scholarly journals The Enhancer of split complex and adjacent genes in the 96F region of Drosophila melanogaster are required for segregation of neural and epidermal progenitor cells.

Genetics ◽  
1992 ◽  
Vol 132 (2) ◽  
pp. 481-503 ◽  
Author(s):  
H Schrons ◽  
E Knust ◽  
J A Campos-Ortega
Keyword(s):  
Drosophila Melanogaster ◽  
Dna Binding ◽  
Progenitor Cells ◽  
Binding Proteins ◽  
Indirect Evidence ◽  
Dna Binding Proteins ◽  
Transcription Unit ◽  
The Third ◽  
Early Neurogenesis ◽  
Enhancer Of Split

Abstract The Enhancer of split complex [E(spl)-C] of Drosophila melanogaster is located in the 96F region of the third chromosome and comprises at least seven structurally related genes, HLH-m delta, HLH-m gamma, HLH-m beta, HLH-m3, HLH-m5, HLH-m7 and E(spl). The functions of these genes are required during early neurogenesis to give neuroectodermal cells access to the epidermal pathway of development. Another gene in the 96F region, namely groucho, is also required for this process. However, groucho is not structurally related to, and appears to act independently of, the genes of the E(spl)-C; the possibility is discussed that groucho acts upstream to the E(spl)-C genes. Indirect evidence suggests that a neighboring transcription unit (m4) may also take part in the process. Of all these genes, only gro is essential; m4 is a dispensable gene, the deletion of which does not produce detectable morphogenetic abnormalities, and the genes of the E(spl)-C are to some extent redundant and can partially substitute for each other. This redundancy is probably due to the fact that the seven genes of the E(spl)-C encode highly conserved putative DNA-binding proteins of the bHLH family. The genes of the complex are interspersed among other genes which appear to be unrelated to the neuroepidermal lineage dichotomy.

scholarly journals Novel DNA-Binding Proteins in the Cyanobacterium Anabaena sp. Strain PCC 7120

2002 ◽  
Vol 184 (14) ◽  
pp. 3931-3940 ◽  
Author(s):  
Olga A. Koksharova ◽  
C. Peter Wolk
Keyword(s):  
Dna Binding ◽  
Insertional Mutagenesis ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Vegetative Cells ◽  
Heterocyst Differentiation ◽  
The Third ◽  
Cyanobacterium Anabaena ◽  
Anabaena Sp ◽  
Pcc 7120

ABSTRACT As an approach towards elucidation of the biochemical regulation of the progression of heterocyst differentiation in Anabaena sp. strain PCC 7120, we have identified proteins that bind to a 150-bp sequence upstream from hepC, a gene that plays a role in the synthesis of heterocyst envelope polysaccharide. Such proteins were purified in four steps from extracts of vegetative cells of Anabaena sp. Two of these proteins (Abp1 and Abp2) are encoded by neighboring genes in the Anabaena sp. chromosome. The genes that encode the third (Abp3) and fourth (Abp4) proteins are situated at two other loci in that chromosome. Insertional mutagenesis of abp2 and abp3 blocked expression of hepC and hepA and prevented heterocyst maturation and aerobic fixation of N2.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

Sign in / Sign up

Export Citation Format

Share Document