scholarly journals Study of Functional and Allosteric Sites in Protein Superfamilies

Acta Naturae ◽  
2015 ◽  
Vol 7 (4) ◽  
pp. 34-45 ◽  
Author(s):  
D. А. Suplatov ◽  
V. К. Švedas
Keyword(s):  
Structure Function ◽  
Binding Sites ◽  
Active Sites ◽  
Molecular Mechanisms ◽  
Protein Structures ◽  
Evolutionary Relationship ◽  
Enzyme Engineering ◽  
Homologous Proteins ◽  
Functional Sites ◽  
Protein Superfamilies

The interaction of proteins (enzymes) with a variety of low-molecular-weight compounds, as well as protein-protein interactions, is the most important factor in the regulation of their functional properties. To date, research effort has routinely focused on studying ligand binding to the functional sites of proteins (active sites of enzymes), whereas the molecular mechanisms of allosteric regulation, as well as binding to other pockets and cavities in protein structures, remained poorly understood. Recent studies have shown that allostery may be an intrinsic property of virtually all proteins. Novel approaches are needed to systematically analyze the architecture and role of various binding sites and establish the relationship between structure, function, and regulation. Computational biology, bioinformatics, and molecular modeling can be used to search for new regulatory centers, characterize their structural peculiarities, as well as compare different pockets in homologous proteins, study the molecular mechanisms of allostery, and understand the communication between topologically independent binding sites in protein structures. The establishment of an evolutionary relationship between different binding centers within protein superfamilies and the discovery of new functional and allosteric (regulatory) sites using computational approaches can improve our understanding of the structure-function relationship in proteins and provide new opportunities for drug design and enzyme engineering.

RECOGNIZING COMPLEX, ASYMMETRIC FUNCTIONAL SITES IN PROTEIN STRUCTURES USING A BAYESIAN SCORING FUNCTION

2003 ◽  
Vol 01 (01) ◽  
pp. 119-138 ◽  
Author(s):  
LIPING WEI ◽  
RUSS B. ALTMAN
Keyword(s):  
Binding Sites ◽  
Calcium Binding ◽  
Active Sites ◽  
Large Scale ◽  
Protein Structures ◽  
Scoring Function ◽  
Chemical Properties ◽  
Data Bank ◽  
Functional Sites ◽  
Calcium Binding Sites

The increase in known three-dimensional protein structures enables us to build statistical profiles of important functional sites in protein molecules. These profiles can then be used to recognize sites in large-scale automated annotations of new protein structures. We report an improved FEATURE system which recognizes functional sites in protein structures. FEATURE defines multi-level physico-chemical properties and recognizes sites based on the spatial distribution of these properties in the sites' microenvironments. It uses a Bayesian scoring function to compare a query region with the statistical profile built from known examples of sites and control nonsites. We have previously shown that FEATURE can accurately recognize calcium-binding sites and have reported interesting results scanning for calcium-binding sites in the entire Protein Data Bank. Here we report the ability of the improved FEATURE to characterize and recognize geometrically complex and asymmetric sites such as ATP-binding sites and disulfide bond-forming sites. FEATURE does not rely on conserved residues or conserved residue geometry of the sites. We also demonstrate that, in the absence of a statistical profile of the sites, FEATURE can use an artificially constructed profile based on a priori knowledge to recognize the sites in new structures, using redoxin active sites as an example.

scholarly journals Application Fields, Positions, and Bioinformatic Mining of Non-active Sites: A Mini-Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Qinyuan Ma ◽  
Jian Shen ◽  
Bin Wang ◽  
Xiuzhen Gao ◽  
...  
Keyword(s):  
Active Sites ◽  
Recent Progress ◽  
Molecular Mechanisms ◽  
Vital Role ◽  
Enzyme Engineering ◽  
Major Research ◽  
Enzyme Families ◽  
Application Fields ◽  
Enzymatic Mechanisms ◽  
Biocatalytic Process

