Methods for Detecting Critical Residues in Proteins

Haspel, Nurit; Jagodzinski, Filip

doi:10.1007/978-1-4939-6472-7_15

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…On the same breath, HR2P have nine common residues (Leu780, Pro767, Tyr1142, Leu729, Leu731, Val1168, Val770, Pro1143, Val790) with AP00225, AP00180, and AP00744. These residues may be considered as key or critical and may play a major role in the protein protein-interaction [32] and might inhibit the formation of the six-helical bundle (6-HB). Further studies may help to understand the role of these residues in drug binding mechanism.…”

Section: Discussionmentioning

confidence: 99%

Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach

Mustafa

Balkhy

Gabere

2019

Advances in Bioinformatics

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There is no effective therapeutic or vaccine for Middle East Respiratory Syndrome and this study attempts to find therapy using peptide by establishing a basis for the peptide-protein interactions through in silico docking studies for the spike protein of MERS-CoV. The antimicrobial peptides (AMPs) were retrieved from the antimicrobial peptide database (APD3) and shortlisted based on certain important physicochemical properties. The binding mode of the shortlisted peptides was measured based on the number of clusters which forms in a protein-peptide docking using Piper. As a result, we identified a list of putative AMPs which binds to the spike protein of MERS-CoV, which may be crucial in providing the inhibitory action. It is observed that seven putative peptides have good binding score based on cluster size cutoff of 208. We conclude that seven peptides, namely, AP00225, AP00180, AP00549, AP00744, AP00729, AP00764, and AP00223, could possibly have binding with the active site of the MERS-CoV spike protein. These seven AMPs could serve as a therapeutic option for MERS and enhance its treatment outcome.

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Section: Discussionmentioning

confidence: 99%

Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach

Mustafa

Balkhy

Gabere

2019

Advances in Bioinformatics

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“…Another way to infer protein activity is through the prediction of critical sites; these sites include the amino acid residues that are critical for the activity of the protein. These sites may be predicted from the protein sequence or protein structure [30]; in this last case, the critical residues may be related to central residues from the protein contact map connectivity [31], an aspect that is relevant for protein structure prediction, as I will describe later. This set of critical residues may be represented by a vector; hence, Equation (1) can be represented as follows:…”

Section: Modeling Protein Structure-activity Relationshipmentioning

confidence: 99%

Challenges in the Computational Modeling of the Protein Structure—Activity Relationship

Río

2021

Computation

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Living organisms are composed of biopolymers (proteins, nucleic acids, carbohydrates and lipid polymers) that are used to keep or transmit information relevant to the state of these organisms at any given time. In these processes, proteins play a central role by displaying different activities required to keep or transmit this information. In this review, I present the current knowledge about the protein sequence–structure–activity relationship and the basis for modeling this relationship. Three representative predictors relevant to the modeling of this relationship are summarized to highlight areas that require further improvement and development. I will describe how a basic understanding of this relationship is fundamental in the development of new methods to design proteins, which represents an area of multiple applications in the areas of health and biotechnology.

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“…Cys–Cys residue contacts and the contacts between residues with opposite charges are more frequent in PPI sites [ 39 ]. Besides, protein interface regions are less flexible [ 40 ] and demonstrate higher sequence conservation rates [ 38 , 41 ] than other non-binding regions. Conserved interfaces are critical for the maintenance of PPIs throughout evolution.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of 3D Zernike descriptors and SVM for protein–protein interface prediction

Daberdaku

Ferrari

2018

BMC Bioinformatics

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BackgroundThe correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.ResultsIn this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI).ConclusionsThe 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.

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Methods for Detecting Critical Residues in Proteins

Cited by 4 publications

References 27 publications

Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach

Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach

Challenges in the Computational Modeling of the Protein Structure—Activity Relationship

Exploring the potential of 3D Zernike descriptors and SVM for protein–protein interface prediction

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