Pepscan Approach for the Identification of Protein-Protein Interfaces: Lessons from Experiment.
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ABSTRACT:
SUBMITTER: Rebollo A
PROVIDER: S-EPMC8224071 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation. RESULTS: ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions. CONCLUSION: Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip.
| S-EPMC4177600 | biostudies-literature
Project description:Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein-protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node's ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein-protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.
| S-EPMC4626657 | biostudies-literature
Project description:Proteins perform many of their biological roles through protein-protein, protein-DNA or protein-ligand interfaces. The identification of the amino acids comprising these interfaces often enhances our understanding of the biological function of the proteins. Many methods for the detection of functional interfaces have been developed, and large-scale analyses have provided assessments of their accuracy. Among them are those that consider the size of the protein interface, its amino acid composition and its physicochemical and geometrical properties. Other methods to this effect use statistical potential functions of pairwise interactions, and evolutionary information. The rationale of the evolutionary approach is that functional and structural constraints impose selective pressure; hence, biologically important interfaces often evolve at a slower pace than do other external regions of the protein. Recently, an algorithm, Rate4Site, and a web-server, ConSurf (http://consurf.tau.ac.il/), for the identification of functional interfaces based on the evolutionary relations among homologous proteins as reflected in phylogenetic trees, were developed in our laboratory. The explicit use of the tree topology and branch lengths makes the method remarkably accurate and sensitive. Here we demonstrate its potency in the identification of the functional interfaces of a hypothetical protein, the structure of which was determined as part of the international structural genomics effort. Finally, we propose to combine complementary procedures, in order to enhance the overall performance of methods for the identification of functional interfaces in proteins.
| S-EPMC2447364 | biostudies-other
Project description:Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.
| S-EPMC5000613 | biostudies-literature
Project description:Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry was used to identify peptic fragments from protein complexes that retained deuterium under hydrogen exchange conditions due to decreased solvent accessibility at the interface of the complex. Short deuteration times allowed preferential labeling of rapidly exchanging surface amides so that primarily solvent accessibility changes and not conformational changes were detected. A single mass spectrum of the peptic digest mixture was analyzed to determine the deuterium content of all proteolytic fragments of the protein. The protein-protein interface was reliably indicated by those peptides that retained more deuterons in the complex compared with control experiments in which only one protein was present. The method was used to identify the kinase inhibitor [PKI(5-24)] and ATP-binding sites in the cyclic-AMP-dependent protein kinase. Three overlapping peptides identified the ATP-binding site, three overlapping peptides identified the glycine-rich loop, and two peptides identified the PKI(5-24)-binding site. A complex of unknown structure also was analyzed, human alpha-thrombin bound to an 83-aa fragment of human thrombomodulin [TMEGF(4-5)]. Five peptides from thrombin showed significantly decreased solvent accessibility in the complex. Three peptides identified the anion-binding exosite I, confirming ligand competition experiments. Two peptides identified a new region of thrombin near the active site providing a potential mechanism of how thrombomodulin alters thrombin substrate specificity.
| S-EPMC24513 | biostudies-literature
Project description:Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein-protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein-protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein-ligand docking. The proposed strategy will be useful for many protein-protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.
| S-EPMC7679229 | biostudies-literature
Project description:BACKGROUND: Biological evolution conserves protein residues that are important for structure and function. Both protein stability and function often require a certain degree of structural co-operativity between spatially neighboring residues and it has previously been shown that conserved residues occur clustered together in protein tertiary structures, enzyme active sites and protein-DNA interfaces. Residues comprising protein interfaces are often more conserved compared to those occurring elsewhere on the protein surface. We investigate the extent to which conserved residues within protein-protein interfaces are clustered together in three-dimensions. RESULTS: Out of 121 and 392 interfaces in homodimers and heterocomplexes, 96.7 and 86.7%, respectively, have the conserved positions clustered within the overall interface region. The significance of this clustering was established in comparison to what is seen for the subsets of the same size of randomly selected residues from the interface. Conserved residues occurring in larger interfaces could often be sub-divided into two or more distinct sub-clusters. These structural cluster(s) comprising conserved residues indicate functionally important regions within the protein-protein interface that can be targeted for further structural and energetic analysis by experimental scanning mutagenesis. Almost 60% of experimental hot spot residues (with DeltaDeltaG > 2 kcal/mol) were localized to these conserved residue clusters. An analysis of the residue types that are enriched within these conserved subsets compared to the overall interface showed that hydrophobic and aromatic residues are favored, but charged residues (both positive and negative) are less common. The potential use of this method for discriminating binding sites (interfaces) versus random surface patches was explored by comparing the clustering of conserved residues within each of these regions--in about 50% cases the true interface is ranked among the top 10% of all surface patches. CONCLUSIONS: Protein-protein interaction sites are much larger than small molecule biding sites, but still conserved residues are not randomly distributed over the whole interface and are distinctly clustered. The clustered nature of evolutionarily conserved residues within interfaces as compared to those within other surface patches not involved in binding has important implications for the identification of protein-protein binding sites and would have applications in docking studies.
| S-EPMC2894039 | biostudies-literature
Project description:Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology-based methods, protein-protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.
| S-EPMC5061451 | biostudies-literature
Project description:A common goal across different fields (e.g. separations, biosensors, biomaterials, pharmaceuticals) is to understand how protein behavior at solid-liquid interfaces is affected by environmental conditions. Temperature, pH, ionic strength, and the chemical and physical properties of the solid surface, among many factors, can control microscopic protein dynamics (e.g. adsorption, desorption, diffusion, aggregation) that contribute to macroscopic properties like time-dependent total protein surface coverage and protein structure. These relationships are typically studied through a top-down approach in which macroscopic observations are explained using analytical models that are based upon reasonable, but not universally true, simplifying assumptions about microscopic protein dynamics. Conclusions connecting microscopic dynamics to environmental factors can be heavily biased by potentially incorrect assumptions. In contrast, more complicated models avoid several of the common assumptions but require many parameters that have overlapping effects on predictions of macroscopic, average protein properties. Consequently, these models are poorly suited for the top-down approach. Because the sophistication incorporated into these models may ultimately prove essential to understanding interfacial protein behavior, this article proposes a bottom-up approach in which direct observations of microscopic protein dynamics specify parameters in complicated models, which then generate macroscopic predictions to compare with experiment. In this framework, single-molecule tracking has proven capable of making direct measurements of microscopic protein dynamics, but must be complemented by modeling to combine and extrapolate many independent microscopic observations to the macro-scale. The bottom-up approach is expected to better connect environmental factors to macroscopic protein behavior, thereby guiding rational choices that promote desirable protein behaviors.
| S-EPMC4028386 | biostudies-literature
Project description:BackgroundProtein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods.ResultsHere, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones.ConclusionsPreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .
| S-EPMC7495898 | biostudies-literature