Prediction of binding hot spot residues by using structural and evolutionary parameters

Higa, R. H.; Tozzi, Clésio Luis

doi:10.1590/s1415-47572009000300029

Cited by 20 publications

(11 citation statements)

References 42 publications

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“…These physicochemical features also correlate well with distinct geometric aspects of PPI binding sites, such as residue buriedness, side chain protrusion, and surface curvature [ 258 ]. Due to the diversity exhibited by PPIs, it is difficult to rely on any individual feature for binding site and hot spot prediction, and it has repeatedly been shown that the combination of several attributes has more success in providing adequate information for PPI surfaces [ 259 ]. Knowledge-based PPI binding site and hot spot prediction algorithms are exemplified in tools like ANCHOR [ 260 ], HomPPI [ 261 ], KFC2 (Knowledge-based FADE and Contacts) [ 262 , 263 , 264 ], HotPoint [ 265 ], FTMAP [ 266 ], MINERVA [ 267 ], and PredHS [ 268 ].…”

Section: Emerging In Silico Approaches For Ppi Drug Discoverymentioning

confidence: 99%

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

et al. 2018

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The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

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Section: Emerging In Silico Approaches For Ppi Drug Discoverymentioning

confidence: 99%

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

et al. 2018

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“…These parameters can be grouped into the following types: amino acid type, evolutionary profile, conservation score, surface area, solvation energy, and geometry [81]. When using the dataset compiled by Darnell et al [100], this method had a performance of 60.4% (measured by F-Measure) that corresponded to a recall of 78.1% and a precision of 49.5% [81]. This work is significant in that it does not require the complex; it can be used with only knowledge of the monomer.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Computational Prediction of Protein Hot Spot Residues

Morrow¹,

Zhang²

2012

CPD

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Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

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“… Grosdidier and Fernández-Recio (2008) have developed an energy-based (Docking) tool with a normalized interface propensity technique. Higa and Tozzi (2009) have developed a tool based on structural and evolutionary methods with the SVM technique. Rajamani et al (2004) has developed a tool based on sidechain ΔASA (accessible surface area) with the MD technique.…”

Section: In Silico Hotspot Prediction Toolsmentioning

confidence: 99%

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein–Protein Interactions

Hashemi

Zarei

Fath

et al. 2021

Front. Mol. Biosci.

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Large contact surfaces of protein–protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; “hotspot” identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

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Prediction of binding hot spot residues by using structural and evolutionary parameters

Cited by 20 publications

References 42 publications

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

Computational Prediction of Protein Hot Spot Residues

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein–Protein Interactions

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