Predicted protein-protein interaction sites from local sequence information

Protein-protein interactions are facilitated by a myriad of residue-residue contacts on the interacting proteins. Identifying the site of interaction in the protein is a key for deciphering its functional mechanisms, and is crucial for drug development. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a neural network that identifies protein-protein interfaces from sequence. For the most strongly predicted sites (in 34 of 333 proteins), 94% of the predictions were confirmed experimentally. When 70% of our predictions were right, we correctly predicted at least one interaction site in 20% of the complexes (66/333). These results indicate that the prediction of some interaction sites from sequence alone is possible. Incorporating evolutionary and predicted structural information may improve our method. However, even at this early stage, our tool might already assist wet-lab biology.     

Predicting protein interaction sites from residue spatial sequence profile and evolution rate

This paper proposes a novel method that can predict protein interaction sites in heterocomplexes using residue spatial sequence profile and evolution rate approaches. The former represents the information of multiple sequence alignments while the latter corresponds to a residue's evolutionary conservation score based on a phylogenetic tree. Three predictors using a support vector machines algorithm are constructed to predict whether a surface residue is a part of a protein-protein interface. The efficiency and the effectiveness of our proposed approach is verified by its better prediction performance compared with other models. The study is based on a non-redundant data set of heterodimers consisting of 69 protein chains.     

Prediction of protein interaction sites from sequence profile and residue neighbor list.

Protein-protein interaction sites are predicted from a neural network with sequence profiles of neighboring residues and solvent exposure as input. The network was trained on 615 pairs of nonhomologous complex-forming proteins. Tested on a different set of 129 pairs of nonhomologous complex-forming proteins, 70% of the 11,004 predicted interface residues are actually located in the interfaces. These 7732 correctly predicted residues account for 65% of the 11,805 residues making up the 129 interfaces. The main strength of the network predictor lies in the fact that neighbor lists and solvent exposure are relatively insensitive to structural changes accompanying complex formation. As such, it performs equally well with bound or unbound structures of the proteins. For a set of 35 test proteins, when the input was calculated from the bound and unbound structures, the correct fractions of the predicted interface residues were 69 and 70%, respectively.     

A recurrent DNA sequence at sites of protein interaction

Variation in the observed spin letter lattice relaxation rate (R_ob) interpreted as proton exchange dominated in sequences corresponding to part of promoters where RNA polymerase initiates mRNA synthesis has been observed by both Patel et al. (64) and Reid and co-workers (43). A higher R_obs was also seen in the TA pair of the GTG/CAC in the sequence corressponding to the λ phage cro repressor binding site by Kyogoku et al. (44). As we pointed out in the introduction, the one case where a three-dimensional structure for a turn of a helix is known shows clear structural heterogeneity which has led to detailed consideration of geometry of regulatory regions. Nussinov and collaborators have generalized the details of the Dickerson dodecomer to note potential similarities in operators including the lac system (53) and the enhancer sequences described above (65). Like the steric considerations of Calladine (66) and Dickerson (67) and nearest neighbor structure analysis of Bubienko et al. (68), the focus is on the geometry of a sequence leading to base tilt angles and potential overlap since they are measurable parameters.With the observation that the DNA molecule is both structurally (14, 69) and dynamically flexible (70–73), there will no doubt be many new variables that can be made as a function of DNA sequence. Biophysical chemists in some ways are like the intoxicated person searching for a lost key at a site different from where it was lost because that is where the light is best. Thus, each physical method has its most convenient observable. It is hoped that the above discussion illustrates that a very large and diverse set of biochemical results are awaiting detailed explanation in molecular terms using the illumination of high resolution physical techniques.     

STAR: predicting recombination sites from amino acid sequence

Background  

Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probability that hybrid sequences have the desired properties is increased dramatically. The prohibitive requirements for applying current tools led us to investigate machine learning to assist in finding useful recombination sites from amino acid sequence alone.     

Predicting protein sumoylation sites from sequence features

Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In this study, a new machine learning approach has been developed for predicting sumoylation sites from protein sequence information. Random forests (RFs) and support vector machines (SVMs) were trained with the data collected from the literature. Domain-specific knowledge in terms of relevant biological features was used for input vector encoding. It was shown that RF classifier performance was affected by the sequence context of sumoylation sites, and 20 residues with the core motif ΨKXE in the middle appeared to provide enough context information for sumoylation site prediction. The RF classifiers were also found to outperform SVM models for predicting protein sumoylation sites from sequence features. The results suggest that the machine learning approach gives rise to more accurate prediction of protein sumoylation sites than the other existing methods. The accurate classifiers have been used to develop a new web server, called seeSUMO (http://bioinfo.ggc.org/seesumo/), for sequence-based prediction of protein sumoylation sites.     

