funtrp: identifying protein positions for variation driven functional tuning

Abstract Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. An in-depth understanding of sequence changes is also fundamental for synthetic protein design and stability assessments. However, the variant effect predictor performance gain observed in recent years has not kept up with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene and protein features for modeling variant effects, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the variation observable in vivo is arguably weaker in its impact, thus requiring evaluation at a higher level of resolution. Here, we describe functionNeutral/Toggle/Rheostatpredictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types can improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

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fuNTRp: Identifying protein positions for variation driven functional tuning

10.1101/578757 ◽

2019 ◽

Cited By ~ 3

Author(s):

Maximilian Miller ◽

Daniel Vitale ◽

Peter Kahn ◽

Burkhard Rost ◽

Yana Bromberg

Keyword(s):

Protein Design ◽

Computational Method ◽

Protein Activity ◽

Protein Functions ◽

Disease Associations ◽

Position Type ◽

Variant Effect ◽

Evolution Understanding ◽

The Impact

ABSTRACTEvaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. Understanding corresponding sequence changes is also fundamental for synthetic protein design and stability assessments. However, the performance gain of variant effect predictors observed in recent years is not in line with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene/protein features for modeling variant effect, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the in vivo observable variation is arguably weaker in its impact and, thus, requires evaluation at a higher level of resolution. Here we describe function Neutral/Toggle/Rheostat predictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types reflect experimentally determined functional effects and can thus improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

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Deep-belief network for predicting potential miRNA-disease associations

Briefings in Bioinformatics ◽

10.1093/bib/bbaa186 ◽

2020 ◽

Author(s):

Xing Chen ◽

Tian-Hao Li ◽

Yan Zhao ◽

Chun-Chun Wang ◽

Chi-Chi Zhu

Keyword(s):

Computational Models ◽

Cross Validation ◽

Biological Data ◽

Deep Belief Network ◽

Computational Method ◽

Restricted Boltzmann Machines ◽

Belief Network ◽

Disease Associations ◽

Leave One Out ◽

The Impact

Abstract MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.

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PremPS: Predicting the impact of missense mutations on protein stability

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008543 ◽

2020 ◽

Vol 16 (12) ◽

pp. e1008543

Author(s):

Yuting Chen ◽

Haoyu Lu ◽

Ning Zhang ◽

Zefeng Zhu ◽

Shuqin Wang ◽

...

Keyword(s):

Protein Stability ◽

Protein Design ◽

Large Scale ◽

Molecular Mechanisms ◽

Predictive Performance ◽

Computational Method ◽

Single Mutation ◽

Missense Mutations ◽

Benchmark Datasets ◽

The Impact

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

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Exploring absent protein function in yeast: assaying post translational modification and human genetic variation

Microbial Cell ◽

10.15698/mic2021.08.756 ◽

2021 ◽

Vol 8 (8) ◽

pp. 164-183

Author(s):

Christina S. Moesslacher ◽

Johanna M. Kohlmayr ◽

Ulrich Stelzl

Keyword(s):

Protein Function ◽

Model Organism ◽

Global Scale ◽

Test System ◽

Post Translational Modification ◽

Protein Activity ◽

Protein Modifications ◽

In Vivo Test ◽

The Impact

Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.

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Multiscale computational modeling of cancer growth using features derived from microCT images

Scientific Reports ◽

10.1038/s41598-021-97966-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Hossein Zangooei ◽

Ryan Margolis ◽

Kenneth Hoyt

Keyword(s):

Computational Model ◽

Initial Conditions ◽

Growth Patterns ◽

Computational Method ◽

Rectangular Lattice ◽

Microvascular Network ◽

Network Growth ◽

Experimental Findings ◽

The Impact

AbstractAdvances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

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Experimental Study of the Impact of Alisma plantago-aquatica Secretions on Pathogenic Bacteria

Agricultural science and practice ◽

10.15407/agrisp1.03.003 ◽

2014 ◽

Vol 1 (3) ◽

pp. 3-7

Author(s):

O. Zhukorskyy ◽

O. Hulay

Keyword(s):

