Akiyama: "Improvement of the Protein-Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: an Application to Interaction Pathway Analysis", In Proceedings of The 7th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB2012), Lecture Note in Bioinformatics 7632, 178-187, Springer Heidelberg, 2012.
http://link.springer.com/chapter/10.1007%2F978-3-642-34123-6_16
Accurate protein-protein docking with rapid calculationMasahito Ohue
In PRIB2012 Talk (http://prib2012.org)
<reference>
Masahito Ohue, Yuri Matsuzaki, Takashi Ishida, Yutaka Akiyama: "Improvement of the Protein-Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: an Application to Interaction Pathway Analysis", In Proceedings of The 7th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB2012), Lecture Note in Bioinformatics 7632, 178-187, Springer Heidelberg, 2012.
http://link.springer.com/chapter/10.1007%2F978-3-642-34123-6_16
Predicting protein–protein interactions based only on sequences information
Juwen Shen, Jian Zhang, Xiaomin Luo, Weiliang Zhu, Kunqian Yu, Kaixian Chen, Yixue Li and Hualiang Jiang
Proc Natl Acad Sci USA, 2007, 104(11), 4337-4341.
Protein-Protein Interaction Prediction Based on Template-Based and de Novo Do...Masahito Ohue
GLBIO2013, Original Research (Proceedings) Presentation.
"Highly Precise Protein-Protein Interaction Prediction Based on Consensus Between Template-Based and de Novo Docking Method"
Changsheng Zhang, Bo Tang, Qian Wang and Luhua Lai.
Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.
Proteins, 2014 (early access on May 26)
Parallelized pipeline for whole genome shotgun metagenomics with GHOSTZ-GPU a...Masahito Ohue
Masahito Ohue, Marina Yamasawa, Kazuki Izawa, Yutaka Akiyama: Parallelized pipeline for whole genome shotgun metagenomics with GHOSTZ-GPU and MEGAN,
In Proceedings of the 19th annual IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE 2019), 152-156, 2019. doi: 10.1109/BIBE.2019.00035
Molecular Activity Prediction Using Graph Convolutional Deep Neural Network C...Masahito Ohue
Molecular Activity Prediction Using Graph Convolutional Deep Neural Network Considering Distance on a Molecular Graph
Int’l Workshop on Mathematical Modeling and Problem Solving (MPS)
2019 Int’l Conference on Parallel and Distributed Processing Techniques & Applications (PDPTA’19)
Session 2. July 29, 2019 @Luxor, Las Vegas
https://americancse.org/events/csce2019/program/pdp_csc_ipc_msv_gcc_29
Predicting protein–protein interactions based only on sequences information
Juwen Shen, Jian Zhang, Xiaomin Luo, Weiliang Zhu, Kunqian Yu, Kaixian Chen, Yixue Li and Hualiang Jiang
Proc Natl Acad Sci USA, 2007, 104(11), 4337-4341.
Protein-Protein Interaction Prediction Based on Template-Based and de Novo Do...Masahito Ohue
GLBIO2013, Original Research (Proceedings) Presentation.
"Highly Precise Protein-Protein Interaction Prediction Based on Consensus Between Template-Based and de Novo Docking Method"
Changsheng Zhang, Bo Tang, Qian Wang and Luhua Lai.
Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.
Proteins, 2014 (early access on May 26)
Parallelized pipeline for whole genome shotgun metagenomics with GHOSTZ-GPU a...Masahito Ohue
Masahito Ohue, Marina Yamasawa, Kazuki Izawa, Yutaka Akiyama: Parallelized pipeline for whole genome shotgun metagenomics with GHOSTZ-GPU and MEGAN,
In Proceedings of the 19th annual IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE 2019), 152-156, 2019. doi: 10.1109/BIBE.2019.00035
Molecular Activity Prediction Using Graph Convolutional Deep Neural Network C...Masahito Ohue
Molecular Activity Prediction Using Graph Convolutional Deep Neural Network Considering Distance on a Molecular Graph
Int’l Workshop on Mathematical Modeling and Problem Solving (MPS)
2019 Int’l Conference on Parallel and Distributed Processing Techniques & Applications (PDPTA’19)
Session 2. July 29, 2019 @Luxor, Las Vegas
https://americancse.org/events/csce2019/program/pdp_csc_ipc_msv_gcc_29
Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a...Masahito Ohue
Thirteenth International Conference on Intelligent Computing (ICIC2017)
R13: Protein and Gene Bioinformatics: Analysis, Algorithms and Applications, Aug 9, 2017.
Masahito Ohue, Takuro Yamazaki, Tomohiro Ban, Yutaka Akiyama.
