Detecting protein atom correlations using correlation of probability of recurrence

Fataftah, Hiba; Karain, Wael I.

doi:10.1002/prot.24574

Cited by 19 publications

(17 citation statements)

References 66 publications

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“…The results showed that the binding of TEM-1 BLIP is tighter in comparison to the SHV-1 BLIP binding, which is in accordance with experimentally determined results [90,104]. Additionally, Fataftah et al [115] conducted MD simulations and a dynamic cross-correlation map (DCCM) to study the distances of the C

_{α}

atom in BLIP. The DCCM results provide a big picture analysis of high correlation among distant residues and should be further investigated.…”

Section: β-Lactamase Inhibitor Proteinssupporting

confidence: 76%

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

Eiamphungporn

Schaduangrat

Malik

et al. 2018

IJMS

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β-Lactams are the most widely used and effective antibiotics for the treatment of infectious diseases. Unfortunately, bacteria have developed several mechanisms to combat these therapeutic agents. One of the major resistance mechanisms involves the production of β-lactamase that hydrolyzes the β-lactam ring thereby inactivating the drug. To overcome this threat, the small molecule β-lactamase inhibitors (e.g., clavulanic acid, sulbactam and tazobactam) have been used in combination with β-lactams for treatment. However, the bacterial resistance to this kind of combination therapy has evolved recently. Therefore, multiple attempts have been made to discover and develop novel broad-spectrum β-lactamase inhibitors that sufficiently work against β-lactamase producing bacteria. β-lactamase inhibitory proteins (BLIPs) (e.g., BLIP, BLIP-I and BLIP-II) are potential inhibitors that have been found from soil bacterium Streptomyces spp. BLIPs bind and inhibit a wide range of class A β-lactamases from a diverse set of Gram-positive and Gram-negative bacteria, including TEM-1, PC1, SME-1, SHV-1 and KPC-2. To the best of our knowledge, this article represents the first systematic review on β-lactamase inhibitors with a particular focus on BLIPs and their inherent properties that favorably position them as a source of biologically-inspired drugs to combat antimicrobial resistance. Furthermore, an extensive compilation of binding data from β-lactamase–BLIP interaction studies is presented herein. Such information help to provide key insights into the origin of interaction that may be useful for rationally guiding future drug design efforts.

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_{α}

atom in BLIP. The DCCM results provide a big picture analysis of high correlation among distant residues and should be further investigated.…”

Section: β-Lactamase Inhibitor Proteinssupporting

confidence: 76%

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

Eiamphungporn

Schaduangrat

Malik

et al. 2018

IJMS

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“…The starting BLIP protein structure is downloaded from the protein data bank (PDB: 3gmu) [ 45 ]. The simulation details are described elsewhere in detail [ 28 ]. The phase space trajectory for each protein residue carbon alpha atom is constructed from a scalar time series of the root mean square deviation RMSD of its position over time [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…The simulation details are described elsewhere in detail [ 28 ]. The phase space trajectory for each protein residue carbon alpha atom is constructed from a scalar time series of the root mean square deviation RMSD of its position over time [ 28 ]. The RMSD values are calculated after removing translational and rotational atomic motions using least square fitting.…”

Section: Methodsmentioning

confidence: 99%

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Weighted protein residue networks based on joint recurrences between residues

Karain

Qaraeen

2015

BMC Bioinformatics

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BackgroundWeighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.ResultsThe joint recurrence weighted network approach performs well in pointing out key protein residues. Furthermore, it seems to emphasize residues with medium to high relative solvent accessibility that lie in loop regions between secondary structure elements of the protein.ConclusionsProtein residue networks that use joint recurrences extracted from molecular dynamics simulations of a solvated protein perform well in pointing to hotspot residues and hotspot clusters. This approach uses no distance cutoff threshold, and does not exclude any interactions between the residues, including water-mediated interactions.

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“…8 A quantitative version known as recurrence quantification analysis RQA 9 has found extensive use in various applications, including protein structure and dynamics. [10][11][12] A suitable context to compare the performance of WK and RPWK in the field of protein molecular dynamics simulations is the protein-solvent complex energy landscape, which has long been a topic of intense research due to the importance of the role played by the hydration water on the functioning of proteins. 4,5,[13][14][15] This is especially true for the protein dynamical transition, which occurs at temperatures ranging between 200 and 240 K, and is strongly coupled to the solvent.…”

Section: Introductionmentioning

confidence: 99%

THz frequency spectrum of protein–solvent interaction energy using a recurrence plot‐based Wiener–Khinchin method

Karain

2016

Proteins

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The dynamics of a protein and the water surrounding it are coupled via nonbonded energy interactions. This coupling can exhibit a complex, nonlinear, and nonstationary nature. The THz frequency spectrum for this interaction energy characterizes both the vibration spectrum of the water hydrogen bond network, and the frequency range of large amplitude modes of proteins. We use a Recurrence Plot based Wiener-Khinchin method RPWK to calculate this spectrum, and the results are compared to those determined using the classical auto-covariance-based Wiener-Khinchin method WK. The frequency spectra for the total nonbonded interaction energy extracted from molecular dynamics simulations between the β-Lactamase Inhibitory Protein BLIP, and water molecules within a 10 Å distance from the protein surface, are calculated at 150, 200, 250, and 310 K, respectively. Similar calculations are also performed for the nonbonded interaction energy between the residues 49ASP, 53TYR, and 142PHE in BLIP, with water molecules within 10 Å from each residue respectively at 150, 200, 250, and 310 K. A comparison of the results shows that RPWK performs better than WK, and is able to detect some frequency data points that WK fails to detect. This points to the importance of using methods capable of taking the complex nature of the protein-solvent energy landscape into consideration, and not to rely on standard linear methods. In general, RPWK can be a valuable addition to the analysis tools for protein molecular dynamics simulations. Proteins 2016; 84:1549-1557. © 2016 Wiley Periodicals, Inc.

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Detecting protein atom correlations using correlation of probability of recurrence

Cited by 19 publications

References 66 publications

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

Weighted protein residue networks based on joint recurrences between residues

THz frequency spectrum of protein–solvent interaction energy using a recurrence plot‐based Wiener–Khinchin method

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