Iosif I. Vaisman

Delaunay Tessellation of Proteins: Four Body Nearest Neighbor Propensities of Amino Acid Residues

Delaunay tessellation is applied for the first time in the analysis of protein structure. By representing amino acid residues in protein chains by C alpha atoms, the protein is described as a set of points in three-dimensional space. Delaunay tessellation of a protein structure generates an aggregate of space-filling irregular tetrahedra, or Delaunay simplices. The vertices of each simplex define objectively four nearest neighbor C alpha atoms, i.e., four nearest-neighbor residues. A simplex classification scheme is introduced in which simplices are divided into five classes based on the relative positions of vertex residues in protein primary sequence. Statistical analysis of the residue composition of Delaunay simplices reveals nonrandom preferences for certain quadruplets of amino acids to be clustered together. This nonrandom preference may be used to develop a four-body potential that can be used in evaluating sequence-structure compatibility for the purpose of inverted structure prediction.

Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis

MOTIVATION Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal. RESULTS We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and its ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results. AVAILABILITY A web server with supporting documentation is available at http://proteins.gmu.edu/automute.

Discrimination of thermophilic and mesophilic proteins

BackgroundThere is a considerable literature on the source of the thermostability of proteins from thermophilic organisms. Understanding the mechanisms for this thermostability would provide insights into proteins generally and permit the design of synthetic hyperstable biocatalysts.ResultsWe have systematically tested a large number of sequence and structure derived quantities for their ability to discriminate thermostable proteins from their non-thermostable orthologs using sets of mesophile-thermophile ortholog pairs. Most of the quantities tested correspond to properties previously reported to be associated with thermostability. Many of the structure related properties were derived from the Delaunay tessellation of protein structures.ConclusionsCarefully selected sequence based indices discriminate better than purely structure based indices. Combined sequence and structure based indices improve performance somewhat further. Based on our analysis, the strongest contributors to thermostability are an increase in ion pairs on the protein surface and a more strongly hydrophobic interior.

Cell Surface Expression of CD147/EMMPRIN Is Regulated by Cyclophilin 60

CD147, also known as extracellular matrix metalloproteinase inducer, is a regulator of matrix metalloproteinase production and also serves as a signaling receptor for extracellular cyclophilins. Previously, we demonstrated that cell surface expression of CD147 is sensitive to cyclophilin-binding drug cyclosporin A, suggesting involvement of a cyclophilin in the regulation of intracellular transport of CD147. In this report, we identify this cyclophilin as cyclophilin 60 (Cyp60), a distinct member of the cyclophilin family of proteins. CD147 co-immunoprecipitated with Cyp60, and confocal immunofluorescent microscopy revealed intracellular co-localization of Cyp60 and CD147. This interaction with Cyp60 involved proline 211 of CD147, which was shown previously to be critical for interaction between CD147 and another cyclophilin, cyclophilin A, in solution. Mutation of this proline residue abrogated co-immunoprecipitation of CD147 and Cyp60 and reduced surface expression of CD147 on the plasma membrane. Suppression of Cyp60 expression using RNA interference had an effect similar to that of cyclosporin A: reduction of cell surface expression of CD147. These results suggest that Cyp60 plays an important role in the translocation of CD147 to the cell surface. Therefore, Cyp60 may present a novel target for therapeutic interventions in diseases where CD147 functions as a pathogenic factor, such as cancer, human immunodeficiency virus infection, or rheumatoid arthritis.

Accurate prediction of enzyme mutant activity based on a multibody statistical potential

MOTIVATION An important area of research in biochemistry and molecular biology focuses on characterization of enzyme mutants. However, synthesis and analysis of experimental mutants is time consuming and expensive. We describe a machine-learning approach for inferring the activity levels of all unexplored single point mutants of an enzyme, based on a training set of such mutants with experimentally measured activity. RESULTS Based on a Delaunay tessellation-derived four-body statistical potential function, a perturbation vector measuring environmental changes relative to wild type (wt) at every residue position uniquely characterizes each enzyme mutant for model development and prediction. First, a measure of model performance utilizing area (AUC) under the receiver operating characteristic (ROC) curve surpasses 0.83 and 0.77 for data sets of experimental HIV-1 protease and T4 lysozyme mutants, respectively. Additionally, a novel method is introduced for evaluating statistical significance associated with the number of correct test set predictions obtained from a trained model. Third, 100 stratified random splits of the protease and T4 lysozyme mutant data sets into training and test sets achieve 77.0% and 80.8% mean accuracy, respectively. Next, protease and T4 lysozyme models trained with experimental mutants are used to predict activity levels for all remaining mutants; a subsequent search for publications reporting on dozens of these test mutants reveals that experimental results are matched by 79% and 86% of predictions, respectively. Finally, learning curves for each mutant enzyme system indicate the influence of training set size on model performance. AVAILABILITY Prediction databases at http://proteins.gmu.edu/automute/

AUTO-MUTE: web-based tools for predicting stability changes in proteins due to single amino acid replacements

Utilizing cutting-edge supervised classification and regression algorithms, three web-based tools have been developed for predicting stability changes upon single residue substitutions in proteins with known native structures. Trained models classify independent mutant test sets with accuracies ranging from 87 to 94%. Attributes representing each mutant protein are based on a computational mutagenesis methodology relying on a four-body statistical potential, illustrating a novel integration of both energy-based and machine learning approaches. The servers are written in PHP and hosted on a Linux platform, and they can be freely accessed online along with detailed data sets, documentation and performance results at http://proteins.gmu.edu/automute.

Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms

Certain genetic variations in the human population are associated with heritable diseases, and single nucleotide polymorphisms (SNPs) represent the most common form of such differences in DNA sequence. In particular, substantial interest exists in determining whether a non-synonymous SNP (nsSNP), leading to a single residue replacement in the translated protein product, is neutral or disease-related. The nature of protein structure-function relationships suggests that nsSNP effects, either benign or leading to aberrant protein function possibly associated with disease, are dependent on relative structural changes introduced upon mutation. In this study, we characterize a representative sampling of 1790 documented neutral and disease-related human nsSNPs mapped to 243 diverse human protein structures, by quantifying environmental perturbations in the associated proteins with the use of a computational mutagenesis methodology that relies on a four-body, knowledge-based, statistical contact potential. These structural change data are used as attributes to generate a vector representation for each nsSNP, in combination with additional features reflecting sequence and structure of the corresponding protein. A trained model based on the random forest supervised classification algorithm achieves 76% cross-validation accuracy. Our classifier performs at least as well as other methods that use significantly larger datasets of nsSNPs for model training, and the novelty of our attributes differentiates the model as an orthogonal approach that can be utilized in conjunction with other techniques. A dedicated server for obtaining predictions, as well as supporting datasets and documentation, is available at http://proteins.gmu.edu/automute.

Simplicial neighborhood analysis of protein packing (SNAPP): a computational geometry approach to studying proteins.

Publisher Summary This chapter describes the application of computational geometry methodology to protein-structure analysis and comparison. The Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method is applied to the problem of automatically identifying recurrent substructures in a large database of diverse protein structures. The chapter presents a novel approach to mapping protein cores with application to fold recognition via structural templates. It also describes the use of the SNAPP methodology for recognizing functional patterns characteristic of three unique protein families. SNAPP employs Delaunay tessellation to identify recurrent tertiary packing motifs that may be characteristic of protein structural and functional families. This method includes automatic identification of elementary tertiary packing motifs recurring in a large database of protein structures, automatic identification of global patterns of protein structure organization by recognizing the proteins hydrophobic core, and identification of functional signature motifs in a family of proteins that can assist structure-based annotation.

A Simple Topological Representation of Protein Structure: Implications for New, Fast, and Robust Structural Classification

A topological representation of proteins is developed that makes use of two metrics: the Euclidean metric for identifying natural nearest neighboring residues via the Delaunay tessellation in Cartesian space and the distance between residues in sequence space. Using this representation, we introduce a quantitative and computationally inexpensive method for the comparison of protein structural topology. The method ultimately results in a numerical score quantifying the distance between proteins in a heuristically defined topological space. The properties of this scoring scheme are investigated and correlated with the standard Cα distance root‐mean‐square deviation measure of protein similarity calculated by rigid body structural alignment. The topological comparison method is shown to have a characteristic dependence on protein conformational differences and secondary structure. This distinctive behavior is also observed in the comparison of proteins within families of structural relatives. The ability of the comparison method to successfully classify proteins into classes, superfamilies, folds, and families that are consistent with standard classification methods, both automated and human‐driven, is demonstrated. Furthermore, it is shown that the scoring method allows for a fine‐grained classification on the family, protein, and species level that agrees very well with currently established phylogenetic hierarchies. This fine classification is achieved without requiring visual inspection of proteins, sequence analysis, or the use of structural superimposition methods. Implications of the method for a fast, automated, topological hierarchical classification of proteins are discussed. Proteins 2004.

Comprehensive mutagenesis of HIV-1 protease: a computational geometry approach.

A computational geometry technique based on Delaunay tessellation of protein structure, represented by C(alpha) atoms, is used to study effects of single residue mutations on sequence-structure compatibility in HIV-1 protease. Profiles of residue scores derived from the four-body statistical potential are constructed for all 1881 mutants of the HIV-1 protease monomer and compared with the profile of the wild-type protein. The profiles for an isolated monomer of HIV-1 protease and the identical monomer in a dimeric state with an inhibitor are analyzed to elucidate changes to structural stability. Protease residues shown to undergo the greatest impact are those forming the dimer interface and flap region, as well as those known to be involved in inhibitor binding.