Protein-protein docking with multiple residue conformations and residue substitutions

The protein docking problem has two major aspects: sampling conformations and orientations, and scoring them for fit. To investigate the extent to which the protein docking problem may be attributed to the sampling of ligand side‐chain conformations, multiple conformations of multiple residues were calculated for the uncomplexed (unbound) structures of protein ligands. These ligand conformations were docked into both the complexed (bound) and unbound conformations of the cognate receptors, and their energies were evaluated using an atomistic potential function. The following questions were considered: (1) does the ensemble of precalculated ligand conformations contain a structure similar to the bound form of the ligand? (2) Can the large number of conformations that are calculated be efficiently docked into the receptors? (3) Can near‐native complexes be distinguished from non‐native complexes? Results from seven test systems suggest that the precalculated ensembles do include side‐chain conformations similar to those adopted in the experimental complexes. By assuming additivity among the side chains, the ensemble can be docked in less than 12 h on a desktop computer. These multiconformer dockings produce near‐native complexes and also non‐native complexes. When docked against the bound conformations of the receptors, the near‐native complexes of the unbound ligand were always distinguishable from the non‐native complexes. When docked against the unbound conformations of the receptors, the near‐native dockings could usually, but not always, be distinguished from the non‐native complexes. In every case, docking the unbound ligands with flexible side chains led to better energies and a better distinction between near‐native and non‐native fits. An extension of this algorithm allowed for docking multiple residue substitutions (mutants) in addition to multiple conformations. The rankings of the docked mutant proteins correlated with experimental binding affinities. These results suggest that sampling multiple residue conformations and residue substitutions of the unbound ligand contributes to, but does not fully provide, a solution to the protein docking problem. Conformational sampling allows a classical atomistic scoring function to be used; such a function may contribute to better selectivity between near‐native and non‐native complexes. Allowing for receptor flexibility may further extend these results.     

The adaptive nature of protein residue networks

Protein residue networks PRNs are used to describe proteins. These networks are usually based on an average structure for the protein. However, proteins are dynamic entities that are affected by their surroundings. In this work, we study the effect of temperatures above and below the protein dynamical transition temperature(≈200 K), on three important network parameters gleaned from weighted PRNs for the solvated β‐lactamase inhibitory protein BLIP: the betweenness centrality B, the closeness centrality C, and the clustering coefficient CC. The B and C values will be extracted for each node from PRNs at six different temperatures: 150 K, 180 K, 200 K, 220 K, 250 K, and 310 K respectively. The average value for the CC for each PRN will also be calculated at each temperature, respectively. We find that at temperatures ≤200 K, the network nodes with the most significant B and C values tend to have lower relative solvent accessibility RSA values, and to fall within the protein secondary structure elements (α helices and β sheets). At temperatures >200 K, the significant nodes in terms of B and C tend to have larger RSA values, and to fall on the connecting loops in the protein. The average CC decreases in value for the PRNs up to 200 K, and then remains basically constant above 200 K. This clearly shows that any conclusions based on static PRNs should be handled with care. The dynamic nature of proteins and its coupling to the surrounding environment should be taken into consideration when using the PRN paradigm. Proteins 2017; 85:917–923. © 2016 Wiley Periodicals, Inc.     

Conserved residue clustering and protein structure prediction

Protein residues that are critical for structure and function are expected to be conserved throughout evolution. Here, we investigate the extent to which these conserved residues are clustered in three-dimensional protein structures. In 92% of the proteins in a data set of 79 proteins, the most conserved positions in multiple sequence alignments are significantly more clustered than randomly selected sets of positions. The comparison to random subsets is not necessarily appropriate, however, because the signal could be the result of differences in the amino acid composition of sets of conserved residues compared to random subsets (hydrophobic residues tend to be close together in the protein core), or differences in sequence separation of the residues in the different sets. In order to overcome these limits, we compare the degree of clustering of the conserved positions on the native structure and on alternative conformations generated by the de novo structure prediction method Rosetta. For 65% of the 79 proteins, the conserved residues are significantly more clustered in the native structure than in the alternative conformations, indicating that the clustering of conserved residues in protein structures goes beyond that expected purely from sequence locality and composition effects. The differences in the spatial distribution of conserved residues can be utilized in de novo protein structure prediction: We find that for 79% of the proteins, selection of the Rosetta generated conformations with the greatest clustering of the conserved residues significantly enriches the fraction of close-to-native structures.     