O-glycosylation sites identified from mucin core-1 type glycopeptides from human serum

In this work O-linked glycopeptides bearing mucin core-1 type structures were enriched from human serum. Since about 70 % of the O-glycans in human serum bind to the plant lectin Jacalin, we tested a previously successful protocol that combined Jacalin affinity enrichment on the protein- and peptide-level with ERLIC chromatography as a further enrichment step in between, to eliminate the high background of unmodified peptides. In parallel, we developed a simpler and significantly faster new workflow that used two lectins sequentially: wheat germ agglutinin and then Jacalin. The first lectin provides general glycopeptide enrichment, while the second specifically enriches O-linked glycopeptides with Galβ1-3GalNAcα structures. Mass spectrometric analysis of enriched samples showed that the new sample preparation method is more selective and sensitive than the former. Altogether, 52 unique glycosylation sites in 20 proteins were identified in this study.     

Rare and spatially segregated release sites mediate a synaptic interaction between two identified network neurons

Laser‐scanning confocal microscopy (LSCM), electron microcopy (EM), and cellular electrophysiology were used in combination to study the structural basis of an inhibitory synapse between two identified neurons of the same network. To achieve this, we examined the chemical inhibitory synapse between identified neurons belonging to the lobster (Homarus gammarus) pyloric network: the pyloric dilator (PD) and the lateral pyloric (LP) neurons. In order to visualize simultaneously these two neurons, we used intrasomatic injection of Lucifer Yellow (LY) in one and rhodamine/horseradish peroxydase (HRP) in the other. Under LSCM, we found only two zones of close apposition in a restricted part of the neuritic tree of the two network neurons. Then, within these two zones, the synaptic release sites were searched using EM. To this end, photoconversion of LY with immunogold and development of HRP with DAB were performed on the previously observed preparations. Structural evidence was found for only one release site per zone. To confirm this result, and because the zones of contact were always segregated in a restricted part of the dendrites, we used laser photoablation to selectively delete, either pre‐ or postsynaptically, the branches on which the release sites were located. In both cases, such restrictive ablation completely abolished the functional interaction between these neurons. Our results therefore demonstrate that an inhibitory synapse that is essential for the operation of a neural network relies on only very few sites of contact localized in a highly restricted part of each neuron's dendritic arbor. © 2002 Wiley Periodicals, Inc. J Neurobiol 50: 150–163, 2002; DOI 10.1002/neu.10023     

Structural and dynamic effects of cholesterol at preferred sites of interaction with rhodopsin identified from microsecond length molecular dynamics simulations

An unresolved question about GPCR function is the role of membrane components in receptor stability and activation. In particular, cholesterol is known to affect the function of membrane proteins, but the details of its effect on GPCRs are still elusive. Here, we describe how cholesterol modulates the behavior of the TM1‐TM2‐TM7‐helix 8(H8) functional network that comprises the highly conserved NPxxY(x)_5,6F motif, through specific interactions with the receptor. The inferences are based on the analysis of microsecond length molecular dynamics (MD) simulations of rhodopsin in an explicit membrane environment. Three regions on the rhodopsin exhibit the highest cholesterol density throughout the trajectory: the extracellular end of TM7, a location resembling the high‐density sterol area from the electron microscopy data; the intracellular parts of TM1, TM2, and TM4, a region suggested as the cholesterol binding site in the recent X‐ray crystallography data on β₂‐adrenergic GPCR; and the intracellular ends of TM2‐TM3, a location that was categorized as the high cholesterol density area in multiple independent 100 ns MD simulations of the same system. We found that cholesterol primarily affects specific local perturbations of the helical TM domains such as the kinks in TM1, TM2, and TM7. These local distortions, in turn, relate to rigid‐body motions of the TMs in the TM1‐TM2‐TM7‐H8 bundle. The specificity of the effects stems from the nonuniform distribution of cholesterol around the protein. Through correlation analysis we connect local effects of cholesterol on structural perturbations with a regulatory role of cholesterol in the structural rearrangements involved in GPCR function. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Allosteric sites can be identified based on the residue–residue interaction energy difference

