Computational analyses of high-throughput protein-protein interaction data

Protein-protein interactions play important roles in nearly all events that take place in a cell. High-throughput experimental techniques enable the study of protein-protein interactions at the proteome scale through systematic identification of physical interactions among all proteins in an organism. High-throughput protein-protein interaction data, with ever-increasing volume, are becoming the foundation for new biological discoveries. A great challenge to bioinformatics is to manage, analyze, and model these data. In this review, we describe several databases that store, query, and visualize protein-protein interaction data. Comparison between experimental techniques shows that each high-throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions and they are complementary to each other. In silico methods using protein/DNA sequences, domain and structure information to predict protein-protein interaction can expand the scope of experimental data and increase the confidence of certain protein-protein interaction pairs. Protein-protein interaction data correlate with other types of data, including protein function, subcellular location, and gene expression profile. Highly connected proteins are more likely to be essential based on the analyses of the global architecture of large-scale interaction network in yeast. Use of protein-protein interaction networks, preferably in conjunction with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in our study on the yeast signal transduction pathway for amino acid transport, integration of high-throughput data with traditional biology resources can transform the protein-protein interaction data from noisy information into knowledge of cellular mechanisms.     

Molecular aptamers for real-time protein-protein interaction study

Protein-protein interactions play critical roles in cellular functions, but current techniques for real-time study of these interactions are limited. We report the real-time monitoring of protein-protein interactions without labeling either of the two interacting proteins; this procedure poses minimum effects on the binding properties of the proteins. Our strategy uses a protein/aptamer complex to probe the interactions in a competitive assay where the binding of an aptamer to its target protein is altered by a second protein that interacts with the target protein. Two signal transduction strategies, fluorescence resonance energy transfer (FRET) and fluorescence anisotropy, have been designed to study the interactions of human alpha-thrombin with different proteins by using two aptamers specific for two binding sites on alpha-thrombin. Our method has been shown to be simple and effective, does not require labeling of proteins, makes use of easily obtainable aptamers, provides detailed protein-protein interaction information and has excellent sensitivity for protein detection and protein-protein interaction studies. The FRET and the fluorescent anisotropy approaches complement each other in providing insight into the kinetics, mechanisms, binding sites and binding dynamics of the interacting proteins.     

Computational methods for remote homolog identification

As more and more protein sequences are available, homolog identification becomes increasingly important for functional, structural, and evolutional studies of proteins. Many homologous proteins were separated a very long time ago in their evolutionary history and thus their sequences share low sequence identity. These remote homologs have become a research focus in bioinformatics over the past decade, and some significant advances have been achieved. In this paper, we provide a comprehensive review on computational techniques used in remote homolog identification based on different methods, including sequence-sequence comparison, and sequence-structure comparison, and structure-structure comparison. Other miscellaneous approaches are also summarized. Pointers to the online resources of these methods and their related databases are provided. Comparisons among different methods in terms of their technical approaches, their strengths, and limitations are followed. Studies on proteins in SARS-CoV are shown as an example for remote homolog identification application.     

Multidimensional detection methods for separations and their application in food analysis

The use of the multidimensional detection systems, mass spectrometry and NMR, with separation techniques is discussed with a consideration of their actual or potential application in food analysis. The features of the most commonly used interfaces for coupling liquid chromatography (LC) and mass spectrometry (MS) are briefly examined and examples from the literature on the use of LC-MS in the analysis of natural components in foodstuffs are reported. The potential capabilities for food analysis of LC-NMR, supercritical fluid chromatography (SFC)-MS and capillary electrophoresis (CE)-MS are highlighted.     

Biomarkers for ischemic stroke subtypes: A protein-protein interaction analysis

According to the Trial of Org 10172 in Acute Stroke Treatment, ischemic stroke is classified into five subtypes. However, the predictive biomarkers of ischemic stroke subtypes are still largely unknown. The utmost objective of this study is to map, construct and analyze protein-protein interaction (PPI) networks for all subtypes of ischemic stroke, and to suggest the predominant biological pathways for each subtypes. Through 6285 protein data retrieved from PolySearch2 and STRING database, the first PPI networks for all subtypes of ischemic stroke were constructed. Notably, F2 and PLG were identified as the critical proteins for large artery atherosclerosis (LAA), lacunar, cardioembolic, stroke of other determined etiology (SOE) and stroke of undetermined etiology (SUE). Gene ontology and DAVID analysis revealed that GO:0030193 regulation of blood coagulation and GO:0051917 regulation of fibrinolysis were the important functional clusters for all the subtypes. In addition, inflammatory pathway was the key etiology for LAA and lacunar, while FOS and JAK2/STAT3 signaling pathways might contribute to cardioembolic stroke. Due to many risk factors associated with SOE and SUE, the precise etiology for these two subtypes remained to be concluded.     