Structural characterization of proteins using residue environments

A primary challenge for structural genomics is the automated functional characterization of protein structures. We have developed a sequence-independent method called S-BLEST (Structure-Based Local Environment Search Tool) for the annotation of previously uncharacterized protein structures. S-BLEST encodes the local environment of an amino acid as a vector of structural property values. It has been applied to all amino acids in a nonredundant database of protein structures to generate a searchable structural resource. Given a query amino acid from an experimentally determined or modeled structure, S-BLEST quickly identifies similar amino acid environments using a K-nearest neighbor search. In addition, the method gives an estimation of the statistical significance of each result. We validated S-BLEST on X-ray crystal structures from the ASTRAL 40 nonredundant dataset. We then applied it to 86 crystallographically determined proteins in the protein data bank (PDB) with unknown function and with no significant sequence neighbors in the PDB. S-BLEST was able to associate 20 proteins with at least one local structural neighbor and identify the amino acid environments that are most similar between those neighbors.     

Scoring residue conservation

The importance of a residue for maintaining the structure and function of a protein can usually be inferred from how conserved it appears in a multiple sequence alignment of that protein and its homologues. A reliable metric for quantifying residue conservation is desirable. Over the last two decades many such scores have been proposed, but none has emerged as a generally accepted standard. This work surveys the range of scores that biologists, biochemists, and, more recently, bioinformatics workers have developed, and reviews the intrinsic problems associated with developing and evaluating such a score. A general formula is proposed that may be used to compare the properties of different particular conservation scores or as a measure of conservation in its own right.     

Clustering biomolecular complexes by residue contacts similarity

Inaccuracies in computational molecular modeling methods are often counterweighed by brute-force generation of a plethora of putative solutions. These are then typically sieved via structural clustering based on similarity measures such as the root mean square deviation (RMSD) of atomic positions. Albeit widely used, these measures suffer from several theoretical and technical limitations (e.g., choice of regions for fitting) that impair their application in multicomponent systems (N > 2), large-scale studies (e.g., interactomes), and other time-critical scenarios. We present here a simple similarity measure for structural clustering based on atomic contacts--the fraction of common contacts--and compare it with the most used similarity measure of the protein docking community--interface backbone RMSD. We show that this method produces very compact clusters in remarkably short time when applied to a collection of binary and multicomponent protein-protein and protein-DNA complexes. Furthermore, it allows easy clustering of similar conformations of multicomponent symmetrical assemblies in which chain permutations can occur. Simple contact-based metrics should be applicable to other structural biology clustering problems, in particular for time-critical or large-scale endeavors.     

Correlated mutations and residue contacts in proteins

The maintenance of protein function and structure constrains the evolution of amino acid sequences. This fact can be exploited to interpret correlated mutations observed in a sequence family as an indication of probable physical contact in three dimensions. Here we present a simple and general method to analyze correlations in mutational behavior between different positions in a multiple sequence alignment. We then use these correlations to predict contact maps for each of 11 protein families and compare the result with the contacts determined by crystallography. For the most strongly correlated residue pairs predicted to be in contact, the prediction accuracy ranges from 37 to 68% and the improvement ratio relative to a random prediction from 1.4 to 5.1. Predicted contact maps can be used as input for the calculation of protein tertiary structure, either from sequence information alone or in combination with experimental information. © 1994 John Wiley & Sons, Inc.     

Protein topology from predicted residue contacts

Residue contacts predicted from correlated positions in a multiple sequence alignment are often sparse and uncertain. To some extent, these limitations in the data can be overcome by grouping the contacts by secondary structure elements and enumerating the possible packing arrangements of these elements in a combinatorial manner. Strong interactions appear frequently but inconsistent interactions are down-weighted and missing interactions up-weighted. The resulting improved consistency in the predicted interactions has allowed the method to be successfully applied to proteins up to 200 residues in length which is larger than any structure previously predicted using sequence data alone.     

