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

OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling

Predicting the effect of protein mutation is crucial in many applications such as protein design, protein evolution, and genetic disease analysis. Structurally, mutation is basically the replacement of the side chain of a particular residue. Therefore, accurate side-chain modeling is useful in studying the effect of mutation. Here, we propose a computational method, namely, OPUS-Mut, which significantly outperforms other backbone-dependent side-chain modeling methods including our previous method OPUS-Rota4. We evaluate OPUS-Mut by four case studies on Myoglobin, p53, HIV-1 protease, and T4 lysozyme. The results show that the predicted structures of side chains of different mutants are consistent well with their experimentally determined results. In addition, when the residues with significant structural shifts upon the mutation are considered, it is found that the extent of the predicted structural shift of these affected residues can be correlated reasonably well with the functional changes of the mutant measured by experiments. OPUS-Mut can also help one to identify the harmful and benign mutations and thus may guide the construction of a protein with relatively low sequence homology but with a similar structure.

Modeling functional changes to Escherichia coli thymidylate synthase upon single residue replacements: a structure-based approach

Escherichia coli thymidylate synthase (TS) is an enzyme that is indispensable to DNA synthesis and cell division, as it provides the only de novo source of dTMP by catalyzing the reductive methylation of dUMP, thus making it a key target for chemotherapeutic agents. High resolution X-ray crystallographic structures are available for TS and, owing to its relatively small size, successful experimental mutagenesis studies have been conducted on the enzyme. In this study, an in silico mutagenesis technique is used to investigate the effects of single amino acid substitutions in TS on enzymatic activity, one that employs the TS protein structure as well as a knowledge-based, four-body statistical potential. For every single residue TS variant, this approach yields both a global structural perturbation score and a set of local environmental perturbation scores that characterize the mutated position as well as all structurally neighboring residues. Global scores for the TS variants are capable of uniquely characterizing groups of residue positions in the enzyme according to their physicochemical, functional, or structural properties. Additionally, these global scores elucidate a statistically significant structure–function relationship among a collection of 372 single residue TS variants whose activity levels have been experimentally determined. Predictive models of TS variant activity are subsequently trained on this dataset of experimental mutants, whose respective feature vectors encode information regarding the mutated position as well as its six nearest residue neighbors in the TS structure, including their environmental perturbation scores.

Prediction of enzyme mutant activity using computational mutagenesis and incremental transduction

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets.

A combined sequence-structure approach for predicting resistance to the non-nucleoside HIV-1 reverse transcriptase inhibitor Nevirapine

The development of drug resistance to antiretroviral medications used to treat infection with HIV-1 is a major concern. Given the cost and time constraints associated with phenotypic resistance testing, computational approaches leading to accurate predictive models of resistance based on a patient's mutational patterns in the target protein would provide a welcome alternative. A combined sequence-structure computational mutagenesis procedure is used to generate attribute vectors for each of 222 mutational patterns of HIV-1 reverse transcriptase that were isolated and sequenced from patients. Phenotypic fold-levels of resistance to the non-nucleoside inhibitor Nevirapine are known for over 25% of these mutants, whose values are used to assign each assayed mutant to a drug susceptibility class, either sensitive or resistant. Support vector machine and random forest supervised learning algorithms applied to this subset respectively classify mutants based on drug susceptibility with 85% and 92% cross-validation accuracy. The trained models are used to predict susceptibility to Nevirapine for all remaining mutant isolates, and predictions are in agreement for 90% of the test cases.

Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage

Background

HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An in silico mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions.

Results

Classifier performance is evaluated based on stratified 10-fold cross-validation, stratified dataset splits (2/3 training, 1/3 validation), and leave-one-out cross-validation. Best reported values of sensitivity (85%), specificity (100%), and precision (98%) for predicting X4-capable HIV-1 virus, overall accuracy (97%), Matthew's correlation coefficient (89%), balanced error rate (0.08), and ROC area (0.97) all reach critical thresholds, suggesting that the models outperform six other state-of-the-art methods and come closer to competing with phenotype assays.

Conclusions

The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information. The classifiers are available online at http://proteins.gmu.edu/automute.

Discrimination of thermophilic and mesophilic proteins

Background

There 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.

Results

We 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.

Conclusions

Carefully 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.

