How long does it take to predict protein stability?
Moreover, the stability curve prediction of a target protein is very fast: it takes less than a minute. SCooP can thus potentially be applied on a structurome scale. This opens new perspectives of large-scale analyses of protein stability, which is of considerable interest for protein engineering.
Is the protstab method suitable for large scale prediction of protein stability?
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.
Can we predict protein stability changes for single amino acid mutations?
Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and s …
What are the factors that affect protein stability?
Moreover, the amino acid interactions and their delicate energetic balance are influenced by a wide series of factors such as the temperature, the pH, the ionic strength and sometimes even the protein concentration. The goal of accurately and rapidly predicting protein stability, and in particular thermal stability, is highly challenging.
How do you determine protein stability?
Methods of Determining Protein StabilityDetermining Protein Stability: Some of the Most Common Methods Used. … Differential Scanning Calorimetry (DSC) … Pulse-Chase Method. … Bleach-chase method. … Cycloheximide-chase method. … Circular Dichroism (CD) Spectroscopy. … Fluorescence-based Activity Assays.
How protein structure is predicted?
Currently, the main techniques used to determine protein 3D structure are X-ray crystallography and nuclear magnetic resonance (NMR). In X-ray crystallography the protein is crystallized and then using X-ray diffraction the structure of protein is determined.
What factors affect protein stability?
Many factors affect the process of protein folding, including conformational and compositional stability, cellular environment including temperature and pH, primary and secondary structure, solvation, hydrogen bonding, salt bridges, hydrophobic effects, van der Waals (vdW) forces, ligand binding, cofactor binding, ion …
What is meant by protein stability?
The term protein stability refers to the energy difference between the folded and unfolded state of the protein in solution. Remarkably, the free energy difference between these states is usually between 20 and 80 kJ/mol, which is of the magnitude of one to four hydrogen bonds.
Why is it difficult to predict the structure of a protein?
Another reason why protein structure prediction is so difficult is because a polypeptide is very flexible, with the ability to rotate in multiple ways at each amino acid, which means that the polypeptide is able to fold into a staggering number of different shapes.
What is protein structure prediction in bioinformatics?
Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design.
Which protein structure is most stable?
the most stable protein structure tertiary.the three dimensional structure of the protein is referred to the tertiary structure of he protein.in this structure, the molecules of the protein bend and twist in such a way that it achieves the maximum stability and has the lowest energy state.More items…•
What stabilizes protein structure?
Tertiary Structure Hydrogen bonding in the polypeptide chain and between amino acid “R” groups helps to stabilize protein structure by holding the protein in the shape established by the hydrophobic interactions.
How does pH affect protein stability?
Changing the pH disrupts the hydrogen bonds, and this changes the shape of the protein.
How do mutations affect protein?
Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein …
Why are predictions of positive and negative data different?
Correct predictions of positive and negative data have different meanings because the effects of mutations are not always detrimental to protein function. One of the purposes of predicting protein stability changes is to identify the mechanisms of structural stability change upon single amino acid mutation; another goal is to apply this knowledge to protein design to modify proteins into more stable and thermal-tolerant forms. Since it is equally important to understand the mechanisms underlying stabilizing and destabilizing mutations, we expect an integrated predictor to make correct predictions in both cases. Since the minority result could be the right answer, we want to prove that iStable 2.0, with training, would know right from wrong and not just pick the majority answer. Accuracy (Acc), sensitivity (Sn), specificity (Sp), and the Matthews correlation coefficient (MCC) were used to evaluate the predictive ability of each system, calculated as follows: Acc = TP + T N TP + F P + T N + F N Sp = TN TP + F N Sn = TP TP + F N and MCC = TP × T N – FN × F P TP + F N × TN + F P × TP + F P × TN + F N where TP, FP, FN and TN are the true positives, false positives, false negatives, and true negatives, respectively. Sn and Sp represent the ratio of true positives to the number of all correctly classified items and of true negatives to the number of all incorrectly classified items, respectively. Acc is the overall accuracy of prediction, and the MCC is a measure of the quality of the classifications, whose value may range between −1 (an inverse prediction) and +1 (a perfect prediction), with 0 denoting a random prediction .
What is integrated prediction?
Machine learning. 1. Introduction. When the amino acid of a protein is changed, it may affect the structural stability, hydrogen bonding, activity, etc., of the protein and then may affect protein function and may even cause disease , , . In protein engineering, drug design, and the optimization …
How does iStable 2.0 work?
iStable 2.0 successfully integrates sequence- and structure-based tools to improve the predictive performance of protein stability changes, which compare to various machine learning methods and prediction tools. In the evaluations of the training and test sets, it was found that these tools provide predicted results of protein stability using predicted ddG values and have high PCC and low MCC performance. According to our experimental results obtained from converting regression to classification, we found that training of both regression and classification models was necessary. In addition, there are some issues which should be considered when we adopt an integrated approach: 1) different input and output formats from different tools, 2) how to determine which tools should be integrated, 3) how to improve the performance of the integrated system, and 4) how to maintain system performance when the integration system fails. Majority Voting is a simple and intuitive integration method; this strategy is often used by biologists for many prediction tools. However, the predicted performance of iStable 2.0 using machine learning integration is better than the Majority Voting method because majority vote cannot consider the confidence score in the prediction results from different prediction tools. Our integration strategy only considers the execution time of the integrated tools but not the performance in order to complete the prediction of the integration calculation within a limited time, and from the feature analysis, the integration tools with low performance also provide contribution to the model. We additionally trained models that relied on the Stand-alone Module (SAM) and Sequence Coding Module (SCM). The integrated tools that cannot grasp the computing status are divided into an Online Server Module (OSM) so when access cannot be obtained by the integrated tools, the system performance will depend on SAM and SCM. iStable 2.0 is more effective at predicting point mutations between pH 6 ~ 8 than any integrated tools. However, each tool has its own advantages, such as a certain temperature, pH range, or protein type. Determining how to integrate the strengths of each tool into a model to enhance the performance will be a further improvement.
The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy Δ G ( T ).
Despite the many experimental and computational efforts that have been carried out over the past few decades, the quantitative understanding of protein stability as a function of the temperature remains a challenging objective.
2 Materials and methods
To train and validate our prediction model, we built four different datasets, whose construction is detailed and whose entries are listed in Supporting Information. These datasets are:
The prediction scores obtained with SCooP in leave-one-out cross validation are quite good, as shown in Table 2. The linear correlation coefficient r between predicted and experimental values of Δ H m, Δ C p and Tm are equal to 0.80, 0.83 and 0.72, respectively.
The SCooP method and webserver constitute a milestone in stability predictions, as it is the first bionformatics tool for the derivation of the full stability curve of a target protein as a function of the temperature, based on its experimental or modeled 3D structure.
Datasets used in this paper S [ Ref], S [ Temp], S [ NMR] , and S[Tenv]
The Belgian Fund for Scientific Research (FNRS) is acknowledged for financial support through a PDR research project (grant T.0100.16). FP is Postdoctoral researcher and MR Research Director at the FNRS.