Active sites of enzymes play a vital role in catalysis, and researchhas been focused on the interactions between active sites and substrates to understand the biocatalytic process. However, the active sites distal to the catalytic cavity also participate in catalysis by maintaining the catalytic conformations. Therefore, some researchers have begun to investigate the roles of non-active sites in proteins, especially for enzyme families with different functions. In this mini-review, we focused on recent progress in research on non-active sites of enzymes. First, we outlined two major research methodswith non-active sites as direct targets, including understanding enzymatic mechanisms and enzyme engineering. Second, we classified the positions of reported non-active sites in enzyme structures and studied the molecular mechanisms underlying their functions, according to the literature on non-active sites. Finally, we summarized the results of bioinformatic analysisof mining non-active sites as targets for protein engineering.

scholarly journals virusMED: an atlas of hotspots of viral proteins

IUCrJ ◽  
2021 ◽  
Vol 8 (6) ◽  
Author(s):  
HuiHui Zhang ◽  
Pei Chen ◽  
Haojie Ma ◽  
Magdalena Woińska ◽  
Dejian Liu ◽  
...  
Keyword(s):  
Binding Sites ◽  
Viral Protein ◽  
Rational Design ◽  
Critical Role ◽  
Protein Structures ◽  
Viral Proteins ◽  
Drug Binding ◽  
Rapid Screening ◽  
Virus Species ◽  
Functional Sites

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

scholarly journals Tracing the origins of SARS-COV-2 in coronavirus phylogenies: a review

2021 ◽  
Vol 19 (2) ◽  
pp. 769-785 ◽  
Author(s):  
Erwan Sallard ◽  
José Halloy ◽  
Didier Casane ◽  
Etienne Decroly ◽  
Jacques van Helden
Keyword(s):  
Sequence Analysis ◽  
Structure Function ◽  
Molecular Mechanisms ◽  
Laboratory Strain ◽  
Domestic Animals ◽  
Scientific Policy ◽  
Human Coronavirus ◽  
Viral Dissemination ◽  
Link Type ◽  
Intensive Breeding

AbstractSARS-CoV-2 is a new human coronavirus (CoV), which emerged in China in late 2019 and is responsible for the global COVID-19 pandemic that caused more than 97 million infections and 2 million deaths in 12 months. Understanding the origin of this virus is an important issue, and it is necessary to determine the mechanisms of viral dissemination in order to contain future epidemics. Based on phylogenetic inferences, sequence analysis and structure–function relationships of coronavirus proteins, informed by the knowledge currently available on the virus, we discuss the different scenarios on the origin—natural or synthetic—of the virus. The data currently available are not sufficient to firmly assert whether SARS-CoV2 results from a zoonotic emergence or from an accidental escape of a laboratory strain. This question needs to be solved because it has important consequences on the risk/benefit balance of our interactions with ecosystems, on intensive breeding of wild and domestic animals, on some laboratory practices and on scientific policy and biosafety regulations. Regardless of COVID-19 origin, studying the evolution of the molecular mechanisms involved in the emergence of pandemic viruses is essential to develop therapeutic and vaccine strategies and to prevent future zoonoses. This article is a translation and update of a French article published in Médecine/Sciences, August/September 2020 (10.1051/medsci/2020123).

scholarly journals Crystal structures of aconitase X enzymes from bacteria and archaea provide insights into the molecular evolution of the aconitase superfamily

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Seiya Watanabe ◽  
Yohsuke Murase ◽  
Yasunori Watanabe ◽  
Yasuhiro Sakurai ◽  
Kunihiko Tajima
Keyword(s):  
Molecular Evolution ◽  
Agrobacterium Tumefaciens ◽  
Crystal Structures ◽  
Epr Spectroscopy ◽  
Binding Sites ◽  
Active Sites ◽  
Type I ◽  
Type Ii ◽  
Thermococcus Kodakarensis ◽  
Evolutionary Scenario

