Distinct roles of overlapping and non-overlapping regions of hub protein interfaces in recognition of multiple partners

Cellular functions of an organism are maintained by protein-protein interactions. Those proteins that bind multiple partners asynchronously (date hub proteins) are important to make the interaction network coordinated. It is known that many date hub proteins bind different partners at overlapping (OV) interfaces. To understand how OV interfaces of date hub proteins can recognize multiple partners, we analyzed the difference between OV and non-overlapping (Non-OV) regions of interfaces involved in the binding of different partners. By using the structures of 16 date hub proteins with various interaction partners (ranging from 5 to 33), we compared buried surface area, compositions of amino acid residues and secondary structures, and side-chain orientations. It was found that buried interface residues are important for recognizing multiple partners, while exposed interface residues are important for determining specificity to a particular ligand. In addition, our analyses reveal that residue compositions in OV and Non-OV regions are different and that residues in OV region show diverse side-chain torsion angles to accommodate binding to multiple targets.     

Residue frequencies and pairing preferences at protein-protein interfaces

We used a nonredundant set of 621 protein-protein interfaces of known high-resolution structure to derive residue composition and residue-residue contact preferences. The residue composition at the interfaces, in entire proteins and in whole genomes correlates well, indicating the statistical strength of the data set. Differences between amino acid distributions were observed for interfaces with buried surface area of less than 1,000 A(2) versus interfaces with area of more than 5,000 A(2). Hydrophobic residues were abundant in large interfaces while polar residues were more abundant in small interfaces. The largest residue-residue preferences at the interface were recorded for interactions between pairs of large hydrophobic residues, such as Trp and Leu, and the smallest preferences for pairs of small residues, such as Gly and Ala. On average, contacts between pairs of hydrophobic and polar residues were unfavorable, and the charged residues tended to pair subject to charge complementarity, in agreement with previous reports. A bootstrap procedure, lacking from previous studies, was used for error estimation. It showed that the statistical errors in the set of pairing preferences are generally small; the average standard error is approximately 0.2, i.e., about 8% of the average value of the pairwise index (2.9). However, for a few pairs (e.g., Ser-Ser and Glu-Asp) the standard error is larger in magnitude than the pairing index, which makes it impossible to tell whether contact formation is favorable or unfavorable. The results are interpreted using physicochemical factors and their implications for the energetics of complex formation and for protein docking are discussed. Proteins 2001;43:89-102.     

Analysing six types of protein-protein interfaces

Non-covalent residue side-chain interactions occur in many different types of proteins and facilitate many biological functions. Are these differences manifested in the sequence compositions and/or the residue-residue contact preferences of the interfaces? Previous studies analysed small data sets and gave contradictory answers. Here, we introduced a new data-mining method that yielded the largest high-resolution data set of interactions analysed. We introduced an information theory-based analysis method. On the basis of sequence features, we were able to differentiate six types of protein interfaces, each corresponding to a different functional or structural association between residues. Particularly, we found significant differences in amino acid composition and residue-residue preferences between interactions of residues within the same structural domain and between different domains, between permanent and transient interfaces, and between interactions associating homo-oligomers and hetero-oligomers. The differences between the six types were so substantial that, using amino acid composition alone, we could predict statistically to which of the six types of interfaces a pool of 1000 residues belongs at 63-100% accuracy. All interfaces differed significantly from the background of all residues in SWISS-PROT, from the group of surface residues, and from internal residues that were not involved in non-trivial interactions. Overall, our results suggest that the interface type could be predicted from sequence and that interface-type specific mean-field potentials may be adequate for certain applications.     

Folding of beta/alpha-unit scrambled forms of S. cerevisiae triosephosphate isomerase: Evidence for autonomy of substructure formation and plasticity of hydrophobic and hydrogen bonding interactions in core of (beta/alpha)8-barrel

We show that residues at the interfaces of protein-protein complexes have higher side-chain energy than other surface residues. Eight different sets of protein complexes were analyzed. For each protein pair, the complex structure was used to identify the interface residues in the unbound monomer structures. Side-chain energy was calculated for each surface residue in the unbound monomer using our previously developed scoring function.1 The mean energy was calculated for the interface residues and the other surface residues. In 15 of the 16 monomers, the mean energy of the interface residues was higher than that of other surface residues. By decomposing the scoring function, we found that the energy term of the buried surface area of non-hydrogen-bonded hydrophilic atoms is the most important factor contributing to the high energy of the interface regions. In spite of lacking hydrophilic residues, the interface regions were found to be rich in buried non-hydrogen-bonded hydrophilic atoms. Although the calculation results could be affected by the inaccuracy of the scoring function, patch analysis of side-chain energy on the surface of an isolated protein may be helpful in identifying the possible protein-protein interface. A patch was defined as 20 residues surrounding the central residue on the protein surface, and patch energy was calculated as the mean value of the side-chain energy of all residues in the patch. In 12 of the studied monomers, the patch with the highest energy overlaps with the observed interface. The results are more remarkable when only three residues with the highest energy in a patch are averaged to derive the patch energy. All three highest-energy residues of the top energy patch belong to interfacial residues in four of the eight small protomers. We also found that the residue with the highest energy score on the surface of a small protomer is very possibly the key interaction residue.     

