Atomic contact vectors in protein-protein recognition

The ability to analyze and compare protein-protein interactions on the structural level is critical to our understanding of various aspects of molecular recognition and the functional interplay of components of biochemical networks. In this study, we introduce atomic contact vectors (ACVs) as an intuitive way to represent the physico-chemical characteristics of a protein-protein interface as well as a way to compare interfaces to each other. We test the utility of ACVs in classification by using them to distinguish between homodimers and crystal contacts. Our results compare favorably with those reported by other authors. We then apply ACVs to mine the PDB for all known protein-protein complexes and separate transient recognition complexes from permanent oligomeric ones. Getting at the basis of this difference is important for our understanding of recognition and we achieved a success rate of 91% for distinguishing these two classes of complexes. Although accessible surface area of the interface is a major discriminating feature, we also show that there are distinct differences in the contact preferences between the two kinds of complexes. Illustrating the superiority of ACVs as a basic comparison measure over a sequence-based approach, we derive a general rule of thumb to determine whether two protein-protein interfaces are redundant. With this method, we arrive at a nonredundant set of 209 recognition complexes--the largest set reported so far.     

A dissection of specific and non-specific protein-protein interfaces

We compare the geometric and physical-chemical properties of interfaces involved in specific and non-specific protein-protein interactions in crystal structures reported in the Protein Data Bank. Specific interactions are illustrated by 70 protein-protein complexes and by subunit contacts in 122 homodimeric proteins; non-specific interactions are illustrated by 188 pairs of monomeric proteins making crystal-packing contacts selected to bury more than 800 A2 of protein surface. A majority of these pairs have 2-fold symmetry and form "crystal dimers" that cannot be distinguished from real dimers on the basis of the interface size or symmetry. The chemical and amino acid compositions of the large crystal-packing interfaces resemble the protein solvent-accessible surface. These interfaces are less hydrophobic than in homodimers and contain much fewer fully buried atoms. We develop a residue propensity score and a hydrophobic interaction score to assess preferences seen in the chemical and amino acid compositions of the different types of interfaces, and we derive indexes to evaluate the atomic packing, which we find to be less compact at non-specific than at specific interfaces. We test the capacity of these parameters to identify homodimeric proteins in crystal structures, and show that a simple combination of the non-polar interface area and the fraction of buried interface atoms assigns the quaternary structure of 88% of the homodimers and 77% of the monomers in our data set correctly. These success rates increase to 93-95% when the residue propensity score of the interfaces is taken into consideration.     

Extent and nature of contacts between protein molecules in crystal lattices and between subunits of protein oligomers

A survey was compiled of several characteristics of the intersubunit contacts in 58 oligomeric proteins, and of the intermolecular contacts in the lattice for 223 protein crystal structures. The total number of atoms in contact and the secondary structure elements involved are similar in the two types of interfaces. Crystal contact patches are frequently smaller than patches involved in oligomer interfaces. Crystal contacts result from more numerous interactions by polar residues, compared with a tendency toward nonpolar amino acids at oligomer interfaces. Arginine is the only amino acid prominent in both types of interfaces. Potentials of mean force for residue–residue contacts at both crystal and oligomer interfaces were derived from comparison of the number of observed residue–residue interactions with the number expected by mass action. They show that hydrophobic interactions at oligomer interfaces favor aromatic amino acids and methionine over aliphatic amino acids; and that crystal contacts form in such a way as to avoid inclusion of hydrophobic interactions. They also suggest that complex salt bridges with certain amino acid compositions might be important in oligomer formation. For a protein that is recalcitrant to crystallization, substitution of lysine residues with arginine or glutamine is a recommended strategy. Proteins 28:494–514, 1997. © 1997 Wiley-Liss, Inc.     