Role of residue Y99 in tissue plasminogen activator

The crystal structure of the fibrinolytic enzyme tissue plasminogen activator (tPA) shows that the bulky side chain of Y99 hinders access to the active site by partially occluding the S2 site and may be responsible for the low catalytic activity of tPA toward plasminogen. We have tested the role of Y99 by replacing it with Leu, the residue found in more proficient proteases like trypsin and thrombin. The Y99L replacement results in an increase in the k(cat)/Km for chromogenic substrates due to enhanced diffusion into the active site. The increase is modest (threefold) for substrates specific for tPA that carry Pro or Gly at P2, but reaches 80-fold for less specific substrates carrying Arg at P2. On the other hand, the Y99L mutation has no effect on the activity of tPA toward the natural substrate plasminogen, that carries Gly at P2, and reduces more than 10-fold the inhibition of tPA by plasminogen activator inhibitor-1 (PAI-1), that carries Ala at P2. We conclude that the steric hindrance provided by Y99 in the crystal structure affects mostly nonphysiological substrates with bulky residues at P2. In addition, residue Y99 plays an active role in the recognition of PAI-1, but not plasminogen. Mutations of Y99 could therefore afford a resistance to inhibition by PAI-1 without compromising the fibrinolytic potency of tPA, a result of potential therapeutic relevance.     

CONFOLD: Residue‐residue contact‐guided ab initio protein folding

Predicted protein residue–residue contacts can be used to build three‐dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two‐stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM‐score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM‐score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/ . Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.     

A united residue force-field for calcium-protein interactions

United-residue potentials are derived for interactions of the calcium cation with polypeptide chains in energy-based prediction of protein structure with a united-residue (UNRES) force-field. Specific potentials were derived for the interaction of the calcium cation with the Asp, Glu, Asn, and Gln side chains and the peptide group. The analytical expressions for the interaction energies for each of these amino acids were obtained by averaging the electrostatic interaction energy, expressed by a multipole series over the dihedral angles not considered in the united-residue model, that is, the side-chain dihedral angles chi and the dihedral angles lambda for the rotation of peptide groups about the C(alpha)...C(alpha) virtual-bond axes. For the side-chains that do not interact favorably with calcium, simple excluded-volume potentials were introduced. The parameters of the potentials were obtained from ab initio quantum mechanical calculations of model systems at the Restricted Hartree-Fock (RHF) level with the 6-31G(d,p) basis set. The energy surfaces of pairs consisting of Ca(2+)-acetate, Ca(2+)-propionate, Ca(2+)-acetamide, Ca(2+)-propionamide, and Ca(2+)-N-methylacetamide systems (modeling the Ca(2+)-Asp(-), Ca(2+)-Glu(-), Ca(2+)-Asn, Ca(2+)-Gln, and Ca(2+)-peptide group interactions) at different distances and orientations were calculated. For each pair, the restricted free energy (RFE) surfaces were calculated by numerical integration over the degrees of freedom lost when switching from the all-atom model to the united-residue model. Finally, the analytical expressions for each pair were fitted to the RFE surfaces. This force-field was able to distinguish the EF-hand motif from all potential binding sites in the crystal structures of bovine alpha-lactalbumin, whiting parvalbumin, calbindin D9K, and apo-calbindin D9K.     

Dissection, residue conservation, and structural classification of protein-DNA interfaces

The basic DNA-binding modules of 128 protein-DNA interfaces have been analyzed. Although these are less planar, like the protein-protein interfaces, the protein-DNA interfaces can also be dissected into core regions in which all the fully-buried atoms are located, and rim regions having atoms with residual accessibilities. The sequence entropy of the core residues is smaller than those in the rim, indicating that the former are better conserved and possibly contribute more towards the binding free energy, as has been implicated in protein-protein interactions. On the protein side, 1014 A(2) of the surface is buried of which 63% belong to the core. There are some differences in the propensities of residues to occur in the core and the rim. In the DNA strands, the nucleotide(s) containing fully-buried atoms in all three components usually occupy central positions of the binding region. A new classification scheme for the interfaces has been introduced based on the composition of secondary structural elements of residues and the results compared with the conventional classification of DNA-binding proteins, as well as the protein class of the molecule. It appears that a common framework may be developed to understand both protein-protein and protein-DNA interactions.     

Mutational analysis of residue roles in AraC function

The previously isolated hemiplegic, induction-negative, repression-positive mutants, H80R and Y82C, were found to be defective in the binding of arabinose. Randomization of other residues close to arabinose in the three-dimensional structure of AraC or that make strong interactions with arabinose yielded induction-negative, repression-positive mutants. The induction and repression properties of mutants obtained by randomizing individual residues of the N-terminal arm of AraC allowed identification of the domain with which that residue very likely makes its predominant interactions. Residues 8-14 of the arm appear to make their predominant interaction with the DNA-binding domain. Although the side-chain of residue 15 interacts directly with arabinose bound to the N-terminal dimerization domain, the properties of mutant F15L indicate that this mutation increases the affinity of the arm for the DNA-binding domain.     

