Computational Alanine Scanning To Probe Protein−Protein Interactions:  A Novel Approach To Evaluate Binding Free Energies

1999 ◽  
Vol 121 (36) ◽  
pp. 8133-8143 ◽  
Author(s):  
Irina Massova ◽  
Peter A. Kollman
Keyword(s):  
Protein Interactions ◽  
Free Energies ◽  
Protein Protein Interactions ◽  
Alanine Scanning ◽  
Novel Approach ◽  
Computational Alanine Scanning ◽  
Binding Free Energies

Computational Alanine Scanning with Interaction Entropy for Protein–Ligand Binding Free Energies

2018 ◽  
Vol 14 (3) ◽  
pp. 1772-1780 ◽  
Author(s):  
Xiao Liu ◽  
Long Peng ◽  
Yifan Zhou ◽  
Youzhi Zhang ◽  
John Z. H. Zhang
Keyword(s):  
Ligand Binding ◽  
Free Energies ◽  
Alanine Scanning ◽  
Computational Alanine Scanning ◽  
Binding Free Energies

Accounting for conformational entropy in predicting binding free energies of protein-protein interactions

2010 ◽  
Vol 79 (2) ◽  
pp. 444-462 ◽  
Author(s):  
Hetunandan Kamisetty ◽  
Arvind Ramanathan ◽  
Chris Bailey-Kellogg ◽  
Christopher James Langmead
Keyword(s):  
Protein Interactions ◽  
Free Energies ◽  
Protein Protein Interactions ◽  
Conformational Entropy ◽  
Binding Free Energies

scholarly journals PPCheck: A Webserver for the Quantitative Analysis of Protein-Protein Interfaces and Prediction of Residue Hotspots

2015 ◽  
Vol 9 ◽  
pp. BBI.S25928 ◽  
Author(s):  
Anshul Sukhwal ◽  
Ramanathan Sowdhamini
Keyword(s):  
Protein Interactions ◽  
Test Cases ◽  
Protein Interface ◽  
Protein Protein Interactions ◽  
Alanine Scanning ◽  
Conserved Residues ◽  
Protein Interfaces ◽  
Computational Alanine Scanning ◽  
Key Residues ◽  
Better Than

Background Modeling protein-protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein-protein interfaces, which contribute more in stabilizing a protein-protein interface. Results We had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate ‘native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning. Conclusions PPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein-protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking

2016 ◽  
Vol 18 (32) ◽  
pp. 22129-22139 ◽  
Author(s):  
Fu Chen ◽  
Hui Liu ◽  
Huiyong Sun ◽  
Peichen Pan ◽  
Youyong Li ◽  
...  
Keyword(s):  
Protein Binding ◽  
Protein Interactions ◽  
Protein Docking ◽  
Biological Processes ◽  
Free Energies ◽  
Protein Protein Interactions ◽  
Binding Free Energies

Understanding protein–protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance.

scholarly journals BAlaS: fast, interactive and accessible computational alanine-scanning using BudeAlaScan

2020 ◽  
Vol 36 (9) ◽  
pp. 2917-2919 ◽  
Author(s):  
Christopher W Wood ◽  
Amaurys A Ibarra ◽  
Gail J Bartlett ◽  
Andrew J Wilson ◽  
Derek N Woolfson ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Interface Design ◽  
Web Application ◽  
Side Chain ◽  
Protein Interface ◽  
Protein Protein Interactions ◽  
Alanine Scanning Mutagenesis ◽  
Alanine Scanning ◽  
Computational Alanine Scanning

Abstract Motivation In experimental protein engineering, alanine-scanning mutagenesis involves the replacement of selected residues with alanine to determine the energetic contribution of each side chain to forming an interaction. For example, it is often used to study protein–protein interactions. However, such experiments can be time-consuming and costly, which has led to the development of programmes for performing computational alanine-scanning mutagenesis (CASM) to guide experiments. While programmes are available for this, there is a need for a real-time web application that is accessible to non-expert users. Results Here, we present BAlaS, an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design. Availability and implementation BAlaS works on all modern browsers and is available through the following website: https://balas.app. The project is open source, distributed using an MIT license and is available on GitHub (https://github.com/wells-wood-research/balas).

