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21 pages, 2011 KiB

Open AccessArticle

Benchmark Evaluation of Protein–Protein Interaction Prediction Algorithms

by Brandan Dunham and Madhavi K. Ganapathiraju

Molecules 2022, 27(1), 41; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27010041 - 22 Dec 2021

Cited by 16 | Viewed by 3812

Protein–protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to [...] Read more.

Protein–protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on ‘illogical’ and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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17 pages, 3148 KiB

Open AccessArticle

Domain Analysis and Motif Matcher (DAMM): A Program to Predict Selectivity Determinants in Monosiga brevicollis PDZ Domains Using Human PDZ Data

by Haley A. Wofford, Josh Myers-Dean, Brandon A. Vogel, Kevin Alexander Estrada Alamo, Frederick A. Longshore-Neate, Filip Jagodzinski and Jeanine F. Amacher

Molecules 2021, 26(19), 6034; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26196034 - 05 Oct 2021

Cited by 2 | Viewed by 1995

Abstract

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three [...] Read more.

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational software suite, Domain Analysis and Motif Matcher (DAMM), that analyzes peptide-binding cleft sequence identity as compared with human PDZ domains and that can be used in combination with literature searches of known human PDZ-interacting sequences to predict target specificity in choanoflagellate PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with <100 μM affinity, a value commonly considered the threshold for cellular PDZ–peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contribute to investigations into choanoflagellate signaling and how it informs metazoan evolution. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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15 pages, 3384 KiB

Open AccessArticle

Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method

by Jie Pan, Li-Ping Li, Zhu-Hong You, Chang-Qing Yu, Zhong-Hao Ren and Yao Chen

Molecules 2021, 26(17), 5359; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26175359 - 03 Sep 2021

Cited by 4 | Viewed by 2057

Abstract

Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order [...] Read more.

Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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13 pages, 4020 KiB

Open AccessArticle

Development of a Platform for Noncovalent Coupling of Full Antigens to Tobacco Etch Virus-Like Particles by Means of Coiled-Coil Oligomerization Motifs

by Lorena Zapata-Cuellar, Jorge Gaona-Bernal, Carlos Alberto Manuel-Cabrera, Moisés Martínez-Velázquez, Carla Sánchez-Hernández, Darwin Elizondo-Quiroga, Tanya Amanda Camacho-Villegas and Abel Gutiérrez-Ortega

Molecules 2021, 26(15), 4436; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26154436 - 23 Jul 2021

Viewed by 2464

Abstract

Virus-like particles are excellent inducers of the adaptive immune response of humans and are presently being used as scaffolds for the presentation of foreign peptides and antigens derived from infectious microorganisms for subunit vaccine development. The most common approaches for peptide and antigen [...] Read more.

Virus-like particles are excellent inducers of the adaptive immune response of humans and are presently being used as scaffolds for the presentation of foreign peptides and antigens derived from infectious microorganisms for subunit vaccine development. The most common approaches for peptide and antigen presentation are translational fusions and chemical coupling, but some alternatives that seek to simplify the coupling process have been reported recently. In this work, an alternative platform for coupling full antigens to virus-like particles is presented. Heterodimerization motifs inserted in both Tobacco etch virus coat protein and green fluorescent protein directed the coupling process by simple mixing, and the obtained complexes were easily taken up by a macrophage cell line. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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19 pages, 2544 KiB

Open AccessArticle

Heavy Vacuum Gas Oil Upregulates the Rhamnosyltransferases and Quorum Sensing Cascades of Rhamnolipids Biosynthesis in Pseudomonas sp. AK6U

by Sarah A. Alkhalaf, Ahmed R. Ramadan, Christian Obuekwe, Ashraf M. El Nayal, Nasser Abotalib and Wael Ismail

Molecules 2021, 26(14), 4122; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26144122 - 06 Jul 2021

Cited by 3 | Viewed by 2001

Abstract

We followed a comparative approach to investigate how heavy vacuum gas oil (HVGO) affects the expression of genes involved in biosurfactants biosynthesis and the composition of the rhamnolipid congeners in Pseudomonas sp. AK6U. HVGO stimulated biosurfactants production as indicated by the lower surface [...] Read more.