Characterizing conserved structural contacts by pair-wise relative contacts and relative packing groups

To adequately deal with the inherent complexity of interactions between protein side-chains, we develop and describe here a novel method for characterizing protein packing within a fold family. Instead of approaching side-chain interactions absolutely from one residue to another, we instead consider the relative interactions of contacting residue pairs. The basic element, the pair-wise relative contact, is constructed from a sequence alignment and contact analysis of a set of structures and consists of a cluster of similarly oriented, interacting, side-chain pairs. To demonstrate this construct's usefulness in analyzing protein structure, we used the pair-wise relative contacts to analyze two sets of protein structures as defined by SCOP: the diverse globin-like superfamily (126 structures) and the more uniform heme binding globin family (a 94 structure subset of the globin-like superfamily). The superfamily structure set produced 1266 unique pair-wise relative contacts, whereas the family structure subset gave 1001 unique pair-wise relative contacts. For both sets, we show that these constructs can be used to accurately and automatically differentiate between fold classes. Furthermore, these pair-wise relative contacts correlate well with sequence identity and thus provide a direct relationship between changes in sequence and changes in structure. To capture the complexity of protein packing, these pair-wise relative contacts can be superimposed around a single residue to create a multi-body construct called a relative packing group. Construction of convex hulls around the individual packing groups provides a measure of the variation in packing around a residue and defines an approximate volume of space occupied by the groups interacting with a residue. We find that these relative packing groups are useful in understanding the structural quality of sequence or structure alignments. Moreover, they provide context to calculate a value for structural randomness, which is important in properly assessing the quality of a structural alignment. The results of this study provide the framework for future analysis for correlating sequence changes to specific structure changes.     

Probing the structure of complex macromolecular interactions by homolog specificity scanning: the P1 and P7 plasmid partition systems.

The P1 plasmid partition locus, P1 par, actively distributes plasmid copies to Escherichia coli daughter cells. It encodes two DNA sites and two proteins, ParA and ParB. Plasmid P7 uses a similar system, but the key macromolecular interactions are species specific. Homolog specificity scanning (HSS) exploits such specificities to map critical contact points between component macromolecules. The ParA protein contacts the par operon operator for operon autoregulation, and the ParB contacts the parS partition site during partition. Here, we refine the mapping of these contacts and extend the use of HSS to map protein-protein contacts. We found that ParB participates in autoregulation at the operator site by making a specific contact with ParA. Similarly, ParA acts in partition by making a specific contact with ParB bound at parS. Both these interactions involve contacts between a C-terminal region of ParA and the extreme N-terminus of ParB. As a single type of ParA-ParB complex appears to be involved in recognizing both DNA sites, the operator and the parS sites may both be occupied by a single protein complex during partition. The general HSS strategy may aid in solving the three-dimensional structures of large complexes of macromolecules.     

Gaussian neighborhood: A new measure of accessibility for residues of protein molecules

We introduce a new method for assessing the extent of residue exposure in proteins. For each atom of every residue a Gaussian-weighted atomic surroundings value (the G-neighborhood) is calculated. A normalized sum of G-neighborhood values over all the atoms of a residue is complementary to conventional surface accessibility characteristics. The G-0neighborhood value of a residue is a sensitive indicator of its location, strongly dependent on the 3D structure of a the protein. Correlations between secondary structures and patterns of G-neighborhood values for six different protein molecules are discussed. Comparison of the distribution of hydrophobic and charged residues in the 3D structure for the alcohol-soluble protein crambin and that of five water-soluble proteins (cytochrome c, flavodoxin, myoglobin, rhodanese, and Bence–Jones protein) shows striking differences in their G-neighborhood patterns. Contacts between the prosthetic group and the peptide portion of a protein as well as protein interdomain contacts and monomer–monomer contacts are characterized. © 1993 Wiley-Liss, Inc.     

Conservation helps to identify biologically relevant crystal contacts.

