dbMPIKT: A database of kinetic and thermodynamic mutant protein interactions

© 2018 The Author(s). Background: Protein-protein interactions (PPIs) play important roles in biological functions. Studies of the effects of mutants on protein interactions can provide further understanding of PPIs. Currently, many databases collect experimental mutants to assess protein interactions, but most of these databases are old and have not been updated for several years. Results: To address this issue, we manually curated a kinetic and thermodynamic database of mutant protein interactions (dbMPIKT) that is freely accessible at our website. This database contains 5291 mutants in protein interactions collected from previous databases and the literature published within the last three years. Furthermore, some data analysis, such as mutation number, mutation type, protein pair source and network map construction, can be performed online. Conclusion: Our work can promote the study on PPIs, and novel information can be mined from the new database. Our database is available in http://DeepLearner.ahu.edu.cn/web/dbMPIKT/for use by all, including both academics and non-academics

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots

Specialized dynamical properties of promiscuous residues revealed by simulated conformational ensembles

The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein-protein interaction prediction and design methods. © 2013 American Chemical Society

Pulling apart the intermolecular interactions of the Parkinson’s disease linked protein alpha synuclein

Amyloidoses are a group of protein misfolding diseases that are characterised by the abnormal accumulation of highly ordered filamentous assemblies known as amyloid. This phenomenon is associated with more than 50 human diseases, some of which are the most debilitating disorders that threaten human health today. Many of these disorders have age as the main contributing risk factor and, therefore, pose an ever-increasing risk in the developed world with aging societies. Despite intense research, much remains unknown about the fundamental processes driving protein aggregation in these diseases and there are few disease modifying treatments available. A protein that undergoes amyloid formation and causes disease is the intrinsically disordered neuronal protein α-synuclein (αSyn), the aggregation of which leads to several diseases including Parkinson’s disease (PD) which is the second-most common neurodegenerative disorder that affects 2–3% of the population ≥65 years of age. Importantly, the toxic species on the aggregation pathway are difficult to identify and determine in molecular detail. This thesis was motivated by this fact and aimed to study the initial intermolecular events in αSyn self-assembly (dimerisation) on a single molecule scale. Single molecule force spectroscopy (SMFS) methodologies were therefore utilised in order to study these early protein-protein interaction events. A display system was firstly designed and validated in which small regions of highly aggregation-prone sequences can be presented in a protein scaffold in a robust and reproducible manner for SMFS studies. It was demonstrated that intermolecular interactions of these sequences could be analysed by implementing this system. A novel heterodimeric interaction between the central aggregation-prone regions of αSyn (residues 71-82) and the same region of its human homologue γSynuclein (γSyn), were revealed by using this system. Further study led to the finding that this interaction played a role in the inhibiting the aggregation of αSyn. The dimerisation interaction of full length αSyn has also been analysed in this thesis and several important findings have been demonstrated. The SMFS experiments show that force-resistant structure forms in the dimeric species of αSyn and that this structure is dependent on the environmental conditions. SMFS utilising different immobilisation regimes of αSyn have also allowed the location of a novel interaction interface involving the N-terminal region of the protein. Further SMFS experiments investigating the effects of salt and hydrophobicity have on dimerisation, alongside bioinformatics analyses of the protein sequence led to the hypotheses that the dimeric interaction is driven by hydrophobic stretches in the N-terminal region, but modulated by local electrostatics. In vitro aggregation assays and SMFS on non-aggregation-prone synuclein homologues (β- and γSyn) indicated that that this interaction is protective against aggregation, considering these finding with existing literature prompted speculation that the interactions observed in SMFS may indeed be physiologically relevant. This may therefore be an important finding in regards to targeting the aggregation process with disease modifying agents

Computational Insight into the Selective Allosteric Inhibition for PTP1B versus TCPTP: a molecular modelling study

