scholarly journals PON-Sol2: Prediction of Effects of Variants on Protein Solubility

2021 ◽  
Vol 22 (15) ◽  
pp. 8027
Author(s):  
Yang Yang ◽  
Lianjie Zeng ◽  
Mauno Vihinen
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Solid Phase ◽  
Protein Solubility ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Correct Prediction ◽  
Blind Test ◽  
Solvent Interactions ◽  
Central Protein

Genetic variations have a multitude of effects on proteins. A substantial number of variations affect protein–solvent interactions, either aggregation or solubility. Aggregation is often related to structural alterations, whereas solubilizable proteins in the solid phase can be made again soluble by dilution. Solubility is a central protein property and when reduced can lead to diseases. We developed a prediction method, PON-Sol2, to identify amino acid substitutions that increase, decrease, or have no effect on the protein solubility. The method is a machine learning tool utilizing gradient boosting algorithm and was trained on a large dataset of variants with different outcomes after the selection of features among a large number of tested properties. The method is fast and has high performance. The normalized correct prediction rate for three states is 0.656, and the normalized GC2 score is 0.312 in 10-fold cross-validation. The corresponding numbers in the blind test were 0.545 and 0.157. The performance was superior in comparison to previous methods. The PON-Sol2 predictor is freely available. It can be used to predict the solubility effects of variants for any organism, even in large-scale projects.

scholarly journals ProTstab – predictor for cellular protein stability

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yang Yang ◽  
Xuesong Ding ◽  
Guanchen Zhu ◽  
Abhishek Niroula ◽  
Qiang Lv ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Sequence Data ◽  
Limited Proteolysis ◽  
Absolute Error ◽  
Cellular Protein ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Protein Chain ◽  
Blind Test

Abstract Background Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples. Results We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities. Conclusions The Pearson’s correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.

Parallel PPI Prediction Performance Study on HPC Platforms

2015 ◽  
Vol 24 (05) ◽  
pp. 1550074 ◽  
Author(s):  
Ali A. El-Moursy ◽  
Wael S. Afifi ◽  
Fadi N. Sibai ◽  
Salwa M. Nassar
Keyword(s):  
Protein Interactions ◽  
Execution Time ◽  
High Performance ◽  
Large Scale ◽  
Parallel Implementation ◽  
Prediction Method ◽  
Protein Protein Interactions ◽  
Performance Study ◽  
Ppi Prediction ◽  
Performance Computing

STRIKE is an algorithm which predicts protein–protein interactions (PPIs) and determines that proteins interact if they contain similar substrings of amino acids. Unlike other methods for PPI prediction, STRIKE is able to achieve reasonable improvement over the existing PPI prediction methods. Although its high accuracy as a PPI prediction method, STRIKE consumes a large execution time and hence it is considered to be a compute-intensive application. In this paper, we develop and implement a parallel STRIKE algorithm for high-performance computing (HPC) systems. Using a large-scale cluster, the execution time of the parallel implementation of this bioinformatics algorithm was reduced from about a week on a serial uniprocessor machine to about 16.5 h on 16 computing nodes, down to about 2 h on 128 parallel nodes. Communication overheads between nodes are thoroughly studied.

scholarly journals Network-based cancer gene relationship prediction method reveals perturbations in the cancer gene network

2020 ◽  
Author(s):  
Jiajun Qiu ◽  
Kui Chen ◽  
Chunlong Zhong ◽  
Sihao Zhu ◽  
Xiao Ma
Keyword(s):  
High Performance ◽  
Gene Network ◽  
Large Scale ◽  
Matthews Correlation Coefficient ◽  
Prediction Method ◽  
Gene Interaction ◽  
Cancer Gene ◽  
Hub Genes ◽  
Scale Perturbation ◽  
Relationship Network

