Use B-factor related features for accurate classification between protein binding interfaces and crystal packing contacts

© 2014 Liu et al.; licensee BioMed Central Ltd. Background: Distinction between true protein interactions and crystal packing contacts is important for structural bioinformatics studies to respond to the need of accurate classification of the rapidly increasing protein structures. There are many unannotated crystal contacts and there also exist false annotations in this rapidly expanding volume of data. Previous tools have been proposed to address this problem. However, challenging issues still remain, such as low performance when the training and test data contain mixed interfaces having diverse sizes of contact areas. Methods and results: B factor is a measure to quantify the vibrational motion of an atom, a more relevant feature than interface size to characterize protein binding. We propose to use three features related to B factor for the classification between biological interfaces and crystal packing contacts. The first feature is the sum of the normalized B factors of the interfacial atoms in the contact area, the second is the average of the interfacial B factor per residue in the chain, and the third is the average number of interfacial atoms with a negative normalized B factor per residue in the chain. We investigate the distribution properties of these basic features and a compound feature on four datasets of biological binding and crystal packing, and on a protein binding-only dataset with known binding affinity. We also compare the cross-dataset classification performance of these features with existing methods and with a widely-used and the most effective feature interface area. The results demonstrate that our features outperform the interface area approach and the existing prediction methods remarkably for many tests on all of these datasets. Conclusions: The proposed B factor related features are more effective than interface area to distinguish crystal packing from biological binding interfaces. Our computational methods have a potential for large-scale and accurate identification of biological interactions from the experimentally determined structural data stored at PDB which may have diverse interface sizes

Development of computational approaches for structural classification, analysis and prediction of molecular recognition regions in proteins

The vast and growing volume of 3D protein structural data stored in the PDB contains abundant information about macromolecular complexes, and hence, data about protein interfaces. Non-covalent contacts between amino acids are the basis of protein interactions, and they are responsible for binding afinity and specificity in biological processes. In addition, water networks in protein interfaces can also complement direct interactions contributing significantly to molecular recognition, although their exact role is still not well understood. It is estimated that protein complexes in the PDB are substantially underrepresented due to their crystallization dificulties. Methods for automatic classifification and description of the protein complexes are essential to study protein interfaces, and to propose putative binding regions. Due to this strong need, several protein-protein interaction databases have been developed. However, most of them do not take into account either protein-peptide complexes, solvent information or a proper classification of the binding regions, which are fundamental components to provide an accurate description of protein interfaces. In the firest stage of my thesis, I developed the SCOWLP platform, a database and web application that structurally classifies protein binding regions at family level and defines accurately protein interfaces at atomic detail. The analysis of the results showed that protein-peptide complexes are substantially represented in the PDB, and are the only source of interacting information for several families. By clustering the family binding regions, I could identify 9,334 binding regions and 79,803 protein interfaces in the PDB. Interestingly, I observed that 65% of protein families interact to other molecules through more than one region and in 22% of the cases the same region recognizes different protein families. The database and web application are open to the research community (www.scowlp.org) and can tremendously facilitate high-throughput comparative analysis of protein binding regions, as well as, individual analysis of protein interfaces. SCOWLP and the other databases collect and classify the protein binding regions at family level, where sequence and structure homology exist. Interestingly, it has been observed that many protein families also present structural resemblances within each other, mostly across folds. Likewise, structurally similar interacting motifs (binding regions) have been identified among proteins with different folds and functions. For these reasons, I decided to explore the possibility to infer protein binding regions independently of their fold classification. Thus, I performed the firest systematic analysis of binding region conservation within all protein families that are structurally similar, calculated using non-sequential structural alignment methods. My results indicate there is a substantial molecular recognition information that could be potentially inferred among proteins beyond family level. I obtained a 6 to 8 fold enrichment of binding regions, and identified putative binding regions for 728 protein families that lack binding information. Within the results, I found out protein complexes from different folds that present similar interfaces, confirming the predictive usage of the methodology. The data obtained with my approach may complement the SCOWLP family binding regions suggesting alternative binding regions, and can be used to assist protein-protein docking experiments and facilitate rational ligand design. In the last part of my thesis, I used the interacting information contained in the SCOWLP database to help understand the role that water plays in protein interactions in terms of affinity and specificity. I carried out one of the firest high-throughput analysis of solvent in protein interfaces for a curated dataset of transient and obligate protein complexes. Surprisingly, the results highlight the abundance of water-bridged residues in protein interfaces (40.1% of the interfacial residues) that reinforces the importance of including solvent in protein interaction studies (14.5% extra residues interacting only water- mediated). Interestingly, I also observed that obligate and transient interfaces present a comparable amount of solvent, which contrasts the old thoughts saying that obligate protein complexes are expected to exhibit similarities to protein cores having a dry and hydrophobic interfaces. I characterized novel features of water-bridged residues in terms of secondary structure, temperature factors, residue composition, and pairing preferences that differed from direct residue-residue interactions. The results also showed relevant aspects in the mobility and energetics of water-bridged interfacial residues. Collectively, my doctoral thesis work can be summarized in the following points: 1. I developed SCOWLP, an improved framework that identiffies protein interfaces and classifies protein binding regions at family level. 2. I developed a novel methodology to predict alternative binding regions among structurally similar protein families independently of the fold they belong to. 3. I performed a high-throughput analysis of water-bridged interactions contained in SCOWLP to study the role of solvent in protein interfaces. These three components of my thesis represent novel methods for exploiting existing structural information to gain insights into protein- protein interactions, key mechanisms to understand biological processes

