Comprehensive statistical analysis of residues interaction specificity at protein-protein interfaces

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Biophysical Reviews' "Meet the Councilor"-a profile of Anastasia A. Anashkina

As one of the twelve Councilors of the International Union of Pure and Applied Biophysics elected in summer 2021, I have been asked to provide this short biographical sketch for the journal readers. I am a new member of the IUPAB Council. I hold a specialist degree in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology and PhD in Biophysics from Moscow State University. I have spent my entire professional career at Engelhardt Institute of Molecular Biology of the Russian Academy of Sciences in Moscow, where I am currently a senior researcher. I am Associate Professor at the Digital Health Institute of the I.M. Sechenov First Moscow State Medical University since 2018, and have trained undergraduate students in structural biology, biophysics, and bioinformatics. In addition, I serve as the Guest Editor of special journal issues of International Journal of Molecular Sciences and Frontiers in Genetics BMC genomics. Now I joined Biophysical Reviews Editorial Board as IUPAB Councilor. I am a Secretary of National Committee of Russian Biophysicists, and have helped to organize scientific conferences and workshops, such as the VI Congress of Russian Biophysicists.

Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.

Comparative analyses of quaternary arrangements in homo-oligomeric proteins in superfamilies: Functional implications

A comprehensive analysis of the quaternary features of distantly related homo-oligomeric proteins is the focus of the current study. This study has been performed at the levels of quaternary state, symmetry, and quaternary structure. Quaternary state and quaternary structure refers to the number of subunits and spatial arrangements of subunits, respectively. Using a large dataset of available 3D structures of biologically relevant assemblies, we show that only 53% of the distantly related homo-oligomeric proteins have the same quaternary state. Considering these homologous homo-oligomers with the same quaternary state, conservation of quaternary structures is observed only in 38% of the pairs. In 36% of the pairs of distantly related homo-oligomers with different quaternary states the larger assembly in a pair shows high structural similarity with the entire quaternary structure of the related protein with lower quaternary state and it is referred as "Russian doll effect." The differences in quaternary state and structure have been suggested to contribute to the functional diversity. Detailed investigations show that even though the gross functions of many distantly related homo-oligomers are the same, finer level differences in molecular functions are manifested by differences in quaternary states and structures. Comparison of structures of biological assemblies in distantly and closely related homo-oligomeric proteins throughout the study differentiates the effects of sequence divergence on the quaternary structures and function. Knowledge inferred from this study can provide insights for improved protein structure classification and function prediction of homo-oligomers. Proteins 2016; 84:1190-1202. © 2016 Wiley Periodicals, Inc.

Salvage of failed protein targets by reductive alkylation

The growth of diffraction-quality single crystals is of primary importance in protein X-ray crystallography. Chemical modification of proteins can alter their surface properties and crystallization behavior. The Midwest Center for Structural Genomics (MCSG) has previously reported how reductive methylation of lysine residues in proteins can improve crystallization of unique proteins that initially failed to produce diffraction-quality crystals. Recently, this approach has been expanded to include ethylation and isopropylation in the MCSG protein crystallization pipeline. Applying standard methods, 180 unique proteins were alkylated and screened using standard crystallization procedures. Crystal structures of 12 new proteins were determined, including the first ethylated and the first isopropylated protein structures. In a few cases, the structures of native and methylated or ethylated states were obtained and the impact of reductive alkylation of lysine residues was assessed. Reductive methylation tends to be more efficient and produces the most alkylated protein structures. Structures of methylated proteins typically have higher resolution limits. A number of well-ordered alkylated lysine residues have been identified, which make both intermolecular and intramolecular contacts. The previous report is updated and complemented with the following new data; a description of a detailed alkylation protocol with results, structural features, and roles of alkylated lysine residues in protein crystals. These contribute to improved crystallization properties of some proteins.

The development of a universal in silico predictor of protein-protein interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.

