Solvation Models and Computational Prediction of Orientations of Peptides and Proteins in Membranes

2013 ◽  
pp. 125-142 ◽  
Author(s):  
Andrei L. Lomize ◽  
Irina D. Pogozheva
Keyword(s):  
Computational Prediction ◽  
Solvation Models

Covalent-Fragment Screening of Brd4 Identifies a Ligandable Site Orthogonal to the Acetyl-Lysine Binding Sites

2019 ◽  
Author(s):  
Michael Olp ◽  
Daniel Sprague ◽  
Stefan Kathman ◽  
Ziyang Xu ◽  
Alexandar Statsyuk ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Binding Site ◽  
Binding Sites ◽  
Computational Prediction ◽  
Chemical Probes ◽  
Computational Docking ◽  
Fragment Screening ◽  
Covalent Inhibitors ◽  
Bet Protein ◽  
Proof Of Principle

<p>Brd4, a member of the bromodomain and extraterminal domain (BET) family, has emerged as a promising epigenetic target in cancer and inflammatory disorders. All reported BET family ligands bind within the bromodomain acetyl-lysine binding sites and competitively inhibit BET protein interaction with acetylated chromatin. Alternative chemical probes that act orthogonally to the highly-conserved acetyl-lysine binding sites may exhibit selectivity within the BET family and avoid recently reported toxicity in clinical trials of BET bromodomain inhibitors. Here, we report the first identification of a ligandable site on a bromodomain outside the acetyl-lysine binding site. Inspired by our computational prediction of hotspots adjacent to non-homologous cysteine residues within the <i>C</i>-terminal Brd4 bromodomain (Brd4-BD2), we performed a mid-throughput mass spectrometry screen to identify cysteine-reactive fragments that covalently and selectively modify Brd4. Subsequent mass spectrometry, NMR and computational docking analyses of electrophilic fragment hits revealed a novel ligandable site near Cys356 that is unique to Brd4 among all human bromodomains. This site is orthogonal to the Brd4-BD2 acetyl-lysine binding site as Cys356 modification did not impact binding of the pan-BET bromodomain inhibitor JQ1 in fluorescence polarization assays. Finally, we tethered covalent fragments to JQ1 and performed NanoBRET assays to provide proof of principle that this orthogonal site can be covalently targeted in intact human cells. Overall, we demonstrate the potential of targeting sites orthogonal to bromodomain acetyl-lysine binding sites to develop bivalent and covalent inhibitors that displace Brd4 from chromatin.</p>

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Computational Prediction of Protein Hot Spot Residues

2012 ◽  
Vol 18 (9) ◽  
pp. 1255-1265 ◽  
Author(s):  
John Kenneth Morrow ◽  
Shuxing Zhang
Keyword(s):  
Hot Spot ◽  
Computational Prediction

Solvation Models: Theory and Validation

2014 ◽  
Vol 20 (20) ◽  
pp. 3266-3280 ◽  
Author(s):  
Enrico Purisima ◽  
Traian Sulea
Keyword(s):  
Solvation Models

Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

Computational Approaches to Predict the Non-canonical DNAs

2019 ◽  
Vol 14 (6) ◽  
pp. 470-479 ◽  
Author(s):  
Nazia Parveen ◽  
Amen Shamim ◽  
Seunghee Cho ◽  
Kyeong Kyu Kim
Keyword(s):  
Computational Methods ◽  
Genetic Instability ◽  
Computational Prediction ◽  
Structure And Function ◽  
Sequence Motifs ◽  
Computational Approaches ◽  
Functional Roles ◽  
And Function ◽  
Genetic Events ◽  
Insight Into

Background: Although most nucleotides in the genome form canonical double-stranded B-DNA, many repeated sequences transiently present as non-canonical conformations (non-B DNA) such as triplexes, quadruplexes, Z-DNA, cruciforms, and slipped/hairpins. Those noncanonical DNAs (ncDNAs) are not only associated with many genetic events such as replication, transcription, and recombination, but are also related to the genetic instability that results in the predisposition to disease. Due to the crucial roles of ncDNAs in cellular and genetic functions, various computational methods have been implemented to predict sequence motifs that generate ncDNA. Objective: Here, we review strategies for the identification of ncDNA motifs across the whole genome, which is necessary for further understanding and investigation of the structure and function of ncDNAs. Conclusion: There is a great demand for computational prediction of non-canonical DNAs that play key functional roles in gene expression and genome biology. In this study, we review the currently available computational methods for predicting the non-canonical DNAs in the genome. Current studies not only provide an insight into the computational methods for predicting the secondary structures of DNA but also increase our understanding of the roles of non-canonical DNA in the genome.

A Computational Prediction of Conserved MicroRNA Targets of Ion Channels in Vertebrates

2013 ◽  
Vol 8 (1) ◽  
pp. 93-111
Author(s):  
Priyadarshan Kathirvel ◽  
Gopal Ramesh Kumar ◽  
Kavitha Sankaranarayanan
Keyword(s):  
Ion Channels ◽  
Computational Prediction ◽  
Microrna Targets
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