Thermodynamic database for proteins: features and applications

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

Artificial intelligence challenges for predicting the impact of mutations on protein stability

Stability is a key ingredient of protein fitness, and its modification through targeted mutations has applications in various fields, such as protein engineering, drug design, and deleterious variant interpretation. Many studies have been devoted over the past decades to build new, more effective methods for predicting the impact of mutations on protein stability based on the latest developments in artificial intelligence. We discuss their features, algorithms, computational efficiency, and accuracy estimated on an independent test set. We focus on a critical analysis of their limitations, the recurrent biases toward the training set, their generalizability, and interpretability. We found that the accuracy of the predictors has stagnated at around 1 kcal/mol for over 15 years. We conclude by discussing the challenges that need to be addressed to reach improved performance.

In-depth interrogation of protein thermal unfolding data with MoltenProt

Protein stability is a key factor in successful structural and biochemical research. However, the approaches for systematic comparison of protein stability are limited by sample consumption or compatibility with sample buffer components. Here we describe how miniaturized measurement of intrinsic tryptophan fluorescence (NanoDSF assay) in combination with a simplified description of protein unfolding can be used to interrogate the stability of a protein sample. We demonstrate that improved protein stability measures, such as apparent Gibbs free energy of unfolding, rather than melting temperature Tm , should be used to rank the results of thermostability screens. The assay is compatible with protein samples of any composition, including protein complexes and membrane proteins. Our data analysis software, MoltenProt, provides an easy and robust way to perform characterization of multiple samples. Potential applications of MoltenProt and NanoDSF include buffer and construct optimization for X-ray crystallography and cryo-electron microscopy, screening for small-molecule binding partners and comparison of effects of point mutations.

Proteus: An algorithm for proposing stabilizing mutation pairs based on interactions observed in known protein 3D structures

Background

Protein engineering has many applications for industry, such as the development of new drugs, vaccines, treatment therapies, food, and biofuel production. A common way to engineer a protein is to perform mutations in functionally essential residues to optimize their function. However, the discovery of beneficial mutations for proteins is a complex task, with a time-consuming and high cost for experimental validation. Hence, computational approaches have been used to propose new insights for experiments narrowing the search space and reducing the costs.

Results

In this study, we developed Proteus (an acronym for Protein Engineering Supporter), a new algorithm for proposing mutation pairs in a target 3D structure. These suggestions are based on contacts observed in other known structures from Protein Data Bank (PDB). Proteus’ basic assumption is that if a non-interacting pair of amino acid residues in the target structure is exchanged to an interacting pair, this could enhance protein stability. This trade is only allowed if the main-chain conformation of the residues involved in the contact is conserved. Furthermore, no steric impediment is expected between the proposed mutations and the surrounding protein atoms. To evaluate Proteus, we performed two case studies with proteins of industrial interests. In the first case study, we evaluated if the mutations suggested by Proteus for four protein structures enhance the number of inter-residue contacts. Our results suggest that most mutations proposed by Proteus increase the number of interactions into the protein. In the second case study, we used Proteus to suggest mutations for a lysozyme protein. Then, we compared Proteus’ outcomes to mutations with available experimental evidence reported in the ProTherm database. Four mutations, in which our results agree with the experimental data, were found. This could be initial evidence that changes in the side-chain of some residues do not cause disturbances that harm protein structure stability.

Conclusion

We believe that Proteus could be used combined with other methods to give new insights into the rational development of engineered proteins. Proteus user-friendly web-based tool is available at <http://proteus.dcc.ufmg.br>.

KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy–maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.

Global computational mutagenesis provides a critical stability framework in protein structures

A protein’s amino acid sequence dictates the folds and final structure the macromolecule will form. We propose that by identifying critical residues in a protein’s atomic structure, we can select a critical stability framework within the protein structure essential to proper protein folding. We use global computational mutagenesis based on the unfolding mutation screen to test the effect of every possible missense mutation on the protein structure to identify the residues that cannot tolerate a substitution without causing protein misfolding. This method was tested using molecular dynamics to simulate the stability effects of mutating critical residues in proteins involved in inherited disease, such as myoglobin, p53, and the 15^th sushi domain of complement factor H. In addition we prove that when the critical residues are in place, other residues may be changed within the structure without a stability loss. We validate that critical residues are conserved using myoglobin to show that critical residues are the same for crystal structures of 6 different species and comparing conservation indices to critical residues in 9 eye disease-related proteins. Our studies demonstrate that by using a selection of critical elements in a protein structure we can identify a critical protein stability framework. The frame of critical residues can be used in genetic engineering to improve small molecule binding for drug studies, identify loss-of-function disease-causing missense mutations in genetics studies, and aide in identifying templates for homology modeling.

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

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

MAESTRO--multi agent stability prediction upon point mutations

BACKGROUND

Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.

RESULTS

We aim to contribute a novel method for predicting changes in stability upon point mutation in proteins called MAESTRO. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds. The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.

CONCLUSIONS

MAESTRO is a versatile tool in the field of stability change prediction upon point mutations. Executables for the Linux and Windows operating systems are freely available to non-commercial users from http://biwww.che.sbg.ac.at/MAESTRO.

