Biology subjects

Giri, N.

Publications and source records attributed to Giri, N..

3 recordsLinked to original sources

bioRxiv2023

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and ranked first out of 24 predictors in estimating the global accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analayzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA. The source code of MULTICOM_qa is available at https://github.com/BioinfoMachineLearning/MULTICOM_qa.

bioinformatics↗

bioRxiv2022

A Deep Learning Bioinformatics Approach to Modeling Protein-Ligand Interaction with cryo-EM Data in 2021 Ligand Model Challenge

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps.This results demonstrate that the deep learning bioinformatics approach is a promising direction to model protein-ligand interaction on cryo-EM data using prior structural information. The source code, data, and instruction to reproduce the results are available on GitHub repository : https://github.com/jianlin-cheng/DeepProLigand

bioinformatics↗

bioRxiv2022

Yeast galactokinase in closed conformation can switch between catalytic and signal transducer states.

S.cerevisiae galactokinase (ScGal1p), in closed conformation catalyzes the phosphorylation of galactose to galactose 1-phopshate using ATP as the phosphate donor as well as allosterically activates the GAL genetic switch in response to galactose and ATP as ligands. How both kinase and signaling activities of ScGal1p are associated with closed conformation of the protein is not understood. Conformational sampling of ScGal1p indicated that this protein samples closed kinase and closed non-kinase conformers. Closed non-kinase conformers are catalytically incompetent to phosphorylate galactose and act as a bonafide signal transducer. It was observed that toggling of side chain of highly conserved K266 of ScGal1p between S171and catalytic base D217 is responsible for transitioning of ScGal1p between signal transducer and kinase states. Interestingly in ScGal3p, the paralog of ScGal1p, which has only signal transduction activity and lacks kinase activity, a H bond between a non-conserved Y433, and a highly conserved Y57, gets broken during MD simulation. The corresponding H-bond present in ScGal1p between residues Y441 and Y63 respectively, remains intact throughout the simulations of ScGal1p.Therefore, we predicted that K266 and Y441 have a role in bifunctionality of ScGal1p. To test the above predictions, we monitored the signaling and kinase activity of ScGal1K266Rp and ScGal1Y441Ap variants. Signaling activity increased in both ScGal1Y441Ap and ScGal1K266Rp variants as compared to ScGal1wtp, whereas the kinase activity increased in ScGal1Y441Ap, but decreased in ScGal1K266Rp Based on the above, we propose that K266 and Y441 are crucial for conferring bifunctionality to ScGal1p. Author summaryGalactokinase of S.cerevisiae(ScGal1p), the first enzyme of Leloir pathway of galactose metabolism, phosphorylates galactose using ATP as the phosphate donor. ScGal1p also functions as a signal transducer of GAL regulon wherein galactose and ATP allosterically activate galactokinase. The active form of galactokinase, then sequesters the repressor ScGal80p, to activate the GAL switch. ScGal1p has a single site each for binding to galactose and ATP. How ScGal1p, a monomeric protein, performs the above two mutually exclusive activities using the same set of substrates/ligands, with the same site acting as the active site for enzymatic activity as well as allosteric site for signal transduction activity is unclear. Our findings are that this protein has a distinct conformational state for functioning as a signal transducer and a distinct conformational state for functioning as a kinase. A highly conserved lysine residue (K266) present only in fungal galactokinases, triggers the interconversion between catalysis and signal transduction states. This interconversion is subdued by H bond between Y441 and Y63. These studies suggest that the two activities of ScGal1p are fine tuned by evolution to regulate metabolism through transcriptional control.

genetics↗