Allosteric drugs act at a distance to regulate protein functions. They have several advantages over conventional orthosteric drugs, including diverse regulation types and fewer side effects. However, the rational design of allosteric ligands remains a challenge, especially when it comes to the identification allosteric binding sites. As the binding of allosteric ligands may induce changes in the pattern of residue–residue interactions, we calculated the residue–residue interaction energies within the allosteric site based on the molecular mechanics generalized Born surface area energy decomposition scheme. Using a dataset of 17 allosteric proteins with structural data for both the apo and the ligand‐bound state available, we used conformational ensembles generated by molecular dynamics simulations to compute the differences in the residue–residue interaction energies in known allosteric sites from both states. For all the known sites, distinct interaction energy differences (>25%) were observed. We then used CAVITY, a binding site detection program to identify novel putative allosteric sites in the same proteins. This yielded a total of 31 “druggable binding sites,” of which 21 exhibited >25% difference in residue interaction energies, and were hence predicted as novel allosteric sites. Three of the predicted allosteric sites were supported by recent experimental studies. All the predicted sites may serve as novel allosteric sites for allosteric ligand design. Our study provides a computational method for identifying novel allosteric sites for allosteric drug design. Proteins 2015; 83:1375–1384. © 2014 Wiley Periodicals, Inc.     

Rare and spatially segregated release sites mediate a synaptic interaction between two identified network neurons.

Laser-scanning confocal microscopy (LSCM), electron microcopy (EM), and cellular electrophysiology were used in combination to study the structural basis of an inhibitory synapse between two identified neurons of the same network. To achieve this, we examined the chemical inhibitory synapse between identified neurons belonging to the lobster (Homarus gammarus) pyloric network: the pyloric dilator (PD) and the lateral pyloric (LP) neurons. In order to visualize simultaneously these two neurons, we used intrasomatic injection of Lucifer Yellow (LY) in one and rhodamine/horseradish peroxydase (HRP) in the other. Under LSCM, we found only two zones of close apposition in a restricted part of the neuritic tree of the two network neurons. Then, within these two zones, the synaptic release sites were searched using EM. To this end, photoconversion of LY with immunogold and development of HRP with DAB were performed on the previously observed preparations. Structural evidence was found for only one release site per zone. To confirm this result, and because the zones of contact were always segregated in a restricted part of the dendrites, we used laser photoablation to selectively delete, either pre- or postsynaptically, the branches on which the release sites were located. In both cases, such restrictive ablation completely abolished the functional interaction between these neurons. Our results therefore demonstrate that an inhibitory synapse that is essential for the operation of a neural network relies on only very few sites of contact localized in a highly restricted part of each neuron's dendritic arbor.     

Genome-wide inference of protein interaction sites: lessons from the yeast high-quality negative protein-protein interaction dataset

High-throughput studies of protein interactions may have produced, experimentally and computationally, the most comprehensive protein–protein interaction datasets in the completely sequenced genomes. It provides us an opportunity on a proteome scale, to discover the underlying protein interaction patterns. Here, we propose an approach to discovering motif pairs at interaction sites (often 3–8 residues) that are essential for understanding protein functions and helpful for the rational design of protein engineering and folding experiments. A gold standard positive (interacting) dataset and a gold standard negative (non-interacting) dataset were mined to infer the interacting motif pairs that are significantly overrepresented in the positive dataset compared to the negative dataset. Four negative datasets assembled by different strategies were evaluated and the one with the best performance was used as the gold standard negatives for further analysis. Meanwhile, to assess the efficiency of our method in detecting potential interacting motif pairs, other approaches developed previously were compared, and we found that our method achieved the highest prediction accuracy. In addition, many uncharacterized motif pairs of interest were found to be functional with experimental evidence in other species. This investigation demonstrates the important effects of a high-quality negative dataset on the performance of such statistical inference.     

Search for folding initiation sites from amino acid sequence

A crucial event in protein folding is the formation of a folding nucleus, which is a structured part of the protein chain in the transition state. We demonstrate a correlation between locations of residues involved in the folding nuclei and locations of predicted amyloidogenic regions. The average Phi-values are significantly greater inside amyloidogenic regions than outside them. We have found that fibril formation and normal folding involve many of the same key residues, giving an opportunity to outline the folding initiation site in protein chains. The search for folding initiation sites for apomyoglobin and ribonuclease. A coincides with the predictions made by other approaches.     

