Correlation of Local Changes in the Temperature-Dependent Conformational Flexibility of Thioredoxins with Their Thermostability

For the development of a method capable of predicting single point mutations substantially affecting protein thermostability, we studied the effect of the E85R and R82E mutations on the thermostability of thioredoxins from Escherichia coli (Trx) andBacillus acidocaldarius (BacTrx), respectively. The basic method of investigation was the molecular dynamics simulation of 3D protein models in an explicit solvent at different temperatures (300 and 373 K). Some thermolabile regions in Trx, BacTrx, and their mutants were revealed by analyzing the temperature effect on the molecular dynamics of the protein molecule. The effect of single point mutations on the temperature changes of the protein conformation flexibility in several thermolabile regions was found. The results of the simulations are in accord with experimental data indicating that the mutation E85R increases Trx thermostability, whereas the mutation R82E decreases BacTrx thermostability. The thermostability of these proteins was revealed to depend on ionic interactions between the thermolabile regions. The single point mutations change the parameters of these interactions and make them more favorable in the E85R-Trx mutant and less favorable in the R82E-BacTrx mutant.     

PROTS: a fragment based protein thermo-stability potential

Designing proteins with enhanced thermo-stability has been a main focus of protein engineering because of its theoretical and practical significance. Despite extensive studies in the past years, a general strategy for stabilizing proteins still remains elusive. Thus effective and robust computational algorithms for designing thermo-stable proteins are in critical demand. Here we report PROTS, a sequential and structural four-residue fragment based protein thermo-stability potential. PROTS is derived from a nonredundant representative collection of thousands of thermophilic and mesophilic protein structures and a large set of point mutations with experimentally determined changes of melting temperatures. To the best of our knowledge, PROTS is the first protein stability predictor based on integrated analysis and mining of these two types of data. Besides conventional cross validation and blind testing, we introduce hypothetical reverse mutations as a means of testing the robustness of protein thermo-stability predictors. In all tests, PROTS demonstrates the ability to reliably predict mutation induced thermo-stability changes as well as classify thermophilic and mesophilic proteins. In addition, this white-box predictor allows easy interpretation of the factors that influence mutation induced protein stability changes at the residue level.     

iSEE: Interface structure,evolution, and energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.     

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.     

The influence of local changes in the temperature-dependent conformational mobility of thioredoxins on their thermostability

For the development of a method for the prediction of single point mutations substantially affecting protein thermostability, we studied the effect of the E85R and R82E mutations on the thermostability of thioredoxins from Escherichia coli (Trx) and Bacillus acidocaldarius (BacTrx), respectively. The basic method of investigation was the molecular dynamics simulation of 3D protein models in a particular solvent at different temperatures (300 and 373 K). Some thermolabile regions in Trx, BacTrx, and their mutants were revealed by analyzing the temperature effect on the molecular dynamics of the protein molecule. The effect of single point mutations on the temperature changes of the protein conformation mobility in several thermolabile regions was found. The results of the calculations are in accord with the experimental data indicating that the mutation E85R increases Trx thermostability, whereas the mutation R82E decreases BacTrx thermostability. The thermostability of these proteins was revealed to depend on ionic interactions between the thermolabile regions. The single point mutations change the parameters of these interactions and make them more favorable in the E85R-Trx mutant and less favorable in the R82E-BacTrx mutant. The English version of the paper: Russian Journal of Bioorganic Chemistry, 2004, vol. 30, no. 5; see also http: // www.maik.ru.     

Neighborhood properties are important determinants of temperature sensitive mutations

Temperature-sensitive (TS) mutants are powerful tools to study gene function in vivo. These mutants exhibit wild-type activity at permissive temperatures and reduced activity at restrictive temperatures. Although random mutagenesis can be used to generate TS mutants, the procedure is laborious and unfeasible in multicellular organisms. Further, the underlying molecular mechanisms of the TS phenotype are poorly understood. To elucidate TS mechanisms, we used a machine learning method-logistic regression-to investigate a large number of sequence and structure features. We developed and tested 133 features, describing properties of either the mutation site or the mutation site neighborhood. We defined three types of neighborhood using sequence distance, Euclidean distance, and topological distance. We discovered that neighborhood features outperformed mutation site features in predicting TS mutations. The most predictive features suggest that TS mutations tend to occur at buried and rigid residues, and are located at conserved protein domains. The environment of a buried residue often determines the overall structural stability of a protein, thus may lead to reversible activity change upon temperature switch. We developed TS prediction models based on logistic regression and the Lasso regularized procedure. Through a ten-fold cross-validation, we obtained the area under the curve of 0.91 for the model using both sequence and structure features. Testing on independent datasets suggested that the model predicted TS mutations with a 50% precision. In summary, our study elucidated the molecular basis of TS mutants and suggested the importance of neighborhood properties in determining TS mutations. We further developed models to predict TS mutations derived from single amino acid substitutions. In this way, TS mutants can be efficiently obtained through experimentally introducing the predicted mutations.     

