Arne Elofsson
Arne Elofsson
The CASP16 experiment provided the first opportunity to benchmark AlphaFold3. In contrast to AlphaFold2, AlphaFold3 can predict the structure of non-protein molecules. According to the benchmark presented by the developers, it is expected t...
Performance of pyDock in 8th CAPRI: Energy-Based Scoring Applied to Docking and AlphaFold Models [0.03%]
pyDock在第八届CAPRI中的表现：基于能量的打分函数应用于分子对接和AlphaFold模型
Luis Angel Rodríguez-Lumbreras,Mireia Rosell,Miguel Romero-Durana et al.
Luis Angel Rodríguez-Lumbreras et al.
The 8th CAPRI edition has shown a significant evolution in the field of protein-protein complex structure prediction. We have participated in all 11 targets proposed in this edition, involving domain-domain, protein-protein, protein-peptide...
Alisia Fadini,Gabriel Studer,Randy J Read
Alisia Fadini
The CASP16 evaluation of model accuracy (EMA) experiment assessed the ability of predictors to estimate the accuracy of predicted models, with a particular emphasis on multimeric assemblies. Expanding on the CASP15 framework, CASP16 introdu...
CASP16 Protein Monomer Structure Prediction Assessment [0.03%]
第十六届Critical Assessment of Techniques for Protein Structure Prediction结构预测评估报告之蛋白质单体结构预测分项评估
Rongqing Yuan,Jing Zhang,Andriy Kryshtafovych et al.
Rongqing Yuan et al.
The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single-domain protein fold prediction is nearly solved-no target folds were incorrectly predicted across ...
A Review on Efficient and Scalable Graph-Based Clustering Algorithms for Protein Complex Identification in PPI Networks [0.03%]
基于图的高效可扩展聚类算法在蛋白质相互作用网络中识别蛋白质复合物的综述
Sabyasachi Patra,Tushar Ranjan Sahoo
Sabyasachi Patra
Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein-protein interaction (PPI) networks. In multiple studies over the past two decades, network clusterin...
Review
Proteins. 2025 Aug 17. DOI:10.1002/prot.70026 2025
John E Cronan
John E Cronan
Although the phenotypes and functions of nonessential proteins can be studied by deletion of their coding sequences (both gene copies in diploid organisms), essential genes cannot be deleted unless loss of the encoded protein can be bypasse...
Review
Proteins. 2025 Aug 15. DOI:10.1002/prot.70039 2025
Alisia Fadini,Recep Adiyaman,Shaima N Alhaddad et al.
Alisia Fadini et al.
Model quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluation of Model Accuracy ...
Enhancing RNA 3D Structure Prediction: A Hybrid Approach Combining Expert Knowledge and Computational Tools in CASP16 [0.03%]
借助专家知识和计算工具的杂交方法提升RNA三维结构预测（CASP16）
Bowen Xiao,Yaohuang Shi,Lin Huang
Bowen Xiao
RNA three-dimensional structures are critical for their roles in gene expression and regulation. However, predicting RNA structures remains challenging due to complex tertiary interactions, ion dependency, molecular flexibility, and the lim...
Accurate Biomolecular Structure Prediction in CASP16 With Optimized Inputs to State-Of-The-Art Predictors [0.03%]
优化输入的最新方法在CASP16中准确预测生物大分子结构
Wenkai Wang,Yuxian Luo,Zhenling Peng et al.
Wenkai Wang et al.
Biomolecular structure prediction has reached an unprecedented level of accuracy, partly attributed to the use of advanced deep learning algorithms. We participated in the CASP16 experiments across the categories of protein domains, protein...
Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning [0.03%]
通过改进的多重序列比对、模型排序和深度学习加强的CASP16中蛋白质寡聚体和RNA的结构建模流程
Yuki Kagaya,Tsukasa Nakamura,Jacob Verburgt et al.
Yuki Kagaya et al.
We present the methods and results of our protein complex and RNA structure predictions at CASP16. Our approach integrated multiple state-of-the-art deep learning models with a consensus-based scoring method. To enhance the depth of multipl...