MotifAE Reveals Functional Motifs from Protein Language Model: Unsupervised Discovery and Interpretability Analysis [0.03%]
基于蛋白质语言模型的无监督功能性基序发现与可解释性分析：MotifAE方法研究
Chao Hou,Di Liu,Yufeng Shen
Chao Hou
Overall, MotifAE provides a general framework for systematic motif discovery and interpretation, with the potential to advance protein function analysis, mutation effect interpretation, and rational protein engineering.
Mutational Disruption of TP53: A Structural Approach to Understanding Chemoresistance [0.03%]
TP53突变引起的耐药性：结构角度理解化学治疗抗性
Ali F Alsulami
Ali F Alsulami
Keywords: COSMIC mutation analysis; TP53 3D structure mapping; TP53 mutations; mutation effect prediction.
Kairi Furui,Koh Sakano,Masahito Ohue
Kairi Furui
We then provide a comprehensive overview of various therapeutic applications of pLMs, including mutation effect prediction, function prediction, and structure prediction.
Quantifying evolutionary rescue probabilities upon standing genetic variation and de novo mutations [0.03%]
基于等位基因库和新突变的进化拯救的概率量化
Paulo R A Campos
Paulo R A Campos
The results provide insights into the governing factors of ER, including the roles of mutation effect size and probability.
Understanding Language Model Scaling on Protein Fitness Prediction [0.03%]
理解语言模型在蛋白质 fitness 预测中的规模效应
Chao Hou,Di Liu,Aziz Zafar et al.
Chao Hou et al.
Protein language models and models incorporating structure or homologous sequences predict sequence likelihoods p(sequence) that reflect the protein fitness landscape and are widely used for mutation effect prediction and protein design.
From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model [0.03%]
从高通量评估到湿实验研究：利用检索增强模型推进变异效应预测
Yang Tan,Ruilin Wang,Banghao Wu et al.
Yang Tan et al.
Both in silico and experimental evaluations not only confirm the reliability of VenusREM as a computational tool for enzyme engineering but also demonstrate a comprehensive evaluation framework for future computational studies in mutation effect prediction.
CATH-ddG: towards robust mutation effect prediction on protein-protein interactions out of CATH homologous superfamily [0.03%]
基于CATH同源超家族的蛋白质-蛋白质相互作用突变效应预测方法研究
Guanglei Yu,Xuehua Bi,Teng Ma et al.
Guanglei Yu et al.
Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and dise...
Revisiting Plasmodium falciparum P-type ATPase 4 in malarial: ADMET, mutation effect, and molecular simulation studies of potential inhibitors [0.03%]
恶性疟原虫P型腺苷三磷酸酶4的再研究：药代动力学、基因突变影响及潜在抑制剂分子模拟研究
Iseoluwa Isaac Ajayi,Toluwase Hezekiah Fatoki,Ayodele Sunday Alonge et al.
Iseoluwa Isaac Ajayi et al.
Malaria, a life-threatening disease caused by Plasmodium parasites, remains a major global health concern, with 247 million cases and approximately 627,000 deaths reported in 2020 across 84 malaria-endemic countries. The Plasmodium falcipar...
EXTENDING PROTEIN LANGUAGE MODELS TO A VIRAL GENOMIC SCALE USING BIOLOGICALLY INDUCED SPARSE ATTENTION [0.03%]
利用生物诱导的稀疏注意将蛋白质语言模型扩展到病毒基因组规模
Thibaut Dejean,Barbra D Ferrell,William Harrigan et al.
Thibaut Dejean et al.
Recent advancements in protein language models have paved the way for significant progress across various domains, including protein function and structure prediction, multiple sequence alignments and mutation effect prediction.
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data [0.03%]
VenusMutHub：对小规模实验数据上蛋白质变异效应预测器的系统评估
Liang Zhang,Hua Pang,Chenghao Zhang et al.
Liang Zhang et al.
In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not ...
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