PremPS: Predicting the impact of missense mutations on protein stability

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.     

An assessment of the uses of homologous interactions

MOTIVATION: Protein-protein interactions have proved to be a valuable starting point for understanding the inner workings of the cell. Computational methodologies have been built which both predict interactions and use interaction datasets in order to predict other protein features. Such methods require gold standard positive (GSP) and negative (GSN) interaction sets. Here we examine and demonstrate the usefulness of homologous interactions in predicting good quality positive and negative interaction datasets. RESULTS: We generate GSP interaction sets as subsets from experimental data using only interaction and sequence information. We can therefore produce sets for several species (many of which at present have no identified GSPs). Comprehensive error rate testing demonstrates the power of the method. We also show how the use of our datasets significantly improves the predictive power of algorithms for interaction prediction and function prediction. Furthermore, we generate GSN interaction sets for yeast and examine the use of homology along with other protein properties such as localization, expression and function. Using a novel method to assess the accuracy of a negative interaction set, we find that the best single selector for negative interactions is a lack of co-function. However, an integrated method using all the characteristics shows significant improvement over any current method for identifying GSN interactions. The nature of homologous interactions is also examined and we demonstrate that interologs are found more commonly within species than across species. CONCLUSION: GSP sets built using our homologous verification method are demonstrably better than standard sets in terms of predictive ability. We can build such GSP sets for several species. When generating GSNs we show a combination of protein features and lack of homologous interactions gives the highest quality interaction sets. AVAILABILITY: GSP and GSN datasets for all the studied species can be downloaded from http://www.stats.ox.ac.uk/~deane/HPIV.     

DBSI: DNA-binding site identifier

In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.     

Application of Rigidity Theory to the Thermostabilization of Lipase A from Bacillus subtilis

Protein thermostability is a crucial factor for biotechnological enzyme applications. Protein engineering studies aimed at improving thermostability have successfully applied both directed evolution and rational design. However, for rational approaches, the major challenge remains the prediction of mutation sites and optimal amino acid substitutions. Recently, we showed that such mutation sites can be identified as structural weak spots by rigidity theory-based thermal unfolding simulations of proteins. Here, we describe and validate a unique, ensemble-based, yet highly efficient strategy to predict optimal amino acid substitutions at structural weak spots for improving a protein’s thermostability. For this, we exploit the fact that in the majority of cases an increased structural rigidity of the folded state has been found as the cause for thermostability. When applied prospectively to lipase A from Bacillus subtilis, we achieved both a high success rate (25% over all experimentally tested mutations, which raises to 60% if small-to-large residue mutations and mutations in the active site are excluded) in predicting significantly thermostabilized lipase variants and a remarkably large increase in those variants’ thermostability (up to 6.6°C) based on single amino acid mutations. When considering negative controls in addition and evaluating the performance of our approach as a binary classifier, the accuracy is 63% and increases to 83% if small-to-large residue mutations and mutations in the active site are excluded. The gain in precision (predictive value for increased thermostability) over random classification is 1.6-fold (2.4-fold). Furthermore, an increase in thermostability predicted by our approach significantly points to increased experimental thermostability (p < 0.05). These results suggest that our strategy is a valuable complement to existing methods for rational protein design aimed at improving thermostability.     

Predicting changes in protein thermostability brought about by single- or multi-site mutations

Background  

An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔG) of thermal denaturation.     

Automatic extraction of protein point mutations using a graph bigram association

Protein point mutations are an essential component of the evolutionary and experimental analysis of protein structure and function. While many manually curated databases attempt to index point mutations, most experimentally generated point mutations and the biological impacts of the changes are described in the peer-reviewed published literature. We describe an application, Mutation GraB (Graph Bigram), that identifies, extracts, and verifies point mutations from biomedical literature. The principal problem of point mutation extraction is to link the point mutation with its associated protein and organism of origin. Our algorithm uses a graph-based bigram traversal to identify these relevant associations and exploits the Swiss-Prot protein database to verify this information. The graph bigram method is different from other models for point mutation extraction in that it incorporates frequency and positional data of all terms in an article to drive the point mutation–protein association. Our method was tested on 589 articles describing point mutations from the G protein–coupled receptor (GPCR), tyrosine kinase, and ion channel protein families. We evaluated our graph bigram metric against a word-proximity metric for term association on datasets of full-text literature in these three different protein families. Our testing shows that the graph bigram metric achieves a higher F-measure for the GPCRs (0.79 versus 0.76), protein tyrosine kinases (0.72 versus 0.69), and ion channel transporters (0.76 versus 0.74). Importantly, in situations where more than one protein can be assigned to a point mutation and disambiguation is required, the graph bigram metric achieves a precision of 0.84 compared with the word distance metric precision of 0.73. We believe the graph bigram search metric to be a significant improvement over previous search metrics for point mutation extraction and to be applicable to text-mining application requiring the association of words.     

