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1. Identification of all-against-all protein–protein interactions based on deep hash learning

   https://doi.org/10.1186/s12859-022-04811-x  

   Jiang, Yue; Wang, Yuxuan; Shen, Lin; Adjeroh, Donald A.; Liu, Zhidong; Lin, Jie (July 2022, BMC Bioinformatics)

   Abstract BackgroundProtein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming. ResultsIn this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database withMproteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship. ConclusionsThe experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from$$O(M^2)$$ $O\left(M^2\right)$to$$O(M\log M)$$ $O(MlogM)$for performing an all-against-all PPI prediction for a database withMproteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets. 

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2. On the Planarity of Validated Complexes of Model Organisms in Protein-Protein Interaction Networks

   https://doi.org/10.1007/978-3-030-50371-0_48  

   Cooper, K; Cornelius, N; Gasper, W; Bhowmick, S.; Ali, H. (May 2020, Internal Conference on Computational Science ICCS 2020)

   Leveraging protein-protein interaction networks to identify groups of proteins and their common functionality is an important problem in bioinformatics. Systems-level analysis of protein-protein interactions is made possible through network science and modeling of high-throughput data. From these analyses, small protein complexes are traditionally represented graphically as complete graphs or dense clusters of nodes. However, there are certain graph theoretic properties that have not been extensively studied in PPI networks, especially as they pertain to cluster discovery, such as planarity. Planarity of graphs have been used to reflect the physical constraints of real-world systems outside of bioinformatics, in areas such as mapping and imaging. Here, we investigate the planarity property in network models of protein complexes. We hypothesize that complexes represented as PPI subgraphs will tend to be planar, reflecting the actual physical interface and limits of components in the complex. When testing the planarity of known complex subgraphs in S. cerevisiae and selected mammalian PPIs, we find that a majority of validated complexes possess this planar property. We discuss the biological motivation of planar versus nonplanar subgraphs, observing that planar subgraphs tend to have longer protein components. Functional classification of planar versus nonplanar complex subgraphs reveals differences in annotation of these groups relating to cellular component organization, structural molecule activity, catalytic activity, and nucleic acid binding. These results provide a new quantitative and biologically motivated measure of real protein complexes in the network model, important for the development of future complex-finding algorithms in PPIs. Accounting for this property paves the way to new means for discovering new protein complexes and uncovering the functionality of unknown or novel proteins. s 

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3. Supervised biological network alignment with graph neural networks

   https://doi.org/10.1093/bioinformatics/btad241  

   Ding, Kerr; Wang, Sheng; Luo, Yunan (June 2023, Bioinformatics)

   Abstract MotivationDespite the advances in sequencing technology, massive proteins with known sequences remain functionally unannotated. Biological network alignment (NA), which aims to find the node correspondence between species’ protein–protein interaction (PPI) networks, has been a popular strategy to uncover missing annotations by transferring functional knowledge across species. Traditional NA methods assumed that topologically similar proteins in PPIs are functionally similar. However, it was recently reported that functionally unrelated proteins can be as topologically similar as functionally related pairs, and a new data-driven or supervised NA paradigm has been proposed, which uses protein function data to discern which topological features correspond to functional relatedness. ResultsHere, we propose GraNA, a deep learning framework for the supervised NA paradigm for the pairwise NA problem. Employing graph neural networks, GraNA utilizes within-network interactions and across-network anchor links for learning protein representations and predicting functional correspondence between across-species proteins. A major strength of GraNA is its flexibility to integrate multi-faceted non-functional relationship data, such as sequence similarity and ortholog relationships, as anchor links to guide the mapping of functionally related proteins across species. Evaluating GraNA on a benchmark dataset composed of several NA tasks between different pairs of species, we observed that GraNA accurately predicted the functional relatedness of proteins and robustly transferred functional annotations across species, outperforming a number of existing NA methods. When applied to a case study on a humanized yeast network, GraNA also successfully discovered functionally replaceable human–yeast protein pairs that were documented in previous studies. Availability and implementationThe code of GraNA is available at https://github.com/luo-group/GraNA. 

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4. Discovering Dynamic Plant Enzyme Complexes in Yeast for Kratom Alkaloid Pathway Identification**

   https://doi.org/10.1002/anie.202307995  

   Wu, Yinan; Liu, Chang; Koganitsky, Anna; Gong, Franklin L.; Li, Sijin (August 2023, Angewandte Chemie International Edition)

   Abstract Discovering natural product biosynthetic pathways of medicinal plants is challenging and laborious. Capturing the coregulation patterns of pathway enzymes, particularly transcriptomic regulation, has proven an effective method to accelerate pathway identification. In this study, we developed a yeast‐based screening method to capture the protein‐protein interactions (PPI) between plant enzymes, which is another useful pattern to complement the prevalent approach. Combining this method with plant multiomics analysis, we discovered four enzyme complexes and their organized pathways from kratom, an alkaloid‐producing plant. The four pathway branches involved six enzymes, including a strictosidine synthase, a strictosidine β‐D‐glucosidase (MsSGD), and four medium‐chain dehydrogenase/reductases (MsMDRs). PPI screening selected six MsMDRs interacting with MsSGD from 20 candidates predicted by multiomics analysis. Four of the six MsMDRs were then characterized as functional, indicating the high selectivity of the PPI screening method. This study highlights the opportunity of leveraging post‐translational regulation features to discover novel plant natural product biosynthetic pathways. 

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5. Thiol‐Acrylate Gel Systems For Frontal Polymerization

   https://doi.org/10.1002/pol.20240800  

   Adrewie, Dominic; Rocha, Monica; Fuller, Mason; Pojman, John A (January 2025, Journal of Polymer Science)

   ABSTRACT A trithiol‐triacrylate gel system for frontal polymerization was explored to establish the gelation time, shelf life, and frontal kinetics. The free‐standing gels were created by triethylamine‐catalyzed Michael addition of trimethylolpropane tris(3‐mercaptopropionate) to trimethylolpropane triacrylate such that sufficient acrylate functional groups were left unreacted to allow free‐radical frontal polymerization with the initiator 1,1‐bis(tert‐butylperoxy)‐3,3,5‐trimethylcyclohexane (Luperox 231). Systems with gelation times between 30 and 60 min that support frontal polymerization after up to 28 days of storage were achieved. The front velocity was found to depend on the 1,1‐bis(tert‐butylperoxy)‐3,3,5‐trimethylcyclohexane concentration. However, the amount of triethylamine, which was used to catalyze gel formation, did not significantly affect front velocity. The gel diameter and addition of milled carbon fiber (Zoltek px35) affected the front velocity. Cracks during frontal polymerization were reduced when Zoltek px35 was added to the formulation, which also increased the mechanical strength. Complex geometries of free‐standing gels were successfully polymerized. This system is potentially useful in situations where molding and reshaping gels are required prior to frontal polymerization, as well as enabling the ability to examine how mechanical forces like stretching and compression can affect front kinetics. 

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