Fuzzle 2.0: Ligand Binding in Natural Protein Building Blocks

Modern proteins have been shown to share evolutionary relationships via subdomain-sized fragments. The assembly of such fragments through duplication and recombination events led to the complex structures and functions we observe today. We previously implemented a pipeline that identified more than 1,000 of these fragments that are shared by different protein folds and developed a web interface to analyze and search for them. This resource named Fuzzle helps structural and evolutionary biologists to identify and analyze conserved parts of a protein but it also provides protein engineers with building blocks for example to design proteins by fragment combination. Here, we describe a new version of this web resource that was extended to include ligand information. This addition is a significant asset to the database since now protein fragments that bind specific ligands can be identified and analyzed. Often the mode of ligand binding is conserved in proteins thereby supporting a common evolutionary origin. The same can now be explored for subdomain-sized fragments within this database. This ligand binding information can also be used in protein engineering to graft binding pockets into other protein scaffolds or to transfer functional sites via recombination of a specific fragment. Fuzzle 2.0 is freely available at https://fuzzle.uni-bayreuth.de/2.0.

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Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations

Scientific Reports ◽

10.1038/s41598-021-84488-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gert-Jan Bekker ◽

Ikuo Fukuda ◽

Junichi Higo ◽

Yoshifumi Fukunishi ◽

Narutoshi Kamiya

Keyword(s):

Ligand Binding ◽

Cancer Progression ◽

Hydrophobic Interactions ◽

Binding Mechanism ◽

Docking Simulations ◽

Large Conformational Change ◽

Binding Pockets ◽

Dynamic Docking ◽

Site Binding ◽

Apo State

AbstractWe have performed multicanonical molecular dynamics (McMD) based dynamic docking simulations to study and compare the binding mechanism between two medium-sized inhibitors (ABT-737 and WEHI-539) that bind to the cryptic site of Bcl-xL, by exhaustively sampling the conformational and configurational space. Cryptic sites are binding pockets that are transiently formed in the apo state or are induced upon ligand binding. Bcl-xL, a pro-survival protein involved in cancer progression, is known to have a cryptic site, whereby the shape of the pocket depends on which ligand is bound to it. Starting from the apo-structure, we have performed two independent McMD-based dynamic docking simulations for each ligand, and were able to obtain near-native complex structures in both cases. In addition, we have also studied their interactions along their respective binding pathways by using path sampling simulations, which showed that the ligands form stable binding configurations via predominantly hydrophobic interactions. Although the protein started from the apo state, both ligands modulated the pocket in different ways, shifting the conformational preference of the sub-pockets of Bcl-xL. We demonstrate that McMD-based dynamic docking is a powerful tool that can be effectively used to study binding mechanisms involving a cryptic site, where ligand binding requires a large conformational change in the protein to occur.

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Architectural Repertoire of Ligand-Binding Pockets on Protein Surfaces

ChemBioChem ◽

10.1002/cbic.200900604 ◽

2010 ◽

Vol 11 (4) ◽

pp. 556-563 ◽

Cited By ~ 18

Author(s):

Martin Weisel ◽

Jan M. Kriegl ◽

Gisbert Schneider

Keyword(s):

Ligand Binding ◽

Protein Surfaces ◽

Binding Pockets

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Towards comprehensive allosteric control over protein activity

10.1101/384198 ◽

2018 ◽

Author(s):

Enrico Guarnera ◽

Igor N. Berezovsky

Keyword(s):

Ligand Binding ◽

Molecular Mechanisms ◽

Allosteric Regulation ◽

Mode Switching ◽

Contact Network ◽

Protein Activity ◽

Theoretical Understanding ◽

Functional Sites ◽

Allosteric Control ◽

Allosteric Communication

AbstractOn the basis of the perturbation nature of allosteric communication, a computational framework is proposed for estimating the energetics of signaling caused by the ligand binding and mutations. The perturbations are modelled as alterations of the strenght of interactions in the protein contact network in the binding sites and neighborhoods of mutated residues. The combination of protein harmonic modelling with effect of perturbations and the estimate of local partition functions allow one to evaluate the energetics of allosteric communication at single residue level. The potential allosteric effect of a protein residue position, modulation range, is given by the difference between responses to stabilizing and destabilizing mutations. We show a versatility of the approach on three case studies of proteins with different mechanisms of allosteric regulation, testing it on their known regulatory and functional sites. Allosteric Signaling Maps (ASMs) obtained on the basis of residue-by-residue scanning are proposed as a comprehensive tool to explore a relationship between mutations allosterically modulating protein activity and those that mainly affect protein stability. Analysis of ASMs shows distance dependence of the mode switching in allosteric signaling, emphasizing the role of domains/subunits in protein allosteric communication as elements of a percolative system. Finally, ASMs can be used to complement and tune already existing signaling and to design new elements of allosteric regulation.SignificanceUniversality of allosteric signaling in proteins, molecular machines, and receptors and great advantages of prospected allosteric drugs in highly specific, non-competitive, and modulatory nature of their actions call for deeper theoretical understanding of allosteric communication. In the energy landscape paradigm underliying the molecular mechanisms of protein function, allosteric signalling is the result of any perturbation, such as ligand binding, mutations, intermolecular interactions etc. We present a computational model, allowing to tackle the problem of modulating the energetics of protein allosteric communication. Using this method, Allosteric Signaling Maps (ASMs) are proposed as an approach to exhaustively describe allosteric signaling in the protein, making it possible to take protein activity under allosteric control.

