DFprot: a webtool for predicting local chain deformability

Garzón, José Ignacio; Kovacs, Julio A.; Abagyan, Ruben; Chacón, Pablo

doi:10.1093/bioinformatics/btm014

Cited by 20 publications

(18 citation statements)

References 23 publications

(25 reference statements)

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“…For example, FlexOracle [26] and HingeProt [27] predict hinge regions in proteins. Furthermore, DFprot [28] predicts main-chain deformability, which corresponds fundamentally to the internal motions described above.…”

Section: Introductionmentioning

confidence: 99%

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

Hirose

Yokota

Kuroda

et al. 2010

BMC Structural Biology

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BackgroundStructural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information.ResultsIn this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors.ConclusionsFinally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the predicted high internal motion regions and the observed conformational change regions.

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Section: Introductionmentioning

confidence: 99%

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

Hirose

Yokota

Kuroda

et al. 2010

BMC Structural Biology

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“…1. For identification purposes, ED peaks (or cor- Table 1 Correlation factors κ as calculated using the NMA, GNM and ANM approaches applied to C α or backbone peak networks of protein structure 5pti [7] 0.754 * --ElNémo [25] 0.620 --DFprot [27] 0.816 --* Calculated from the first six non-zero frequency modes only.…”

Section: Hierarchical Clustering Resultsmentioning

confidence: 99%

“…Several Web servers are available for the evaluation of protein dynamics through GNM [22], ANM [23], and NMA [7,[24][25][26][27] techniques. The GNM and ANM algorithms have been described hereabove.…”

Section: Web Serversmentioning

confidence: 99%

“…Alexandrov et al [26] also developed a Web tool for motion prediction and conformation comparison. In the recently published algorithm DFprot [27], a NMA is performed on the C α atoms, setting the mass of each atom as the total mass of the corresponding residue. For a given protein molecule, its C α atoms are connected with harmonic springs of a strength defined by: Fig.…”

Section: Web Serversmentioning

confidence: 99%

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Collective motions in protein structures: Applications of elastic network models built from electron density distributions

Leherte

Vercauteren

2008

Computer Physics Communications

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“…Servers such as Best/Corex [8], HingeMaster [9], H-predictor [10], Dfprot [11], HingeProt [12] and many others are capable of localizing sequences amenable to 0014 j o u r n a l h o m e p a g e : w w w . F E B S L e t t e r s .…”

Section: Introductionmentioning

confidence: 99%

Hinge sequences as signaling agents?

Stec¹

2012

FEBS Letters

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a b s t r a c tWe report an unexpected finding of common structural principles in two unrelated signaling systems: the FAS death domain transformation that initializes the extrinsic apoptotic pathway and signaling by calmodulin bending. The location and design of the hinge is postulated to be a general principle for creating potential signaling event. We suggest that already existing tool can predict the existence of such a hinge and formulate the hypothesis that the internal instabilities designed into the hinge sequences are necessary devices in effective signaling events. Ó 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.FAS receptor initiates signaling of an extrinsic apoptotic response pathway that culminates with the formation of the death inducing signaling complex (DISC) and the subsequent cell death in Type 1 cells. In our recent letter to Nature [1] we reported an X-ray crystal model for the interaction between the death domain of the FAS receptor and death effector domain (DED) of the FADD protein. This observation shed new light onto important apoptotic signaling events and enhanced our understanding of how the individual pieces of the apoptotic machinery work. However, the main question regarding a protein switch (conformational change) was not addressed in the paper. This communication is designed to fill this gap.In a subsequent study, the conformational changes of all proteins in the PDB were reviewed [2]. Our analysis showed that, despite no sequence similarity, the FAS death domain shares a significant structural similarity with calmodulin [3]. Fig. 1 shows the helical fragments of both molecules that superpose particularly well, with RMSD 2.3 Å along 33 residues. As a result, a significant similarity is found between FAS-FADD and calmodulin based on the common conformational transition.Calmodulin has been thoroughly investigated with particular emphasis on the mobility of the protein [4,5]. In calmodulin, a long, stem helix bends during a signaling event and brings two globular helical domains together, closing on a target [3]. The FAS death domain undergoes a transition from the closed to the open state [1] in a similar manner as calmodulin. The significant difference between these systems is which conformation is adopted in the resting state. While the resting conformation of calmodulin is an open state, the resting conformation of FAS death domain is closed.

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DFprot: a webtool for predicting local chain deformability

Cited by 20 publications

References 23 publications

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

Collective motions in protein structures: Applications of elastic network models built from electron density distributions

Hinge sequences as signaling agents?

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