A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations

Frappier, Vincent; Najmanovich, Rafaël

doi:10.1101/001495

Cited by 4 publications

(4 citation statements)

References 80 publications

(56 reference statements)

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“…To consider this effect and estimate the level of structural destabilization associated with each missense mutation, we predicted quantitative stability changes utilizing multiple programs that calculate the change of the protein structural stability induced by mutations (ΔΔ G ). In order to provide points of comparison, we utilized multiple programs such as I‐Mutant 3.0, Cologne University Proteins Stability Analysis Tool (CUPSAT), and Elastic Network Contact Model (ENCoM) (Table ) …”

Section: Resultsmentioning

confidence: 99%

“…To further explore the effects of the mutations on structure stability and dynamics we used Elastic Network Contact Model ( ENCoM ) (http://bcb.med.usherbrooke.ca/encom), that utilizes a novel mixed coarse‐grained normal mode method to account for the type and extent of pairwise atomic interactions allowing for the calculation of vibrational entropy differences as a result of mutations . ENCoM utilizes a potential function with four terms: (1) covalent bond stretching, (2) angle bending, (3) dihedral torsion and (4) nonbonded interaction while taking into consideration the nature and possible effects that the orientation of the side‐chains have on dynamics within the context of normal mode analysis.…”

Section: Protein Structure Stability and Flexibilitymentioning

confidence: 99%

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Structural mutation analysis of PTEN and its genotype‐phenotype correlations in endometriosis and cancer

Smith

Briggs

2016

Proteins

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The phosphatase and tensin homolog deleted on chromosome ten (PTEN) gene encodes a tumor suppressor phosphatase that has recently been found to be frequently mutated in patients with endometriosis, endometrial cancer and ovarian cancer. Here, we present the first computational analysis of 13 somatic missense PTEN mutations associated with these phenotypes. We found that a majority of the mutations are associated in conserved positions within the active site and are clustered within the signature motif, which contain residues that play a crucial role in loop conformation and are essential for catalysis. In silico analyses were utilized to identify the putative effects of these mutations. In addition, coarse-grained models of both wild-type (WT) PTEN and mutants were constructed using elastic network models to explore the interplay of the structural and global dynamic effects that the mutations have on the relationship between genotype and phenotype. The effects of the mutations reveal that the local structure and interactions affect polarity, protein structure stability, electrostatic surface potential, and global dynamics of the protein. Our results offer new insight into the role in which PTEN missense mutations contribute to the molecular mechanism and genotypic-phenotypic correlation of endometriosis, endometrial cancer and ovarian cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Protein Structure Stability and Flexibilitymentioning

confidence: 99%

Structural mutation analysis of PTEN and its genotype‐phenotype correlations in endometriosis and cancer

Smith

Briggs

2016

Proteins

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“…CG KBPs afford the investigator the same advantages as any other CG model, offering lower computational cost and a greater focus on those elements of a system believed to be relevant compared to the fully atomistic (FA) models . Work in this field has come primarily in the form of a number of elastic network models (ENMs), and a handful of contact‐based potentials of varying complexity . Remarkably, CG KBPs have been found in at least one case to outperform FA potentials on the same task (possibly due to reduced signal‐to‐noise ratio when parameterizing at a lower level of detail).…”

Section: Introductionmentioning

confidence: 99%

Application of information theory to a three‐body coarse‐grained representation of proteins in the PDB: Insights into the structural and evolutionary roles of residues in protein structure

2014

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Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.

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“…Najmanovich and coworkers implemented this strategy in the ENcoM server [126], where they combine FoldX [27] with their ENcoM protocol to rapidly estimate vibrational entropy. ENcoM combines ENM techniques with a pairwise atom-type non-bonded interaction term to include the specific nature of amino acids [127].…”

Section: Protein Stability Improvementmentioning

confidence: 99%

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Monza

Acebes

Lucas

et al. 2017

Directed Enzyme Evolution: Advances and Applications

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Directed evolution creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-319-50413-1_10/fulltext.html of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of art of molecular modeling, discussing their strengths, limitations and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques, and a synergic effort between simulations and experimental validation. IntroductionBiotechnology needs catalysts that can work under harsh conditions, catalyze a broad range of substrates, generate maximum amount of product, and tolerate changes in the environment. Enzymes, which are biodegradable and reusable catalysts [1], in addition to remarkable reaction rates, can work in environmentally friendly pH and temperature ranges, and display control over stereochemistry and regioselectivity which makes them ideal for many applications [2,3]. When thinking about enzymes, people normally associate them to expressions such as "perfect catalysts" or "outstanding reaction rate". In fact, there are examples of enzymes that catalyze reactions at extremely high rates such as triose phosphate isomerase, superoxide dismutase or carbonic anhydrase [4]. These are often limited only by the rate of ligand diffusion into the active site (diffusion-controlled rate). Nevertheless, an extensive analysis by Bar-Even et al., of nearly 2000 enzymes, showed that the median maximal turnover rate value over all measured enzymes is about 10 s −1 nowhere close to the values of 10 5 or 10 6 normally associated with catalysts [5,6].So, it would appear that natural enzymes are "just good enough" for the function they must perform in a given organism [7]. One might conclude that if they had evolved to their optimum performance then trying to improve them (from a kinetic point of view) would be attempting the impossible. On the contrary, as seen by the distribution of reaction rates, k cat , most enzymes function at a lower rate than the diffusion-limit and thus, there is space to further increase their kinetic properties to meet industrial needs. Additionally, we need enzymes capable of catalyzing reactions for which no known enzymes exist, to work with different substrates and for particular conditions that are industrially...

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A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations

Cited by 4 publications

References 80 publications

Structural mutation analysis of PTEN and its genotype‐phenotype correlations in endometriosis and cancer

Structural mutation analysis of PTEN and its genotype‐phenotype correlations in endometriosis and cancer

Application of information theory to a three‐body coarse‐grained representation of proteins in the PDB: Insights into the structural and evolutionary roles of residues in protein structure

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

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