scholarly journals Rational design of proteins that exchange on functional timescales

2017 ◽  
Author(s):  
James A. Davey ◽  
Adam M. Damry ◽  
Natalie K. Goto ◽  
Roberto A. Chica
Keyword(s):  
Rational Design ◽  
Computational Method ◽  
Protein G ◽  
Molecular Processes ◽  
Conformational States ◽  
Standard Methods ◽  
Novel Proteins ◽  
Designed Proteins ◽  
Global Fold ◽  
New Applications

AbstractProteins are intrinsically dynamic molecules that can exchange between multiple conformational states, enabling them to carry out complex molecular processes with extreme precision and efficiency. Attempts to design novel proteins with tailored functions have mostly failed to yield efficiencies matching those found in nature because standard methods do not allow for the design of exchange between necessary conformational states on a functionally-relevant timescale. Here, we develop a broadly-applicable computational method to engineer protein dynamics that we term meta-multistate design. We used this methodology to design spontaneous exchange between two novel conformations introduced into the global fold of Streptococcal protein G domain β1. The designed proteins, named DANCERs, for Dynamic And Native Conformational ExchangeRs, are stably folded and exchange between predicted conformational states on the millisecond timescale. The successful introduction of defined dynamics on functional timescales opens the door to new applications requiring a protein to spontaneously access multiple conformational states.

scholarly journals Engineered pH-Sensitive Protein G / IgG Interaction

2020 ◽  
Author(s):  
Ramesh K. Jha ◽  
Allison Yankey ◽  
Kalifa Shabazz ◽  
Leslie Naranjo ◽  
Nileena Velappan ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Design ◽  
Protein Interactions ◽  
Rational Design ◽  
Protein G ◽  
Acidic Ph ◽  
Protein Protein Interactions ◽  
Natural Interface ◽  
Complex Stimuli ◽  
Igg Purification

ABSTRACTWhile natural protein-protein interactions have evolved to be induced by complex stimuli, rational design of interactions that can be switched-on-demand still remain challenging in the protein design world. Here, we demonstrate a computationally redesigned natural interface for improved binding affinity could further be mutated to adopt a pH switchable interaction. The redesigned interface of Protein G-IgG Fc domain, when incorporated with histidine and glutamic acid on Protein G (PrG-EHHE), showed a switch in binding affinity by 50-fold when pH was altered from mild acidic to mild basic. The wild type (WT) interface only showed negligible switch. The overall binding affinity at mild acidic pH for PrG-EHHE outperformed the WT PrG interaction. The new reagent PrG-EHHE will be revolutionary in IgG purification since the traditional method of using an extreme acidic pH for elution can be circumvented.Abstract Figure

scholarly journals System Reliability Design Analysis of Propulsion Structures

1993 ◽  
Author(s):  
S. Mahadevan ◽  
S. Mehta ◽  
R. G. Tryon ◽  
T. A. Cruse
Keyword(s):  
Failure Probability ◽  
Rational Design ◽  
Failure Modes ◽  
Quantitative Information ◽  
Reliability Estimation ◽  
Computational Method ◽  
System Failure ◽  
Turbine Engine ◽  
Reliability Design ◽  
The Individual

The reliability of a gas turbine engine structure is affected by the uncertainties in the operating environment (speed, temperature etc.) as well as in the structural properties (material properties, geometries, boundary conditions etc.). A computational method for accurate reliability estimation under such uncertainties is described in this paper. Reliability computation for individual failure modes (burst, LCF etc.) as well as overall system failure is addressed. System failure probability is computed through the union of individual mode failures. The method also provides precise sensitivity information about the effect of each uncertain parameter on the individual failure probabilities as well as on the system failure probability. Such quantitative information helps rational design decisions as well as risk assessment and certification.

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features

2021 ◽  
Vol 43 (3) ◽  
pp. 1489-1501
Author(s):  
Muhammad Usman ◽  
Shujaat Khan ◽  
Seongyong Park ◽  
Jeong-A Lee
Keyword(s):  
Feature Space ◽  
Vital Role ◽  
Computational Method ◽  
The Novel ◽  
Novel Proteins ◽  
Discriminating Power ◽  
Latent Space ◽  
Antioxidant Proteins ◽  
Class Labels ◽  
Space Encoding

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.

scholarly journals Cilia interactome with predicted protein–protein interactions reveals connections to Alzheimer’s disease, aging and other neuropsychiatric processes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kalyani B. Karunakaran ◽  
Srilakshmi Chaparala ◽  
Cecilia W. Lo ◽  
Madhavi K. Ganapathiraju
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nervous System ◽  
Drug Targets ◽  
Rational Design ◽  
Primary Cilia ◽  
Intraflagellar Transport ◽  
Neuropsychiatric Disorders ◽  
Computational Method ◽  
Ciliary Protein