PresCont: predicting protein-protein interfaces utilizing four residue properties

An important task of computational biology is to identify those parts of a polypeptide chain, which are involved in interactions with other proteins. For this purpose, we have developed the program PresCont, which predicts in a robust manner amino acids that constitute protein-protein interfaces (PPIs). PresCont reaches state-of-the-art classification quality on the basis of only four residue properties that can be readily deduced from the 3D structure of an individual protein and a multiple sequence alignment (MSA) composed of homologs. The core of PresCont is a support vector machine, which assesses solvent-accessible surface area, hydrophobicity, conservation, and the local environment of each amino acid on the protein surface. For training and performance testing, we compiled three nonoverlapping datasets consisting of permanently formed or transient complexes, respectively. A comparison with SPPIDER, ProMate, and meta-PPISP showed that PresCont compares favorably with these highly sophisticated programs, and that its prediction quality is less dependent on the type of protein complex being considered. This balance is due to a mutual compensation of classification weaknesses observed for individual properties: For PPIs of permanent complexes, solvent-accessible surface and hydrophobicity contribute most to classification quality, for PPIs of transient complexes, the assessment of the local environment is most significant. Moreover, we show that for permanent complexes a segmentation of PPIs into core and rim residues has only a moderate influence on prediction quality. PresCont is available as a web service at http://www-bioinf.uni-regensburg.de/.     

基于质谱技术的蛋白质组学方法应用于 2000 年前食物残留的分析

考古样品中蛋白质残留物的保存状态能否应用质谱技术成功分析,取决于其特定埋藏环境微生物条件和埋藏年代两个主要因素。目前国际上的最新进展,是成功分析距今大约800～600年之间陶器碎片上附着的粘土状残留物,测定出其中的蛋白质成分来源于灰海狮,该样品来自邻近北冰洋的极寒地区。尝试分析距今2000年左右,出土于云南黑玛井遗址两件青铜容器内的内容物样品,其中一件的内容物外观性状为颗粒状,另外一件则为膏状。分析结果发现,颗粒状样品未能测出蛋白质残留成分,而膏状样品则保留了大量蛋白质残留的信息。这一结果表明,基于生物质谱技术的蛋白质组学方法有可能应用于温带埋藏环境以及年代更为古老的考古样品。     

Neural network-based prediction of mutation-induced protein stability changes in Staphylococcal nuclease at 20 residue positions

Protein-based therapeutics are playing an increasingly important role in the treatment of diseases, including diabetes and cancer. The viability of these treatments, however, are highly dependent on the stability of the therapeutic, since stability affects both the shelf life of the therapeutic as well as its active life in the body. Stability engineering can, therefore, be used to increase the effectiveness of protein-based therapeutics. Computational methods of protein stability prediction have been under development for about a decade, but complex molecular interactions make stability prediction difficult and computationally intensive. A rapid computational method of protein stability prediction is developed using feed-forward neural networks and used to predict mutation-induced stability changes in Staphylococcal nuclease. The input to the neural network consisted of sequences of evolutionarily based amino acid similarity scores that were obtained through the comparison of the amino acids in a mutation containing sequence to their positional counterparts in the baseline wild-type amino acid sequence. A training set was created which consisted of similarity score sequences, for which the stabilities of the corresponding amino acid sequences were known, paired with the relative stabilities of the sequences to that of the baseline. Back-propagation of error was used to train the network to output accurate relative stability scores for the sequences in the training set. Neural network-based relative stability predictions for 55 sequences containing mutation combinations not found in the training set had an accuracy of 92.8%.     

Detecting local residue environment similarity for recognizing near‐native structure models

We developed a new representation of local amino acid environments in protein structures called the Side‐chain Depth Environment (SDE). An SDE defines a local structural environment of a residue considering the coordinates and the depth of amino acids that locate in the vicinity of the side‐chain centroid of the residue. SDEs are general enough that similar SDEs are found in protein structures with globally different folds. Using SDEs, we developed a procedure called PRESCO (Protein Residue Environment SCOre) for selecting native or near‐native models from a pool of computational models. The procedure searches similar residue environments observed in a query model against a set of representative native protein structures to quantify how native‐like SDEs in the model are. When benchmarked on commonly used computational model datasets, our PRESCO compared favorably with the other existing scoring functions in selecting native and near‐native models. Proteins 2014; 82:3255–3272. © 2014 Wiley Periodicals, Inc.     

