Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants

TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features

The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.

Proteins' Evolution upon Point Mutations

As the reader must be already aware, state-of-the-art protein folding prediction methods have reached a smashing success in their goal of accurately determining the three-dimensional structures of proteins. Yet, a solution to simple problems such as the effects of protein point mutations on their (i) native conformation; (ii) marginal stability; (iii) ensemble of high-energy nativelike conformations; and (iv) metamorphism propensity and, hence, their evolvability, remains as an unsolved problem. As a plausible solution to the latter, some properties of the amide hydrogen-deuterium exchange, a highly sensitive probe of the structure, stability, and folding of proteins, are assessed from a new perspective. The preliminary results indicate that the protein marginal stability change upon point mutations provides the necessary and sufficient information to estimate, through a Boltzmann factor, the evolution of the amide hydrogen exchange protection factors and, consequently, that of the ensemble of folded conformations coexisting with the native state. This work contributes to our general understanding of the effects of point mutations on proteins and may spur significant progress in our efforts to develop methods to determine the appearance of new folds and functions accurately.

Illustrative Tutorials for ProThermDB: Thermodynamic Database for Proteins and Mutants

ProThermDB (https://web.iitm.ac.in/bioinfo2/prothermdb/index.html) is a primary resource for protein stability, which contains experimentally determined thermodynamic data for proteins and their mutants. The most recent version of ProThermDB accumulates the data obtained from both high- and low-throughput experimental biophysical methods. It includes comprehensive information at four different levels, i.e.: (i) protein sequence and structure; (ii) experimental conditions; (iii) thermodynamic parameters such as Gibbs free energy, melting temperature, enthalpy, etc.; and (iv) literature. In the following protocols, we present detailed tutorials for retrieving data using different search, display and sorting options, interpretation of search results, description of each entry-level information category, data upload and download, cross-links with other databases, and visualization options. This protocol consists of six pictorial exercises, which are useful for biologists/users to understand the contents and organization of data in ProThermDB. Further, potential applications of ProThermDB in protein engineering are discussed. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Retrieval of experimental thermodynamic data for wild-type and mutants of a specific protein using a simple query Basic Protocol 2: Retrieval of stabilizing point mutations, which are located at the interior of α-helical regions, and obtaining data by thermal denaturation methods Basic Protocol 3: Retrieval of destabilizing point mutations, which are in β-sheets of exposed regions, and obtaining data by chemical denaturation methods (urea and GdnHCl) Basic Protocol 4: Retrieval of stabilizing and destabilizing point mutations in a range of physiological conditions (pH: 6-9 and T: 20°C-25°C) and publication years (2010-2020) Support Protocol: Downloading the entire data of the database for academic research purposes and submission of new data in ProThermDB.

Synergistic stabilization of a double mutant in chymotrypsin inhibitor 2 from a library screen in E. coli

Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is often not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay in E. coli to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ mol-1 more stable than the wild-type. In one case, the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ mol-1 more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability.

Avoidance of protein unfolding constrains protein stability in long-term evolution

Every amino acid residue can influence a protein's overall stability, making stability highly susceptible to change throughout evolution. We consider the distribution of protein stabilities evolutionarily permittable under two previously reported protein fitness functions: flux dynamics and misfolding avoidance. We develop an evolutionary dynamics theory and find that it agrees better with an extensive protein stability data set for dihydrofolate reductase orthologs under the misfolding avoidance fitness function rather than the flux dynamics fitness function. Further investigation with ribonuclease H data demonstrates that not any misfolded state is avoided; rather, it is only the unfolded state. At the end, we discuss how our work pertains to the universal protein abundance-evolutionary rate correlation seen across organisms' proteomes. We derive a closed-form expression relating protein abundance to evolutionary rate that captures Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens experimental trends without fitted parameters.

ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification

MOTIVATION

Protein-protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein-protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class.

RESULTS

Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein-protein complexes without structural information.

