A quantitative structure-activity relationship study of some substance P-related peptides. A multivariate approach using PLS and variable selection

Collagen derived Gly-Pro-type DPP-IV inhibitory peptides: Structure-activity relationship, inhibition kinetics and inhibition mechanism

Our previous study has demonstrated that both the amino acid at N3 position and peptide length affected the DPP-IV inhibitory activity of Gly-Pro-type peptides. To further elucidate their molecular mechanism, a combined approach of QSAR modeling, enzymatic kinetics and molecular docking was used. Results showed that the QSAR models of Gly-Pro-type tripeptides and Gly-Pro-type peptides containing 3-12 residues were successfully constructed by 5z-scale descriptor with R2 of 0.830 and 0.797, respectively. The lower values of electrophilicity, polarity, and side-chain bulk of amino acid at N3 position caused higher DPP-IV inhibitory activity of Gly-Pro-type peptides. Moreover, an appropriate increase in the length of Gly-Pro-type peptides did not change their competitive inhibition mode, but decreased their inhibition constants (Ki values) and increased interactions with DPP-IV. More importantly, the interactions between the residues at C-terminal of Gly-Pro-type peptides containing 5 ∼ 6 residues with S2 extensive subsites (Ser209, Phe357, Arg358) of DPP-IV increased the interactions of Gly residue at N1 position with the S2 subsites (Glu205, Glu206, Asn710, Arg125, Tyr662) and decreased the acylation level of DPP-IV-peptide complex, and thereby increasing peptides' DPP-IV inhibitory activity.

Ligand-based pharmacophore modeling and docking studies on vitamin D receptor inhibitors

In recent years, pharmacophore modeling and molecular docking approaches have been extensively used to characterize the structural requirements and explore the conformational space of a ligand in the binding pocket of the selected target protein. Herein, we report a pharmacophore modeling and molecular docking of 45 compounds comprising of the indole scaffold as vitamin D receptor (VDR) inhibitors. Based on the selected best hypothesis (DRRRR.61), an atom-based three-dimensional quantitative structure-activity relationships model was developed to rationalize the structural requirement of biological activity modulating components. The developed model predicted the binding affinity for the training set and test set with R² _(training)  = 0.8869 and R² _(test)  = 0.8139, respectively. Furthermore, molecular docking and dynamics simulation were performed to understand the underpinning of binding interaction and stability of selected VDR inhibitors in the binding pocket. In conclusion, the results presented here, in the form of functional and structural data, agreed well with the proposed pharmacophores and provide further insights into the development of novel VDR inhibitors with better activity.

Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study

The work aiming to unravel the correlation between protein sequence and function in the absence of structural information can be highly rewarding. We present a new way of considering descriptors from the amino acids index database for modeling and predicting the fitness value of a polypeptide chain. This approach includes the following steps: (i) Calculating Q elementary numerical sequences (Ele_SEQ) depending on the encoding of the amino acid residues, (ii) determining an extended numerical sequence (Ext_SEQ) by concatenating the Q elementary numerical sequences, wherein at least one elementary numerical sequence is a protein spectrum obtained by applying fast Fourier transformation (FFT), and (iii) predicting a value of fitness for polypeptide variants (train and/or validation set). These new descriptors were tested on four sets of proteins of different lengths (GLP-2, TNF alpha, cytochrome P450, and epoxide hydrolase) and activities (cAMP activation, binding affinity, thermostability and enantioselectivity). We show that the use of multiple physicochemical descriptors coupled with the implementation of the FFT, taking into account the interactions between residues of amino acids within the protein sequence, could lead to very significant improvement in the quality of models and predictions. The choice of the descriptor or of the combination of descriptors and/or FFT is dependent on the couple protein/fitness. This approach can provide potential users with value added to existing mutant libraries where screening efforts have so far been unsuccessful in finding improved polypeptide mutants for useful applications.