Analysis of side-chain rotamers in transmembrane proteins

We measured the frequency of side-chain rotamers in 14 alpha-helical and 16 beta-barrel membrane protein structures and found that the membrane environment considerably perturbs the rotamer frequencies compared to soluble proteins. Although there are limited experimental data, we found statistically significant changes in rotamer preferences depending on the residue environment. Rotamer distributions were influenced by whether the residues were lipid or protein facing, and whether the residues were found near the N- or C-terminus. Hydrogen-bonding interactions with the helical backbone perturbs the rotamer populations of Ser and His. Trp and Tyr favor side-chain conformations that allow their side chains to extend their polar atoms out of the membrane core, thereby aligning the side-chain polarity gradient with the polarity gradient of the membrane. Our results demonstrate how the membrane environment influences protein structures, providing information that will be useful in the structure prediction and design of transmembrane proteins.     

Amino acid pairing preferences in parallel beta-sheets in proteins

Statistical approaches have been applied to examine amino acid pairing preferences within parallel beta-sheets. The main chain hydrogen bonding pattern in parallel beta-sheets means that, for each residue pair, only one of the residues is involved in main chain hydrogen bonding with the strand containing the partner residue. We call this the hydrogen bonded (HB) residue and the partner residue the non-hydrogen bonded (nHB) residue, and differentiate between the favorability of a pair and that of its reverse pair, e.g. Asn(HB)-Thr(nHB)versus Thr(HB)-Asn(nHB). Significantly (p < or = 0.000001) favoured pairings were rationalised using stereochemical arguments. For instance, Asn(HB)-Thr(nHB) and Arg(HB)-Thr(nHB) were favoured pairs, where the residues adopted favoured chi1 rotamer positions that allowed side-chain interactions to occur. In contrast, Thr(HB)-Asn(nHB) and Thr(HB)-Arg(nHB) were not significantly favoured, and could only form side-chain interactions if the residues involved adopted less favourable chi1 conformations. The favourability of hydrophobic pairs e.g. Ile(HB)-Ile(nHB), Val(HB)-Val(nHB) and Leu(HB)-Ile(nHB) was explained by the residues adopting their most preferred chi1 and chi2 conformations, which enabled them to form nested arrangements. Cysteine-cysteine pairs are significantly favoured, although these do not form intrasheet disulphide bridges. Interactions between positively and negatively charged residues were asymmetrically preferred: those with the negatively charged residue at the HB position were more favoured. This trend was accounted for by the presence of general electrostatic interactions, which, based on analysis of distances between charged atoms, were likely to be stronger when the negatively charged residue is the HB partner. The Arg(HB)-Asp(nHB) interaction was an exception to this trend and its favorability was rationalised by the formation of specific side-chain interactions. This research provides rules that could be applied to protein structure prediction, comparative modelling and protein engineering and design. The methods used to analyse the pairing preferences are automated and detailed results are available (http://www.rubic.rdg.ac.uk/betapairprefsparallel/).     

Protein-protein interfaces: properties, preferences, and projections

Herein, we study the interfaces of a set of 146 transient protein-protein interfaces in order to better understand the principles of their interactions. We define and generate the protein interface using tools from computational geometry and topology and then apply statistical analysis to its residue composition. In addition to counting individual occurrences, we evaluate pairing preferences, both across and as neighbors on one side of an interface. Likelihood correction emphasizes novel and unexpected pairs, such as the His-Cys pair found in most complexes of serine proteases with their diverse inhibitors and the Met-Met neighbor pair found in unrelated protein interfaces. We also present a visualization of the protein interface that allows for facile identification of residue-residue contacts and other biochemical properties.     