AbstractThe landscape of the gene relationship/network (such as activation, expression, phosphorylation, and binding) in cancer is found different from the general (non-disease) situation, and gene network perturbations are supposed to be the main cause of cancer. Thus, it makes no sense to use a regular gene relationship prediction method to map the cancer gene network. Here, we established a novel prediction method that we dubbed network-based cancer gene relationship (NECARE), which achieved a high performance with a Matthews correlation coefficient (MCC) = 0.71±0.01 and an F1 = 89±0.7%. Then, we investigated the cancer interactome atlas and revealed a large-scale perturbation in the gene network in cancer using NECARE. We found 2287 genes, which were named cancer hub genes, that were enriched with gene interaction perturbations, and over 56% of cancer treatment-related genes were hub genes. We further assessed the association of hub genes with the prognosis of 32 types of cancers and found that hub genes were significantly related to the cancer outcomes. Furthermore, the mutations occurring on residues that bind to macromolecules were overrepresented at cancer hub genes. By coimmunoprecipitation (co-IP), we confirmed that the NECARE prediction method was highly reliable and was 90% accurate. NECARE is available at: https://github.com/JiajunQiu/NECARE.

The Structure and Properties of MoSi2 Thin Film in Mos Process

1980 ◽  
Vol 38 ◽  
pp. 326-327
Author(s):  
C.K. Wu ◽  
P. Chang ◽  
N. Godinho
Keyword(s):  
Thin Film ◽  
Integrated Circuits ◽  
High Performance ◽  
Large Scale ◽  
Process Development ◽  
Structure And Properties ◽  
Metal Silicides ◽  
High Oxidation ◽  
Important Approach ◽  
High Oxidation Resistance

Recently, the use of refractory metal silicides as low resistivity, high temperature and high oxidation resistance gate materials in large scale integrated circuits (LSI) has become an important approach in advanced MOS process development (1). This research is a systematic study on the structure and properties of molybdenum silicide thin film and its applicability to high performance LSI fabrication.

RECOMMENDATIONS FOR THE CHOICE OF MILKING INSTALLATIONS IN LOOSE HOUSING SYSTEMS OF COWS

2020 ◽  
Author(s):  
В.В. ГОРДЕЕВ ◽  
В.Е. ХАЗАНОВ
Keyword(s):  
Dairy Cows ◽  
High Performance ◽  
Large Scale ◽  
Dairy Farms ◽  
Economic Indicators ◽  
Technical Level ◽  
Housing Systems ◽  
Working Shift ◽  
Technical And Economic Indicators

При выборе типа доильной установки и ее размера необходимо учитывать максимальное планируемое поголовье дойных коров и размер технологической группы, кратность и время одного доения, продолжительность рабочей смены дояров. Анализ технико-экономических показателей наиболее распространенных на сегодняшний день типов доильных установок одинакового технического уровня свидетельствует, что наилучшие удельные показатели имеет установка типа «Карусель» (1), а установка типа «Елочка» (2) требует более высоких затрат труда и средств. Установка «Параллель» (3) занимает промежуточное положение. Из анализа пропускной способности и количества необходимых операторов: установка 2 рекомендована для ферм с поголовьем дойного стада до 600 голов, 3 — не более 1200 дойных коров, 1 — более 1200 дойных коров. «Карусель» — наиболее рациональный, высокопроизводительный, легко автоматизируемый и, следовательно, перспективный способ доения в залах, особенно для крупных молочных ферм. The choice of the proper type and size of milking installations needs to take into account the maximum planned number of dairy cows, the size of a technological group, the number of milkings per day, and the duration of one milking and the operator's working shift. The analysis of technical and economic indicators of currently most common types of milking machines of the same technical level revealed that the Carousel installation had the best specific indicators while the Herringbone installation featured higher labour inputs and cash costs. The Parallel installation was found somewhere in between. In terms of the throughput and the required number of operators Herringbone is recommended for farms with up to 600 dairy cows, Parallel — below 1200 dairy cows, Carousel — above 1200 dairy cows. Carousel was found the most practical, high-performance, easily automated and, therefore, promising milking system for milking parlours, especially on the large-scale dairy farms.

Sign in / Sign up

Export Citation Format

Share Document