Better library design: data-driven protein engineering

Data-driven protein engineering is increasingly used as an alternative to rational design and combinatorial engineering because it uses available knowledge to limit library size, while still allowing for the identification of unpredictable substitutions that lead to large effects. Recent advances in computational modeling and bioinformatics, as well as an increasing databank of experiments on functional variants, have led to new strategies to choose particular amino acid residues to vary in order to increase the chances of obtaining a variant protein with the desired property. Strategies for limiting diversity at each position, design of small sub-libraries, and the performance of scouting experiments, have also been developed or even automated, further reducing the library size.

Engineering proteinase K using machine learning and synthetic genes

Background

Altering a protein's function by changing its sequence allows natural proteins to be converted into useful molecular tools. Current protein engineering methods are limited by a lack of high throughput physical or computational tests that can accurately predict protein activity under conditions relevant to its final application. Here we describe a new synthetic biology approach to protein engineering that avoids these limitations by combining high throughput gene synthesis with machine learning-based design algorithms.

Results

We selected 24 amino acid substitutions to make in proteinase K from alignments of homologous sequences. We then designed and synthesized 59 specific proteinase K variants containing different combinations of the selected substitutions. The 59 variants were tested for their ability to hydrolyze a tetrapeptide substrate after the enzyme was first heated to 68°C for 5 minutes. Sequence and activity data was analyzed using machine learning algorithms. This analysis was used to design a new set of variants predicted to have increased activity over the training set, that were then synthesized and tested. By performing two cycles of machine learning analysis and variant design we obtained 20-fold improved proteinase K variants while only testing a total of 95 variant enzymes.

Conclusion

The number of protein variants that must be tested to obtain significant functional improvements determines the type of tests that can be performed. Protein engineers wishing to modify the property of a protein to shrink tumours or catalyze chemical reactions under industrial conditions have until now been forced to accept high throughput surrogate screens to measure protein properties that they hope will correlate with the functionalities that they intend to modify. By reducing the number of variants that must be tested to fewer than 100, machine learning algorithms make it possible to use more complex and expensive tests so that only protein properties that are directly relevant to the desired application need to be measured. Protein design algorithms that only require the testing of a small number of variants represent a significant step towards a generic, resource-optimized protein engineering process.

WebProAnalyst: an interactive tool for analysis of quantitative structure-activity relationships in protein families

WebProAnalyst is a web-accessible analysis tool () designed for scanning quantitative structure–activity relationships in protein families. The tool allows users to search correlations between protein activity and physicochemical characteristics (i.e. hydrophobicity or alpha-helical amphipathicity) in queried sequences. WebProAnalyst uses aligned amino acid sequences and data on protein activity (pK, K_m, ED₅₀, among others). WebProAnalyst implements methods of the known ProAnalyst package, including the multiple linear regression analysis and the sequence–activity correlation coefficient. In addition, WebProAnalyst incorporates a method based on neural networks. The WebProAnalyst reports a list of sites in protein family, the regression analysis parameters (including correlation values) for the relationships between the amino acid physicochemical characteristics in the site and the protein activity values. WebProAnalyst is useful in search of the amino acid residues that are important for protein function/activity. Furthermore, WebProAnalyst may be helpful in designing the protein-engineering experiments.

Predicting enzyme function from protein sequence

There are two main reasons to try to predict an enzyme's function from its sequence. The first is to identify the components and thus the functional capabilities of an organism, the second is to create enzymes with specific properties. Genomics, expression analysis, proteomics and metabonomics are largely directed towards understanding how information flows from DNA sequence to protein functions within an organism. This review focuses on information flow in the opposite direction: the applicability of what is being learned from natural enzymes to improve methods for catalyst design.

Engineered protein function by selective amino acid diversification

Almost all protein engineering methods rely upon making changes to naturally occurring proteins that already possess some of the desired properties. This will probably remain the case as long as we lack a complete understanding of the way that an amino acid sequence gives rise to a protein with a precisely defined biological function. Common to all methods for altering an existing protein is the selection of a subset of amino acids in the protein for variation and a choice of which substitutions to make at each position. Variants are then tested empirically and further variants are created based upon their performance. Differences between protein engineering methods are the ways in which amino acids are chosen for variation, the protocols followed for creating the variants, and how information regarding variant properties is used in creating subsequent variants. In this article, we describe these differences and provide examples of how the experimental parameters of specific projects determine which method is most suitable.

