Biology subjects

Cheung, N. J.

Publications and source records attributed to Cheung, N. J..

7 recordsLinked to original sources

bioRxiv2023

Shared Structural Features of Miro Binding Control Mitochondrial Homeostasis

Miro proteins are universally conserved mitochondrial calcium-binding GTPases that regulate a multitude of mitochondrial processes, including transport, clearance and lipid trafficking. Miro binds a variety of client proteins involved in these functions. How this binding is operated at the molecular level and whether and how it is important for mitochondrial health, however, remains unknown. Here, we show that known Miro clients all use a similar short motif to bind the same structural element: a highly conserved hydrophobic pocket in the calcium-binding domain of Miro. Using these Miro-binding motifs, we identified direct interactors de novo, including yeast Mdm34, and mammalian MTFR1/2/1L, VPS13D and Parkin. Given the shared binding mechanism and conservation across eukaryotes, we propose that Miro is a universal mitochondrial adaptor coordinating mitochondrial health. One-Sentence SummaryFunctionally diverse mitochondrial proteins interact with a conserved hydrophobic pocket on the calcium-binding Miro-GTPases.

biochemistry↗

bioRxiv2022

Residue communities reveal evolutionary signatures of γδ T-Cell receptor

Naturally co-occurring amino acids, term coevolution, in a protein family play a significant role in both protein engineering and folding, and it is expanding in recent years from the studies of the effects of single-site mutations to the complete re-design of a protein and its folding, especially in three-dimensional structure prediction. Here, to better characterize such coevolving interactions, we in silico decipher evolutionary couplings from massive homologous sequences using spectral analysis to capture signatures that are important for specific molecular interactions and binding activities. We implement the present approach on the G7 gamma delta T-cell receptor to identify functionally important residues that contribute to its highly distinct binding mode. The analysis indicates the evolutionary signatures (highly ordered networks of coupled amino acids, termed residue communities) of the protein confirm previously identified functional sites that are relevant to dock the receptor underneath the major histocompatibility complex class I-related protein-1 (MR1) antigen presenting groove. Moreover, we analyze the correlation of inter-residue contacts with the activation states of receptors and show that contact patterns closely correlating with activation indeed coincide with these sites. The theoretical results demonstrate our method provides an alternative path towards bridging protein sequence with its function at residue-level without requiring its tertiary structure or highly accurate measurement of its biological activities in vivo/vitro.

bioinformatics↗

bioRxiv2021

Rewiring phospholipid biosynthesis reveals robustness in membrane homeostasis and uncovers lipid regulatory players.

Intracellular transport of lipids by Lipid Transport Proteins (LTPs) is thought to work alongside vesicular transport to shuttle lipids from their place of synthesis to their destinations. Whereas many LTPs have been identified, it is largely unknown which routes and which LTPs a given lipid utilizes to navigate the multiple membranes of eukaryotic cells. The major and essential phospholipids, phosphatidylethanolamine (PE) and phosphatidylcholine (PC) can be produced by multiple pathways and, in the case of PE, also at multiple locations. Here, we present an approach in which we simplify and rewire yeast phospholipid synthesis by redirecting PE and PC synthesis reactions to distinct subcellular locations using chimeric enzymes fused to specific organelle targeting motifs. In rewired conditions, viability is expected to depend on homeostatic adaptation to the ensuing lipostatic perturbations and on efficient interorganelle lipid transport. We therefore performed genetic screens to identify factors involved in both of these processes. Among the candidates identified, we find genes linked to transcriptional regulation of lipid homeostasis, lipid metabolism and transport. In particular, we identify a requirement for Csf1 -an uncharacterized protein harboring a Chorein-N lipid transport domain- for survival under certain rewired conditions as well as lipidomic adaptation to cold, implicating Csf1 in interorganelle lipid transport and homeostatic adaptation.