Thiazolylazo dyes and their application in analytical methods

An overview is presented on thiazolylazo dyes and their analytical applications in the determination, preconcentration or separation of trace metal ions. The article summarizes conventional analytical methods based on spectrophotometry, solid phase extraction, liquid chromatography and liquid–liquid and cloud point extraction. Both in-batch and on-line procedures are considered. General properties about these ligands are also commented on. Finally, the use of thiazolylazo dyes in procedures involving electroanalytical and other methods is presented.     

Charge-transfer interaction in liquid-crystalline materials and their application to photonics

Photochemical phase transition of push-pull type azobenzene liquid crystal (LC), which contains both an electron donor and an acceptor in both ends of the azobenzene moiety, has been explored. Polymer azobenzene LCs were prepared, which show nematic (N) LC behavior in the trans form while no LC phase in the cis isomer. Photoirradiation of a very thin film of the azobenzene LCs (∼ 200 nm) in the trans form resulted in disappearance of the N phase due to transcis photoisomerization of each mesogen, and the N phase recovered quickly when the irradiated sample was kept in the dark because of cis-trans thermal isomerization and reorientation of trans-azobenzenes. Time-resolved measurements by the use of a laser pulse (355 nm; 10 ns FWHM) revealed that the N to isotropic (I) phase transition took place in 200 μs. The thermal I-N phase transition of the push-pull type azobenzene LC occurred in 800 ms at 135 °C. This response is faster by one order of magnitude than the response of azobenzene LCs without charge-transfer interaction.     

Improving neural protein-protein interaction extraction with knowledge selection

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts. This paper proposes a Knowledge Selection Model (KSM) to fuse the selected prior knowledge and context information for PPI extraction. Firstly, two Transformers encode the context sequence of a protein pair according to each protein embedding, respectively. Then, the two outputs are fed to a mutual attention to capture the important context features towards the protein pair. Next, the context features are used to distill the relation embedding by a knowledge selector. Finally, the selected relation embedding and the context features are concatenated for PPI extraction. Experiments on the BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art performance (38.08 % F1-score) by adding knowledge selection.     

Efficient moves for global geometry optimization methods and their application to binary systems

We show that molecular dynamics based moves in the minima hopping method are more efficient than saddle point crossing moves. For binary systems we incorporate identity exchange moves in a way that allows one to avoid the generation of high energy configurations. Using this modified minima hopping method, we re-examine the binary Lennard-Jones benchmark system with up to 100 atoms and we find a large number of new putative global minima.     

Tris(hydroxymethyl)aminomethane-functionalized silica particles and their application for hydrophilic interaction chromatography

A new method is presented for synthesizing a highly hydrophilic silica-based material for use in hydrophilic interaction chromatography. Porous silica particles used as a starting substrate were modified with 3-bromopropyl trichlorosilane and grafted with glycidyl methacrylate by controlled ("living") atom transfer radical polymerization in order to introduce an oxirane-carrying reactive tentacle layer on the silica surface. The grafted material was thereafter subject to an oxirane ring opening reaction with tris(hydroxy-methyl)aminomethane in dimethylformamide to yield a polymer-bound equivalent of the well known and highly hydrophilic "TRIS" buffering substance. Chemical characterization was done by diffuse reflectance FT-IR, X-ray photoelectron spectroscopy, elemental analysis, and (1)H NMR. Porosity and surface area examination was done with Brunauer-Emmett-Teller. Chromatographic application of the material was evaluated by separations of nucleic bases, small organic acids, and common nucleotides under mixed hydrophilic interaction chromatography and weak anion exchange conditions.     

Computational methods for the structural alignment of molecules

In drug design, often enough, no structural information on a particular receptor protein is available. However, frequently a considerable number of different ligands is known together with their measured binding affinities towards a receptor under consideration. In such a situation, a set of plausible relative superpositions of different ligands, hopefully approximating their putative binding geometry, is usually the method of choice for preparing data for the subsequent application of 3D methods that analyze the similarity or diversity of the ligands. Examples are 3D-QSAR studies, pharmacophore elucidation, and receptor modeling. An aggravating fact is that ligands are usually quite flexible and a rigorous analysis has to incorporate molecular flexibility. We review the past six years of scientific publishing on molecular superposition. Our focus lies on automatic procedures to be performed on arbitrary molecular structures. Methodical aspects are our main concern here. Accordingly, plain application studies with few methodical elements are omitted in this presentation. While this review cannot mention every contribution to this actively developing field, we intend to provide pointers to the recent literature providing important contributions to computational methods for the structural alignment of molecules. Finally we provide a perspective on how superposition methods can effectively be used for the purpose of virtual database screening. In our opinion it is the ultimate goal to detect analogues in structure databases of nontrivial size in order to narrow down the search space for subsequent experiments.     

