scholarly journals Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

2017 ◽  
pp. 221-236 ◽  
Author(s):  
Alexander Goncearenco ◽  
Minghui Li ◽  
Franco L. Simonetti ◽  
Benjamin A. Shoemaker ◽  
Anna R. Panchenko
Keyword(s):  
Cancer Therapy ◽  
Protein Interactions ◽  
In Silico ◽  
Drug Targets ◽  
Protein Protein Interactions ◽  
Anti Cancer

scholarly journals New machine learning and physics-based scoring functions for drug discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Isabella A. Guedes ◽  
André M. S. Barreto ◽  
Diogo Marinho ◽  
Eduardo Krempser ◽  
Mélaine A. Kuenemann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Protein Interactions ◽  
In Silico ◽  
Drug Targets ◽  
Support Vector ◽  
Scoring Functions ◽  
Protein Protein Interactions ◽  
Energy Prediction ◽  
Original Class

AbstractScoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

scholarly journals In Silico and In Vitro Studies on the Protein-Protein Interactions between Brugia malayi Immunomodulatory Protein Calreticulin and Human C1q

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106413 ◽  
Author(s):  
Sunita Yadav ◽  
Smita Gupta ◽  
Chandrabose Selvaraj ◽  
Pawan Kumar Doharey ◽  
Anita Verma ◽  
...  
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Brugia Malayi ◽  
In Vitro Studies ◽  
Protein Protein Interactions ◽  
Immunomodulatory Protein

scholarly journals Structural Model of the hUbA1-UbcH10 Quaternary Complex: In Silico and Experimental Analysis of the Protein-Protein Interactions between E1, E2 and Ubiquitin

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e112082 ◽  
Author(s):  
Stefania Correale ◽  
Ivan de Paola ◽  
Carmine Marco Morgillo ◽  
Antonella Federico ◽  
Laura Zaccaro ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Experimental Analysis ◽  
In Silico ◽  
Structural Model ◽  
Protein Protein Interactions ◽  
Quaternary Complex

An integrated strategy for the discovery of drug targets by the analysis of protein–protein interactions

2004 ◽  
Vol 238 (2) ◽  
pp. 119-130 ◽  
Author(s):  
John M. Peltier ◽  
Srdjan Askovic ◽  
Robert R. Becklin ◽  
Cindy Lou Chepanoske ◽  
Yew-Seng J. Ho ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Protein Protein Interactions ◽  
Integrated Strategy

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

2016 ◽  
pp. 249-277
Author(s):  
Sailu Sarvagalla ◽  
Mohane Selvaraj Coumar
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Rational Design ◽  
Kinase Activity ◽  
Hot Spot ◽  
Binding Pocket ◽  
Atp Binding ◽  
Protein Protein Interactions ◽  
Structure Based Drug Design ◽  
Atp Binding Site

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

scholarly journals Protein Dynamics in F-like Bacterial Conjugation

Biomedicines ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 362
Author(s):  
Nicholas Bragagnolo ◽  
Christina Rodriguez ◽  
Naveed Samari-Kermani ◽  
Alice Fours ◽  
Mahboubeh Korouzhdehi ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Protein Interactions ◽  
Drug Targets ◽  
Dynamic Models ◽  
Structural Information ◽  
Imaging Techniques ◽  
Antibiotic Resistance Genes ◽  
Protein Protein Interactions ◽  
Secretion Systems ◽  
Type Iv

Efficient in silico development of novel antibiotics requires high-resolution, dynamic models of drug targets. As conjugation is considered the prominent contributor to the spread of antibiotic resistance genes, targeted drug design to disrupt vital components of conjugative systems has been proposed to lessen the proliferation of bacterial antibiotic resistance. Advancements in structural imaging techniques of large macromolecular complexes has accelerated the discovery of novel protein-protein interactions in bacterial type IV secretion systems (T4SS). The known structural information regarding the F-like T4SS components and complexes has been summarized in the following review, revealing a complex network of protein-protein interactions involving domains with varying degrees of disorder. Structural predictions were performed to provide insight on the dynamicity of proteins within the F plasmid conjugative system that lack structural information.

Targeting protein–protein interactions for cancer therapy

2005 ◽  
Vol 83 (12) ◽  
pp. 955-963 ◽  
Author(s):  
David C. Fry ◽  
Lyubomir T. Vassilev
Keyword(s):  
Cancer Therapy ◽  
Protein Interactions ◽  
Protein Protein Interactions
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