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1. Protein Docking Model Evaluation by Graph Neural Networks

   https://doi.org/10.3389/fmolb.2021.647915  

   Wang, Xiao; Flannery, Sean T.; Kihara, Daisuke (May 2021, Frontiers in Molecular Biosciences)

   null (Ed.)

   Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions, it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning–based approach named Graph Neural Network–based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph, respectively. GNN-DOVE was trained, validated, and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods, including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models. 

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2. Phylogenetic and Protein Structure Analyses Provide Insight into the Evolution and Diversification of the CD36 Domain “Apex” among Scavenger Receptor Class B Proteins across Eukarya

   https://doi.org/10.1093/gbe/evad218  

   Boohar, Reed T.; Vandepas, Lauren E.; Traylor-Knowles, Nikki; Browne, William E.; Williams, ed., Tom (November 2023, Genome Biology and Evolution)

   Abstract The cluster of differentiation 36 (CD36) domain defines the characteristic ectodomain associated with class B scavenger receptor (SR-B) proteins. In bilaterians, SR-Bs play critical roles in diverse biological processes including innate immunity functions such as pathogen recognition and apoptotic cell clearance, as well as metabolic sensing associated with fatty acid uptake and cholesterol transport. Although previous studies suggest this protein family is ancient, SR-B diversity across Eukarya has not been robustly characterized. We analyzed SR-B homologs identified from the genomes and transcriptomes of 165 diverse eukaryotic species. The presence of highly conserved amino acid motifs across major eukaryotic supergroups supports the presence of a SR-B homolog in the last eukaryotic common ancestor. Our comparative analyses of SR-B protein structure identify the retention of a canonical asymmetric beta barrel tertiary structure within the CD36 ectodomain across Eukarya. We also identify multiple instances of independent lineage-specific sequence expansions in the apex region of the CD36 ectodomain—a region functionally associated with ligand-sensing. We hypothesize that a combination of both sequence expansion and structural variation in the CD36 apex region may reflect the evolution of SR-B ligand-sensing specificity between diverse eukaryotic clades. 

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3. Protein docking model evaluation by 3D deep convolutional neural networks

   https://doi.org/10.1093/bioinformatics/btz870  

   Wang, Xiao; Terashi, Genki; Christoffer, Charles W; Zhu, Mengmeng; Kihara, Daisuke; Ponty, Yann (November 2019, Bioinformatics)

   Abstract Motivation Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models. Results We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein–protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions. Availability and implementation Codes available at http://github.com/kiharalab/DOVE, http://kiharalab.org/dove/. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. RNA molecules display distinctive organization at nuclear speckles

   https://doi.org/10.1016/j.isci.2024.109603  

   Paul, Sneha; Arias, Mauricio A.; Wen, Li; Liao, Susan E.; Zhang, Jiacheng; Wang, Xiaoshu; Regev, Oded; Fei, Jingyi (May 2024, iScience)

   RNA molecules often play critical roles in assisting the formation of membraneless organelles in eukaryotic cells. Yet, little is known about the organization of RNAs within membraneless organelles. Here, using super-resolution imaging and nuclear speckles as a model system, we demonstrate that different sequence domains of RNA transcripts exhibit differential spatial distributions within speckles. Specifically, we image transcripts containing a region enriched in binding motifs of serine/arginine-rich (SR) proteins and another region enriched in binding motifs of heterogeneous nuclear ribonucleoproteins (hnRNPs). We show that these transcripts localize to the outer shell of speckles, with the SR motif-rich region localizing closer to the speckle center relative to the hnRNP motif-rich region. Further, we identify that this intra-speckle RNA organization is driven by the strength of RNA-protein interactions inside and outside speckles. Our results hint at novel functional roles of nuclear speckles and likely other membraneless organelles in organizing RNA substrates for biochemical reactions. 

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5. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

   https://doi.org/10.3389/fmolb.2023.1110567  

   Shin, Woong-Hee; Kumazawa, Keiko; Imai, Kenichiro; Hirokawa, Takatsugu; Kihara, Daisuke (February 2023, Frontiers in Molecular Biosciences)

   Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer . 

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