Biology subjects

Kihara, D.

Publications and source records attributed to Kihara, D..

44 records · Page 3Linked to original sources

bioRxiv2020

AttentiveDist: Protein Inter-Residue Distance Prediction Using Deep Learning with Attention on Quadruple Multiple Sequence Alignments

Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSAs feature at the inter-residue level, we added an attention layer to the deep neural network. The model is trained in a multi-task fashion to also predict backbone and orientation angles further improving the inter-residue distance prediction. We show that AttentiveDist outperforms the top methods for contact prediction in the CASP13 structure prediction competition. To aid in structure modeling we also developed two new deep learning-based sidechain center distance and peptide-bond nitrogen-oxygen distance prediction models. Together these led to a 12% increase in TM-score from the best server method in CASP13 for structure prediction.

bioinformatics↗

bioRxiv2020

Emap2sec+: Detecting Protein and DNA/RNA Structures in Cryo-EM Maps of Intermediate Resolution Using Deep Learning

An increasing number of density maps of macromolecular structures, including proteins and protein and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 [A] resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.

biophysics↗

bioRxiv2020

Specific OCRL1 patient mutations differentially impact Lowe Syndrome cellular phenotypes

Lowe Syndrome (LS) is a lethal genetic disorder caused by mutations in the OCRL1 gene which encodes the lipid 5 phosphatase Ocrl1. Patients exhibit a characteristic triad of symptoms including eyes, brain and kidneys abnormalities with renal failure as the most common cause of premature death. Over 200 OCRL1 mutations have been identified in LS, but their specific impact on cellular processes is unknown. Despite observations of heterogeneity in patient symptom severity, there is little understanding of the correlation between genotype and its impact on phenotype. Here, we show that different mutations had diverse effects on protein localization and on triggering LS cellular phenotypes. In addition, some mutations affecting specific domains imparted unique characteristics to the resulting mutated protein. We also propose that certain mutations conformationally affect the 5-phosphatase domain of the protein, resulting in loss of enzymatic activity and causing common and specific phenotypes. This study is the first to show the differential effect of patient 5-phosphatase mutations on cellular phenotypes and introduces a conformational disease component in LS. This work provides a framework that can help stratify patients as well as to produce a more accurate prognosis depending on the nature and location of the mutation within the OCRL1 gene.

cell biology↗

bioRxiv2020

Protein Contact Map Denoising Using Generative Adversarial Networks

Protein residue-residue contact prediction from protein sequence information has undergone substantial improvement in the past few years, which has made it a critical driving force for building correct protein tertiary structure models. Improving accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. Here, we show a novel contact map denoising method, ContactGAN, which uses Generative Adversarial Networks (GAN) to refine predicted protein contact maps. ContactGAN was able to make a consistent and significant improvement over predictions made by recent contact prediction methods when tested on two datasets including protein structure modeling targets in CASP13. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy.

bioinformatics↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2019

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗

bioRxiv2018

Kinetic And Structural Parameters Governing Fic-Mediated Adenylylation/AMPylation of the Hsp70 chaperone, BiP/GRP78

Fic (filamentation induced by cAMP) proteins regulate diverse cell signaling events by post-translationally modifying their protein targets, predominantly by the addition of an AMP (adenosine monophosphate). This modification is called Fic-mediated Adenylylation or AMPylation. We previously reported that the human Fic protein, HYPE/FicD, is a novel regulator of the unfolded protein response (UPR) that maintains homeostasis in the endoplasmic reticulum (ER) in response to stress from misfolded proteins. Specifically, HYPE regulates UPR by adenylylating the ER chaperone, BiP/GRP78, which serves as a sentinel for UPR activation. Maintaining ER homeostasis is critical for determining cell fate, thus highlighting the importance of the HYPE-BiP interaction. Here, we study the kinetic and structural parameters that determine the HYPE-BiP interaction. By measuring the binding and kinetic efficiencies of HYPE in its activated (Adenylylation-competent) and wild type (de-AMPylation-competent) forms for BiP in its wild type and ATP-bound conformations, we determine that HYPE displays a nearly identical preference for the wild type and ATP-bound forms of BiP in vitro and preferentially de-AMPylates the wild type form of adenylylated BiP. We also show that AMPylation at BiPs Thr366 versus Thr518 sites differentially affect its ATPase activity, and that HYPE does not adenylylate UPR accessory proteins like J-protein ERdJ6. Using molecular docking models, we explain how HYPE is able to adenylylate Thr366 and Thr518 sites in vitro. While a physiological role for AMPylation at both the Thr366 and Thr518 sites has been reported, our molecular docking model supports Thr518 as the structurally preferred modification site. This is the first such analysis of the HYPE-BiP interaction and offers critical insights into substrate specificity and target recognition.

biochemistry↗

bioRxiv2018

PP-2, a src-kinase inhibitor, is a potential corrector for F508del-CFTR in cystic fibrosis

Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The most common mutation in CF, an in-frame deletion of phenylalanine 508, leads to a trafficking defect and endoplasmic reticulum retention of the protein where it becomes targeted for degradation. Successful clinical deployments of ivacaftor and ivacaftor/lumacaftor combination have been an exciting translational development in treating CF. However, their therapeutic effects are variable between subjects and remain insufficient. We used the Library of Integrated Network-based Cellular Signatures (LINCS) database as our chemical pool to screen for candidates. For in silico screening, we integrated connectivity mapping and CF systems biology to identify candidate therapeutic compounds for CF. Following in silico screening, we validated our candidate compounds with (i) an enteroid-based compound screening assay using CF ({Delta}F508/{Delta}F508-CFTR) patient-derived enteroids, (ii) short-circuit current analysis using polarized CF primary human airway epithelial cells and (iii) Western blots to measure F508-del-CFTR protein maturation. We identified 184 candidate compounds with in silico screening and tested 24 of them with enteroid-based forskolin-induced swelling (FIS) assay. The top hit compound was PP2, a known src-kinase inhibitor that induced swelling in enteroid comparable to known CF corrector (lumacaftor). Further validation with Western blot and short-circuit current analysis showed that PP-2 could correct mutant CFTR mis-folding and restore CFTR-mediated transmembrane current. We have identified PP2, a known src-kinase inhibitor, as a novel corrector of {Delta}F508-CFTR. Based on our studies and previous reports, src kinase inhibition may represent a novel paradigm of multi-action therapeutics - corrector, anti-inflammatory, and anti-infective - in CF.

systems biology↗