Computational studies of COX-2 inhibitors: 3D-QSAR and docking

The 3D-QSAR (three-dimensional quantitative structure-activity relationships) studies for 88 selective COX-2 (cyclooxygenase-2) inhibitors belonging to three chemical classes (triaryl rings, diaryl cycloalkanopyrazoles, and diphenyl hydrazides) were conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Partial least squares analysis produced statistically significant models with q(2) values of 0.84 and 0.79 for CoMFA and CoMSIA, respectively. The binding energies calculated from flexible docking were correlated with inhibitory activities by the least-squares fit method. The three chemical classes of inhibitors showed reasonable internal predictability (r(2)=0.51, 0.49, and 0.54), but the sulfonyl-containing inhibitors demonstrated distinctively low binding energy compared to the others. The electrostatic interaction energy between the Arg513 of the COX-2 active site and sulfonyl group of the triaryl rings seemed to have the responsibility for difference in binding energy. Comparative binding energy (COMBINE) analyses gave q(2) values of 0.64, 0.63, and 0.50 for triaryl rings, diaryl cycloalkanopyrazoles, and diphenyl hydrazides, respectively. In this COMBINE model, some protein residues were highlighted as particularly important for inhibitory activity. The combination of ligand-based and structure-based models provided an improved understanding in the interaction between the three chemical classes and the COX-2.

Increased activity of antagonists of growth hormone-releasing hormone substituted at positions 8, 9, and 10

Antagonists of human growth hormone-releasing hormone (hGHRH) with increased potency and improved enzymatic and chemical stability are needed for potential clinical applications. We synthesized 21 antagonistic analogs of hGHRH(1-29)NH(2), substituted at positions 8, 9, and 10 of the common core sequence [phenylacetyl-Tyr(1), d-Arg(2,28), para-chloro-phenylalanine 6, Arg(9)/homoarginine 9, Tyr(10)/O-methyltyrosine 10, alpha-aminobutyric acid 15, norleucine 27, Har(29)] hGHRH(1-29)NH(2). Inhibitory effects on hGHRH-induced GH release were evaluated in vitro in a superfused rat pituitary system, as well as in vivo after i.v. injection into rats. The binding affinities of the peptides to pituitary GHRH receptors were also determined. Introduction of para-amidinophenylalanine 10 yielded antagonists JV-1-62 and -63 with the highest activities in vitro and lowest receptor dissociation constants (K(i) = 0.057-0.062 nM). Antagonists JV-1-62 and -63 also exhibited the strongest effect in vivo, significantly (P < 0.05-0.001) inhibiting hGHRH-induced GH release for at least 1 h. Para-aminophenylalanine 10 and O-ethyltyrosine 10 substitutions yielded antagonists potent in vitro, but His(10), 3,3'-diphenylalanine 10, 2-naphthylalanine 10, and cyclohexylalanine 10 modifications were detrimental. Antagonists containing citrulline 9 (in MZ-J-7-72), amidinophenylalanine 9 (in JV-1-65), His(9), d-Arg(9), citrulline 8, Ala(8), d-Ala(8), or alpha-aminobutyric acid 8 substituents also had high activity and receptor affinity in vitro. However, in vitro potencies of analogs with substitution in position 9 correlated poorly with acute endocrine effects in vivo, as exemplified by the weak and/or short inhibitory actions of antagonists JV-1-65 and MZ-J-7-72 on GH release in vivo. Nevertheless, antagonist JV-1-65 was more potent than JV-1-63 in tests on inhibition of the growth of human prostatic and lung cancer lines xenografted into nude mice. This indicates that oncological activity may be based on several mechanisms. hGHRH antagonists with improved efficacy could be useful for treatment of cancers that depend on insulin-like growth factors or GHRH.

