Patterns for addition to C59BHElectronic supplementary information (ESI) available: Heats of formation and single-point energies for heterofullerenes. See http://www.rsc.org/suppdata/cp/b3/b300458a/

2003 ◽  
Vol 5 (9) ◽  
pp. 1739-1743 ◽  
Author(s):  
Yunxiao Liang ◽  
Guichang Wang ◽  
Xiufang Xu ◽  
Zunsheng Cai ◽  
Yinming Pan ◽  
...  
Keyword(s):  
Single Point ◽  
Heats Of Formation ◽  
Supplementary Information

AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation

2019 ◽  
Author(s):  
Puneet Rawat ◽  
R Prabakaran ◽  
Sandeep Kumar ◽  
M Michael Gromiha
Keyword(s):  
Mathematical Model ◽  
Protein Aggregation ◽  
Correlation Coefficient ◽  
Single Point ◽  
Three Dimensional ◽  
Point Mutations ◽  
Supplementary Information ◽  
P Value ◽  
Aggregation Kinetics ◽  
Aggregation Rate

Abstract Motivation Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known for their mechanistic roles in protein aggregation do not correlate well with the aggregation kinetics. This limits the practical utility of predictive algorithms. Results We analyzed experimental data on 183 unique single point mutations that lead to change in aggregation rates for 23 polypeptides and proteins. Our initial mathematical model obtained a correlation coefficient of 0.43 between predicted and experimental change in aggregation rate upon mutation (P-value <0.0001). However, when the dataset was classified based on protein length and conformation at the mutation sites, the average correlation coefficient almost doubled to 0.82 (range: 0.74–0.87; P-value <0.0001). We observed that distinct sequence and structure-based properties determine protein aggregation kinetics in each class. In conclusion, the protein aggregation kinetics are impacted by local factors and not by global ones, such as overall three-dimensional protein fold, or mechanistic factors such as the presence of aggregation-prone regions. Availability and implementation The web server is available at http://www.iitm.ac.in/bioinfo/aggrerate-pred/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CScape-somatic: distinguishing driver and passenger point mutations in the cancer genome

2020 ◽  
Vol 36 (12) ◽  
pp. 3637-3644 ◽  
Author(s):  
Mark F Rogers ◽  
Tom R Gaunt ◽  
Colin Campbell
Keyword(s):  
Human Cancer ◽  
Single Point ◽  
Point Mutations ◽  
Cancer Genome ◽  
Alternative Methods ◽  
Supplementary Information ◽  
Driver Mutations ◽  
Cancer Data ◽  
Coding Regions ◽  
Single Point Mutations

Abstract Motivation Next-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations. Results For somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions. Availability and implementation Predictions and software are available at http://CScape-somatic.biocompute.org.uk/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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