cell biology↗

bioRxiv2021

Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network

The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of proteins. The backbone torsion angles, as an important structural constraint, play a critical role in protein structure prediction, and accurately predicting the angles can considerably advance the tertiary structure prediction by accelerating efficient sampling of the large conformational space for low energy structures. On account of the rapid growth of protein databases and striking breakthroughs in deep learning algorithms, computational advances allow us to extract knowledge from large-scale data to address key biological questions. Here we propose evolutionary signatures that are computed from protein sequence profiles, and a deep neural network, termed ESIDEN, that adopts a straightforward architecture of recurrent neural networks with a small number of learnable parameters. The proposed ESIDEN is validated on three benchmark datasets, including D2020, TEST2016/2018, and CASPs datasets. On the D2020, using the combination of the four novel features and basic features, the ESIDEN achieves the mean absolute error (MAE) of 15.7 and 19.8 for{phi} and{psi} , respectively. Comparing to the best-so-far methods, we show that the ESIDEN significantly improves the angle{psi} by the MAE decrements of more than 3.5 degrees on both TEST2016 and TEST2018 and achieves better MAE of the angle{phi} by decrements of at least 0.3 degrees although it adopts simple architecture and fewer learnable parameters. On fifty-nine template-free modeling targets, the ESIDEN achieves high accuracy by reducing the MAEs by 0.6 and more than 2.3 degrees on average for the torsion angles{phi} and{psi} in the CASPs, respectively. Using the predicted torsion angles, we infer the tertiary structures of four representative template-free modeling targets that achieve high precision with regard to the root-mean-square deviation and TM-score by comparing them to the native structures. The results demonstrate that the ESIDEN can make accurate predictions of the torsion angles by leveraging the evolutionary signatures. The proposed evolutionary signatures would be also used as alternative features in predicting residue-residue distance, protein structure, and protein-ligand binding sites. Moreover, the high-precision torsion angles predicted by the ESIDEN can be used to accurately infer protein tertiary structures, and the ESIDEN would potentially pave the way to improve protein structure prediction.

bioinformatics↗

bioRxiv2020

Leri: a web-server for identifying protein functional networks from evolutionary couplings

Information on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed "residue communities", relevant to protein function. By taking advantage of recent developments in high-performance and parallel computing, we constructed a web-server, Leri, that identifies relevant residue communities to allow researchers to investigate how a protein evolves and folds for function(s). All the data of the computational results including high-quality images can be downloaded and presented for publication. This web-server, written in C++, is sufficiently rapid to enable the studies on proteins of up to 400 amino acids.

bioinformatics↗

bioRxiv2018

Sibe: a computation tool to apply protein sequence statistics to folding and design

Statistical analysis plays a significant role in both protein sequences and structures, expanding in recent years from the studies of co-evolution guided single-site mutations to protein folding in silico. Here we describe a computational tool, termed Sibe, with a particular focus on protein sequence analysis, folding and design. Since Sibe has various easy-interface modules, expressive architecture and extensible codes, it is powerful in statistically analyzing sequence data and building energetic potentials in boosting both protein folding and design. In this study, Sibe is used to capture positionally conserved couplings between pairwise amino acids and help rational protein design, in which the pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several blocks. A human {beta}2-adrenergic receptor ({beta}2AR) was used to demonstrated that those blocks could contribute rational design at functional residues. In addition, Sibe provides protein folding modules based on both the positionally conserved couplings and well-established statistical potentials. Sibe provides various easy to use command-line interfaces in C++ and/or Python. Sibe was developed for compatibility with the big data era, and it primarily focuses on protein sequence analysis, in silico folding and design, but it is also applicable to extend for other modeling and predictions of experimental measurements.

bioinformatics↗

bioRxiv2018

De novo protein structure prediction using ultra-fast molecular dynamics simulation

Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology is playing a more important role in protein structure prediction than ever. Here, we present a system of de novo predictor, termed NiDelta, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. The proposed approach is demonstrated by calculations on a set of eighteen large proteins from different fold classes. The results show that the ultra-fast molecular dynamics simulation could dramatically reduce the gap between the sequence and its structure at atom level, and it could also present high efficiency in protein structure determination if sparse experimental data is available.

bioinformatics↗