Some crystal contacts are biologically relevant, most are not. We assess the utility of combining measures of size and conservation to discriminate between biological and non-biological contacts. Conservation and size information is calculated for crystal contacts in 53 families of homodimers and 65 families of monomers. Biological contacts are shown to be usually conserved and typically the largest contact in the crystal. A range of neural networks accepting different combinations and encodings of this information is used to answer the following questions: (1) is a given crystal contact biological, and (2) given all crystal contacts in a homodimer, which is the biological one? Predictions for (1) are performed on both homodimer and monomer datasets. The best performing neural network combined size and conservation inputs. For the homodimers, it correctly classified 48 out of 53 biological contacts and 364 out of 366 non-biological contacts, giving a combined accuracy of 98.3 %. A more robust performance statistic, the phi-coefficient, which accounts for imbalances in the dataset, gave a value of 0.92. Taking all 535 non-biological contacts from the 65 monomers, this predictor made erroneous classifications only 4.3 % of the time. Predictions for (2) were performed on homodimers only. The best performing network achieved a prediction accuracy of 98.1 % using size information alone. We conclude that in answering question (1) size and conservation combined discriminate biological from non-biological contacts better than either measure alone. For answering question (2), we conclude that in our dataset size is so powerful a discriminant that conservation adds little predictive benefit.     

A combinatorial score to distinguish biological and nonbiological protein-protein interfaces

With the large amount of protein-protein complex structural data available, to understand the key features governing the specificity of protein-protein recognition and to define a suitable scoring function for protein-protein interaction predictions, we have analyzed the protein interfaces from geometric and energetic points of view. Atom-based potential of mean force (PMFScore), packing density, contact size, and geometric complementarity are calculated for crystal contacts in 74 homodimers and 91 monomers, which include real biological interactions in dimers and nonbiological contacts in monomers and dimers. Simple cutoffs were developed for single and combinatorial parameters to distinguish biological and nonbiological contacts. The results show that PMFScore is a better discriminator between biological and nonbiological interfaces comparable in size. The combination of PMFScore and contact size is the most powerful pairwise discriminator. A combinatorial score (CFPScore) based on the four parameters was developed, which gives the success rate of the homodimer discrimination of 96.6% and error rate of the monomer discrimination of 6.0% and 19.8% according to Valdar's and our definition, respectively. Compared with other statistical learning models, the cutoffs for the four parameters and their combinations are directly based on physical models, simple, and can be easily applied to protein-protein interface analysis and docking studies.     

Helix-helix packing and interfacial pairwise interactions of residues in membrane proteins

Helix-helix packing plays a critical role in maintaining the tertiary structures of helical membrane proteins. By examining the overall distribution of voids and pockets in the transmembrane (TM) regions of helical membrane proteins, we found that bacteriorhodopsin and halorhodopsin are the most tightly packed, whereas mechanosensitive channel is the least tightly packed. Large residues F, W, and H have the highest propensity to be in a TM void or a pocket, whereas small residues such as S, G, A, and T are least likely to be found in a void or a pocket. The coordination number for non-bonded interactions for each of the residue types is found to correlate with the size of the residue. To assess specific interhelical interactions between residues, we have developed a new computational method to characterize nearest neighboring atoms that are in physical contact. Using an atom-based probabilistic model, we estimate the membrane helical interfacial pairwise (MHIP) propensity. We found that there are many residue pairs that have high propensity for interhelical interactions, but disulfide bonds are rarely found in the TM regions. The high propensity pairs include residue pairs between an aromatic residue and a basic residue (W-R, W-H, and Y-K). In addition, many residue pairs have high propensity to form interhelical polar-polar atomic contacts, for example, residue pairs between two ionizable residues, between one ionizable residue and one N or Q. Soluble proteins do not share this pattern of diverse polar-polar interhelical interaction. Exploratory analysis by clustering of the MHIP values suggests that residues similar in side-chain branchness, cyclic structures, and size tend to have correlated behavior in participating interhelical interactions. A chi-square test rejects the null hypothesis that membrane protein and soluble protein have the same distribution of interhelical pairwise propensity. This observation may help us to understand the folding mechanism of membrane proteins.     

