Prediction of DNA-Binding Protein–Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature

Wang, Wei; Liu, Dong; Zhang, HongJun; Wang, Xianfang; Zhou, Yun

doi:10.3389/fbioe.2022.822392

Cited by 3 publications

(5 citation statements)

References 39 publications

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“…Second, according to Meiler's method, 27 we added seven selected properties (spatial parameters, hydrophobicity, polarizability, volume, helical probability, isoelectric point, and sheet probability) from AAindex, 27 illustrating the physicochemical properties of each residue. In previous studies, we also demonstrated the important influence of these seven amino acid properties on protein function 28–30 . Therefore, these seven amino acid properties play an important role in the flexible prediction.…”

Section: Methodsmentioning

confidence: 59%

“…In previous studies, we also demonstrated the important influence of these seven amino acid properties on protein function. 28 , 29 , 30 Therefore, these seven amino acid properties play an important role in the flexible prediction. Finally, with calculating the dihedral angles phi (φ) and psi (ψ) of proteins, illustrating the preference of backbone dihedral angles for each amino acid type will help to accurately predict protein flexibility.…”

Section: Methodsmentioning

confidence: 99%

“…In previous studies, we also demonstrated the important influence of these seven amino acid properties on protein function. [28][29][30] Therefore, these seven amino acid properties play an important role in the flexible prediction.…”

Section: Feature Encodingmentioning

confidence: 99%

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PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

Wang

Liu

et al. 2022

Proteins

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The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA‐binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA‐binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA‐binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA‐binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°–120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

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Section: Methodsmentioning

confidence: 59%

Section: Methodsmentioning

confidence: 99%

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PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

Wang

Liu

et al. 2022

Proteins

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“…From the structural point of view, there is an increment of amino acids having the highest strand propensity, whereas the opposite is found regarding the number of residues with a high ability to form a turn. From the drug-targeting perspective, AP-2δ was also investigated for the content of leucine (L), isoleucine (I), phenylalanine (F), and methionine (M), seeing that the recent study by Wang et al indicated that drugs frequently bind to these amino acids [ 41 ]. Analyzing the entire protein sequences (acquired from the UniProt database), AP-2δ contains the largest sum of “LIFM” amino acids, whereas in the “first half” it is surpassed only by AP-2ε.…”

Section: Resultsmentioning

confidence: 99%

AP-2δ Is the Most Relevant Target of AP-2 Family-Focused Cancer Therapy and Affects Genome Organization

Kołat

Zhao

Kciuk

et al. 2022

Cells

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Formerly hailed as “undruggable” proteins, transcription factors (TFs) are now under investigation for targeted therapy. In cancer, this may alter, inter alia, immune evasion or replicative immortality, which are implicated in genome organization, a process that accompanies multi-step tumorigenesis and which frequently develops in a non-random manner. Still, targeting-related research on some TFs is scarce, e.g., among AP-2 proteins, which are known for their altered functionality in cancer and prognostic importance. Using public repositories, bioinformatics tools, and RNA-seq data, the present study examined the ligandability of all AP-2 members, selecting the best one, which was investigated in terms of mutations, targets, co-activators, correlated genes, and impact on genome organization. AP-2 proteins were found to have the conserved “TF_AP-2” domain, but manifested different binding characteristics and evolution. Among them, AP-2δ has not only the highest number of post-translational modifications and extended strands but also contains a specific histidine-rich region and cleft that can received a ligand. Uterine, colon, lung, and stomach tumors are most susceptible to AP-2δ mutations, which also co-depend with cancer hallmark genes and drug targets. Considering AP-2δ targets, some of them were located proximally in the spatial genome or served as co-factors of the genes regulated by AP-2δ. Correlation and functional analyses suggested that AP-2δ affects various processes, including genome organization, via its targets; this has been eventually verified in lung adenocarcinoma using expression and immunohistochemistry data of chromosomal conformation-related genes. In conclusion, AP-2δ affects chromosomal conformation and is the most appropriate target for cancer therapy focused on the AP-2 family.

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“…While these computational tools have greatly supported molecular biology research, their limitations in conducting large-scale functional studies have required the integration of artificial intelligence (AI) strategies and machine learning algorithms (ML) (Wang et al, 2022c). This integration has accelerated the discovery of new DNA-binding proteins, focusing on predicting interaction sites, hotspots, and transcription factor binding sites (Wang et al, 2022a; Pan et al, 2020; Zhang et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

RUDEUS, a machine learning classification system to study DNA-Binding proteins

Medina-Ortiz,

Cabas-Mora,

Moya-Barría

et al. 2024

Preprint

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DNA-binding proteins are essential in different biological processes, including DNA replication, transcription, packaging, and chromatin remodelling. Exploring their characteristics and functions has become relevant in diverse scientific domains. Computational biology and bioinformatics have assisted in studying DNA-binding proteins, complementing traditional molecular biology methods. While recent advances in machine learning have enabled the integration of predictive systems with bioinformatic approaches, there still needs to be generalizable pipelines for identifying unknown proteins as DNA-binding and assessing the specific type of DNA strand they recognize. In this work, we introduce RUDEUS, a Python library featuring hierarchical classification models designed to identify DNA-binding proteins and assess the specific interaction type, whether single-stranded or double-stranded. RUDEUS has a versatile pipeline capable of training predictive models, synergizing protein language models with supervised learning algorithms, and integrating Bayesian optimization strategies. The trained models have high performance, achieving a precision rate of 95% for DNA-binding identification and 89% for discerning between single-stranded and doublestranded interactions. RUDEUS includes an exploration tool for evaluating unknown protein sequences, annotating them as DNA-binding, and determining the type of DNA strand they recognize. Moreover, a structural bioinformatic pipeline has been integrated into RUDEUS for validating the identified DNA strand through DNA-protein molecular docking. These comprehensive strategies and straightforward implementation demonstrate comparable performance to high-end models and enhance usability for integration into protein engineering pipelines.

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Prediction of DNA-Binding Protein–Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature

Cited by 3 publications

References 39 publications

PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

AP-2δ Is the Most Relevant Target of AP-2 Family-Focused Cancer Therapy and Affects Genome Organization

RUDEUS, a machine learning classification system to study DNA-Binding proteins

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