Coverage of protein domain families with structural protein-protein interactions: current progress and future trends

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

Computational Approaches to Prioritize Cancer Driver Missense Mutations

Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.

Advances in template-based protein docking by utilizing interfaces towards completing structural interactome

The increase in the number of structurally determined protein complexes strengthens template-based docking (TBD) methods for modelling protein-protein interactions (PPIs). These methods utilize the known structures of protein complexes as templates to predict the quaternary structure of the target proteins. The templates may be partial or complete structures. Interface based (partial) methods have recently gained interest due in part to the observation that the interface regions are reusable. We describe how available template interfaces can be used to obtain the structural models of protein interactions. Despite the agreement that a majority of the protein complexes can be modelled using the available Protein Data Bank (PDB) structures, a handful of studies argue that we need more template proteins to increase the structural coverage of PPIs. We also discuss the performance of the interface TBD methods at large scale, and the significance of capturing multiple conformations for improving accuracy.

PPIXpress: construction of condition-specific protein interaction networks based on transcript expression

Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress.

AVAILABILITY AND IMPLEMENTATION

PPIXpress is available at https://sourceforge.net/projects/ppixpress/.

Structural Perspectives on the Evolutionary Expansion of Unique Protein-Protein Binding Sites

Structures of protein complexes provide atomistic insights into protein interactions. Human proteins represent a quarter of all structures in the Protein Data Bank; however, available protein complexes cover less than 10% of the human proteome. Although it is theoretically possible to infer interactions in human proteins based on structures of homologous protein complexes, it is still unclear to what extent protein interactions and binding sites are conserved, and whether protein complexes from remotely related species can be used to infer interactions and binding sites. We considered biological units of protein complexes and clustered protein-protein binding sites into similarity groups based on their structure and sequence, which allowed us to identify unique binding sites. We showed that the growth rate of the number of unique binding sites in the Protein Data Bank was much slower than the growth rate of the number of structural complexes. Next, we investigated the evolutionary roots of unique binding sites and identified the major phyletic branches with the largest expansion in the number of novel binding sites. We found that many binding sites could be traced to the universal common ancestor of all cellular organisms, whereas relatively few binding sites emerged at the major evolutionary branching points. We analyzed the physicochemical properties of unique binding sites and found that the most ancient sites were the largest in size, involved many salt bridges, and were the most compact and least planar. In contrast, binding sites that appeared more recently in the evolution of eukaryotes were characterized by a larger fraction of polar and aromatic residues, and were less compact and more planar, possibly due to their more transient nature and roles in signaling processes.

Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.