A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with Cα-only representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.

iStable: off-the-shelf predictor integration for predicting protein stability changes

BACKGROUND

Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users.

RESULTS

We proposed an integrated predictor, iStable, with grid computing architecture constructed by using sequence information and prediction results from different element predictors. In the learning model, several machine learning methods were evaluated and adopted the support vector machine as an integrator, while not just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available with two different input types: structural and sequential. After training and cross-validation, iStable has better performance than all of the element predictors on several datasets. Under different classifications and conditions for validation, this study has also shown better overall performance in different types of secondary structures, relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental conditions.

CONCLUSIONS

The trained and validated version of iStable provides an accurate approach for prediction of protein stability changes. iStable is freely available online at: http://predictor.nchu.edu.tw/iStable.

Predicting folding free energy changes upon single point mutations

MOTIVATION

The folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies.

RESULTS

We report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters.

AVAILABILITY

All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB_pdb.rar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The evolution of cefotaximase activity in the TEM β-lactamase

The development of a molecular-level understanding of drug resistance through β-lactamase is critical not only in designing newer-generation antibacterial agents but also in providing insight into the evolutionary mechanisms of enzymes in general. In the present study, we have evaluated the effect of four drug resistance mutations (A42G, E104K, G238S, and M182T) on the cefotaximase activity of the TEM-1 β-lactamase. Using computational methods, including docking and molecular mechanics calculations, we have been able to correctly identify the relative order of catalytic activities associated with these four single point mutants. Further analyses suggest that the changes in catalytic efficiency for mutant enzymes are correlated to structural changes within the binding site. Based on the energetic and structural analyses of the wild-type and mutant enzymes, structural rearrangement is suggested as a mechanism of evolution of drug resistance through TEM β-lactamase. The present study not only provides molecular-level insight into the effect of four drug resistance mutations on the structure and function of the TEM β-lactamase but also establishes a foundation for a future molecular-level analysis of complete evolutionary trajectory for this class of enzymes.

SDM--a server for predicting effects of mutations on protein stability and malfunction

The sheer volume of non-synonymous single nucleotide polymorphisms that have been generated in recent years from projects such as the Human Genome Project, the HapMap Project and Genome-Wide Association Studies means that it is not possible to characterize all mutations experimentally on the gene products, i.e. elucidate the effects of mutations on protein structure and function. However, automatic methods that can predict the effects of mutations will allow a reduced set of mutations to be studied. Site Directed Mutator (SDM) is a statistical potential energy function that uses environment-specific amino-acid substitution frequencies within homologous protein families to calculate a stability score, which is analogous to the free energy difference between the wild-type and mutant protein. Here, we present a web server for SDM (http://www-cryst.bioc.cam.ac.uk/~sdm/sdm.php), which has obtained more than 10 000 submissions since being online in April 2008. To run SDM, users must upload a wild-type structure and the position and amino acid type of the mutation. The results returned include information about the local structural environment of the wild-type and mutant residues, a stability score prediction and prediction of disease association. Additionally, the wild-type and mutant structures are displayed in a Jmol applet with the relevant residues highlighted.

Local/bulk determinants of conformational stability of exchangeable apolipoproteins

GuHCl-induced denaturation of human plasma apoA-I, apoA-II, apoA-IV, apoE3 and three recombinant apoE isoforms in solution and discoidal complexes with phosphatidylcholine (only plasma proteins) was studied. The protein conformational stability (ΔG(H(2)O)) and a slope of linear dependence of free energy of unfolding on GuHCl concentration (m-value) were estimated with the three equilibrium schemes. The data for all proteins, except apoA-II, fit with the three-state model, thus evidencing two-domain structure. The predicted folding rate of the four apoE in solution correlated with conformational stability. The dependence disappeared at the inclusion of apoA-I and apoA-IV into analysis and the m-values, adjusted for residue number in helices (m(rh)), differed between those for apoE and apoA-I/apoA-IV. However, the m(rh)-values for six proteins correlated positively with the fractional change in accessible surface area at unfolding for Phe, Lys and Asn, while negatively for Arg, Ala and Gly residues. The difference between the adjusted ΔG(rh)(H(2)O) values for apolipoproteins in complexes and in solution decreased at the increase of reduced temperature (T(obs)-T(t))/T(t). The induction of intrinsic disorder by arginine residues may be of primary importance in metabolism and function of exchangeable apolipoproteins, while their stability in nascent discoidal HDL is controlled by the physical state of phosphatidylcholine.

Utilizing natural diversity to evolve protein function: applications towards thermostability

Protein evolution relies on designing a library of sequences that capture meaningful functional diversity in a limited number of protein variants. Several approaches take advantage of the sequence space already explored through natural selection by incorporating sequence diversity available from modern genomes (and their ancestors) when designing these libraries. The success of these approaches is, partly, owing to the fact that modern sequence diversity has already been subjected to evolutionary selective forces and thus the diversity has already been deemed 'fit to survive'. Five of these approaches will be discussed in this review to highlight how protein engineers can use evolutionary sequence history/diversity of homologous proteins in unique ways to design protein libraries.