Predicting DNA-binding sites of proteins from amino acid sequence

Background  

Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.     

Prediction of zinc-binding sites in proteins from sequence.

MOTIVATION: Motivated by the abundance, importance and unique functionality of zinc, both biologically and physiologically, we have developed an improved method for the prediction of zinc-binding sites in proteins from their amino acid sequences. RESULTS: By combining support vector machine (SVM) and homology-based predictions, our method predicts zinc-binding Cys, His, Asp and Glu with 75% precision (86% for Cys and His only) at 50% recall according to a 5-fold cross-validation on a non-redundant set of protein chains from the Protein Data Bank (PDB) (2727 chains, 235 of which bind zinc). Consequently, our method predicts zinc-binding Cys and His with 10% higher precision at different recall levels compared to a recently published method when tested on the same dataset. AVAILABILITY: The program is available for download at www.fos.su.se/~nanjiang/zincpred/download/     

Discover protein sequence signatures from protein-protein interaction data

Background  

The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-protein interaction (PPI) datasets. Mining these datasets for underlying biological knowledge has, however, remained a challenge.     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.     

ISMapper: identifying transposase insertion sites in bacterial genomes from short read sequence data

Background

Insertion sequences (IS) are small transposable elements, commonly found in bacterial genomes. Identifying the location of IS in bacterial genomes can be useful for a variety of purposes including epidemiological tracking and predicting antibiotic resistance. However IS are commonly present in multiple copies in a single genome, which complicates genome assembly and the identification of IS insertion sites. Here we present ISMapper, a mapping-based tool for identification of the site and orientation of IS insertions in bacterial genomes, directly from paired-end short read data.

Results

ISMapper was validated using three types of short read data: (i) simulated reads from a variety of species, (ii) Illumina reads from 5 isolates for which finished genome sequences were available for comparison, and (iii) Illumina reads from 7 Acinetobacter baumannii isolates for which predicted IS locations were tested using PCR. A total of 20 genomes, including 13 species and 32 distinct IS, were used for validation. ISMapper correctly identified 97 % of known IS insertions in the analysis of simulated reads, and 98 % in real Illumina reads. Subsampling of real Illumina reads to lower depths indicated ISMapper was able to correctly detect insertions for average genome-wide read depths >20x, although read depths >50x were required to obtain confident calls that were highly-supported by evidence from reads. All ISAba1 insertions identified by ISMapper in the A. baumannii genomes were confirmed by PCR. In each A. baumannii genome, ISMapper successfully identified an IS insertion upstream of the ampC beta-lactamase that could explain phenotypic resistance to third-generation cephalosporins. The utility of ISMapper was further demonstrated by profiling genome-wide IS6110 insertions in 138 publicly available Mycobacterium tuberculosis genomes, revealing lineage-specific insertions and multiple insertion hotspots.

Conclusions

ISMapper provides a rapid and robust method for identifying IS insertion sites directly from short read data, with a high degree of accuracy demonstrated across a wide range of bacteria.     

Microsomal dexamethasone binding sites identified by affinity labelling

Binding studies with [3H]dexamethasone identified a class of binding sites on male rat liver microsomes. The binding sites were glucocorticoid-dependent and specific for glucocorticoids and progestins. Scatchard binding parameters, competition studies with triamcinolone acetonide, a synthetic glucocorticoid which competes well for the glucocorticoid receptor, and immunoblotting with an antiglucocorticoid receptor antibody indicated that these sites are distinct from the cytosolic glucocorticoid receptor. Affinity labelling experiments with [3H]dexamethasone 21-mesylate revealed two specifically labelled peptides, one at approx. 66 kDa and a doublet at 45 kDa. The 66 kDa peptide had been previously identified in serum and may be present as a result of serum contamination of the microsomal preparation. The 45 kDa doublet, on the other hand, had been shown to be absent from rat serum. The characteristics of the 45 kDa peptide(s) were identical to those of the dexamethasone binding site identified in the binding studies. [3H]Dexamethasone binding characteristics and affinity labelling of microsomal subfractions, separated by isopycnic centrifugation, showed that the binding sites are located in the endoplasmic reticulum. The identification and role of the 45 kDa peptide doublet remain to be determined.