Protein Interactions for Functional Genomics

2012 ◽  
Vol 3 (4) ◽  
pp. 15-30
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Protein Interactions ◽  
Biological Networks ◽  
State Of The Art ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

scholarly journals Virus–Host Protein–Protein Interactions between Human Papillomavirus 16 E6 A1 and D2/D3 Sub-Lineages: Variances and Similarities

2020 ◽  
Vol 21 (21) ◽  
pp. 7980
Author(s):  
Guillem Dayer ◽  
Mehran L. Masoom ◽  
Melissa Togtema ◽  
Ingeborg Zehbe
Keyword(s):  
Human Papillomavirus ◽  
Asian American ◽  
Protein Interactions ◽  
Host Protein ◽  
Cellular Proteins ◽  
Loss Of Function ◽  
Protein Protein Interactions ◽  
Human Papillomavirus 16 ◽  
Novel Approach ◽  
Causative Agents

High-risk strains of human papillomavirus are causative agents for cervical and other mucosal cancers, with type 16 being the most frequent. Compared to the European Prototype (EP; A1), the Asian-American (AA; D2/D3) sub-lineage seems to have increased abilities to promote carcinogenesis. Here, we studied protein–protein interactions (PPIs) between host proteins and sub-lineages of the key transforming E6 protein. We transduced human keratinocyte with EP or AA E6 genes and co-immunoprecipitated E6 proteins along with interacting cellular proteins to detect virus–host binding partners. AAE6 and EPE6 may have unique PPIs with host cellular proteins, conferring gain or loss of function and resulting in varied abilities to promote carcinogenesis. Using liquid chromatography-mass spectrometry and stringent interactor selection criteria based on the number of peptides, we identified 25 candidates: 6 unique to AAE6 and EPE6, along with 13 E6 targets common to both. A novel approach based on pathway selection discovered 171 target proteins: 90 unique AAE6 and 61 unique EPE6 along with 20 common E6 targets. Interpretations were made using databases, such as UniProt, BioGRID, and Reactome. Detected E6 targets were differentially implicated in important hallmarks of cancer: deregulating Notch signaling, energetics and hypoxia, DNA replication and repair, and immune response.

Multidimensional Integration Analysis of Autophagy-related Modules in Colorectal Cancer

2019 ◽  
Vol 16 (4) ◽  
pp. 340-346
Author(s):  
Yang Zhang ◽  
Zheng Zhang ◽  
Dong Wang ◽  
Jianzhen Xu ◽  
Yanhui Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Protein Interactions ◽  
Colon Adenocarcinoma ◽  
Undifferentiated Carcinoma ◽  
Protein Protein Interactions ◽  
Integration Strategy ◽  
Novel Approach ◽  
Multidimensional Integration ◽  
Regulation Network ◽  
Integration Analysis

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract occurring in the colon, which mainly divided into adenocarcinoma, mucinous adenocarcinoma, and undifferentiated carcinoma. However, autophagy is related to the occurrence and development of various kinds of human diseases such as cancer. There is little research on the relationship between CRC and autophagy. Hence, we performed multidimensional integration analysis to systematically explore potential relationship between autophagy and CRC. Based on gene expression datasets of colon adenocarcinoma (COAD) and protein-protein interactions (PPIs), we first identified 12 autophagy-related modules in COAD using WGCNA. Then, 9 module pairs which with significantly crosstalk were deciphered, a total of 6 functional modules. Autophagy-related genes in these modules were closely related with CRC, emphasizing that the important role of autophagy-related genes in CRC, including PPP2CA and EIF4E, etc. In addition to, by integrating transcription factor (TF)-target and RNA-associated interactions, a regulation network was constructed, in which 42 TFs (including SMAD3 and TP53, etc.) and 20 miRNAs (including miR-20 and miR-30a, etc.) were identified as pivot regulators. Pivot TFs were mainly involved in cell cycle, cell proliferation and pathways in cancer. And pivot miRNAs were demonstrated associated with CRC. It suggests that these pivot regulators might be have an effect on the development of CRC by regulating autophagy. In a word, our results suggested that multidimensional integration strategy provides a novel approach to discover potential relationships between autophagy and CRC, and further improves our understanding of autophagy and tumor in human.

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