Abstract Cilia are dynamic microtubule-based organelles present on the surface of many eukaryotic cell types and can be motile or non-motile primary cilia. Cilia defects underlie a growing list of human disorders, collectively called ciliopathies, with overlapping phenotypes such as developmental delays and cognitive and memory deficits. Consistent with this, cilia play an important role in brain development, particularly in neurogenesis and neuronal migration. These findings suggest that a deeper systems-level understanding of how ciliary proteins function together may provide new mechanistic insights into the molecular etiologies of nervous system defects. Towards this end, we performed a protein–protein interaction (PPI) network analysis of known intraflagellar transport, BBSome, transition zone, ciliary membrane and motile cilia proteins. Known PPIs of ciliary proteins were assembled from online databases. Novel PPIs were predicted for each ciliary protein using a computational method we developed, called High-precision PPI Prediction (HiPPIP) model. The resulting cilia “interactome” consists of 165 ciliary proteins, 1,011 known PPIs, and 765 novel PPIs. The cilia interactome revealed interconnections between ciliary proteins, and their relation to several pathways related to neuropsychiatric processes, and to drug targets. Approximately 184 genes in the cilia interactome are targeted by 548 currently approved drugs, of which 103 are used to treat various diseases of nervous system origin. Taken together, the cilia interactome presented here provides novel insights into the relationship between ciliary protein dysfunction and neuropsychiatric disorders, for e.g. interconnections of Alzheimer’s disease, aging and cilia genes. These results provide the framework for the rational design of new therapeutic agents for treatment of ciliopathies and neuropsychiatric disorders.

scholarly journals Rational design of boron-dipyrromethene (BODIPY) reporter dyes for cucurbit[7]uril

2018 ◽  
Vol 14 ◽  
pp. 1961-1971 ◽  
Author(s):  
Mohammad A Alnajjar ◽  
Jürgen Bartelmeß ◽  
Robert Hein ◽  
Pichandi Ashokkumar ◽  
Mohamed Nilam ◽  
...  
Keyword(s):  
Rational Design ◽  
Photophysical Properties ◽  
Visible Spectral Region ◽  
Correlation Spectroscopy ◽  
Binding Motif ◽  
Binding Affinities ◽  
Binding Motifs ◽  
Host Guest Complex ◽  
Boron Dipyrromethene ◽  
New Applications

We introduce herein boron-dipyrromethene (BODIPY) dyes as a new class of fluorophores for the design of reporter dyes for supramolecular host–guest complex formation with cucurbit[7]uril (CB7). The BODIPYs contain a protonatable aniline nitrogen in the meso-position of the BODIPY chromophore, which was functionalized with known binding motifs for CB7. The unprotonated dyes show low fluorescence due to photoinduced electron transfer (PET), whereas the protonated dyes are highly fluorescent. Encapsulation of the binding motif inside CB7 positions the aniline nitrogen at the carbonyl rim of CB7, which affects the pK a value, and leads to a host-induced protonation and thus to a fluorescence increase. The possibility to tune binding affinities and pK a values is demonstrated and it is shown that, in combination with the beneficial photophysical properties of BODIPYs, several new applications of host–dye reporter pairs can be implemented. This includes indicator displacement assays with favourable absorption and emission wavelengths in the visible spectral region, fluorescence correlation spectroscopy, and noncovalent surface functionalization with fluorophores.

scholarly journals A Computational Method to Dissect Colonization Resistance of the Gut Microbiota against Pathogens

2022 ◽  
Author(s):  
Shanlin Ke ◽  
Yandong Xiao ◽  
Scott T. Weiss ◽  
Xinhua Chen ◽  
Ciaran P. Kelly ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Rational Design ◽  
Computational Method ◽  
Colonization Resistance ◽  
Gut Microbes ◽  
Population Dynamics Model ◽  
Microbiome Research ◽  
Enteric Infections ◽  
Health And Disease ◽  
Microbiome Data

The indigenous gut microbes have co-evolved with their hosts for millions of years. Those gut microbes play a crucial role in host health and disease. In particular, they protect the host against incursion by exogenous and often harmful microorganisms, a mechanism known as colonization resistance (CR). Yet, identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a fundamental challenge in microbiome research. Here, we develop a computational method --- Generalized Microbe-Phenotype Triangulation (GMPT) to systematically identify causal microbes that directly influence the microbiota-mediated CR against a pathogen. We systematically validate GMPT using a classical population dynamics model in community ecology, and then apply it to microbiome data from two mouse studies on C. difficile infection. The developed method will not only significantly advance our understanding of CR mechanisms but also pave the way for the rational design of microbiome-based therapies for preventing and treating enteric infections.