In Proceedings of the Thirteenth International Conference On Intelligent Computing (ICIC2017) (Lecture Notes in Computer Science), 10362, 549-558, Liverpool,UK August 7-10, 2017
https://link.springer.com/chapter/10.1007/978-3-319-63312-1_48
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
3. Fernandezらについて
• ドッキングソフトPyDOCK開発チーム
– FTDockがベース
– CAPRIでも結構強い
• FTDockが元
References
Grosdidier, S., Pons, C., Solernou, A., & Fernández-Recio, J. (2007).
Prediction and scoring of docking poses with pyDock. Proteins, 69(4), 852-8.
doi: 10.1002/prot.21796.
Cheng, T. M., Blundell, T. L., & Fernandez-Recio, J. (2007).
pyDock: electrostatics and desolvation for effective scoring of rigid-body
protein-protein docking. Proteins, 68(2), 503-15.
doi: 10.1002/prot.21419.
Pons, C., Solernou, A., Perez-Cano, L., Grosdidier, S., & Fernandez-Recio, J. (2010).
Optimization of pyDock for the new CAPRI challenges: Docking of homology-
based models, domain-domain assembly and protein-RNA binding. Proteins, 1-7.
doi: 10.1002/prot.22773. 3
8. イントロダクション
• Protein-RNA複合体の相互作用面の情報等
から相互作用の特徴の理解を目指した研究
– 水素結合の全原子統計的ポテンシャル
[3] Chen, Y., Kortemme, T., Robertson, T., Baker, D., & Varani, G.
(2004). A new hydrogen-bonding potential for the design of
protein-RNA interactions predicts specific contacts and
discriminates decoys. Nucleic acids research, 32(17), 5147-62.
doi: 10.1093/nar/gkh785.
• Rosetta(リガンドドッキングシステム)を使って
decoy生成,near nativeとそうでないのを比較して
チューニング
8
9. イントロダクション
• 統計的ポテンシャルに関する研究(一部)
[12] Lejeune, D., Delsaux, N., Charloteaux, B., Thomas, A., &
Brasseur, R. (2005). Protein-nucleic acid recognition: statistical
analysis of atomic interactions and influence of DNA structure.
Proteins, 61(2), 258-71. doi: 10.1002/prot.20607.
[9] Ellis, J. J., Broom, M., & Jones, S. (2007). Protein – RNA
Interactions : Structural Analysis and Functional Classes.
Bioinformatics, 911(December 2006), 903-911.
[13] Jeong, E., Kim, H., Lee, S., & Han, K. (2003). Discovering the
interaction propensities of amino acids and nucleotides from protein-
RNA complexes. Molecules and cells, 16(2), 161-7.
9
10. イントロダクション
• 類似の論文
[6] Pérez-Cano, L., & Fernández-Recio, J. (2010). Optimal protein-RNA
area, OPRA: a propensity-based method to identify RNA-binding sites
on proteins. Proteins, 78(1), 25-35. doi: 10.1002/prot.22527.
10
20. Protein-RNA rigid-body docking and scoring
10,000個のFTDock生成decoy中にNNSがあったもの(12複合体中7複合体)
・fnat is the fraction of RNA-protein contacts that is also found in the
native (target) structure
・fnon-nat is the fraction of RNA-protein contacts that is found, but that is
not present in the native (target) structure
・FTDock&Propensity はスコアの和(重み付けなし)
20
24. Example of successful prediction
PDB id : 2QUX
unbound protein vs. bound RNA
RMSD = 8.7Å
(Propensityで1位が当たったやつ)
シアン:予測
マゼンタ:X-ray
タンパク質表面での位置が
結構近いから良いんじゃね
24
27. 他のpropensity score
[16]Treger, M., & Westhof, E. (2001). Statistical analysis of atomic
contacts at RNA-protein interfaces. Journal of molecular recognition :
JMR, 14(4), 199-214. doi: 10.1002/jmr.534.
[9] Ellis, J. J., Broom, M., & Jones, S. (2007). Protein-RNA
interactions: structural analysis and functional classes. Proteins,
66(4), 903-11. John Wiley & Sons. doi: 10.1002/prot.21211. 27
28. 他のpropensity score
[15]Jones, S., Daley, D. T., Luscombe,
N. M., Berman, H. M., & Thornton, J.
M. (2001). Protein-RNA interactions:
a structural analysis. Nucleic acids
research, 29(4), 943-54.
28
29. 他のpropensity score
[13]Jeong, E., Kim, H., Lee, S., & Han, K. (2003). Discovering the
interaction propensities of amino acids and nucleotides from protein-
RNA complexes. Molecules and cells, 16(2), 161-7. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/14651256.
29