We followed a comparative approach to investigate how heavy vacuum gas oil (HVGO) affects the expression of genes involved in biosurfactants biosynthesis and the composition of the rhamnolipid congeners in Pseudomonas sp. AK6U. HVGO stimulated biosurfactants production as indicated by the lower surface tension (26 mN/m) and higher yield (7.8 g/L) compared to a glucose culture (49.7 mN/m, 0.305 g/L). Quantitative real-time PCR showed that the biosurfactants production genes rhlA and rhlB were strongly upregulated in the HVGO culture during the early and late exponential growth phases. To the contrary, the rhamnose biosynthesis genes algC, rmlA and rmlC were downregulated in the HVGO culture. Genes of the quorum sensing systems which regulate biosurfactants biosynthesis exhibited a hierarchical expression profile. The lasI gene was strongly upregulated (20-fold) in the HVGO culture during the early log phase, whereas both rhlI and pqsE were upregulated during the late log phase. Rhamnolipid congener analysis using high-performance liquid chromatography-mass spectrometry revealed a much higher proportion (up to 69%) of the high-molecularweight homologue Rha–Rha–C₁₀–C₁₀ in the HVGO culture. The results shed light on the temporal and carbon source-mediated shifts in rhamonlipids’ composition and regulation of biosynthesis which can be potentially exploited to produce different rhamnolipid formulations tailored for specific applications. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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Review

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9 pages, 1327 KiB

Open AccessReview

Quantitative FRET (qFRET) Technology for the Determination of Protein–Protein Interaction Affinity in Solution

by Jiayu Liao, Vipul Madahar, Runrui Dang and Ling Jiang

Molecules 2021, 26(21), 6339; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26216339 - 20 Oct 2021

Cited by 15 | Viewed by 3550

Abstract

Protein–protein interactions play pivotal roles in life, and the protein interaction affinity confers specific protein interaction events in physiology or pathology. Förster resonance energy transfer (FRET) has been widely used in biological and biomedical research to detect molecular interactions in vitro and in [...] Read more.

Protein–protein interactions play pivotal roles in life, and the protein interaction affinity confers specific protein interaction events in physiology or pathology. Förster resonance energy transfer (FRET) has been widely used in biological and biomedical research to detect molecular interactions in vitro and in vivo. The FRET assay provides very high sensitivity and efficiency. Several attempts have been made to develop the FRET assay into a quantitative measurement for protein–protein interaction affinity in the past. However, the progress has been slow due to complicated procedures or because of challenges in differentiating the FRET signal from other direct emission signals from donor and receptor. This review focuses on recent developments of the quantitative FRET analysis and its application in the determination of protein–protein interaction affinity (K_D), either through FRET acceptor emission or donor quenching methods. This paper mainly reviews novel theatrical developments and experimental procedures rather than specific experimental results. The FRET-based approach for protein interaction affinity determination provides several advantages, including high sensitivity, high accuracy, low cost, and high-throughput assay. The FRET-based methodology holds excellent potential for those difficult-to-be expressed proteins and for protein interactions in living cells. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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14 pages, 1994 KiB

Open AccessReview

ZNF224 Protein: Multifaceted Functions Based on Its Molecular Partners

by Elena Cesaro, Angelo Lupo, Roberta Rapuano, Arianna Pastore, Michela Grosso and Paola Costanzo

Molecules 2021, 26(20), 6296; https://doi.org/10.3390/molecules26206296 - 18 Oct 2021

Cited by 5 | Viewed by 2223

Abstract

The transcription factor ZNF224 is a Kruppel-like zinc finger protein that consists of 707 amino acids and contains 19 tandemly repeated C₂H₂ zinc finger domains that mediate DNA binding and protein–protein interactions. ZNF224 was originally identified as a transcriptional repressor [...] Read more.