Putting engineering back into protein engineering: bioinformatic approaches to catalyst design

Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.

VSMP: a novel variable selection and modeling method based on the prediction

The use of numerous descriptors that are indicative of molecular structure and topology is becoming more common in quantitative structure-activity relationship (QSAR). How to choose the adequate descriptors for QSAR studies is important but difficult because there are no absolute rules to govern this choice. A variety of variable selection techniques including stepwise, partial least squares/principal component analysis (PLS/PCA), neural network, and evolutionary algorithm such as genetic algorithm have been applied to this common problem. All-subsets regression (ASR) is capable of finding out the best variable subset from among a large pool. In this paper, a novel variable selection and modeling method based on the prediction, for short VSMP, has been developed. Here two controllable parameters, the interrelation coefficient between the pairs of the independent variables (r(int)) and the correlation coefficient (q(2)) obtained using the leave-one-out (LOO) cross-validation technique, are introduced into the ASR to improve its performances. This technique differs from the other variable selection procedures related to the ASR by two main features: (1) The search of various optimal subset search is controlled by the statistic q(2) or root-mean-square error (RMSEP) in the LOO cross-validation step rather than the correlation coefficient obtained in the modeling step (r(2)). (2) The searching speed of all optimal subsets is expedited by the statistic r(int) together with q(2). A comparison of the results of the VSMP applied to the Selwood data set (n = 31 compounds, m = 53 descriptors) with those obtained from alternative algorithms shows the good performance of the technique.

QSAR studies applied to the prediction of antigen-antibody interaction kinetics as measured by BIACORE

The objective of this work was to investigate the potential of the quantitative structure-activity relationships (QSAR) approach for predictive modulation of molecular interaction kinetics. A multivariate QSAR approach involving modifications in peptide sequence and buffer composition was recently used in an attempt to predict the kinetics of peptide-antibody interactions as measured by BIACORE. Quantitative buffer-kinetics relationships (QBKR) and quantitative sequence-kinetics relationships (QSKR) models were developed. Their predictive capacity was investigated in this study by comparing predicted and observed kinetic dissociation parameters (k(d)) for new antigenic peptides, or in new buffers. The range of experimentally measured k(d) variations was small (300-fold), limiting the practical value of the approach for this particular interaction. However, the models were validated from a statistical point of view. In QSKR, the leave-one-out cross validation gave Q(2) = 0.71 for 24 peptides (all but one outlier), compared to 0.81 for 17 training peptides. A more precise model (Q(2) = 0.92) could be developed when removing sets of peptides sharing distinctive structural features, suggesting that different peptides use slightly different binding modes. All models share the most important factor and are informative for structure-kinetics relationships. In QBKR, the measured effect on k(d) of individual additives in the buffers was consistent with the effect predicted from multivariate buffers. Our results open new perspectives for the predictive optimization of interaction kinetics, with important implications in pharmacology and biotechnology.

Mathematical modelling of insect neuropeptide potencies. Are quantitatively predictive models possible?

The potencies of natural adipokinetic hormones and synthetic variants have been determined in Locusta migratoria using the lipid mobilisation assay in vivo, and/or the acetate uptake assay in vitro. These data are combinations of previously published and unpublished data (a total of sixty-nine analogues), and form data sets for the construction of mathematical models of the hormone potencies. The sequence variations of amino acids in both natural and artificial adipokinetic hormone analogues were described using continuous descriptor scales z(1)', z(2)', and z(3)', each previously published scale being derived from various properties of the amino acids. By means of these z'-scales and partial least squares regression we attempted to model the potencies in Locusta migratoria of adipokinetic hormones in the two assays. Correlations (r(2) values) between predicted and actual potencies of the different peptides were up to 0.73. We discuss the potential of the partial least squares method for formulating quantitative relationships between different hormone structures and their potencies, and describe how the procedure might be used in structure-activity prediction with the construction of an optimised peptide data set.

