Yanrong Ren

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.

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.

Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity

Quantitative structure–activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482–491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.

Halogen-Ionic Bridges: Do They Exist in the Biomolecular World?

If considering that the pronouncedly charged halide anions are ubiquitous in the biological world, then it is interesting to ask whether the halogen-ionic bridges-this term is named by us to describe the interaction motif of a nonbonded halogen ion with two or more electrophiles simultaneously-commonly exist in biomolecules and how they contribute to the stability and specificity of biomolecular folding and binding? To address these problems, we herein present a particularly systematic investigation on the geometrical profile and the energy landscape of halogen ions interacting with and bridging between polar and charged molecular moieties in small model systems and real crystal structures, by means of ab initio calculation, database survey, continuum electrostatic analysis, and hybrid quantum mechanics/molecular mechanics examination. All of these unequivocally demonstrate that this putative halide motif is broadly distributed in biomolecular systems (>6000) and can confer a substantial stabilization for the architecture of proteins and their complexes with nucleic acids and small ligands. This stabilization energy is estimated to be generally more than 100 kcal·mol(-1) for gas-phase states or about 20 kcal·mol(-1) for solution conditions, which is much greater than that found in sophisticated water-mediated (<10 kcal·mol(-1)) and salt (∼ 3.66 kcal·mol(-1)) bridges. In this respect, we would expect that the proposed halogen-ionic bridge, which has long been unrecognized in the arena of biological repertoires, could be appreciated in chemistry and biology communities and might be exploited as a new and versatile tool for rational drug design and bioengineering.

Systematic classification and analysis of themes in protein-DNA recognition.

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.

Gaussian process: a promising approach for the modeling and prediction of Peptide binding affinity to MHC proteins.

On the basis of Bayesian probabilistic inference, Gaussian process (GP) is a powerful machine learning method for nonlinear classification and regression, but has only very limited applications in the new areas of computational vaccinology and immunoinformatics. In the current work, we present a paradigmatic study of using GP regression technique to quantitatively model and predict the binding affinities of over 7000 immunodominant peptide epitopes to six types of human major histocompatibility complex (MHC) proteins. In this procedure, the sequence patterns of diverse peptides are characterized quantitatively and the resulting variables are then correlated with the experimentally measured affinities between different MHC and their peptide ligands, by using a linearity- and nonlinearity-hybrid GP approach. We also make systematical comparisons between the GP and two sophisticated modeling methods as partial least square (PLS) regression and support vector machine (SVM) with respect to their fitting ability, predictive power and generalization capability. The results suggest that GP could be a new and effective tool for the modeling and prediction of MHC-peptide interactions and would be promising in the field of computer-aided vaccine design (CAVD).

Quantitative prediction of the thermal motion and intrinsic disorder of protein cofactors in crystalline state: A case study on halide anions

The thermal motion and intrinsic disorder of protein cofactors are highly correlated with their biological functions and can be at least in part measured by atomic temperature factor or B-factor. However, this crystallographic parameter, which actually shares the equal importance with the atomic coordinate in describing the complete profile of crystal structures, has long been underappreciated in the field of biology. In the present study, we attempt to put the first step towards the quantitative prediction of the B-factor values of halide anions, which were recently found to play a fundamental role in conferring stability and specificity to the architecture of proteins and their complexes with nucleic acids and small ligands. In this procedure, the local nonbonding landscapes of halide anions bound in proteins are characterized by electrostatic and dispersion potentials, and then the resulting descriptors of the characterization are statistically correlated with experimentally measured B-factors by using both linear and nonlinear machine learning approaches. From the modeling results and the comparison of these results to those obtained previously for predicting protein B-factors, we demonstrate that the dynamic behavior of halide anions in protein crystals is primarily governed by the local features of nonbonding potential landscapes and, owing to the non-ignorable noise existing in experimental data, the relationship between the B-factor values and the local nonbonding landscapes can only be modeled at a moderate level of accuracy even using the complicated nonlinear methods. These findings are consistent well with that concluding from previous studies of protein B-factors.

Toward a Quantitative Model and Prediction of the Cloud Point of Normal Nonionic Surfactants and Novel Gemini Surfactants with Heuristic Method and Gaussian Process

A systematic study of integrating statistical modeling and experimental analysis to investigate the cloud point (CP) and environmental risk of 82 structurally diverse nonionic surfactants is performed. During this procedure, the structural profiles of the studied compounds are characterized using hydrophilic domain and the whole molecular. Hundreds of descriptors, including constitutional, topological, geometrical, and electrostatic were calculated by the CODESSA program, and the resulting variables of the characterization selected by heuristic method are then modeled by the Gaussian process (GP). A variety of regression techniques, including MLR, PLS, SVM, and LSSVM are performed to a comprehensive comparison with GP on the basis of statistical analysis and experimental properties, in conjunction with the sophisticated variable selection methods, that is, empirical heuristic strategy. Among all the built models, the most predictable one is constructed based on the GP modeling combination of heuristic variable selection related to hydrophilic domain, with its predictive coefficient of determination ( ) and root-mean-square error of prediction (RMSP) on external independent test set of 0.962 and 5.200, respectively. The statistic model shows that the CP phenomenon is a comprehensive interaction of relative molecular weight, moments of inertia A, and topological structure account for hydrophilic part.