All over the world, diabetes mellitus type 2 has spread as a problematic pandemic. Despite currently available treatments, approved drugs still show undesirable side effects and loss of efficacy or target symptoms instead of causes. Protein tyrosine phosphatase 1B (PTP1B), since its discovery, has emerged as a very promising target against this disease. Although the information regarding the enzyme is immense, little is known about the selectivity between this enzyme and its closest homologue, lymphocyte T tyrosine phosphatase (TCPTP), which is responsible for complicated side effects. In this study, on the basis of different computational approaches, we are able to highlight the importance of a phenylalanine residue located in PTP1B, but not in TCPTP, as a crucial hotspot that causes selectivity and stability for the whole ligand bound system. These results not only allow to explain the selectivity determinants of PTP1B but also provide a useful guide for the design of new allosteric inhibitors. Communicated by Ramaswamy H. Sarm

Designing Small Molecule Inhibitors of RNA-Binding Protein Musashi Using New Biochemical and Computational Approaches

ABSTRACT RNA-binding proteins (RBPs) are key regulators of post-transcriptional gene expression, and underlie many disease-relevant processes. However, they have historically been challenging to target with drug-like compounds. Inspired by the “anchor residues” of protein-protein interactions, we developed a computational approach for rationally designing small-molecule inhibitors of RBPs. In this dissertation, we first selected Musashi-1 and Musashi-2 to apply our “RNA mimicry” approach. Both Musashi proteins are well-studied RBPs, known principally as stem‑cell markers that are upregulated in many cancers. In the future, we hope our “RNA mimicry” approach can be generally applied to inhibitor design of diverse target RBPs. To design inhibitors of Musashi proteins, we applied our strategy by mimicking the three-dimensional interactions in the protein-RNA complex. As described in Chapter II, by using pharmacophoric screening, we searched for drug-like compounds that can present the same geometric arrangement of functional groups as the RNA in the complex. We hypothesized that such ligands would engage Musashi in a similar manner as the RNA binds to Musashi. Since the interaction geometries can be quite distinct from one another for different RBPs, we anticipated that this strategy would lead to inhibitors that were selective for Musashi. To facilitate characterization of these candidate Musashi inhibitors, I developed the “isothermal analysis” approach. As described in Chapter III, this method allows us to calculate quantitative binding constants by using differential scanning fluorimetry (DSF) data. The method requires only the protein unfolding information at a given temperature as a function of ligand concentration, and thus no thermodynamic parameters are included in the calculation. Finally, I describe the use of computational docking to better understand the basis for PROTAC-mediated degradation of target proteins. PROteolysis TArgeting Chimeras (PROTACs) are heterobifunctional small molecules which can induce target protein degradation through cell ubiquitination process. Rational design of PROTACs is still challenging, however, because of the limited structural understanding of their mechanism. In Chapter IV, I seek to predict the formation of the ternary structure complex by including both effects of the protein-protein interaction and effects of the chemical linker. Looking ahead, I hope to use these ternary structure models to explain the activity and selectivity of the given PROTAC molecules, and ultimately to use our designed Musashi inhibitors as a starting point for building new PROTACs to degrade Musashi. The text of Chapter II is a manuscript that is in preparation for publication as: Bai N‡, Adeshina Y‡, Lan L, Makhov PB, Xia Y, Gowthaman R, Miller SA, Johnson DK, Boumber Y, Xu L, Karanicolas J. Rationally designing inhibitors of the Musashi protein-RNA interaction by hotspot mimicry. ‡equally contributing co-authors The supporting information for this chapter is included as Appendix A.1. The text of Chapter III is a reprint of the material from: Bai N, Roder H, Dickson A, Karanicolas J. Isothermal analysis of ThermoFluor data can readily provide quantitative binding affinities. Sci. Rep. 9, p. 2650 (2019). Note: the software disseminated with this paper has accumulated 1000 downloads in the 9 months since publication The supporting information for this chapter is included as Appendix A.2. The text of Chapter IV is a manuscript that is in preparation for publication as: Bai N, Karanicolas J. Predicting PROTAC-mediated ternary complex formation using Rosetta. The supporting information for this chapter is included as Appendix A.3