Design and structure of peptide and peptidomimetic antagonists of protein-protein interaction

Peptides based on the amino acid sequences found at protein-protein interaction sites make excellent leads for antagonist development. A statistical picture of amino acids involved in protein-protein interactions indicates that proteins recognize and interact with one another through the restricted set of specialized interface amino acid residues, Pro, Ile, Tyr, Trp, Asp and Arg. These amino acids represent residues from each of the three classes of amino acids, hydrophobic, aromatic and charged, with one anionic and one cationic residue at neutral pH. The use of peptides as drug leads has been successfully used to search for antagonists of cell-surface receptors. Peptide, peptidomimetic, and non-peptide organic inhibitors of a class of cell surface receptors, the integrins, currently serve as therapeutic and diagnostic imaging agents. In this review, we discuss the structural features of protein-protein interactions as well as the design of peptides, peptidomimetics, and small organic molecules for the inhibition of protein-protein interactions. Information gained from studying inhibitors of integrin functions is now being applied to the design and testing of inhibitors of other protein-protein interactions. Most drug development progress in the past several decades has been made using the enzyme binding-pocket model of drug targets. Small molecules are designed to fit into the substrate-binding pockets of proteins based on a lock-and-key, induced-fit, or conformational ensemble model of the protein binding site. Traditionally, enzymes have been used as therapeutic drug targets because it was easier to develop rapid, sensitive screening assays, and to find low molecular weight inhibitors that blocked the active site. However, for proteins which interact with other proteins, rather than with small substrate molecules, the lack of binding pockets means that this approach will not generally succeed. There exist many diseases in which the inhibition of protein-protein interactions would provide therapeutic benefit, but there are no general methods available to address such problems. The focus of the first part of this review is to discuss the features of protein-protein interactions which may serve as general guidelines for the development and design of inhibitors for protein-protein interactions. In the second part we focus on the design of peptides (lead compounds) and their conversion into peptidomimetics or small organic molecules for the inhibition of protein-protein interactions. We draw examples from the important and emerging area of integrin-based cell adhesion and show how the principles of protein-protein interactions are followed in the discovery, optimization and usage of specific protein interface peptides as drug leads.     

Relationship between degree-rank function and degree distribution of protein-protein interaction networks

It is argued that both the degree-rank function r=f(d), which describes the relationship between the degree d and the rank r of a degree sequence, and the degree distribution P(k), which describes the probability that a randomly chosen vertex has degree k, are important statistical properties to characterize protein-protein interaction (PPI) networks, both rank-degree plot and frequency-degree plot are reliable tools to analyze PPI networks. An exact mathematical relationship between degree-rank functions and degree distributions of PPI networks is derived. It is demonstrated that a power law degree distribution is equivalent to a power law degree-rank function only if scaling exponent is greater than 2. The puzzle that the degree distributions of some PPI networks follow a power law using frequency-degree plots, whereas the degree sequences do not follow a power law using rank-degree plots is explained using the mathematical relationship.     

Efficient methods for configuration interaction calculations

Advanced techniques are developed to provide efficient economic treatment of the large scale eigenvalue problem posed when configuration interaction is carried out on SCF basis sets of moderate size. When the characteristic properties of the hamiltonian matrix are examined in light of the type of solution required, partitioning of the configuration space is shown to result in an expansion of the problem about a limited core of states, where the small but cumulative interactions of vast regions of the remaining space are reduced to the form of an effective potential. With proper selection of the core, the evaluation of this potential can be readily and accurately truncated to a level involving minimum expenditure in time and effort. In particular only diagonal elements and a strip of the full CI matrix are required to achieve an accuracy of 1 – 5 kcal/mole with complete treatment for configuration spaces of order tens of thousands. In addition, a close look at current theory on the generation of matrix elements between spin symmetry adapted configurations leads to simplified expressions where the matrix elements are derived in the form of a weighted sum of molecular integrals in which the weighting coefficients represent the integrated value of the wavefunctions over spin coordinates. For typical cases of low multiplicity and limited numbers of open shells the list of unique parameters needed to generate all weights are shown to be readily stored as a program library. Actual times for matrix element generation are believed to be an order of magnitude faster than current techniques. Practical demonstration of the accuracy and efficiency of the method is provided by calculations on formaldehyde, water, and ethylene.     

Computational methods for the identification and optimisation of high quality leads

Lead identification and optimisation have evolved into multidimensional, multidisciplinary and information-driven processes. Herein, we review the contribution of computational chemistry to these processes. We focus on computational approaches developed for modelling biopharmaceutical properties, including in vitro activity, selectivity, absorption, distribution, metabolism, excretion and toxicity. Whenever possible, successful applications are mentioned.     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.