Geometrically centered region: A “wet” model of protein binding hot spots not excluding water molecules

Prediction of quaternary structure by analysis of hot spot residues in protein-protein interfaces: the case of anthranilate phosphoribosyltransferases

It is an important goal of computational biology to correctly predict the association state of a protein based on its amino acid sequence and the structures of known homologues. We have pursued this goal on the example of anthranilate phosphoribosyltransferase (AnPRT), an enzyme that is involved in the biosynthesis of the amino acid tryptophan. Firstly, known crystal structures of naturally occurring homodimeric AnPRTs were analyzed using the Protein Interfaces, Surfaces, and Assemblies (PISA) service of the European Bioinformatics Institute (EBI). This led to the identification of two hydrophobic "hot spot" amino acids in the protein-protein interface that were predicted to be essential for self-association. Next, in a comprehensive multiple sequence alignment (MSA), naturally occurring AnPRT variants with hydrophilic or charged amino acids in place of hydrophobic residues in the two hot spot positions were identified. Representative variants were characterized in terms of thermal stability, enzymatic activity, and quaternary structure. We found that AnPRT variants with charged residues in both hot spot positions exist exclusively as monomers in solution. Variants with hydrophilic amino acids in one hot spot position occur in both forms, monomer and dimer. The results of the present study provide a detailed characterization of the determinants of the AnPRT monomer-dimer equilibrium and show that analysis of hot spots in combination with MSAs can be a valuable tool in prediction of protein quaternary structures.

Burial Level Change Defines a High Energetic Relevance for Protein Binding Interfaces

Protein-protein interfaces defined through atomic contact or solvent accessibility change are widely adopted in structural biology studies. But, these definitions cannot precisely capture energetically important regions at protein interfaces. The burial depth of an atom in a protein is related to the atom's energy. This work investigates how closely the change in burial level of an atom/residue upon complexation is related to the binding. Burial level change is different from burial level itself. An atom deeply buried in a monomer with a high burial level may not change its burial level after an interaction and it may have little burial level change. We hypothesize that an interface is a region of residues all undergoing burial level changes after interaction. By this definition, an interface can be decomposed into an onion-like structure according to the burial level change extent. We found that our defined interfaces cover energetically important residues more precisely, and that the binding free energy of an interface is distributed progressively from the outermost layer to the core. These observations are used to predict binding hot spots. Our approach's F-measure performance on a benchmark dataset of alanine mutagenesis residues is much superior or similar to those by complicated energy modeling or machine learning approaches.

Hot spot-based design of small-molecule inhibitors for protein-protein interactions

Protein-protein interactions (PPIs) are important targets for the development of chemical probes and therapeutic agents. From the initial discovery of the existence of hot spots at PPI interfaces, it has been proposed that hot spots might provide the key for developing small-molecule PPI inhibitors. However, there has been no review on the ways in which the knowledge of hot spots can be used to achieve inhibitor design, nor critical examination of successful examples. This Digest discusses the characteristics of hot spots and the identification of druggable hot spot pockets. An analysis of four examples of hot spot-based design reveals the importance of this strategy in discovering potent and selective PPI inhibitors. A general procedure for hot spot-based design of PPI inhibitors is outlined.

Solvent-accessible surface area: How well can be applied to hot-spot detection?

A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set.

Beta atomic contacts: identifying critical specific contacts in protein binding interfaces

Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the β-skeletons in computational geometry, requires that there is no other atom in the contact spheres defined by two contact atoms; this sphere is similar to the van der Waals spheres of atoms. The statistical analysis on a large dataset shows that β contacts are only a small fraction of conventional distance-based contacts. To empirically quantify the importance of β contacts, we design βACV, an SVM classifier with β contacts as input, to classify homodimers from crystal packing. We found that our βACV is able to achieve the state-of-the-art classification performance superior to SVM classifiers with distance-based contacts as input. Our βACV also outperforms several existing methods when being evaluated on several datasets in previous works. The promising empirical performance suggests that β contacts can truly identify critical specific contacts in protein binding interfaces. β contacts thus provide a new model for more precise description of atomic organization in protein quaternary structures than distance-based contacts.