AbstractAconitase superfamily members catalyze the homologous isomerization of specific substrates by sequential dehydration and hydration and contain a [4Fe-4S] cluster. However, monomeric and heterodimeric types of function unknown aconitase X (AcnX) have recently been characterized as a cis-3-hydroxy-L-proline dehydratase (AcnXType-I) and mevalonate 5-phosphate dehydratase (AcnXType-II), respectively. We herein elucidated the crystal structures of AcnXType-I from Agrobacterium tumefaciens (AtAcnX) and AcnXType-II from Thermococcus kodakarensis (TkAcnX) without a ligand and in complex with substrates. AtAcnX and TkAcnX contained the [2Fe-2S] and [3Fe-4S] clusters, respectively, conforming to UV and EPR spectroscopy analyses. The binding sites of the [Fe-S] cluster and substrate were clearlydifferent from those that were completely conserved in other aconitase enzymes; however, theoverall structural frameworks and locations of active sites were partially similar to each other.These results provide novel insights into the evolutionary scenario of the aconitase superfamilybased on the recruitment hypothesis.

scholarly journals Machine learning differentiates enzymatic and non-enzymatic metals in proteins

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Feehan ◽  
Meghan W. Franklin ◽  
Joanna S. G. Slusky
Keyword(s):  
Machine Learning ◽  
Metal Binding ◽  
Binding Sites ◽  
Active Sites ◽  
De Novo ◽  
Enzyme Design ◽  
Metal Binding Sites ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Physicochemical Features

AbstractMetalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic  metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model’s ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.

scholarly journals Multidrug Antimicrobial Resistance and Molecular Detection of mcr-1 Gene in Salmonella Species Isolated from Chicken

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 206
Author(s):  
Md Bashir Uddin ◽  
S.M. Bayejed Hossain ◽  
Mahmudul Hasan ◽  
Mohammad Nurul Alam ◽  
Mita Debnath ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Antimicrobial Resistance ◽  
In Silico ◽  
Salmonella Enterica ◽  
Molecular Mechanisms ◽  
Antimicrobial Agents ◽  
Genetic Relationships ◽  
Evolutionary Relationship ◽  
Salmonella Spp ◽  
Gram Negative

Colistin (polymyxin E) is widely used in animal and human medicine and is increasingly used as one of the last-resort antibiotics against Gram-negative bacilli. Due to the increased use of colistin in treating infections caused by multidrug-resistant Gram-negative bacteria, resistance to this antibiotic ought to be monitored. The study was undertaken to elucidate the molecular mechanisms, genetic relationships and phenotype correlations of colistin-resistant isolates. Here, we report the detection of the mcr-1 gene in chicken-associated Salmonella isolates in Bangladesh and its in-silico functional analysis. Out of 100 samples, 82 Salmonella spp. were isolated from chicken specimens (liver, intestine). Phenotypic disc diffusion and minimum inhibitory concentration (MIC) assay using different antimicrobial agents were performed. Salmonella isolates were characterized using PCR methods targeting genus-specific invA and mcr-1 genes with validation for the functional analysis. The majority of the tested Salmonella isolates were found resistant to colistin (92.68%), ciprofloxacin (73.17%), tigecycline (62.20%) and trimethoprim/sulfamethoxazole (60.98%). When screened using PCR, five out of ten Salmonella isolates were found to carry the mcr-1 gene. One isolate was confirmed for Salmonella enterica subsp. enterica serovar Enteritidis, and other four isolates were confirmed for Salmonella enterica subsp. enterica serovar Typhimurium. Sequencing and phylogenetic analysis revealed a divergent evolutionary relationship between the catalytic domain of Neisseria meningitidis lipooligosaccharide phosphoethanolamine transferase A (LptA) and MCR proteins, rendering them resistant to colistin. Three-dimensional homology structural analysis of MCR-1 proteins and molecular docking interactions suggested that MCR-1 and LptA share a similar substrate binding cavity, which could be validated for the functional analysis. The comprehensive molecular and in-silico analyses of the colistin resistance mcr-1 gene of Salmonella spp. of chicken origin in the present study highlight the importance of continued monitoring and surveillance for antimicrobial resistance among pathogens in food chain animals.

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