Pathogenic Bacteria ◽

Initial Density ◽

Biologically Active ◽

Erysipelothrix Rhusiopathiae ◽

Natural Focus ◽

Test Species ◽

Popula Tions ◽

And Control ◽

The Impact

Aim. To estimate the impact of in vivo secretions of water plantain (Alisma plantago-aquatica) on the popula- tions of pathogenic bacteria Erysipelothrix rhusiopathiae. Methods. The plants were isolated from their natural conditions, the roots were washed from the substrate residues and cultivated in laboratory conditions for 10 days to heal the damage. Then the water was changed; seven days later the selected samples were sterilized using fi lters with 0.2 μm pore diameter. The dilution of water plantain root diffusates in the experimental samples was 1:10–1:10,000. The initial density of E. rhusiopathiae bacteria populations was the same for both experimental and control samples. The estimation of the results was conducted 48 hours later. Results. When the dilution of root diffusates was 1:10, the density of erysipelothrixes in the experimental samples was 11.26 times higher than that of the control, on average, the dilution of 1:100 − 6.16 times higher, 1:1000 – 3.22 times higher, 1:10,000 – 1.81 times higher, respectively. Conclusions. The plants of A. plantago-aquatica species are capable of affecting the populations of E. rhusiopathiae pathogenic bacteria via the secretion of biologically active substances into the environment. The consequences of this interaction are positive for the abovementioned bacteria, which is demon- strated by the increase in the density of their populations in the experiment compared to the control. The intensity of the stimulating effect on the populations of E. rhusiopathiae in the root diffusates of A. plantago-aquatica is re- ciprocally dependent on the degree of their dilution. The investigated impact of water plantain on erysipelothrixes should be related to the topical type of biocenotic connections, the formation of which between the test species in the ecosystems might promote maintaining the potential of natural focus of rabies. Keywords: Alisma plantago-aquatica, in vivo secretions, Erysipelothrix rhusiopathiae, population density, topical type of connections.

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Curcumin-containing Silver Nanoparticles prevent carbon tetrachlorideinduced hepatotoxicity in mice

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666201211100830 ◽

2020 ◽

Vol 23 ◽

Author(s):

Hossam Ebaid ◽

Mohamed Habila ◽

Iftekhar Hassan ◽

Jameel Al-Tamimi ◽

Mohamed S. Omar ◽

...

Keyword(s):

Dna Damage ◽

Silver Nanoparticles ◽

Nuclear Dna ◽

Liquid Paraffin ◽

Tail Length ◽

Hepatic Tissue ◽

Tissue Destruction ◽

Group 2 ◽

The Impact

Background: Hepatotoxicity remains an important clinical challenge. Hepatotoxicity observed in response to toxins and hazardous chemicals may be alleviated by delivery of the curcumin in silver nanoparticles (AgNPs-curcumin). In this study, we examined the impact of AgNPs-curcumin in a mouse model of carbon tetrachloride (CCl4)-induced hepatic injury. Methods: Male C57BL/6 mice were divided into three groups (n=8 per group). Mice in group 1 were treated with vehicle control alone, while mice in Group 2 received a single intraperitoneal injection of 1 ml/kg CCl4 in liquid paraffin (1:1 v/v). Mice in group 3 were treated with 2.5 mg/kg AgNPs-curcumin twice per week for three weeks after the CCl4 challenge. Results: Administration of CCL4 resulted in oxidative dysregulation, including significant reductions in reduced glutathione and concomitant elevations in the level of malondialdehyde (MDA). CCL4 challenge also resulted in elevated levels of serum aspartate transaminase (AST) and alanine transaminase (ALT); these findings were associated with the destruction of hepatic tissues. Treatment with AgNPs-curcumin prevented oxidative imbalance, hepatic dysfunction, and tissue destruction. A comet assay revealed that CCl4 challenge resulted in significant DNA damage as documented by a 70% increase in nuclear DNA tail-length; treatment with AgNPs-curcumin inhibited the CCL4-mediated increase in nuclear DNA tail-length by 34%. Conclusion: Administration of AgNPs-curcumin resulted in significant antioxidant activity in vivo. This agent has the potential to prevent the hepatic tissue destruction and DNA damage that results from direct exposure to CCL4.

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Approach in improving potency and selectivity of kinase inhibitors: Allosteric kinase inhibitors

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1389557521666201222144355 ◽

2020 ◽

Vol 21 ◽

Author(s):

Shangfei Wei ◽

Tianming Zhao ◽

Jie Wang ◽

Xin Zhai

Keyword(s):

Protein Interactions ◽

Kinase Inhibitors ◽

Binding Modes ◽

Allosteric Inhibitors ◽

Wide Range ◽

Allosteric Sites ◽

Protein Functions ◽

Regulate Protein

: Allostery is an efficient and particular regulatory mechanism to regulate protein functions. Different from conserved orthosteric sites, allosteric sites have distinctive functional mechanism to form the complex regulatory network. In drug discovery, kinase inhibitors targeting the allosteric pockets have received extensive attention for the advantages of high selectivity and low toxicity. The approval of trametinib as the first allosteric inhibitor validated that allosteric inhibitors could be used as effective therapeutic drugs for treatment of diseases. To date, a wide range of allosteric inhibitors have been identified. In this perspective, we outline different binding modes and potential advantages of allosteric inhibitors. In the meantime, the research processes of typical and novel allosteric inhibitors are described briefly in terms of structureactivity relationships, ligand-protein interactions and in vitro and in vivo activity. Additionally, challenges as well as opportunities are presented.

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Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Current Gene Therapy ◽

10.2174/1566523219666190917155959 ◽

2019 ◽

Vol 19 (4) ◽

pp. 232-241 ◽

Cited By ~ 5

Author(s):

Xuegong Chen ◽

Wanwan Shi ◽

Lei Deng

Keyword(s):

Protein Interactions ◽

Experimental Studies ◽

Treatment Strategies ◽

Computational Method ◽

Biological Information ◽

Support Vector ◽

Protein Protein Interactions ◽

Efficient Treatment ◽

Disease Associations ◽

Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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