Enhancing RGI lyase thermostability by targeted single point mutations

Rhamnogalacturonan I lyase (RGI lyase) (EC 4.2.2.-) catalyzes the cleavage of rhamnogalacturonan I in pectins by β-elimination. In this study the thermal stability of a RGI lyase (PL 11) originating from Bacillus licheniformis DSM 13/ATCC14580 was increased by a targeted protein engineering approach involving single amino acid substitution. Nine individual amino acids were selected as targets for site-saturated mutagenesis by the use of a predictive consensus approach in combination with prediction of protein mutant stability changes and B-factor iteration testing. After extensive experimental verification of the thermal stability of the designed mutants versus the original wild-type RGI lyase, several promising single point mutations were obtained, particularly in position Glu434 on the surface of the enzyme protein. The best mutant, Glu434Leu, produced a half-life of 31 min at 60 °C, corresponding to a 1.6-fold improvement of the thermal stability compared to the original RGI lyase. Gly55Val was the second best mutation with a thermostability half-life increase of 27 min at 60 °C, and the best mutations following were Glu434Trp, Glu434Phe, and Glu434Tyr, respectively. The data verify the applicability of a combinatorial predictive approach for designing a small site saturation library for improving enzyme thermostability. In addition, new thermostable RGI lyases suitable for enzymatic upgrading of pectinaceous plant biomass materials at elevated temperatures were produced.     

枯草杆菌蛋白酶嗜热性改造单突变效果评判方法研究

对蛋白质进行嗜热性改造是蛋白质工程的主要问题之一,残基突变方法被广泛运用于其中。本文以枯草杆菌蛋白酶（SUBTILISIN BPN＇）为研究对象,旨在建立评判嗜热性改造效果的方法,选取了有可靠实验资料的9个突变点,运用分子动力学模拟方法,在四种不同模拟条件下,对其中的6个突变体和1个野生型蛋白进行了多种参量的对比分析,提取4个特征有效参量,建立了蛋白酶嗜热性改造单突变效果评判方法;利用该方法对其它3个突变效果进行评判,评判结果与实验资料完全吻合,证明该方法可用于枯草杆菌蛋白酶嗜热性改造单突变效果的评判。     

Viral protein instability enhances host-range evolvability

Viruses are highly evolvable, but what traits endow this property? The high mutation rates of viruses certainly play a role, but factors that act above the genetic code, like protein thermostability, are also expected to contribute. We studied how the thermostability of a model virus, bacteriophage λ, affects its ability to evolve to use a new receptor, a key evolutionary transition that can cause host-range evolution. Using directed evolution and synthetic biology techniques we generated a library of host-recognition protein variants with altered stabilities and then tested their capacity to evolve to use a new receptor. Variants fell within three stability classes: stable, unstable, and catastrophically unstable. The most evolvable were the two unstable variants, whereas seven of eight stable variants were significantly less evolvable, and the two catastrophically unstable variants could not grow. The slowly evolving stable variants were delayed because they required an additional destabilizing mutation. These results are particularly noteworthy because they contradict a widely supported contention that thermostabilizing mutations enhance evolvability of proteins by increasing mutational robustness. Our work suggests that the relationship between thermostability and evolvability is more complex than previously thought, provides evidence for a new molecular model of host-range expansion evolution, and identifies instability as a potential predictor of viral host-range evolution.     

