Feifei Tian

Fluorine Bonding — How Does It Work In Protein−Ligand Interactions?

Although fluorination of pharmacologically active compounds has long been a common strategy to increase their metabolic stability and membrane permeation, the functionality of protein-ligand interactions involving fluorine atoms (fluorine bonding) was only recently recognized in the chemistry and biology communities. In this study, the geometric characteristics and the energetic behaviors of fluorine bonding were systematically investigated by combining two quite disparate but complementary approaches: X-ray structural analysis and theoretical calculations. We found that the short contacts involving fluorine atoms (generalized fluorine bonding) between proteins and fluorinated ligands are very frequent, and these contacts, compared to those routine hydrogen/halogen bonding, are more similar to sulfur-involved hydrogen bonding observed in proteins. ONIOM-based quantum mechanics/molecular mechanics analysis further revealed that fluorine bonding does play an essential role in protein-ligand binding, albeit the strength of isolated fluorine bonding is quite modest. Furthermore, 14 quantum mechanics (QM) and molecular mechanics (MM) methods were performed to reproduce fluorine bonding energies obtained at the rigorous MP2/aug-cc-pVDZ level of theory, and the results showed that most QM and very few MM methods perform well in the reproducibility; the MPWLYP functional and MMFF94 force field are recommended to study moderate and large fluorine bonding systems, respectively.

Geometric characteristics of hydrogen bonds involving sulfur atoms in proteins

Sulfur atoms have been known to participate in hydrogen bonds (H‐bonds) and these sulfur‐containing H‐bonds (SCHBs) are suggested to play important roles in certain biological processes. This study aims to comprehensively characterize all the SCHBs in 500 high‐resolution protein structures (≤1.8 Å). We categorized SCHBs into six types according to donor/acceptor behaviors and used explicit hydrogen approach to distinguish SCHBs from those of nonhydrogen bonding interactions. It is revealed that sulfur atom is a very poor H‐bond acceptor, but a moderately good H‐bond donor. In α‐helix, considerable SCHBs were found between the sulphydryl group of cysteine residue i and the carbonyl oxygen of residue i‐4, and these SCHBs exert effects in stabilizing helices. Although for other SCHBs, they possess no specific secondary structural preference, their geometric characteristics in proteins and in free small compounds are significantly distinct, indicating the protein SCHBs are geometrically distorted. Interestingly, sulfur atom in the disulfide bond tends to form bifurcated H‐bond whereas in cysteine‐cysteine pairs prefer to form dual H‐bond. These special H‐bonds remarkably boost the interaction between H‐bond donor and acceptor. By oxidation/reduction manner, the mutual transformation between the dual H‐bonds and disulfide bonds for cysteine‐cysteine pairs can accurately adjust the structural stability and biological function of proteins in different environments. Furthermore, few loose H‐bonds were observed to form between the sulphydryl groups and aromatic rings, and in these cases the donor H is almost over against the rim rather than the center of the aromatic ring. Proteins 2009.

Computational Peptidology: A New and Promising Approach to Therapeutic Peptide Design

The recent focus on protein-protein interaction networks has increasingly been shifted towards the disruption of protein complexes, which either are mediated by the binding of a globular domain in one protein to a short peptide stretch in another, or involve flat, large, and hydrophobic interfaces that classical small-molecule agents are not always ideally suited. Rational design of therapeutic peptides with high affinity targeting such interactions has emerged as a new and promising tool in discovery of potential drug candidates against associated diseases. The design is commonly based on bioinformatics methods or molecular modeling techniques, indirectly exploiting structure-activity relationship at the level of peptide sequence or directly deriving lead entities from protein complex architecture. Here, a newly rising subfield called computational peptidology that focuses on the use of computational and theoretical approaches to treat peptide-related problems is comprehensively reviewed on the design and discovery of peptide agents targeting protein-protein interactions. We address a systematic discussion on several representative cases in which the computational peptidology is successfully employed to develop peptide therapeutics. Besides, some problems and pitfalls accompanied with the current use of computational methods in peptide modeling and design are also present.

