Understanding E3 ligase and target substrate interactions are important for cell biology and therapeutic development. However, experimental identification of E3 target relationships is not an easy task due to the labor-intensive nature of the experiments. In this article, a sequence-based E3-target prediction model is proposed for the first time. The proposed framework utilizes composition of k-spaced amino acid pairs (CKSAAP) to learn the relationship between E3 ligases and their target protein. A class separable latent space encoding scheme is also devised that provides a compressed representation of feature space. A thorough ablation study is performed to identify an optimal gap size for CKSAAP and the number of latent variables that can represent the E3-target relationship successfully. The proposed scheme is evaluated on an independent dataset for a variety of standard quantitative measures. In particular, it achieves an average accuracy of $70.63\%$ on an independent dataset. The source code and datasets used in the study are available at the author's GitHub page (https://github.com/psychemistz/E3targetPred).
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins
Centre for Biotechnology and Bioengineering, Universidad de Chile（智利大学生物技术与生物工程中心）；Division of Chemistry and Chemical Engineering, California Institute of Technology（加州理工学院化学与化学工程系）；Max Planck Institute for Dynamics and Self-Organization（马克斯·普朗克动力学与自组织研究所）；Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias, Física y Matemáticas, Universidad de Chile（智利大学科学、物理与数学学院化学工程、生物技术与材料工程系）；Departamento de Ingeniería En Computación, Universidad de Magallanes（麦哲伦大学计算机工程系）；Escuela de Medicina, Universidad de Magallanes（麦哲伦大学医学院）
Encoding Domain Information with Sparse Priors for Inferring Explainable Latent Variables
German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Goethe University Frankfurt（法兰克福歌德大学）；Frankfurt Cancer Insititute（法兰克福癌症研究所）
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；Tsinghua University（清华大学）；HEC Montréal（蒙特利尔高等商学院）
Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Signal Processing Laboratory (LTS2), EPFL, Lausanne, Switzerland（信号处理实验室（LTS2），瑞士洛桑联邦理工学院）；Institute of Bioengineering, EPFL, Lausanne, Switzerland（生物工程研究所，瑞士洛桑联邦理工学院）；Bioinformatics Group, University & Research Wageningen, The Netherlands（生物信息学组，荷兰瓦赫宁根大学及研究学院）；Ludwig Institute for Cancer Research, Lausanne, Switzerland（癌症研究路易斯研究所，瑞士洛桑）
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
School of Computing, KAIST（韩国科学技术院计算机学院）；Center for Mathematical and Computational Sciences, Institute for Basic Science（基础科学研究院数学与计算科学中心）；Center for Biomolecular and Cellular Structure, Institute for Basic Science（基础科学研究院生物分子与细胞结构中心）；Department of Biological Sciences, KAIST（韩国科学技术院生物科学系）
Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Ecole Polytechnique Fédérale de Lausanne（瑞士联邦理工学院）；Oxford University（牛津大学）；VantAI；Aithyra Research Institute for Biomedical Artificial Intelligence（Aithyra生物医学人工智能研究所）；National Centre of Competence in Research (NCCR) Catalysis EPFL, Switzerland（瑞士国家研究能力中心（NCCR）催化EPFL，瑞士）
STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding
Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；State Key Laboratory of Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）；Renmin University of China（中国人民大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
ConforNets: Latents-Based Conformational Control in OpenFold3
Department of Systems Biology, Columbia University, NY, USA（系统生物学系，哥伦比亚大学，纽约，美国）；Department of Computer Science, Columbia University, NY, USA（计算机科学系，哥伦比亚大学，纽约，美国）；Department of Computer Science, Princeton University, Princeton, NJ, USA（计算机科学系，普林斯顿大学，普林斯顿，新泽西州，美国）
OneProt: Towards Multi-Modal Protein Foundation Models
PriorLabs；Helmholtz AI（亥姆霍兹人工智能）；Technical University of Munich（慕尼黑工业大学）；Munich Data Science Institute（慕尼黑数据科学研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Institute of Bio- and Geosciences (IBG-4: Bioinformatics)（生物与地球科学研究所（IBG-4：生物信息学））；Institute for Pharmaceutical and Medicinal Chemistry（药物与医药化学研究所）；Helmholtz Munich（慕尼黑亥姆霍兹中心）
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
Harvard Medical School（哈佛医学院）；University of Copenhagen（哥本哈根大学）；Novo Nordisk A/S（诺和诺德公司）；Microsoft Research（微软研究院）；Technical University of Denmark（丹麦技术大学）
KTH Royal Institute of Technology（皇家理工学院）；University of Amsterdam（阿姆斯特丹大学）；University of Cambridge（剑桥大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Delft University of Technology（代尔夫特理工大学）
ResCap-DBP: A Lightweight Residual-Capsule Network for Accurate DNA-Binding Protein Prediction Using Global ProteinBERT Embeddings
Department of Biomedical Engineering, Bangladesh University of Engineering（生物医学工程系，孟加拉国工程大学）；School of Mathematics, Physics and Computing（数学、物理与计算学院）；University of Southern Queensland, Springfield, QLD 4300, Australia（南方昆士兰大学，斯普林菲尔德，QLD 4300，澳大利亚）
本文提出 ResCap-DBP 轻量级残差-胶囊网络，融合全局 ProteinBERT 嵌入直接预测 DNA 结合蛋白，并在多组基准上取得优于现有方法的 AUC 与泛化表现。