Modeling the functional consequences of single residue replacements in bacteriophage f1 gene V protein

A computational mutagenesis methodology utilizing a four-body, knowledge-based, statistical contact potential is applied toward globally quantifying relative environmental perturbations (residual scores) in bacteriophage f1 gene V protein (GVP) due to single amino acid substitutions. We show that residual scores correlate well with experimentally measured relative changes in protein function upon mutation. Residual scores also distinguish between GVP amino acid positions grouped according to protein structural or functional roles or based on similarities in physicochemical characteristics. For each mutant, the in silico mutagenesis additionally yields local measures of environmental change (EC scores) occurring at every residue position (residual profile) relative to the native protein. Implementation of the random forest (RF) algorithm, utilizing experimental GVP mutants whose feature vector components include EC scores at the mutated position and at six structurally nearest neighbors, correctly classifies mutants based on function with up to 77% cross-validation accuracy while achieving 0.82 area under the receiver operating characteristic curve. A control experiment highlights the effectiveness of mutant feature vector signals, and a variety of learning curves are generated to analyze the impact of GVP mutant data set size on performance measures. An optimally trained RF model is subsequently used for inferring function for all the remaining unexplored GVP mutants.

HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method

The Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method was used to predict the effect of mutagenesis on the enzymatic activity of the HIV-1 protease (HIVP). SNAPP relies on a four-body statistical scoring function derived from the analysis of spatially nearest neighbor residue compositional preferences in a diverse and representative subset of protein structures from the Protein Data Bank. The method was applied to the analysis of HIVP mutants with residue substitutions in the hydrophobic core as well as at the interface between the two protease monomers. Both wild-type and tethered structures were employed in the calculations. We obtained a strong correlation, with R(2) as high as 0.96, between DeltaSNAPP score (i.e., the difference in SNAPP scores between wild-type and mutant proteins) and the protease catalytic activity for tethered structures. However, a weaker but significant correlation was obtained for nontethered structures. Our analysis identified residues both in the hydrophobic core and at the dimeric interface that are very important for the protease function. This study demonstrates a potential utility of the SNAPP method for rational design of mutagenesis studies and protein engineering.

Predicting the transactivation activity of p53 missense mutants using a four-body potential score derived from Delaunay tessellations

We describe a novel statistical scoring method based on a computational geometry approach to predict the functional impact (transactivation activity) of missense mutations in the DNA-binding domain (DBD) of the tumor suppressor TP53, which is the most frequently mutated gene in human cancer. Residual scores (RS) for each residue were calculated to reflect differences in the compositional preferences of four nearest-neighbor residues between mutant and wild-type proteins. The RS were then combined into a residual score profile (RSP) representing the RS values for all 194 residues in the DBD. Mutants were grouped into functional categories based on their transactivation activities experimentally measured in yeast functional assays using p53-response elements from eight different promoters. While these functional categories showed significant differences in average RS, the latter lacked resolution power to predict the transactivation activities of individual mutants. In contrast, using decision tree models, we found that the RSP predicted transactivation with an accuracy varying between 64.2% and 78.5% depending on the promoter. Lastly, we used the best model to predict the functional outcome of all missense mutants in the DBD of p53 and compared the predictions with their frequency of occurrence in human cancers. We found that mutants predicted as functional (F) accounted for approximately 14% of all missense mutants found in cancers, while mutants predicted as nonfunctional (NF) represented approximately 86% of the mutants. These results show that this computational approach provides a fast and reliable method for predicting the functional impact of p53 mutants associated with cancer.

Statistical geometry approach to the study of functional effects of human nonsynonymous SNPs

The ability to predict the effect of nonsynonymous SNPs (nsSNPs) on protein function is important for the success of genetic disease association studies. Here we present a statistical geometry approach to nsSNP classification based on Delaunay tessellation, whereby the impact of nsSNPs on protein function is correlated with the change in the four-body statistical potential (DeltaQ) of the protein caused by the amino acid substitution. We observed that the DeltaQ of polymorphic proteins with disease-associated nsSNPs (daSNPs) was on average significantly lower than the DeltaQ of the proteins with neutral SNPs (ntSNPs). Clustering amino acid substitutions into conservative and nonconservative groups, and using a three-letter alphabet based on side-chain polarity showed significantly lower DeltaQ in nonconservative changes to daSNPs and when hydrophobic residues were substituted by charged or by polar residues. We also found that the daSNPs in the protein core caused much lower DeltaQ than surface daSNPs. This approach demonstrates a strong correlation between the computed DeltaQ and SNP classification. Integration of our approach with the existing models will help achieve a more precise recognition of nsSNPs that underlie polygenic diseases. All of the programs were written in Java and are available from the authors upon request.

