Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model

Atılgan, Ali Rana; Durell, Stewart R.; Jernigan, Robert L.; Demirel, Melik C.; Keskin, Özlem; Bahar, İvet

doi:10.1016/s0006-3495(01)76033-x

Cited by 1,536 publications

(2,125 citation statements)

References 49 publications

(56 reference statements)

Supporting

Mentioning

2,094

Contrasting

Order By: Relevance

“…34 ENM is equivalent to a NMA with an ENM at the C a level and the Hessian is based on a harmonic potential form. 41 ENM uses the coordinates of a structure from PDB database; 64 with the adjacent residues connected based on a cut-off distance with a spring to build a Hessian connectivity matrix.…”

Section: Elastic Network Model and Generating New Conformations Usingmentioning

confidence: 99%

“…On the other hand, the ENM [34][35][36][37][38][39][40][41] which is based on a purely mechanical model, and view a protein structure as an elastic network, have been applied to many proteins to obtain slowest (i.e., functionally related) fluctuations. The nodes of the elastic network are a-carbons where identical springs connect the ''interacting'' a-carbons in their native fold.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A flexible docking scheme to explore the binding selectivity of PDZ domains

Gerek

Ozkan

2010

Protein Science

View full text Add to dashboard Cite

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTALIGAND, we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density-95/Dlg/ZO-1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å . We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.

show abstract

Section: Elastic Network Model and Generating New Conformations Usingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A flexible docking scheme to explore the binding selectivity of PDZ domains

Gerek

Ozkan

2010

Protein Science

View full text Add to dashboard Cite

show abstract

“…Despite their coarse-grained nature, ENMs capture the overall geometry of proteins efficiently and modes from ENMs have been shown to be significantly accurate at reproducing experimental temperature factors for a number of crystal structures. 55–58 All the elastic network models and fluctuations were implemented using the ‘bio3d’ package 59 in R. We specifically used two variations of elastic network models implemented in the bio3d package: the anisotropic network model (ANM) 60 and the Hinsen’s network model 61 (referred to as HNM in this paper). In the ANM, all springs between residue i and j are assumed to have the same stiffness (spring constant ) whereas in the HNM, springs between sequentially adjacent C α atoms are represented as and those between non-adjacent C α atoms as where is the distance between residues and ; and and are constants as previously discussed.…”

Section: Methodsmentioning

confidence: 99%

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Sankar¹,

Hoi²,

Yuan

et al. 2018

mAbs

View full text Add to dashboard Cite

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.

show abstract

“…This allows each aminoacid to be represented with its contacts and the topology of the network around it. Representing the structure as a graph allows for sub-graph matching to find reoccurring common motifs in a data set [3], use of elastic network models for normal mode analysis [4] and other algorithms that can employ the graph theoretical properties.…”

Section: Introductionmentioning

confidence: 99%

Representation of Protein Secondary Structure Using Bond-Orientational Order Parameters

Meydan

Sezerman

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Structural studies of proteins for motif mining and other pattern recognition techniques require the abstraction of the structure into simpler elements for robust matching. In this study, we propose the use of bondorientational order parameters, a well-established metric usually employed to compare atom packing in crystals and liquids. Creating a vector of orientational order parameters of residue centers in a sliding window fashion provides us with a descriptor of local structure and connectivity around each residue that is easy to calculate and compare. To test whether this representation is feasible and applicable to protein structures, we tried to predict the secondary structure of protein segments from those descriptors, resulting in 0.99 AUC (area under the ROC curve). Clustering those descriptors to 6 clusters also yield 0.93 AUC, showing that these descriptors can be used to capture and distinguish local structural information.Keywords: bond-orientational order, secondary structure, machine learning, structural alphabet. IntroductionIn analysis protein structures, different models of representations on various levels of structural details are used. From coarse-grained to all-atom models, simplified lattice to continuous representations, each model can be used in different areas of research. The need for abstraction in computational methods (such as structure search and comparison, fold matching, structural motif mining and other areas of pattern recognition) is especially high. The very high amount of data and precision in the 3D coordinates makes computational analysis very complex and very rigid in its applicability. Simplified models capture relevant information and hide unimportant details through abstraction, conferring the ability to group complex 3D information into manageable clusters that can be searched for, compared and "learned" by machine-learning algorithms in a flexible fashion.The most common simplified representation of the protein states are the secondary structural assignments to the coordinates, which can be overlaid onto the sequence to create a 1D representation.There have been other studies with aims to create local structural alphabets to represent the structure as a 1D sequence of structural blocks [1]. A structural alphabet

show abstract

Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model

Cited by 1,536 publications

References 49 publications

A flexible docking scheme to explore the binding selectivity of PDZ domains

A flexible docking scheme to explore the binding selectivity of PDZ domains

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Representation of Protein Secondary Structure Using Bond-Orientational Order Parameters

Contact Info

Product

Resources

About