Arginine 383 is a crucial residue in ABCG2 biogenesis

ABCG2 is an ATP-binding cassette half-transporter initially identified in multidrug-resistant cancer cell lines and recently suggested to play an important role in pharmacokinetics. Here we report studies of a conserved arginine predicted to localize near the cytoplasmic side of TM1. First, we determined the effect of losing charge and bulk at this position via substitutions with glycine and alanine. The R383G mutant when transfected into HEK cells was not detectable on immunoblot or by functional assay, while the R383A mutant exhibited detectable but significantly decreased levels compared to wild-type, partial retention in the ER and altered glycosylation. Efflux of the ABCG2-substrates mitoxantrone and pheophorbide a was observed. Our experiments suggested rapid degradation of the R383A mutant by the proteasome via a kifunensine-insensitive pathway. Interestingly, overnight treatment of the R383A mutant with mitoxantrone assisted in protein maturation as evidenced by a shift to the N-glycosylated form. The R383A mutant when expressed in insect cells, though detected on the surface, had no measurable ATPase activity. In addition, substitution with the positively charged lysine resulted in significantly decreased protein expression levels in HEK cells, while retaining function. In conclusion, arginine 383 is a crucial residue for ABCG2 biogenesis, where even the most conservative mutations have a large impact.     

A single residue of GDF-5 defines binding specificity to BMP receptor IB

Growth and differentiation factor 5 (GDF-5), a member of the TGF-beta superfamily, is involved in many developmental processes, like chondrogenesis and joint formation. Mutations in GDF-5 lead to diseases, e.g. chondrodysplasias like Hunter-Thompson, Grebe and DuPan syndromes and brachydactyly. Similar to other TGF-beta superfamily members, GDF-5 transmits signals through binding to two different types of membrane-bound serine-/threonine-kinase receptors termed type I and type II. In contrast to the large number of ligands, only seven type I and five type II receptors have been identified to date, implicating a limited promiscuity in ligand-receptor interaction. However, in contrast to other members of the TGF-beta superfamily, GDF-5 shows a pronounced specificity in type I receptor interaction in cross-link experiments binding only to BMP receptor IB (BMPR-IB). In mice, deletion of either GDF-5 or BMPR-IB results in a similar phenotype, indicating that GDF-5 signaling is highly dependent on BMPR-IB. Here, we demonstrate by biosensor analysis that GDF-5 also binds to BMP receptor IA (BMPR-IA) but with approximately 12-fold lower affinity. Structural and mutational analyses revealed a single residue of GDF-5, Arg57 located in the pre-helix loop, being solely responsible for the high binding specificity to BMPR-IB. In contrast to wild-type GDF-5, variant GDF-5R57A interacts with BMPR-IA and BMPR-IB with a comparable high binding affinity. These results provide important insights into how receptor-binding specificity is generated at the molecular level and might be useful for the generation of receptor subtype specific activators or inhibitors.     

Analyses of the general rule on residue pair frequencies in local amino acid sequences of soluble,ordered proteins

The amino acid sequences of soluble, ordered proteins with stable structures have evolved due to biological and physical requirements, thus distinguishing them from random sequences. Previous analyses have focused on extracting the features that frequently appear in protein substructures, such as α‐helix and β‐sheet, but the universal features of protein sequences have not been addressed. To clarify the differences between native protein sequences and random sequences, we analyzed 7368 soluble, ordered protein sequences, by inspecting the observed and expected occurrences of 400 amino acid pairs in local proximity, up to 10 residues along the sequence in comparison with their expected occurrence in random sequence. We found the trend that the hydrophobic residue pairs and the polar residue pairs are significantly decreased, whereas the pairs between a hydrophobic residue and a polar residue are increased. This trend was universally observed regardless of the secondary structure content but was not observed in protein sequences that include intrinsically disordered regions, indicating that it can be a general rule of protein foldability. The possible benefits of this rule are discussed from the viewpoints of protein aggregation and disorder, which are both caused by low‐complexity regions of hydrophobic or polar residues.