Computational mutagenesis studies of protein structure‐function correlations

Topological scores, measures of sequence-structure compatibility, are calculated for all 1,881 single point mutants of the human immunodeficiency virus (HIV)-1 protease using a four-body statistical potential function based on Delaunay tessellation of protein structure. Comparison of the mutant topological score data with experimental data from alanine scan studies specifically on the dimer interface residues supports previous findings that 1) L97 and F99 contribute greatly to the Gibbs energy of HIV-1 protease dimerization, 2) Q2 and T4 contribute the least toward the Gibbs energy, and 3) C-terminal residues are more sensitive to mutations than those at the N-terminus. For a more comprehensive treatment of the relationship between protease structure and function, mutant topological scores are compared with the activity levels for a set of 536 experimentally synthesized protease mutants, and a significant correlation is observed. Finally, this structure-function correlation is similarly identified by examining model systems consisting of 2,015 single point mutants of bacteriophage T4 lysozyme as well as 366 single point mutants of HIV-1 reverse transcriptase and is hypothesized to be a property generally applicable to all proteins.

Graph theoretic properties of networks formed by the Delaunay tessellation of protein structures

The Delaunay tessellation of several sets of real and simplified model protein structures has been used to explore graph theoretic properties of residue contact networks. The system of contacts defined by residues joined by edges in the Delaunay simplices can be thought of as a graph or network and analyzed using techniques from elementary graph theory and the theory of complex networks. Such analysis indicates that protein contact networks have small world character, but technically are not small world networks. This approach also indicates that networks formed by native structures and by most misfolded decoys can be differentiated by their respective graph properties. The characteristic features of residue contact networks can be used for the detection of structural elements in proteins, such as the ubiquitous closed loops consisting of 22-32 consecutive residues, where terminal residues are Delaunay neighbors.

New method for protein secondary structure assignment based on a simple topological descriptor

A simple, five-element descriptor, derived from the Delaunay tessellation of a protein structure in a single point per residue representation, can be assigned to each residue in the protein. The descriptor characterizes main-chain topology and connectivity in the neighborhood of the residue and does not explicitly depend on putative hydrogen bonds or any geometric parameter, including bond length, angles, and areas. Rules based on this descriptor can be used for accurate, robust, and computationally efficient secondary structure assignment that correlates well with the existing methods.

The key role of atom types, reference states, and interaction cutoff radii in the knowledge-based method: New variational approach

We present a variational method to derive knowledge-based potentials. The method is based on an optimization procedure of objective variables: atom types, reference states, and interaction cutoff radii. We suggest and apply new unsymmetrical reference states. The cutoff radii and atom types are optimized to improve docking accuracy of the corresponding potentials. The atom types are varied along an atom type tree, with 6 root and 49 top atom types, and the set of 18 optimal atom types is obtained. We demonstrate strong dependence between the choice of atom types and the docking accuracy of the potentials derived with these atom types. The averaged root-mean square deviations (RMSDs) of the ligand docked positions relative to the experimentally determined positions decrease when the elements C, N, O are split into the optimal types.

Evaluation of the relative stability of liganded versus ligand-free protein conformations using Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method

Many proteins change their conformation upon ligand binding. For instance, bacterial periplasmic binding proteins (bPBPs), which transport nutrients into the cytoplasm, generally consist of two globular domains connected by strands, forming a hinge. During ligand binding, hinge motion changes the conformation from the open to the closed form. Both forms can be crystallized without a ligand, suggesting that the energy difference between them is small. We applied Simplicial Neighborhood Analysis of Protein Packing (SNAPP) as a method to evaluate the relative stability of open and closed forms in bPBPs. Using united residue representation of amino acids, SNAPP performs Delaunay tessellation of the protein, producing an aggregate of space-filling, irregular tetrahedra with nearest neighbor residues at the vertices. The SNAPP statistical scoring function is derived from log-likelihood scores for all possible quadruplet compositions of amino acids found in a representative subset of the Protein Data Bank, and the sum of the scores for a given protein provides the total SNAPP score. Results of scoring for bPBPs suggest that in most cases, the unliganded form is more stable than the liganded form, and this conclusion is corroborated by similar observations of other proteins undergoing conformation changes upon binding their ligands. The results of these studies suggest that the SNAPP method can be used to predict the relative stability of accessible protein conformations. Furthermore, the SNAPP method allows delineation of the role of individual residues in protein stabilization, thereby providing new testable hypotheses for rational site-directed mutagenesis in the context of protein engineering.