An investigation of protein subunit and domain interfaces

Protein structures were collected from the Brookhaven Database of tertiary architectures that displayed oligomeric association (24 molecules) or whose polypeptide folding revealed domains (34 proteins). The subunit and domain interfaces for these proteins were respectively examined from the following aspects: percentage water-accessible surface area buried by the respective associations, surface compositions and physical characteristics of the residues involved in the subunit and domain contacts, secondary structural state of the interface amino acids, preferred polar and non-polar interactions, spatial distribution of polar and non-polar residues on the interface surface, same residue interactions in the oligomeric contacts, and overall cross-section and shape of the contact surfaces. A general, consistent picture emerged for both the domain and subunit interfaces.     

Asymmetric recognition of DNA local distortion. Structure-based functional studies of eukaryotic Msh2-Msh6

Crystal structures of bacterial MutS homodimers bound to mismatched DNA reveal asymmetric interactions of the two subunits with DNA. A phenylalanine and glutamate of one subunit make mismatched base-specific interactions, and residues of both subunits contact the DNA backbone surrounding the mismatched base, but asymmetrically. A number of amino acids in MutS that contact the DNA are conserved in the eukaryotic Msh2-Msh6 heterodimer. We report here that yeast strains with amino acids substituted for residues inferred to interact with the DNA backbone or mismatched base have elevated spontaneous mutation rates consistent with defective mismatch repair. Purified Msh2-Msh6 with substitutions in the conserved Phe(337) and Glu(339) in Msh6 thought to stack or hydrogen bond, respectively, with the mismatched base do have reduced DNA binding affinity but normal ATPase activity. Moreover, wild-type Msh2-Msh6 binds with lower affinity to mismatches with thymine replaced by difluorotoluene, which lacks the ability to hydrogen bond. The results suggest that yeast Msh2-Msh6 interacts asymmetrically with the DNA through base-specific stacking and hydrogen bonding interactions and backbone contacts. The importance of these contacts decreases with increasing distance from the mismatch, implying that interactions at and near the mismatch are important for binding in a kinked DNA conformation.     

Peptide segments in protein-protein interfaces

An important component of functional genomics involves the understanding of protein association. The interfaces resulting from protein-protein interactions - (i) specific, as represented by the homodimeric quaternary structures and the complexes formed by two independently occurring protein components, and (ii) non-specific, as observed in the crystal lattice of monomeric proteins - have been analysed on the basis of the length and the number of peptide segments. In 1000 A2 of the interface area, contributed by a polypeptide chain, there would be 3.4 segments in homodimers, 5.6 in complexes and 6.3 in crystal contacts. Concomitantly, the segments are the longest (with 8.7 interface residues) in homodimers. Core segments (likely to contribute more towards binding) are more in number in homodimers (1.7) than in crystal contacts (0.5), and this number can be used as one of the parameters to distinguish between the two types of interfaces. Dominant segments involved in specific interactions, along with their secondary structural features, are enumerated.     

Atomic and residue hydrophilicity in the context of folded protein structures

Water-protein interactions drive protein folding, stabilize the folded structure, and influence molecular recognition and catalysis. We analyzed the closest protein contacts of 10,837 water molecules in crystallographic structures to define a specific hydrophilicity scale reflecting specific rather than bulk solvent interactions. The tendencies of different atom and residue types to be the nearest protein neighbors of bound water molecules correlated with other hydrophobicity scales, verified the relevance of crystallographically determined water positions, and provided a direct experimental measure of water affinity in the context of the folded protein. This specific hydrophilicity was highly correlated with hydrogen-bonding capacity, and correlated better with experimental than computationally derived measures of partitioning between aqueous and organic phases. Atoms with related chemistry clustered with respect to the number of bound water molecules. Neutral and negatively charged oxygen atoms were the most hydrophilic, followed by positively-charged then neutral nitrogen atoms, followed by carbon and sulfur atoms. Agreement between observed side-chain specific hydrophilicity values and values derived from the atomic hydrophilicity scale showed that hydrophilicity values can be synthesized for different functional groups, such as unusual side or main chains, discontinuous epitopes, and drug molecules. Two methods of atomic hydrophilicity analysis provided a measure of complementarity in the interfaces of trypsin:pancreatic trypsin inhibitor and HIV protease:U-75875 inhibitor complexes. © 1995 Wiley-Liss, Inc.     