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

Sequence composition and environment effects on residue fluctuations in protein structures

The spectrum and scale of fluctuations in protein structures affect the range of cell phenomena, including stability of protein structures or their fragments, allosteric transitions and energy transfer. The study presents a statistical-thermodynamic analysis of relationship between the sequence composition and the distribution of residue fluctuations in protein-protein complexes. A one-node-per residue elastic network model accounting for the nonhomogeneous protein mass distribution and the inter-atomic interactions through the renormalized inter-residue potential is developed. Two factors, a protein mass distribution and a residue environment, were found to determine the scale of residue fluctuations. Surface residues undergo larger fluctuations than core residues, showing agreement with experimental observations. Ranking residues over the normalized scale of fluctuations yields a distinct classification of amino acids into three groups. The structural instability in proteins possibly relates to the high content of the highly fluctuating residues and a deficiency of the weakly fluctuating residues in irregular secondary structure elements (loops), chameleon sequences and disordered proteins. Strong correlation between residue fluctuations and the sequence composition of protein loops supports this hypothesis. Comparing fluctuations of binding site residues (interface residues) with other surface residues shows that, on average, the interface is more rigid than the rest of the protein surface and Gly, Ala, Ser, Cys, Leu and Trp have a propensity to form more stable docking patches on the interface. The findings have broad implications for understanding mechanisms of protein association and stability of protein structures.Comment: 8 pages, 4 figure

Molecular characterization and evolutionary plasticity of protein-protein interfaces

Abstract The sequencing of the human genome provides the parts list for understanding cellular processes. However, as 70% of eukaryotic genes work through multi-protein systems, it is only through detailed study of the interactions of these components, that a more complete, systems-level understanding can be gained. This thesis is centred on the establishment of PICCOLO - a comprehensive database of structurally characterized protein interactions. In generating the resource, issues of interface definition, quaternary structure, data redundancy, structural environment and interaction type are addressed. The resource enables a variety of analyses to be performed concerning interface properties including residue propensity, hydropathy, polarity, interface size, sequence entropy and residue contact preference. PICCOLO has been applied to probing the patterns of substitutions that are accepted in protein interfaces across evolution, and whether these patterns are distinguishable from those seen in other structural environments. The derivation of a high-quality set of multiple structural alignments in the form of the database TOCCATA, a prerequisite for such analysis, is described, as well as procedures to derive environment-specific substitution tables. The Blundell group has contributed a series of methods to predict the likely effect of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on protein stability, function and interactions in order to triage the large volumes of data created from high-throughput genetic screening studies, enabling prioritization of those nsSNPs most likely to be phenotypically detrimental. PICCOLO's contribution to these predictions is described. Historically there has been little focus on protein-protein interactions as drug targets for small-molecule therapeutics. However, alanine-scanning mutagenesis studies have revealed that only a subset of residues contribute the greater part of free energy to binding - so-called "hot-spots". Molecular characterization of hot-spots performed using PICCOLO, probes the molecular basis underlying this important phenomenon leading to the possibility of predictive methods to identify hot-spots 'in silico'

Substrate-Induced Allosteric Change in the Quaternary Structure of the Spermidine N-Acetyltransferase SpeG

AbstractThe spermidine N-acetyltransferase SpeG is a dodecameric enzyme that catalyzes the transfer of an acetyl group from acetyl coenzyme A to polyamines such as spermidine and spermine. SpeG has an allosteric polyamine-binding site and acetylating polyamines regulate their intracellular concentrations. The structures of SpeG from Vibrio cholerae in complexes with polyamines and cofactor have been characterized earlier. Here, we present the dodecameric structure of SpeG from V. cholerae in a ligand-free form in three different conformational states: open, intermediate and closed. All structures were crystallized in C2 space group symmetry and contain six monomers in the asymmetric unit cell. Two hexamers related by crystallographic 2-fold symmetry form the SpeG dodecamer. The open and intermediate states have a unique open dodecameric ring. This SpeG dodecamer is asymmetric except for the one 2-fold axis and is unlike any known dodecameric structure. Using a fluorescence thermal shift assay, size-exclusion chromatography with multi-angle light scattering, small-angle X-ray scattering analysis, negative-stain electron microscopy and structural analysis, we demonstrate that this unique open dodecameric state exists in solution. Our combined results indicate that polyamines trigger conformational changes and induce the symmetric closed dodecameric state of the protein when they bind to their allosteric sites

Integrating water exclusion theory into βcontacts to predict binding free energy changes and binding hot spots

10.1186/1471-2105-15-57BMC Bioinformatics151-BBMI