Epistructural tension promotes protein associations

Epistructural tension is the reversible work per unit area required to span the aqueous interface of a soluble protein structure. The parameter accounts for the free-energy cost of imperfect hydration, involving water molecules with a shortage of hydrogen-bonding partnerships relative to bulk levels. The binding hot spots along protein-protein interfaces are identified with residues that contribute significantly to the epistructural tension in the free subunits. Upon association, such residues either displace or become deprived of low-coordination vicinal water molecules.

Wrapping mimicking in drug-like small molecules disruptive of protein-protein interfaces

The discovery of small-molecule drugs aimed at disrupting protein-protein associations is expected to lead to promising therapeutic strategies. The small molecule binds to the target protein thus replacing its natural protein partner. Noteworthy, structural analysis of complexes between successful disruptive small molecules and their target proteins has suggested the possibility that such ligands might somehow mimic the binding behavior of the protein they replace. In these cases, the molecules show a spatial and "chemical" (i.e., hydrophobicity) similarity with the residues of the partner protein involved in the protein-protein complex interface. However, other disruptive small molecules do not seem to show such spatial and chemical correspondence with the replaced protein. In turn, recent progress in the understanding of protein-protein interactions and binding hot spots has revealed the main role of intermolecular wrapping interactions: three-body cooperative correlations in which nonpolar groups in the partner protein promote dehydration of a two-body electrostatic interaction of the other protein. Hence, in the present work, we study some successful complexes between already discovered small disruptive drug-like molecules and their target proteins already reported in the literature and we compare them with the complexes between such proteins and their natural protein partners. Our results show that the small molecules do in fact mimic to a great extent the wrapping behavior of the protein they replace. Thus, by revealing the replacement the small molecule performs of relevant wrapping interactions, we convey precise physical meaning to the mimicking concept, a knowledge that might be exploited in future drug-design endeavors.

Progressive dry-core-wet-rim hydration trend in a nested-ring topology of protein binding interfaces

BACKGROUND

Water is an integral part of protein complexes. It shapes protein binding sites by filling cavities and it bridges local contacts by hydrogen bonds. However, water molecules are usually not included in protein interface models in the past, and few distribution profiles of water molecules in protein binding interfaces are known.

RESULTS

In this work, we use a tripartite protein-water-protein interface model and a nested-ring atom re-organization method to detect hydration trends and patterns from an interface data set which involves immobilized interfacial water molecules. This data set consists of 206 obligate interfaces, 160 non-obligate interfaces, and 522 crystal packing contacts. The two types of biological interfaces are found to be drier than the crystal packing interfaces in our data, agreeable to a hydration pattern reported earlier although the previous definition of immobilized water is pure distance-based. The biological interfaces in our data set are also found to be subject to stronger water exclusion in their formation. To study the overall hydration trend in protein binding interfaces, atoms at the same burial level in each tripartite protein-water-protein interface are organized into a ring. The rings of an interface are then ordered with the core atoms placed at the middle of the structure to form a nested-ring topology. We find that water molecules on the rings of an interface are generally configured in a dry-core-wet-rim pattern with a progressive level-wise solvation towards to the rim of the interface. This solvation trend becomes even sharper when counterexamples are separated.

CONCLUSIONS

Immobilized water molecules are regularly organized in protein binding interfaces and they should be carefully considered in the studies of protein hydration mechanisms.