scholarly journals Distinct Epigenomic Patterns Are Associated with Haploinsufficiency and Predict Risk Genes of Developmental Disorders

2017 ◽  
Author(s):  
Xinwei Han ◽  
Siying Chen ◽  
Elise Flynn ◽  
Shuang Wu ◽  
Dana Wintner ◽  
...  
Keyword(s):  
Developmental Disorders ◽  
De Novo ◽  
Population Data ◽  
Cell Types ◽  
Computational Method ◽  
Loss Of Function ◽  
Major Mechanism ◽  
Risk Variants ◽  
Sequencing Studies ◽  
New Applications

AbstractHaploinsufficiency is a major mechanism of genetic risk in developmental disorders. Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants in genetic studies. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we showed haploinsufficiency is strongly associated with epigenomic patterns, and then developed a new computational method (Episcore) to predict haploinsufficiency from epigenomic data from a broad range of tissue and cell types using machine learning methods. Based on data from recent exome sequencing studies of developmental disorders, Episcore achieved better performance in prioritizing loss of function de novo variants than current methods. We further showed that Episcore was less biased with gene size, and was complementary to mutation intolerance metrics for prioritizing loss of function variants. Our approach enables new applications of epigenomic data and facilitates discovery and interpretation of novel risk variants in studies of developmental disorders.

scholarly journals Probing Allosteric Hsp70 Inhibitors by Molecular Modelling Studies to Expedite the Development of Novel Combined F508del CFTR Modulators

2021 ◽  
Vol 14 (12) ◽  
pp. 1296
Author(s):  
Roberto Sabbadini ◽  
Emanuela Pesce ◽  
Alice Parodi ◽  
Eleonora Mustorgi ◽  
Santina Bruzzone ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Rational Design ◽  
Binding Mode ◽  
Docking Studies ◽  
Computational Method ◽  
Crystallographic Structure ◽  
Qsar Analysis ◽  
Modelling Studies ◽  
Hsp70 Inhibitors ◽  
Cftr Modulators

Cystic fibrosis (CF) is caused by different mutations related to the cystic fibrosis transmembrane regulator protein (CFTR), with F508del being the most common. Pioneering the development of CFTR modulators, thanks to the development of effective correctors or potentiators, more recent studies deeply encouraged the administration of triple combination therapeutics. However, combinations of molecules interacting with other proteins involved in functionality of the CFTR channel recently arose as a promising approach to address a large rescue of F508del-CFTR. In this context, the design of compounds properly targeting the molecular chaperone Hsp70, such as the allosteric inhibitor MKT-077, proved to be effective for the development of indirect CFTR modulators, endowed with ability to amplify the accumulation of the rescued protein. Herein we performed structure-based studies of a number of allosteric HSP70 inhibitors, considering the recent X-ray crystallographic structure of the human enzyme. This allowed us to point out the main interaction supporting the binding mode of MKT-077, as well as of the related analogues. In particular, cation-π and π–π stacking with the conserve residue Tyr175 deeply stabilized inhibitor binding at the HSP70 cavity. Molecular docking studies had been followed by QSAR analysis and then by virtual screening of aminoaryl thiazoles (I–IIIa) as putative HSP70 inhibitors. Their effectiveness as CFTR modulators has been verified by biological assays, in combination with VX-809, whose positive results confirmed the reliability of the whole applied computational method. Along with this, the “in-silico” prediction of absorption, distribution, metabolism, and excretion (ADME) properties highlighted, once more, that AATs may represent a chemical class to be further investigated for the rational design of novel combination of compounds for CF treatment.

scholarly journals Rational design of FRET sensor proteins based on mutually exclusive domain interactions

2013 ◽  
Vol 41 (5) ◽  
pp. 1201-1205 ◽  
Author(s):  
Maarten Merkx ◽  
Misha V. Golynskiy ◽  
Laurens H. Lindenburg ◽  
Jan L. Vinkenborg
Keyword(s):  
Rational Design ◽  
Large Change ◽  
Robust Method ◽  
Molecular Processes ◽  
Design Strategies ◽  
Modular Architecture ◽  
Domain Interactions ◽  
Sensor Proteins ◽  
Generic Design ◽  
And Control

Proteins that switch between distinct conformational states are ideal to monitor and control molecular processes within the complexity of biological systems. Inspired by the modular architecture of natural signalling proteins, our group explores generic design strategies for the construction of FRET-based sensor proteins and other protein switches. In the present article, I show that designing FRET sensors based on mutually exclusive domain interactions provides a robust method to engineer sensors with predictable properties and an inherently large change in emission ratio. The modularity of this approach should make it easily transferable to other applications of protein switches in fields ranging from synthetic biology, optogenetics and molecular diagnostics.

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