Caught in self-interaction: evolutionary and functional mechanisms of protein homooligomerization

Many soluble and membrane proteins form homooligomeric complexes in a cell which are responsible for the diversity and specificity of many pathways, may mediate and regulate gene expression, activity of enzymes, ion channels, receptors, and cell adhesion processes. The evolutionary and physical mechanisms of oligomerization are very diverse and its general principles have not yet been formulated. Homooligomeric states may be conserved within certain protein subfamilies and might be important in providing specificity to certain substrates while minimizing interactions with other unwanted partners. Moreover, recent studies have led to a greater awareness that transitions between different oligomeric states may regulate protein activity and provide the switch between different pathways. In this paper we summarize the biological importance of homooligomeric assemblies, physico-chemical properties of their interfaces, experimental and computational methods for their identification and prediction. We particularly focus on homooligomer evolution and describe the mechanisms to develop new specificities through the formation of different homooligomeric complexes. Finally, we discuss the possible role of oligomeric transitions in the regulation of protein activity and compile a set of experimental examples with such regulatory mechanisms.

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

PIE-efficient filters and coarse grained potentials for unbound protein-protein docking

Identifying correct binding modes in a large set of models is an important step in protein-protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two-chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near-native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near-native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution.

X-ray-radiation-induced cooperative atomic movements in protein

X-rays interact with biological matter and cause damage. Proteins and other macromolecules are damaged primarily by ionizing X-ray photons and secondarily by reactive radiolytic chemical species. In particular, protein molecules are damaged during X-ray diffraction experiments with protein crystals, which is, in many cases, a serious hindrance to structure solution. The local X-ray-induced structural changes of the protein molecule have been studied using a number of model systems. However, it is still not well understood whether these local chemical changes lead to global structural changes in protein and what the mechanism is. We present experimental evidence at atomic resolution indicating the movement of large parts of the protein globule together with bound water molecules in the early stages of radiation damage to the protein crystal. The data were obtained from a crystal cryocooled to approximately 100 K and diffracting to 1 A. The movement of the protein structural elements occurs simultaneously with the decarboxylation of several glutamate and aspartate residues that mediate contacts between moving protein structural elements and with the rearrangement of the water network. The analysis of the anisotropy of atomic displacement parameters reveals that the observed atomic movements occur at different rates in different unit cells of the crystal. Thus, the examination of the cooperative atomic movement enables us to better understand how radiation-induced local chemical and structural changes of the protein molecule eventually lead to disorder in protein crystals.

Quantitative expression of protein heterogeneity: Response of amino acid side chains to their local environment

A general method has been developed to characterize the hydrophobicity or hydrophilicity of the microenvironment (MENV), in which a given amino acid side chain is immersed, by calculating a quantitative property descriptor (QPD) based on the relative (to water) hydrophobicity of the MENV. Values of the QPD were calculated for a test set of 733 proteins to analyze the modulating effects on amino acid residue properties by the MENV in which they are imbedded. The QPD values and solvent accessibility were used to derive a partitioning of residues based on the MENV hydrophobicities. From this partitioning, a new hydrophobicity scale was developed, entirely in the context of protein structure, where amino acid residues are immersed in one or more "MENVpockets." Thus, the partitioning is based on the residues "sampling" a large number of "solvents" (MENVs) that represent a very large range of hydrophobicity values. It was found that the hydrophobicity of around 80% of amino acid side chains and their MENV are complementary to each other, but for about 20%, the MENV and their imbedded residue can be considered as mismatched. Many of these mismatches could be rationalized in terms of the structural stability of the protein and/or the involvement of the imbedded residue in function. The analysis also indicated a remarkable conservation of local environments around highly conserved active site residues that have similar functions across protein families, but where members have relatively low sequence homology. Thus, quantitative evaluation of this QPD is suggested, here, as a tool for structure-function prediction, analysis, and parameter development for the calculation of properties in proteins.