Using motif-based methods in multiple genome analyses: a case study comparing orthologous mesophilic and thermophilic proteins

Protein motifs represent highly conserved regions within protein families and are generally accepted to describe critical regions required for protein stability and/or function. In this comprehensive analysis, we present a robust, unique approach to identify and compare corresponding mesophilic and thermophilic sequence motifs between all orthologous proteins within 44 microbial genomes. Motif similarity is determined through global sequence alignment of mesophilic and thermophilic motif pairs, which are identified by a greedy algorithm. Our results reveal only modest correlation between motif and overall sequence similarity, highlighting the rationale of motif-based approaches in comprehensive multigenome comparisons. Conserved mutations reflect previously suggested physiochemical principles for conferring thermostability. Additionally, comparisons between corresponding mesophilic and thermophilic motif pairs provide key biochemical insights related to thermostability and can be used to test the evolutionary robustness of individual structural comparisons. We demonstrate the ability of our unique approach to provide key insights in two examples: the TATA-box binding protein and glutamate dehydrogenase families. In the latter example, conserved mutations hint at novel origins leading to structural stability differences within the hexamer structures. Additionally, we present amino acid composition data and average protein length comparisons for all 44 microbial genomes.     

Why are proteins so robust to site mutations?

There have been repeated observations that proteins are surprisingly robust to site mutations, enduring significant numbers of substitutions with little change in structure, stability, or function. These results are almost paradoxical in light of what is known about random heteropolymers and the sensitivity of their properties to seemingly trivial mutations. To address this discrepancy, the preservation of biological protein properties in the presence of mutation has been interpreted as indicating the independence of selective pressure on such properties. Such results also lead to the prediction that de novo protein design should be relatively easy, in contrast to what is observed. Here, we use a computational model with lattice proteins to demonstrate how this robustness can result from population dynamics during the evolutionary process. As a result, sequence plasticity may be a characteristic of evolutionarily derived proteins and not necessarily a property of designed proteins. This suggests that this robustness must be re-interpreted in evolutionary terms, and has consequences for our understanding of both in vivo and in vitro protein evolution.     

A clinical scoring system for selection of patients for PTEN mutation testing is proposed on the basis of a prospective study of 3042 probands

Cowden syndrome (CS) and Bannayan-Riley-Ruvalcaba syndrome are allelic, defined by germline PTEN mutations, and collectively referred to as PTEN hamartoma tumor syndrome. To date, there are no existing criteria based on large prospective patient cohorts to select patients for PTEN mutation testing. To address these issues, we conducted a multicenter prospective study in which 3042 probands satisfying relaxed CS clinical criteria were accrued. PTEN mutation scanning, including promoter and large deletion analysis, was performed for all subjects. Pathogenic mutations were identified in 290 individuals (9.5%). To evaluate clinical phenotype and PTEN genotype against protein expression, we performed immunoblotting (PTEN, P-AKT1, P-MAPK1/2) for a patient subset (n = 423). In order to obtain an individualized estimation of pretest probability of germline PTEN mutation, we developed an optimized clinical practice model to identify adult and pediatric patients. For adults, a semiquantitative score-the Cleveland Clinic (CC) score-resulted in a well-calibrated estimation of pretest probability of PTEN status. Overall, decreased PTEN protein expression correlated with PTEN mutation status; decreasing PTEN protein expression correlated with increasing CC score (p < 0.001), but not with the National Comprehensive Cancer Network (NCCN) criteria (p = 0.11). For pediatric patients, we identified highly sensitive criteria to guide PTEN mutation testing, with phenotypic features distinct from the adult setting. Our model improved sensitivity and positive predictive value for germline PTEN mutation relative to the NCCN 2010 criteria in both cohorts. We present the first evidence-based clinical practice model to select patients for genetics referral and PTEN mutation testing, further supported biologically by protein correlation.     

残基突变对冷休克蛋白热稳定性影响的计算分析研究(英文)

残基突变是提高蛋白质热稳定性最直接有效的方式。在本文中,我们选取一对冷休克蛋白质作为研究对象,其中一个来自嗜温的Bacillus subtilis(Bs-CspB),另一个来自嗜热的Bacillus caldolyticus(Bc-Csp),这两个蛋白质在序列和结构上具有高度的相似性,但两者的耐热能力却相差很大。我们利用全原子模型计算残基突变前后蛋白质的自由能和氨基酸之间相互作用能的变化,分析残基突变对冷休克蛋白热稳定性的影响。通过对比两个蛋白质对应位置上残基的能量,我们成功鉴别出对Bc-Csp的高热稳定性有突出贡献的残基。我们计算了这些残基突变前后,该残基的静电相互作用和范德华相互作用的变化,以分析该残基对Bc-Csp高热稳定性的主要贡献。同时,我们分析了离子键对蛋白质热稳定性的贡献。我们的计算结果和实验结果吻合得很好,关键在于利用该方法可以详细地说明残基突变影响蛋白质热稳定性的根本原因。本文为研究残基突变对蛋白质热稳定性的影响提供了一种计算思路和方法,并有助于设计具有高耐热能力的蛋白质。