Protein-RNA interactions: a structural analysis

A detailed computational analysis of 32 protein–RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein–double-stranded DNA and protein–single-stranded DNA complexes. The interface properties of the protein–RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein–RNA and protein–DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein–RNA complexes, backbone contacts were more dominant in the protein–DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level.     

Towards accurate residue-residue hydrophobic contact prediction for alpha helical proteins via integer linear optimization

A new optimization-based method is presented to predict the hydrophobic residue contacts in alpha-helical proteins. The proposed approach uses a high resolution distance dependent force field to calculate the interaction energy between different residues of a protein. The formulation predicts the hydrophobic contacts by minimizing the sum of these contact energies. These residue contacts are highly useful in narrowing down the conformational space searched by protein structure prediction algorithms. The proposed algorithm also offers the algorithmic advantage of producing a rank ordered list of the best contact sets. This model was tested on four independent alpha-helical protein test sets and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) obtained using the presented method was approximately 66% for single domain proteins. The average true positive and false positive distances were also calculated for each protein test set and they are 8.87 and 14.67 A, respectively.     

All-atom contact potential approach to protein thermostability analysis

In this paper we use all-atom potential energy to define and analyze the inter-residue contacts in mesophilic and thermophilic proteins. Fifteen families of proteins are selected and each family has two representative proteins with greatly different preferred environmental temperatures. We find that both the number and energy of the contacts defined in this way show stronger correlations with the preferred temperatures of proteins than other factors used before. We also find that the charged-polar and charged-nonpolar residue contacts not only have larger contact numbers but also have lower single contact energies. Furthermore, the most important is that most of the thermophilic proteins have more charged-polar and charged-nonpolar residue contacts than their mesophilic counterparts. This suggests that they may play an important role in the thermostability of proteins, except usual charged-charged and nonpolar-nonpolar residue contacts. Charged residues may exert their profound influence by forming contacts not only with other charged residues but also with polar or nonpolar residues, thus further increasing the strength of contact network and then the thermostability of proteins.     

STING Contacts: a web-based application for identification and analysis of amino acid contacts within protein structure and across protein interfaces

Amino acid contacts in terms of atomic interactions are essential factors to be considered in the analysis of the structure of a protein and its complexes. Consequently, molecular biologists do require specific tools for the identification and visualization of all such contacts. Graphical contacts (GC) and interface forming residue graphical contacts (IFRgc) presented here, calculate atomic contacts among amino acids based on a table of predefined pairs of the atom types and their distances, and then display them using number of different forms. The inventory of currently listed contact types by GC and IFRgc include hydrogen bonds (in nine different flavors), hydrophobic interactions, charge-charge interactions, aromatic stacking and disulfide bonds. Such extensive catalog of the interactions, representing the forces that govern protein folding, stability and binding, is the key feature of these two applications. GC and IFRgc are part of STING Millennium Suite. AVAILABILITY: http://sms.cbi.cnptia.embrapa.br/SMS, http://trantor.bioc.columbia.edu/SMS, http://mirrors.rcsb.org//SMS, http://www.es.embnet.org/SMS and http://www.ar.embnet.org/SMS (Options: Graphical Contacts and IFR Graphical Contacts).     

Application of pattern recognition techniques to the analysis of protein crystal structure data. I. Characteristics of per-residue side chain contact frequency distributions

A statistical study of amino acid side chain contact interactions was carried out using a data set based on 36 protein structures. For each type of amino acid, a distribution of per-residue inter-side-chain contacts was obtained, over the observed span of zero to 11 contacts per residue. Significant observations included the following: 1) The mean number of inter-side-chain contacts is proportional to side chain surface area with the exception of Lys and Arg. 2) The mean number of contacts was greater for amino acids in beta-sheet relative to alpha-helical regions. 3) The more polar or surface-loving amino acids exhibited non-normal distributions, whereas distributions for the non-polar or interior-loving amino acids fell within accepted limits of normality.