A C-H triplebond O hydrogen bond stabilized polypeptide chain reversal motif at the C terminus of helices in proteins

The serendipitous observation of a C-H cdots, three dots, centered O hydrogen bond mediated polypeptide chain reversal in synthetic peptide helices has led to a search for the occurrence of a similar motif in protein structures. From a dataset of 634 proteins, 1304 helices terminating in a Schellman motif have been examined. The C-H triplebond O interaction between the T-4 C(alpha)H and T+1 Cz doublebond O group (C triplebond O< or =3.5A) becomes possible only when the T+1 residue adopts an extended beta conformation (T is defined as the helix terminating residue adopting an alpha(L) conformation). In all, 111 examples of this chain reversal motif have been identified and the compositional and conformational preferences at positions T-4, T, and T+1 determined. A marked preference for residues like Ser, Glu and Gln is observed at T-4 position with the motif being further stabilized by the formation of a side-chain-backbone O triplebond H-N hydrogen bond involving the side-chain of residue T-4 and the N-H group of residue T+3. In as many as 57 examples, the segment following the helix was extended with three to four successive residues in beta conformation. In a majority of these cases, the succeeding beta strand lies approximately antiparallel with the helix, suggesting that the backbone C-H triplebond O interactions may provide a means of registering helices and strands in an antiparallel orientation. Two examples were identified in which extended registry was detected with two sets of C-H cdots, three dots, centered O hydrogen bonds between (T-4) C(alpha)H triplebond O (T+1) and (T-8) C(alpha)H triplebondC doublebond O (T+3).     

Structure-activity relationships of oligo-beta-glucoside elicitors of phytoalexin accumulation in soybean.

The abilities of a family of chemically synthesized oligo-beta-glucosides, ranging in size from hexamer to decamer, to induce phytoalexin accumulation in soybean cotyledons were investigated to determine which structural elements of the oligoglucosides are important for their biological activity. The results of the biological assays established that the following structural motif is necessary for the oligo-beta-glucosides to have high elicitor activity: [formula; see text] The branched trisaccharide at the nonreducing end of the oligoglucosides was found to be essential for maximum elicitor activity. Substitution of either the nonreducing terminal backbone glucosyl residue or the side-chain glucosyl residue closest to the nonreducing end with glucosaminyl or N-acetylglucosaminyl residues reduced the elicitor activity of the oligoglucosides between 10-fold and 10,000-fold. Elicitor activity was also reduced 1000-fold if the two side-chain glucosyl residues were attached to adjacent backbone glucosyl residues rather than to glucosyl residues separated by an unbranched residue. In contrast, modifications of the reducing terminal glucosyl residue of an elicitor-active hepta-beta-glucoside by conjugation with tyramine and subsequent iodination had no significant effect on the elicitor activity of the hepta-beta-glucoside. These results demonstrate that oligo-beta-glucosides must have a specific structure to trigger the signal transduction pathway, which ultimately leads to the de novo synthesis of phytoalexins in soybean.     

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.     

Molecular structure and biological activity: A Symposium on Biologically Active Molecules Buffalo,NY, U.S.A., 26 to 28 August 1981

Analysis of the pattern of residue to residue contacts at the interface of 50 helix to helix packings observed in ten proteins of known structure supports a model for helix to helix packing in which the ridges and grooves on the helix surface intercalate. These ridges are formed by rows of residues whose separation in sequence is usually four, occasionally three and rarely one. The model explains the observed predominance of packings whose interhelical angle is ~ ?50 °. Of the 50 packings, 38 agree with the model and the general features of another ten packings are described by an extension to the model in which ridges can pack across each other if a small side-chain occurs at the place where they cross.     

Determinants of protein side-chain packing.

The problem of protein side-chain packing for a given backbone trace is investigated using 3 different prediction models. The first requires an exhaustive search of all possible combinations of side-chain conformers, using the dead-end elimination theorem. The second considers only side-chain-backbone interactions, whereas the third neglects side-chain-backbone interactions and instead keeps side-chain-side-chain interactions. Predictions of side-chain conformations for 11 proteins using all 3 models show that removal of side-chain-side-chain interactions does not cause a large decrease in the prediction accuracy, whereas the model having only side-chain-side-chain interactions still retains a significant level of accuracy. These results suggest that the 2 classes of interactions, side-chain-backbone and side-chain-side-chain, are consistent with each other and work concurrently to stabilize the native conformations. This is confirmed by analyses of energy spectra of the side-chain conformations derived from the fourth prediction model, the Independent model, which gives almost the same quality of the prediction as the dead-end elimination. The analyses indicate that the 2 classes of interactions simultaneously increase the energy difference between the native and nonnative conformations.     

