A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape
This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are pr...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Shengjie Sun Georgialina Rodriguez Gaoshu Zhao Jason E Sanchez Wenhan Guo Dan Du Omar J Rodriguez Moncivais Dehua Hu Jing Liu Robert Arthur Kirken Lin Li Source Type: research
Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization
Brief Bioinform. 2024 Jan 22;25(2):bbae078. doi: 10.1093/bib/bbae078.ABSTRACTAntimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrat...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Rui Wang Tao Wang Linlin Zhuo Jinhang Wei Xiangzheng Fu Quan Zou Xiaojun Yao Source Type: research
Partial order relation-based gene ontology embedding improves protein function prediction
In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is cri...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Wenjing Li Bin Wang Jin Dai Yan Kou Xiaojun Chen Yi Pan Shuangwei Hu Zhenjiang Zech Xu Source Type: research
Cracking the black box of deep sequence-based protein-protein interaction prediction
Brief Bioinform. 2024 Jan 22;25(2):bbae076. doi: 10.1093/bib/bbae076.ABSTRACTIdentifying protein-protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed as cheap alternatives to biological experiments, reporting surprisingly high accuracy estimates. We systematically investigated how much reproducible deep learning models depend on data leakage, sequence similarities and node degree information, and compared them with basic machine learning models. We found that overlaps between training and test sets resulting from random splitting lead to strongly overes...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Judith Bernett David B Blumenthal Markus List Source Type: research
New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics
Brief Bioinform. 2024 Jan 22;25(2):bbae074. doi: 10.1093/bib/bbae074.ABSTRACTBioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents uni...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Majid Mokhtari Samane Khoshbakht Kobra Ziyaei Mohammad Esmaeil Akbari Sayyed Sajjad Moravveji Source Type: research
Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay
This study examines the remarkable effectiveness of Withaferin-A (WA), a withanolide obtained from Withania somnifera (Ashwagandha), in encountering the mortiferous breast malignancy, a global peril. The predominant objective is to investigate WA's intrinsic target proteins and hedgehog (Hh) pathway proteins in breast cancer targeting through the application of in silico computational techniques and network pharmacology predictions. The databases and webtools like Swiss target prediction, GeneCards, DisGeNet and Online Mendelian Inheritance in Man were exploited to identify the common target proteins. The culmination of th...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Mythili Srinivasan Apeksha Gangurde Ashwini Y Chandane Amol Tagalpallewar Anil Pawar Akshay M Baheti Source Type: research
Correction to: From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies
Brief Bioinform. 2024 Jan 22;25(2):bbae100. doi: 10.1093/bib/bbae100.NO ABSTRACTPMID:38446744 | PMC:PMC10917181 | DOI:10.1093/bib/bbae100 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Source Type: research
Prediction of protein-ligand binding affinity via deep learning models
Brief Bioinform. 2024 Jan 22;25(2):bbae081. doi: 10.1093/bib/bbae081.ABSTRACTAccurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational meth...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Huiwen Wang Source Type: research
A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape
This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are pr...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Shengjie Sun Georgialina Rodriguez Gaoshu Zhao Jason E Sanchez Wenhan Guo Dan Du Omar J Rodriguez Moncivais Dehua Hu Jing Liu Robert Arthur Kirken Lin Li Source Type: research
Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization
Brief Bioinform. 2024 Jan 22;25(2):bbae078. doi: 10.1093/bib/bbae078.ABSTRACTAntimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrat...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Rui Wang Tao Wang Linlin Zhuo Jinhang Wei Xiangzheng Fu Quan Zou Xiaojun Yao Source Type: research
Partial order relation-based gene ontology embedding improves protein function prediction
In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is cri...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Wenjing Li Bin Wang Jin Dai Yan Kou Xiaojun Chen Yi Pan Shuangwei Hu Zhenjiang Zech Xu Source Type: research
Cracking the black box of deep sequence-based protein-protein interaction prediction
Brief Bioinform. 2024 Jan 22;25(2):bbae076. doi: 10.1093/bib/bbae076.ABSTRACTIdentifying protein-protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed as cheap alternatives to biological experiments, reporting surprisingly high accuracy estimates. We systematically investigated how much reproducible deep learning models depend on data leakage, sequence similarities and node degree information, and compared them with basic machine learning models. We found that overlaps between training and test sets resulting from random splitting lead to strongly overes...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Judith Bernett David B Blumenthal Markus List Source Type: research
New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics
Brief Bioinform. 2024 Jan 22;25(2):bbae074. doi: 10.1093/bib/bbae074.ABSTRACTBioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents uni...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Majid Mokhtari Samane Khoshbakht Kobra Ziyaei Mohammad Esmaeil Akbari Sayyed Sajjad Moravveji Source Type: research
Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay
This study examines the remarkable effectiveness of Withaferin-A (WA), a withanolide obtained from Withania somnifera (Ashwagandha), in encountering the mortiferous breast malignancy, a global peril. The predominant objective is to investigate WA's intrinsic target proteins and hedgehog (Hh) pathway proteins in breast cancer targeting through the application of in silico computational techniques and network pharmacology predictions. The databases and webtools like Swiss target prediction, GeneCards, DisGeNet and Online Mendelian Inheritance in Man were exploited to identify the common target proteins. The culmination of th...
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Authors: Mythili Srinivasan Apeksha Gangurde Ashwini Y Chandane Amol Tagalpallewar Anil Pawar Akshay M Baheti Source Type: research
Correction to: From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies
Brief Bioinform. 2024 Jan 22;25(2):bbae100. doi: 10.1093/bib/bbae100.NO ABSTRACTPMID:38446744 | DOI:10.1093/bib/bbae100 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - March 6, 2024 Category: Bioinformatics Source Type: research