Investigation of ligand selectivity and activation dynamics of G protein-coupled receptors using enhanced sampling simulations

G protein-coupled receptors (GPCRs) are a large superfamily of transmembrane proteins found in eukaryotes. They play a crucial role in the transduction of signals across the plasma membrane of cells, and are involved in the regulation of a plethora of processes. Due to their function in countless biological pathways they have a primary role in many pathological conditions, and are thus therapeutic targets of great importance. Notwithstanding the growing availability of X-ray and cryo-EM structures and the intense involvement of the scientific community, many gaps are still present in our understanding of the mechanisms of ligand binding, receptor activation and allostery. Computational methods open the possibility for the study of the dynamics of such processes at atomistic resolution, complementing experimental findings. In this work key processes of a number of different GPCRs are explored with the use of computational approaches. Molecular dynamics and enhanced sampling methods are leveraged for sampling rare events of great interest and for the calculation of the associated free energy landscapes. In the first place our study of ligand binding and the selectivity mechanism in adenosine A2a and A1 receptors is reported, elucidating how selectivity arises from an interplay of structural factors. The activation mechanism of glucagon receptor and the coupling with a G protein is then investigated, highlighting the cooperative action of glucagon and G protein in the process. A detailed overview of allosteric antagonism in chemokine receptors is built by mining databases of experimental data and complementing this picture with insights on the dynamics of these receptors. Finally, the performance of TS-PPTIS (Transition State-Partial Path Transition State Sampling), a method for the calculation of kinetic rate constants, is studied for the prediction of ligand binding kinetic rates. The findings of this study add to the understanding of the mechanism of signal transduction through GPCRs, and detail this process from its origin outside the cell to the intracellular medium

Inferring structures, free energy differences, and kinetic rates of biological macromolecular assemblies by integrative modeling

Biological macromolecular assemblies play crucial roles in most cellular processes. The determination of their structures, thermodynamics, and kinetics is essential to understand their function, evolution, modulation, and design. Determining such models, however, remains challenging. One particularly powerful approach to constructing models in general is integrative modeling. Integrative modeling aims to maximize the accuracy, precision, and completeness of models, by simultaneously utilizing all available information, including experimental data, physical principles, statistical analyses, and other prior models. The goal of this thesis is to expand the scope of integrative modeling to the inference of spatial, thermodynamic, and kinetic aspects of macromolecular assemblies. In Chapter I, I introduce the integrative modeling framework for spatiotemporal modeling of biological macromolecular assemblies. In Chapter II, I demonstrate how the synergy between multi-chemistry cross-linking mass spectrometry and integrative modeling can map the structural dynamics of macromolecular assemblies, by application to the human Cop9 signalosome complex. In Chapter III, I present a method for determining structures, free energy differences, and kinetic rates of macromolecular assemblies along their functional cycle, mainly from negative stain electron microscopy (EM). We apply the method to the yeast Hsp90 to estimate the free energy differences and kinetic parameters along its nucleotide hydrolysis cycle, which includes open and closed states of Hsp90. In Chapter IV, I describe a validation of stochastic sampling in integrative modeling. The remaining chapters describe applications of integrative modeling to assemblies of various sizes and scales, using various sources of information, thus illustrating the flexibility of the integrative modeling approach. Specifically, I apply integrative modeling to the human ECM29-Proteasome assembly under oxidative stress (Chapter V), the yeast nuclear pore complex (NPC) cytoplasmic mRNA export platform (Chapter VI), the major membrane ring component of the yeast NPC (Chapter VII), the entire yeast NPC (Chapter VIII), and the reconstruction of 3D structures of MET antibodies (Chapter IX)