AVAILABILITY AND IMPLEMENTATION

Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Current pivotal strategies leading a difficult target protein to a sample suitable for crystallographic analysis

Crystallographic structural analysis is an essential method for the determination of protein structure. However, crystallization of a protein of interest is the most difficult process in the analysis. The process is often hampered during the sample preparation, including expression and purification. Even after a sample has been purified, not all candidate proteins crystallize. In this mini-review, the current methodologies used to overcome obstacles encountered during protein crystallization are sorted. Specifically, the strategy for an effective crystallization is compared with a pipeline where various expression hosts and constructs, purification and crystallization conditions, and crystallization chaperones as target-specific binder proteins are assessed by a precrystallization screening. These methodologies are also developed continuously to improve the process. The described methods are useful for sample preparation in crystallographic analysis and other structure determination techniques, such as cryo-electron microscopy.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

Effects of Single Mutations on Protein Stability Are Gaussian Distributed

The distribution of protein stability effects is known to be well approximated by a Gaussian distribution from previous empirical fits. Starting from first-principles statistical mechanics, we more rigorously motivate this empirical observation by deriving per-residue-position protein stability effects to be Gaussian. Our derivation requires the number of amino acids to be large, which is satisfied by the standard set of 20 amino acids found in nature. No assumption is needed on the number of residues in close proximity in space, in contrast to previous applications of the central limit theorem to protein energetics. We support our derivation results with computational and experimental data on mutant protein stabilities across all types of protein residues.

MPTherm: database for membrane protein thermodynamics for understanding folding and stability

The functions of membrane proteins (MPs) are attributed to their structure and stability. Factors influencing the stability of MPs differ from globular proteins due to the presence of membrane spanning regions. Thermodynamic data of MPs aid to understand the relationship among their structure, stability and function. Although a wealth of experimental data on thermodynamics of MPs are reported in the literature, there is no database available explicitly for MPs. In this work, we have developed a database for MP thermodynamics, MPTherm, which contains more than 7000 thermodynamic data from about 320 MPs. Each entry contains protein sequence and structural information, membrane topology, experimental conditions, thermodynamic parameters such as melting temperature, free energy, enthalpy etc. and literature information. MPTherm assists users to retrieve the data by using different search and display options. We have also provided the sequence and structure visualization as well as cross-links to UniProt and PDB databases. MPTherm database is freely available at http://www.iitm.ac.in/bioinfo/mptherm/. It is implemented in HTML, PHP, MySQL and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Opera. MPTherm would serve as an effective resource for understanding the stability of MPs, development of prediction tools and identifying drug targets for diseases associated with MPs.

Convolution Neural Network-Based Prediction of Protein Thermostability

Most natural proteins exhibit poor thermostability, which limits their industrial application. Computer-aided rational design is an efficient purpose-oriented method that can improve protein thermostability. Numerous machine-learning-based methods have been designed to predict the changes in protein thermostability induced by mutations. However, all of these methods have certain limitations due to existing mutation coding methods that overlook protein sequence features. Here we propose a method to predict protein thermostability using convolutional neural networks based on an in-depth study of thermostability-related protein properties. This method comprises a three-dimensional coding algorithm, including protein mutation information and a strategy to extract neighboring features at protein mutation sites based on multiscale convolution. The accuracies on the S1615 and S388 data sets, which are widely used for protein thermostability predictions, reached 86.4 and 87%, respectively. The Matthews correlation coefficient was nearly double those produced using other methods. Furthermore, a model was constructed to predict the thermostability of Rhizomucor miehei lipase mutants based on the S3661 data set, a single amino acid mutation data set screened from the ProTherm protein thermodynamics database. Compared with the RIF strategy, which consists of three algorithms, i.e., Rosetta ddg monomer, I Mutant 3.0, and FoldX, the accuracy of the proposed method was higher (75.0 vs 66.7%), and the negative sample resolution was simultaneously enhanced. These results indicate that our prediction method more effectively assessed the protein thermostability and distinguished its features, making it a powerful tool to devise mutations that enhance the thermostability of proteins, particularly enzymes.

Protein Melting Temperature Cannot Fully Assess Whether Protein Folding Free Energy Underlies the Universal Abundance-Evolutionary Rate Correlation Seen in Proteins

The protein misfolding avoidance hypothesis explains the universal negative correlation between protein abundance and sequence evolutionary rate across the proteome by identifying protein folding free energy (ΔG) as the confounding variable. Abundant proteins resist toxic misfolding events by being more stable, and more stable proteins evolve slower because their mutations are more destabilizing. Direct supporting evidence consists only of computer simulations. A study taking advantage of a recent experimental breakthrough in measuring protein stability proteome-wide through melting temperature (Tm) (Leuenberger et al. 2017), found weak misfolding avoidance hypothesis support for the Escherichia coli proteome, and no support for the Saccharomyces cerevisiae, Homo sapiens, and Thermus thermophilus proteomes (Plata and Vitkup 2018). I find that the nontrivial relationship between Tm and ΔG and inaccuracy in Tm measurements by Leuenberger et al. 2017 can be responsible for not observing strong positive abundance-Tm and strong negative Tm-evolutionary rate correlations.

Protein-protein interactions: scoring schemes and binding affinity

Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.