Computational analysis of aspartic protease plasmepsin II complexed with EH58 inhibitor: a QM/MM MD study

Silva, Natália de Farias; Lameira, Jerônimo; Alves, Cláudio Nahum

doi:10.1007/s00894-011-0963-1

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…Plasmepsins are expressed by the parasite Plasmodium falciparum and represent highly interesting targets for anti-malaria drugs. A study with plasmepsin II and a similar inhibitor equally showed a preference for a protonated Asp214 [39]. The free binding energy terms ∆G Elect −QM/MM of the two different protonation states with bound inhibitors showed differences of 11.1 and 15.6 kJ/mol, respectively.…”

Section: Mechanisms Of Aspartic Proteasesmentioning

confidence: 95%

Mechanisms of Proteolytic Enzymes and Their Inhibition in QM/MM Studies

Elsässer

Goettig

2021

IJMS

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Experimental evidence for enzymatic mechanisms is often scarce, and in many cases inadvertently biased by the employed methods. Thus, apparently contradictory model mechanisms can result in decade long discussions about the correct interpretation of data and the true theory behind it. However, often such opposing views turn out to be special cases of a more comprehensive and superior concept. Molecular dynamics (MD) and the more advanced molecular mechanical and quantum mechanical approach (QM/MM) provide a relatively consistent framework to treat enzymatic mechanisms, in particular, the activity of proteolytic enzymes. In line with this, computational chemistry based on experimental structures came up with studies on all major protease classes in recent years; examples of aspartic, metallo-, cysteine, serine, and threonine protease mechanisms are well founded on corresponding standards. In addition, experimental evidence from enzyme kinetics, structural research, and various other methods supports the described calculated mechanisms. One step beyond is the application of this information to the design of new and powerful inhibitors of disease-related enzymes, such as the HIV protease. In this overview, a few examples demonstrate the high potential of the QM/MM approach for sophisticated pharmaceutical compound design and supporting functions in the analysis of biomolecular structures.

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Section: Mechanisms Of Aspartic Proteasesmentioning

confidence: 95%

Mechanisms of Proteolytic Enzymes and Their Inhibition in QM/MM Studies

Elsässer

Goettig

2021

IJMS

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“…This catalog revealed a new line of novel proteases for functional characterization. Studies on malarial proteases have been focused on biochemical and molecular characterization [33-46], structural modeling and analysis [47,48], and inhibitor design and screening [49-59]. Although significant progress has been made, much remains to be learned about the roles played by these proteins, including how they interact with other proteins in space and time to coordinate important aspects of growth, transmission, invasion, response to drug treatment and pathogenesis of this devastating pathogen.…”

Section: Introductionmentioning

confidence: 99%

Protease-associated cellular networks in malaria parasite Plasmodium falciparum

et al. 2011

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BackgroundMalaria continues to be one of the most severe global infectious diseases, responsible for 1-2 million deaths yearly. The rapid evolution and spread of drug resistance in parasites has led to an urgent need for the development of novel antimalarial targets. Proteases are a group of enzymes that play essential roles in parasite growth and invasion. The possibility of designing specific inhibitors for proteases makes them promising drug targets. Previously, combining a comparative genomics approach and a machine learning approach, we identified the complement of proteases (degradome) in the malaria parasite Plasmodium falciparum and its sibling species [1-3], providing a catalog of targets for functional characterization and rational inhibitor design. Network analysis represents another route to revealing the role of proteins in the biology of parasites and we use this approach here to expand our understanding of the systems involving the proteases of P. falciparum.ResultsWe investigated the roles of proteases in the parasite life cycle by constructing a network using protein-protein association data from the STRING database [4], and analyzing these data, in conjunction with the data from protein-protein interaction assays using the yeast 2-hybrid (Y2H) system [5], blood stage microarray experiments [6-8], proteomics [9-12], literature text mining, and sequence homology analysis. Seventy-seven (77) out of 124 predicted proteases were associated with at least one other protein, constituting 2,431 protein-protein interactions (PPIs). These proteases appear to play diverse roles in metabolism, cell cycle regulation, invasion and infection. Their degrees of connectivity (i.e., connections to other proteins), range from one to 143. The largest protease-associated sub-network is the ubiquitin-proteasome system which is crucial for protein recycling and stress response. Proteases are also implicated in heat shock response, signal peptide processing, cell cycle progression, transcriptional regulation, and signal transduction networks.ConclusionsOur network analysis of proteases from P. falciparum uses a so-called guilt-by-association approach to extract sets of proteins from the proteome that are candidates for further study. Novel protease targets and previously unrecognized members of the protease-associated sub-systems provide new insights into the mechanisms underlying parasitism, pathogenesis and virulence.

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“…Titration curves for inhibitors were calculated by the ChemAxon software . Asp 214 of PlmII was set to be protonated at the oxygen atom OD2 as has been previously described . The protonation states of remaining ionizable residues and histidine tautomers in the protein were determined with the program PDB2PQR, which uses PROPKA for the prediction of pKa values .…”

Section: Methodsmentioning

confidence: 99%

Protein surface roughness accounts for binding free energy of Plasmepsin II‐ligand complexes

Valdés‐Tresanco

Valdés-Tresanco

Valiente

et al. 2017

J of Molecular Recognition

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The calculation of absolute binding affinities for protein-inhibitor complexes remains as one of the main challenges in computational structure-based ligand design. The present work explored the calculations of surface fractal dimension (as a measure of surface roughness) and the relationship with experimental binding free energies of Plasmepsin II complexes. Plasmepsin II is an attractive target for novel therapeutic compounds to treat malaria. However, the structural flexibility of this enzyme is a drawback when searching for specific inhibitors. Concerning that, we performed separate explicitly solvated molecular dynamics simulations using the available high-resolution crystal structures of different Plasmepsin II complexes. Molecular dynamics simulations allowed a better approximation to systems dynamics and, therefore, a more reliable estimation of surface roughness. This constitutes a novel approximation in order to obtain more realistic values of fractal dimension, because previous works considered only x-ray structures.Binding site fractal dimension was calculated considering the ensemble of structures generated at different simulation times. A linear relationship between binding site fractal dimension and experimental binding free energies of the complexes was observed within 20 ns. Previous studies of the subject did not uncover this relationship. Regression model, coined FD model, was built to estimate binding free energies from binding site fractal dimension values. Leaveone-out cross-validation showed that our model reproduced accurately the absolute binding free energies for our training set (R 2 = 0.76; <|error|> =0.55 kcal/mol; SD error = 0.19 kcal/mol).The fact that such a simple model may be applied raises some questions that are addressed in the article. Most rapid methods for estimation of binding free energies, such as, so-called empirical or knowledge-based (statistical) scoring functions, have been proposed. These methods are based on very simple energy functions 4-6 or on the frequency of occurrence of different atom-atom contact pairs in complexes of known structure. 7,8 The simplicity of the energy function along with the lack of conformational sampling and explicit water treatment makes these approaches very fast, but usually at the cost of accuracy. 9 Thus, further development of fast and accurate methods to complement the already existing ones is still needed.

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Computational analysis of aspartic protease plasmepsin II complexed with EH58 inhibitor: a QM/MM MD study

Cited by 8 publications

References 52 publications

Mechanisms of Proteolytic Enzymes and Their Inhibition in QM/MM Studies

Mechanisms of Proteolytic Enzymes and Their Inhibition in QM/MM Studies

Protease-associated cellular networks in malaria parasite Plasmodium falciparum

Protein surface roughness accounts for binding free energy of Plasmepsin II‐ligand complexes

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