IgE-binding epitopes: a reappraisal

Here, we discuss various questions related to IgE epitopes: What are the technical possibilities and pitfalls, what is currently known, how can we put this information into hypothetical frameworks and the unavoidable question: how useful is this information for patient care or allergenicity prediction? We discuss the information obtained by (i) 3D structures of allergen-antibody complexes; (ii) analysis of allergen analogues; (iii) mimics without obvious structural similarity; (iv) mAbs competing with IgE; (v) repertoire analysis of cloned IgEs, and other developments. Based on limited data, four suggestions are presented in the literature: (i) IgE might be more cross-reactive than IgG; (ii) IgE might be more often directed to immunologically 'uninviting' surfaces; (iii) IgE epitopes may tend to cluster and (iv) IgE paratopes might have a higher intrinsic flexibility. While these are not proven facts, they still can generate hypotheses for future research. The hypothesis is put forward that the IgE repertoire of switched B-cells is less influenced by positive selection, because positive selection might not be able to rescue IgE-switched B cells. While this might be of interest for the discussion about mechanisms leading to allergen-sensitization, we need to be modest in answering the 'clinical relevance' question. Current evidence indicates the IgE-epitope repertoire is too big to make specific IgE epitopes a realistic target for diagnosis, treatment or allergenicity prediction. In-depth analysis of a few selected IgE epitope-peptides or mimitopes derived from allergen-sequences and from random peptide libraries, respectively, might well prove rewarding in relation to diagnosis and prognosis of allergy, particularly food allergy.

Structural analysis of the hot spots in the binding between H1N1 HA and the 2D1 antibody: do mutations of H1N1 from 1918 to 2009 affect much on this binding?

MOTIVATION

Worldwide and substantial mortality caused by the 2009 H1N1 influenza A has stimulated a new surge of research on H1N1 viruses. An epitope conservation has been learned in the HA1 protein that allows antibodies to cross-neutralize both 1918 and 2009 H1N1. However, few works have thoroughly studied the binding hot spots in those two antigen-antibody interfaces which are responsible for the antibody cross-neutralization.

RESULTS

We apply predictive methods to identify binding hot spots at the epitope sites of the HA1 proteins and at the paratope sites of the 2D1 antibody. We find that the six mutations at the HA1's epitope from 1918 to 2009 should not harm its binding to 2D1. Instead, the change of binding free energy on the whole exhibits an increased tendency after these mutations, making the binding stronger. This is consistent with the observation that the 1918 H1N1 neutralizing antibody can cross-react with 2009 H1N1. We identified three distinguished hot spot residues, including Lys(166), common between the two epitopes. These common hot spots again can explain why 2D1 cross-reacted. We believe that these hot spot residues are mutation candidates which may help H1N1 viruses to evade the immune system. We also identified eight residues at the paratope site of 2D1, five from its heavy chain and three from its light chain, that are predicted to be energetically important in the HA1 recognition. The identification of these hot spot residues and their structural analysis are potentially useful to fight against H1N1 viruses.

CONTACT

jinyan.li@uts.edu.au

AVAILABILITY

Z-score is available at http://155.69.2.25/liuqian/indexz.py

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DBAC: a simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts

BACKGROUND

A protein binding hot spot is a cluster of residues in the interface that are energetically important for the binding of the protein with its interaction partner. Identifying protein binding hot spots can give useful information to protein engineering and drug design, and can also deepen our understanding of protein-protein interaction. These residues are usually buried inside the interface with very low solvent accessible surface area (SASA). Thus SASA is widely used as an outstanding feature in hot spot prediction by many computational methods. However, SASA is not capable of distinguishing slightly buried residues, of which most are non hot spots, and deeply buried ones that are usually inside a hot spot.

RESULTS

We propose a new descriptor called "burial level" for characterizing residues, atoms and atomic contacts. Specifically, burial level captures the depth the residues are buried. We identify different kinds of deeply buried atomic contacts (DBAC) at different burial levels that are directly broken in alanine substitution. We use their numbers as input for SVM to classify between hot spot or non hot spot residues. We achieve F measure of 0.6237 under the leave-one-out cross-validation on a data set containing 258 mutations. This performance is better than other computational methods.

CONCLUSIONS

Our results show that hot spot residues tend to be deeply buried in the interface, not just having a low SASA value. This indicates that a high burial level is not only a necessary but also a more sufficient condition than a low SASA for a residue to be a hot spot residue. We find that those deeply buried atoms become increasingly more important when their burial levels rise up. This work also confirms the contribution of deeply buried interfacial atomic contacts to the energy of protein binding hot spot.