Toward quantitative characterization of the binding profile between the human amphiphysin-1 SH3 domain and its peptide ligands

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Background:

Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.

Methods:

We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.

Results:

Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.

Conclusion:

There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Streptococcus pneumoniae Genome-wide Identification and Characterization of BOX Element-binding Domains

The BOX elements are short repetitive DNA sequences that distribute randomly in intergenic regions of the Streptococcus pneumoniae genome. The function and origin of such elements are still unknown, but they were found to modulate expression of neighboring genes. Evidences suggested that the modulation's mechanism can be fulfilled by sequence-specific interaction of BOX elements with transcription factor family proteins. However, the type and function of these BOX-binding proteins still remain largely unexplored to date. In the current study we described a synthetic protocol to investigate the recognition and interaction between a highly conserved site of BOX elements and the DNA-binding domains of a variety of putative transcription factors in the pneumococcal genome. With the protocol we were able to predict those high-affinity domain binders of the conserved BOX DNA site (BOX DNA) in a high-throughput manner, and analyzed sequence-specific interaction in the domainDNA recognition at molecular level. Consequently, a number of putative transcription factor domains with both high affinity and specificity for the BOX DNA were identified, from which the helix-turn-helix (HTH) motif of a small heat shock factor was selected as a case study and tested for its binding capability toward the double-stranded BOX DNA using fluorescence anisotropy analysis. As might be expected, a relatively high affinity was detected for the interaction of HTH motif with BOX DNA with dissociation constant at nanomolar level. Molecular dynamics simulation, atomic structure examination and binding energy analysis revealed a complicated network of intensive nonbonded interactions across the complex interface, which confers both stability and specificity for the complex architecture.

The critical aggregation concentration of peptide surfactants is predictable from dynamic hydrophobic property

Peptide surfactants are a kind of newly emerged functional materials, which have a variety of applications such as building nanoarchitecture, stabilizing membrane proteins and controlling drug release. In the present study, we report the modelling and prediction of critical aggregation concentration (CAC), an important parameter that characterizes the self-assembling behaviour of peptide surfactants through the use of statistical modelling and quantitative structure-property relationship (QSPR) approaches. In order to accurately describe the structural and physicochemical properties of the highly flexible peptide molecules, a new method called molecular dynamics-based hydrophobic cross-field (MD-HCF) is proposed to capture both the hydrophobic profile and dynamic feature of 32 surface-activity, structure-known peptides. A number of statistical models are then developed using partial least squares (PLS) regression with or without improvement by genetic algorithm (GA). We demonstrate that MD-HCF performs much better than the widely used CODESSA method in both its predictability and interpretability. We also highlight the importance of dynamic hydrophobic property in accurate prediction and reasonable explanation of peptide self-assembling behaviour in solution, albeit which is exhaustive to compute compared with those derived directly from peptide static structure. To the best of our knowledge, this study is the first to computationally model and predict the self-assembling behaviour of peptide surfactants.

Exploring the substructural space of indole-3-carboxamide derivatives binding to renin: a novel active-site spatial partitioning approach

Renin has recently attracted much attention in the antihypertensive community, since this enzyme starts the angiotensin-converting cascade and forms the rate-limiting step in this cascade. In the present study, we describe a new method called active-site spatial partitioning (ASSP) for quantitatively characterizing the nonbonding interaction profile between renin and the substructures of indole-3-carboxamide derivatives-a novel class of achiral renin inhibitors that exhibit both high affinity and strong specificity for renin, thus blocking its active state-on the basis of structural models of protein-ligand complexes. It is shown that the ASSP-derived potential parameters are highly correlated with the experimentally measured activities of indole-3-carboxamides; the statistical models linking the parameters and activities using a sophisticated partial least squares regression technique show much promise as an effective and powerful tool for generalizing and predicting the pharmaceutical potencies and the physicochemical properties of other modified derivatives. Furthermore, by visually examining substructure-color plots generated by the ASSP procedure, it is found that the relative importance of nonbonding contributions to the recognition and binding of a ligand by renin is as follows: steric < hydrophobic < electrostatic. The polar and charged moieties that float on the surface of the ligand molecule play a critical role in conferring electrostatic stability and specificity to renin-ligand complexes, whereas the aromatic rings embedded in the core region of the ligand are the main source of hydrophobic and steric potentials that lead to substantial stabilization of the complex architecture.

Predicting the Flexibility Profile of Ribosomal RNAs

Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye-Waller factor or B-factor. Although numerous works have been addressed on theoretical and computational studies of the B-factor profiles of proteins, the methods used for predicting B-factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure-flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B-factor based on primary sequences (sequence-based) and advanced structures (structure-based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B-factors, we demonstrate that (i) rRNA B-factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure-based approach performed much better as compared to sequence-based methods in modeling of rRNA B-factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.

Predicting the binding affinity of epitope-peptides with HLA-A*0201 by encoding atom-pair non-covalent interaction information between receptor and ligands

A structure-based method was used to characterize the non-covalent interactions of HLA-A*0201 with its peptide ligands. In this procedure, protein and peptide atoms were classified into 16 types in terms of their chemical property and local environment, and a 16 x 16 matrix was then defined to describe the interaction mode of 256 atom-pairs between the receptor and ligand in a complex structure. Three biologically related chemical forces as electrostatic, van der Waals, and hydrophobic potentials were separately calculated for each element of the matrix to yield 768 structural descriptors encoding the detailed information about the non-covalent interactions involved in protein-peptide binding. We employed this method to perform quantitative structure-activity relationship (QSAR) study of a data panel consisting of 419 non-apeptides with known binding affinities to HLA-A*0201 protein. Several QSAR models were constructed using partial least square regression (PLS) coupled with or without genetic algorithm (GA)-variable selection, and these models were validated rigorously and investigated systematically by using external test set and one-way analysis of variance. Results show that diverse properties have significant contributions to the HLA-A*0201-peptide binding. Particularly, the hydrophobicity and electrostatic property at the anchor residues of peptides confer a significant specificity and stability for the bound complexes.