Synthesis and biological evaluation of antagonists of growth hormone-releasing hormone with high and protracted in vivo activities

Some antagonists of human growth hormone-releasing hormone (hGH-RH) synthesized previously were shown to inhibit in vivo proliferation of various human cancers in nude mice. However, the activity of these analogs requires an increase to assure clinical efficacy. In an attempt to prepare hGH-RH antagonists with a high and protracted activity, we synthesized and biologically tested 22 antagonistic analogs of hGH-RH(1-29)NH2. The ability of the antagonists to inhibit hGH-RH-induced GH release was evaluated in vitro in a superfused rat pituitary system, as well as in vivo after i.v. injection into rats. The binding affinity of the peptides to GH-RH receptors also was determined. All antagonistic analogs had the common core sequence [PhAc-Tyr1,D-Arg2, Phe(4-Cl)6 (para-chlorophenylalanine), Abu15 (alpha-aminobutyric acid), Nle27]hGH-RH(1-29)NH2 and contained Arg, D-Arg, homoarginine (Har), norleucine (Nle), and other substitutions. The following analogs were determined to have a high and/or protracted antagonistic activity: [PhAc-Tyr1,D-Arg2,Phe(4-Cl)6,Arg9,Abu15,Nle27, D-Arg29]hGH-RH(1-29)NH2 (JV-1-10), [PhAc-Tyr1,D-Arg2,Phe(4-Cl)6, Abu15,Nle27,D-Arg28,Har29]hGH-RH(1-29)NH2 (MZ-6-55), [PhAc-Tyr1, D-Arg2,Phe(4-Cl)6,Arg9,Abu15,Nle27,D-Arg28,Har29 ]hGH-RH(1-29)NH2 (JV-1-36), and [PhAc-Tyr1,D-Arg2,Phe(4-Cl)6,Har9,Tyr(Me)10,Abu15, Nle27,D-Arg28,Har29]hGH-RH(1-29)NH2 (JV-1-38). Among the peptides tested, analog JV-1-36 showed the highest GH-RH antagonistic activity in vitro and also induced a strong and prolonged inhibition of GH release in vivo for at least 30 min. The antagonist JV-1-38 was slightly less potent than JV-1-36 both in vitro and in vivo but proved to be very long-acting in vivo, suppressing the GH-RH-induced GH release even after 60 min. High and protracted in vivo activities of these antagonists indicate an improvement over earlier GH-RH analogs. Some of these hGH-RH antagonists could find clinical applications in the treatment of cancers dependent on insulin-like growth factors I and II.

A validation study of molecular descriptors for the rational design of peptide libraries

Important molecular descriptors used for establishing quantitative structure-activity relationships are investigated to classify similar versus dissimilar peptides. When searching new lead structures, synthesizing and testing compounds which are too similar wastes time and resources. In contrast, any lead optimization program requires the investigation of similar compounds to that lead. Thus, it is important to maximize or minimize the structural diversity of peptides to design useful compound libraries for lead finding or lead refinement projects. If a molecular descriptor is a useful measure of similarity for the design of peptide libraries, small differences in this descriptor for a pair of molecules should only translate into small biological differences. Using this paradigm as a basis for descriptor validation, it was possible to rank different molecular descriptors. Those physicochemical descriptors are 2D fingerprints and five experimentally or theoretically derived principal property scales. Some theoretically derived metrics are obtained by computing interaction energies or similarity indices on predefined 3D grid points using canonical conformations for individual amino acids. The resulting 3D data matrices are analyzed using a principal component analysis leading to three principal properties for CoMFA (Comparative Molecular Field Analysis) or CoMSIA (Comparative Molecular Similarity Index Analysis) derived molecular fields. The descriptor validation results reveal the applicability of design tools on peptide data sets. Experimentally derived descriptors, in general, are more acceptable than computationally derived metrics, while the latter provide a statistically valid alternative to characterize novel building blocks. The CoMSIA metrics perform slightly better than the CoMFA-based principal properties, while GRID-based descriptors are always less acceptable.