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Molecular Capsule Catalysis: Ready to Address Current Challenges in Synthetic Organic Chemistry?

CHIMIA International Journal for Chemistry ◽

10.2533/chimia.2020.561 ◽

2020 ◽

Vol 74 (7) ◽

pp. 561-568

Author(s):

Ivana Némethová ◽

Leonidas-Dimitrios Syntrivanis ◽

Konrad Tiefenbacher

Keyword(s):

Organic Chemistry ◽

Building Blocks ◽

Short Article ◽

Molecular Capsules ◽

Synthetic Organic Chemistry ◽

Size And Shape ◽

Molecular Capsule ◽

Hydrophobic Binding ◽

Binding Pockets ◽

Self Assembled

Self-assembled molecular capsules, host structures that form spontaneously when their building blocks are mixed, have been known since the 1990s. They share some basic similarities with enzyme pockets, as they feature defined hydrophobic binding pockets that are able to bind molecules of appropriate size and shape. The potential to utilize such host structures for catalysis has been explored since their discovery; however, applications that solve current challenges in synthetic organic chemistry have remained limited. In this short article, we discuss the challenges associated with the use of molecular capsules as catalysts, and highlight some recent applications of supramolecular capsules to overcome challenges in synthetic organic chemistry.

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Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BMC Genomics ◽

10.1186/s12864-019-6287-8 ◽

2019 ◽

Vol 20 (S11) ◽

Cited By ~ 3

Author(s):

Shuai Zeng ◽

Zhen Lyu ◽

Siva Ratna Kumari Narisetti ◽

Dong Xu ◽

Trupti Joshi

Keyword(s):

Knowledge Base ◽

Data Storage ◽

High Performance ◽

Data Retrieval ◽

Omics Data ◽

Web Interface ◽

Web Resource ◽

Privilege Management ◽

Multi Level ◽

Comprehensive Framework

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

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BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

Bioinformatics ◽

10.1093/bioinformatics/btaa094 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3077-3083

Author(s):

Wentao Shi ◽

Jeffrey M Lemoine ◽

Abd-El-Monsif A Shawky ◽

Manali Singha ◽

Limeng Pu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Ligand Binding ◽

Binding Sites ◽

Protein Structures ◽

Supplementary Information ◽

Heme Binding ◽

Unseen Data ◽

Ligand Binding Sites ◽

Binding Pockets

Abstract Motivation Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary information Supplementary data are available at Bioinformatics online.

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Programmed assembly of oppositely charged homogeneously decorated and Janus particles

Faraday Discussions ◽

10.1039/c6fd00008h ◽

2016 ◽

Vol 191 ◽

pp. 89-104 ◽

Cited By ~ 8

Author(s):

Alina Kirillova ◽

Georgi Stoychev ◽

Alla Synytska

Keyword(s):

Optical Properties ◽

Electrostatic Interactions ◽

Building Blocks ◽

Janus Particles ◽

Complex Structures ◽

Janus Particle ◽

Micro Structures ◽

Oppositely Charged ◽

Particle Assemblies ◽

Programmed Assembly

The exploitation of colloidal building blocks with morphological and functional anisotropy facilitates the generation of complex structures with unique properties, which are not exhibited by isotropic particle assemblies. Herein, we demonstrate an easy and scalable bottom-up approach for the programmed assembly of hairy oppositely charged homogeneously decorated and Janus particles based on electrostatic interactions mediated by polyelectrolytes grafted onto their surface. Two different assembly routes are proposed depending on the target structures: raspberry-like/half-raspberry-like or dumbbell-like micro-clusters. Ultimately, stable symmetric and asymmetric micro-structures could be obtained in a well-controlled manner for the homogeneous–homogeneous and homogeneous–Janus particle assemblies, respectively. The spatially separated functionalities of the asymmetric Janus particle-based micro-clusters allow their further assembly into complex hierarchical constructs, which may potentially lead to the design of materials with tailored plasmonics and optical properties.

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Precise assembly of complex beta sheet topologies from de novo designed building blocks

eLife ◽

10.7554/elife.11012 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 10

Author(s):

Indigo Chris King ◽

James Gleixner ◽

Lindsey Doyle ◽

Alexandre Kuzin ◽

John F Hunt ◽

...

Keyword(s):

De Novo ◽

Computational Design ◽

Building Blocks ◽

Beta Sheets ◽

Protein Domain ◽

Beta Sheet ◽

Design Models ◽

Natural Protein ◽

Complex Protein ◽

Alpha Beta

Design of complex alpha-beta protein topologies poses a challenge because of the large number of alternative packing arrangements. A similar challenge presumably limited the emergence of large and complex protein topologies in evolution. Here, we demonstrate that protein topologies with six and seven-stranded beta sheets can be designed by insertion of one de novo designed beta sheet containing protein into another such that the two beta sheets are merged to form a single extended sheet, followed by amino acid sequence optimization at the newly formed strand-strand, strand-helix, and helix-helix interfaces. Crystal structures of two such designs closely match the computational design models. Searches for similar structures in the SCOP protein domain database yield only weak matches with different beta sheet connectivities. A similar beta sheet fusion mechanism may have contributed to the emergence of complex beta sheets during natural protein evolution.

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