The transcription factor ZNF224 is a Kruppel-like zinc finger protein that consists of 707 amino acids and contains 19 tandemly repeated C₂H₂ zinc finger domains that mediate DNA binding and protein–protein interactions. ZNF224 was originally identified as a transcriptional repressor of genes involved in energy metabolism, and it was demonstrated that ZNF224-mediated transcriptional repression needs the interaction of its KRAB repressor domain with the co-repressor KAP1 and its zinc finger domains 1–3 with the arginine methyltransferase PRMT5. Furthermore, the protein ZNF255 was identified as an alternative isoform of ZNF224 that possesses different domain compositions mediating distinctive functional interactions. Subsequent studies showed that ZNF224 is a multifunctional protein able to exert different transcriptional activities depending on the cell context and the variety of its molecular partners. Indeed, it has been shown that ZNF224 can act as a repressor, an activator and a cofactor for other DNA-binding transcription factors in different human cancers. Here, we provide a brief overview of the current knowledge on the multifaceted interactions of ZNF224 and the resulting different roles of this protein in various cellular contexts. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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17 pages, 3332 KiB

Open AccessReview

Critical Protein–Protein Interactions Determine the Biological Activity of Elk-1, a Master Regulator of Stimulus-Induced Gene Transcription

by Gerald Thiel, Tobias M. Backes, Lisbeth A. Guethlein and Oliver G. Rössler

Molecules 2021, 26(20), 6125; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26206125 - 11 Oct 2021

Cited by 4 | Viewed by 2469

Abstract

Elk-1 is a transcription factor that binds together with a dimer of the serum response factor (SRF) to the serum-response element (SRE), a genetic element that connects cellular stimulation with gene transcription. Elk-1 plays an important role in the regulation of cellular proliferation [...] Read more.

Elk-1 is a transcription factor that binds together with a dimer of the serum response factor (SRF) to the serum-response element (SRE), a genetic element that connects cellular stimulation with gene transcription. Elk-1 plays an important role in the regulation of cellular proliferation and apoptosis, thymocyte development, glucose homeostasis and brain function. The biological function of Elk-1 relies essentially on the interaction with other proteins. Elk-1 binds to SRF and generates a functional ternary complex that is required to activate SRE-mediated gene transcription. Elk-1 is kept in an inactive state under basal conditions via binding of a SUMO-histone deacetylase complex. Phosphorylation by extracellular signal-regulated protein kinase, c-Jun N-terminal protein kinase or p38 upregulates the transcriptional activity of Elk-1, mediated by binding to the mediator of RNA polymerase II transcription (Mediator) and the transcriptional coactivator p300. Strong and extended phosphorylation of Elk-1 attenuates Mediator and p300 recruitment and allows the binding of the mSin3A-histone deacetylase corepressor complex. The subsequent dephosphorylation of Elk-1, catalyzed by the protein phosphatase calcineurin, facilitates the re-SUMOylation of Elk-1, transforming Elk-1 back to a transcriptionally inactive state. Thus, numerous protein–protein interactions control the activation cycle of Elk-1 and are essential for its biological function. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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19 pages, 5546 KiB

Open AccessReview

Protein–Protein Interactions in Translesion Synthesis

by Radha Charan Dash and Kyle Hadden

Molecules 2021, 26(18), 5544; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26185544 - 13 Sep 2021

Cited by 5 | Viewed by 2870

Abstract

Translesion synthesis (TLS) is an error-prone DNA damage tolerance mechanism used by actively replicating cells to copy past DNA lesions and extend the primer strand. TLS ensures that cells continue replication in the presence of damaged DNA bases, albeit at the expense of [...] Read more.

Translesion synthesis (TLS) is an error-prone DNA damage tolerance mechanism used by actively replicating cells to copy past DNA lesions and extend the primer strand. TLS ensures that cells continue replication in the presence of damaged DNA bases, albeit at the expense of an increased mutation rate. Recent studies have demonstrated a clear role for TLS in rescuing cancer cells treated with first-line genotoxic agents by allowing them to replicate and survive in the presence of chemotherapy-induced DNA lesions. The importance of TLS in both the initial response to chemotherapy and the long-term development of acquired resistance has allowed it to emerge as an interesting target for small molecule drug discovery. Proper TLS function is a complicated process involving a heteroprotein complex that mediates multiple attachment and switching steps through several protein–protein interactions (PPIs). In this review, we briefly describe the importance of TLS in cancer and provide an in-depth analysis of key TLS PPIs, focusing on key structural features at the PPI interface while also exploring the potential druggability of each key PPI. Full article

(This article belongs to the Special Issue Protein-Protein Interactions 2021)

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