In silico quantitative prediction of peptides binding affinity to human MHC molecule : an intuitive quantitative structure-activity relationship approach

In this paper, we have handpicked 23 kinds of electronic properties, 37 kinds of steric properties, 54 kinds of hydrophobic properties and 5 kinds of hydrogen bond properties from thousands of amino acid structural and property parameters. Principal component analysis (PCA) was applied on these parameters and thus ten score vectors involving significant nonbonding properties of 20 coded amino acids were yielded, called the divided physicochemical property scores (DPPS) of amino acids. The DPPS descriptor was then used to characterize the structures of 152 HLA-A*0201-restricted CTL epitopes, and significant variables being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure–activity relationship (QSAR) model by partial least square was established to predict the peptide-HLA-A*0201 molecule interactions. Statistical analysis on the resulted DPPS-based QSAR models were consistent well with experimental exhibits and molecular graphics display. Diversified properties of the different residues in binding peptides may contribute remarkable effect to the interactions between the HLA-A*0201 molecule and its peptide ligands. Particularly, hydrophobicity and hydrogen bond of anchor residues of peptides may have a significant contribution to the interactions. The results showed that DPPS can well represent the structural characteristics of the antigenic peptides and is a promising approach to predict the affinities of peptide binding to HLA-A*0201 in a efficient and intuitive way. We expect that this physical-principle based method can be applied to other protein–peptide interactions as well.

Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm‐Gaussian processes

In this article, we discuss the application of the Gaussian process (GP) and other statistical methods (PLS, ANN, and SVM) for the modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands. Divided physicochemical property scores of amino acids, involving significant hydrogen bond, electronic, hydrophobic, and steric properties, was used to characterize the peptide structures, and quantitative structure‐affinity relationship models were then constructed by PLS, ANN, SVM, and GP coupled with genetic algorithm‐variable selection. The results show that: (i) since the significant flexibility and high complexity possessed in polypeptide structures, linear PLS method was incapable of fulfilling a satisfying behavior on SH3 domain binding peptide dataset; (ii) the overfitting involved in training process has decreased the predictive power of ANN model to some extent; (iii) both SVM and GP have a good performance for SH3 domain binding peptide dataset. Moreover, by combining linear and nonlinear terms in the covariance function, the GP is capable of handling linear and nonlinear‐hybrid relationship, and which thus obtained a more stable and predictable model than SVM. Analyses of GP models showed that diversified properties contribute remarkable effect to the interactions between the SH3 domain and the peptides. Particularly, steric property and hydrophobicity of P2, electronic property of P0, and electronic property and hydrogen bond property of P−3 in decapeptide (P4P3P2P1P0P−1P−2P−3P−4P−5) significantly contribute to the binding affinities of SH3 domain‐peptide interactions.

Halogen-water-hydrogen bridges in biomolecules

The importance of water in biological systems has long been recognized in chemistry and biology communities. In this article we describe a new manner by which water affects biomolecular behaviors, called halogen-water-hydrogen bridge (XWH bridge), that is, one hydrogen bonding (H-bonding) in water-mediated H-bond bridge is replaced by halogen bonding (X-bonding). Although behaving similarly to water-mediated H-bond motif, the XWH bridge usually stands in multifurcated forms and possesses stronger directionality. Quantum mechanical analysis on several model and real systems reveals that the XWH bridges are more thermodynamically stable than other water-involved interactions, and this stability is further enhanced by the cooperation of X-bonding and H-bonding. Crystal structure survey clearly demonstrates the significance of XWH bridges in stabilization of biomolecular conformations and in mediation of protein-protein, protein-nucleic acid, and receptor-ligand recognition and binding. These findings shed light into the potential value of XWH bridges in drug design and biological engineering.

Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome

In this study, we propose a new peptide characterization method that gives attention to both the amino acid composition and the residue local environment. Using this approach, structural characteristics of peptides derived from Escherichia coli proteome were parameterized and, based upon that, the performance profile of eight statistical modelling methods were validated rigorously and compared comprehensively by applying them to modelling relationship between the sequence structure and retention ability for 816 experimentally measured peptides and to predicting normalized retention times for 121,273 unmeasured peptides in liquid chromatography. Results show that the regression models constructed by nonlinear approaches are more robust and predictable but time-consuming than those by linear ones. In these modelling methods, Gaussian process and back-propagation neural network possess the best stability, unbiased ability and predictive power, thus they can be used to accurately model the peptide structure-retention relationships; multiple linear regression and partial least squares regression perform worse compared to nonlinear modelling techniques but they are computationally efficient, so they are promising candidates for solving the qualitative problems involved in massive data. In addition, by investigating the descriptor importance in different models we found that the amino acid composition presents a significantly linear correlation with the retention time of peptides, whereas the residue environment is mainly correlated nonlinearly with peptide retention. The polar Arg and strongly hydrophobic amino acids such as Leu, Ile, Phe, Trp and Val are the critical factors influencing peptide retention behavior.

2D depiction of nonbonding interactions for protein complexes

A program called the 2D‐GraLab is described for automatically generating schematic representation of nonbonding interactions across the protein binding interfaces. The input file of this program takes the standard PDB format, and the outputs are two‐dimensional PostScript diagrams giving intuitive and informative description of the protein–protein interactions and their energetics properties, including hydrogen bond, salt bridge, van der Waals interaction, hydrophobic contact, π–π stacking, disulfide bond, desolvation effect, and loss of conformational entropy. To ensure these interaction information are determined accurately and reliably, methods and standalone programs employed in the 2D‐GraLab are all widely used in the chemistry and biology community. The generated diagrams allow intuitive visualization of the interaction mode and binding specificity between two subunits in protein complexes, and by providing information on nonbonding energetics and geometric characteristics, the program offers the possibility of comparing different protein binding profiles in a detailed, objective, and quantitative manner. We expect that this 2D molecular graphics tool could be useful for the experimentalists and theoreticians interested in protein structure and protein engineering.

What are the ideal properties for functional food peptides with antihypertensive effect? A computational peptidology approach

Peptides with antihypertensive potency have long been attractive to the medical and food communities. However, serving as food additives, rather than therapeutic agents, peptides should have a good taste. In the present study, we explore the intrinsic relationship between the angiotensin I-converting enzyme (ACE) inhibition and bitterness of short peptides in the framework of computational peptidology, attempting to find out the appropriate properties for functional food peptides with satisfactory bioactivities. As might be expected, quantitative structure-activity relationship modeling reveals a significant positive correlation between the ACE inhibition and bitterness of dipeptides, but this correlation is quite modest for tripeptides and, particularly, tetrapeptides. Moreover, quantum mechanics/molecular mechanics analysis of the structural basis and energetic profile involved in ACE-peptide complexes unravels that peptides of up to 4 amino acids long are sufficient to have efficient binding to ACE, and more additional residues do not bring with substantial enhance in their ACE-binding affinity and, thus, antihypertensive capability. All of above, it is coming together to suggest that the tripeptides and tetrapeptides could be considered as ideal candidates for seeking potential functional food additives with both high antihypertensive activity and low bitterness.

Quantitative Sequence-Activity Model (QSAM): Applying QSAR Strategy to Model and Predict Bioactivity and Function of Peptides, Proteins and Nucleic Acids

Traditional quantitative structure-activity relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. Quantitative sequence-activity model (QSAM), applying QSAR strategy to explore sequence-activity/function relationship for biosystems, is greatly meaningful but meanwhile extremely difficult. For biomolecules, high molecular weight, diverse structural morphology and intricate interaction network all bring in traditional QSAR methodologies unprecedented challenges. This article comprehensively reviewed developing process, current state and future perspective of QSAM, concerning its applications into fields of pharmacy, food science, immunology and molecular biology. Besides, discipline-crossing and amalgamation of QSAM with QSAR, bioinformatics and computational biology were also discussed.