Quantitative Characterization of Local Protein Solvation To Predict Solvent Effects on Protein Structure

Characterization of solvent preferences of proteins is essential to the understanding of solvent effects on protein structure and stability. Although it is generally believed that solvent preferences at distinct loci of a protein surface may differ, quantitative characterization of local protein solvation has remained elusive. In this study, we show that local solvation preferences can be quantified over the entire protein surface from extended molecular dynamics simulations. By subjecting microsecond trajectories of two proteins (lysozyme and antibody fragment D1.3) in 4 M glycerol to rigorous statistical analyses, solvent preferences of individual protein residues are quantified by local preferential interaction coefficients. Local solvent preferences for glycerol vary widely from residue to residue and may change as a result of protein side-chain motions that are slower than the longest intrinsic solvation timescale of ～10 ns. Differences of local solvent preferences between distinct protein side-chain conformations predict solvent effects on local protein structure in good agreement with experiment. This study extends the application scope of preferential interaction theory and enables molecular understanding of solvent effects on protein structure through comprehensive characterization of local protein solvation.     

Energy minimization method using automata network for sequence and side-chain conformation prediction from given backbone geometry

Globular proteins have high packing densities as a result of residue side chains in the core achieving a tight, complementary packing. The internal packing is considered the main determinant of native protein structure. From that point of view, we present here a method of energy minimization using an automata network to predict a set of amino acid sequences and their side-chain conformations from a desired backbone geometry for de novo design of proteins. Using discrete side-chain conformations, that is, rotamers, the sequence generation problem from a given backbone geometry becomes one of combinatorial problems. We focused on the residues composing the interior core region and predicted a set of amino acid Sequences and their side-chain conformations only from a given backbone geometry. The kinds of residues were restricted to six hydrophobic amino acids (Ala, Ile, Met, Leu, Phe, and Val) because the core regions are almost always composed of hydrophobic residues. The obtained sequences were well packed as was the native sequence. The method can be used for automated sequence generation in the de novo design of proteins. © 1994 Wiley-Liss, Inc.     

An algorithm for predicting protein-protein interaction sites: Abnormally exposed amino acid residues and secondary structure elements

Multiprotein systems mediate most regulatory processes in living organisms. Although the structures of the individual proteins are often defined, less is known of the structures of multiprotein systems. Computational methods for predicting interfaces, using evolutionary conservation and/or physicochemical data, have been developed. Here we consider the use of solvent accessibility, residue propensity, and hydrophobicity, in conjunction with secondary structure data, as prediction parameters. We analyze the influence of residue type and secondary structure on solvent accessibility and define a measure of "relative exposedness." Clustering abnormally high scoring residues provides a basis for predicting interaction sites. The analysis is extended to investigate abnormally exposed secondary structure elements, particularly beta-sheet strands. We show that surface-exposed beta-strands lacking protective features are more likely to be found at protein-protein interfaces, allowing us to create an algorithm with approximately 68% and approximately 75% accuracy in differentiating between interacting and edge strands in isolated beta-strands and beta-sheet strands, respectively. These methods of identifying abnormally exposed surface regions are combined in an algorithm, which, on a data set of 77 unbound and disjoint (single chain extracted from complex) structures, predicts 79% of the protein-protein interfaces correctly. If enzyme-inhibitor complexes, where the inhibitor mimics a nonprotein substrate, are excluded, the accuracy increases to 85%.     

Residue conservation in viral capsid assembly

To evaluate the evolutionary constraints placed on viral proteins by the structure and assembly of the capsid, we calculate Shannon entropies in the aligned sequences of 45 polypeptide chains in 32 icosahedral viruses, and relate these entropies to the residue location in the three-dimensional structure of the capsids. Three categories of residues have entropies lower than the chain average implying that they are better conserved than average: residues that are buried within a subunit (the protein core), residues that contain atoms buried at an interface between subunits (the interface core), and residues that contribute to several such interfaces. The interface core is also conserved in homomeric proteins and in transient protein-protein complexes, which have only one interface whereas capsids have many. In capsids, the subunit interfaces implicate most of the polypeptide chain: on average, 66% of the capsid residues are at an interface, 34% at more than one, and 47% at the interface core. Nevertheless, we observe that the degree of residue conservation can vary widely between interfaces within a capsid and between regions within an interface. The interfaces and regions of interfaces that show a low sequence variability are likely to play major roles in the self-assembly of the capsid, with implications on its mechanism that we discuss taking adeno-associated virus as an example.     

ISIS: interaction sites identified from sequence

MOTIVATION: Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues. RESULTS: Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone.     

Structure-Based Druggability Assessment of the Mammalian Structural Proteome with Inclusion of Light Protein Flexibility

Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.