Analysis of sequence-reactivity space for protein-protein interactions

Sequence–reactivity space is defined by the relationships between amino acid type choices at some residue positions in a protein and the reactivities of the resulting variants. We are studying Kazal superfamily serine proteinase inhibitors, under substitution of any combination of residue types at 10 binding‐region positions. Reactivities are defined by the standard free energy of association for an inhibitor against an enzyme, and we are interested in both the strength (the free energy value) and specificity (relative free energy values for one inhibitor against different enzymes). Characterizing the structure of such a space poses several interesting questions: (1) How many sequences achieve particular strength and specificity characteristics? (2) What is the best such sequence? (3) What are some nearly‐as‐good alternatives? (4) What are their common residue type characteristics (e.g., conservation and correlation)? Although these problems are all highly combinatorial in nature, this article develops an efficient, integrated mechanism to address them under a data‐driven model that predicts reactivity for given sequences. We employ sampling and a novel deterministic distribution propagation algorithm, in order to determine both the reactivity distribution and sequence composition statistics; integer programming and a novel branch‐and‐bound search algorithm, in order to optimize sequences and enumerate near‐optimal sequences; and correlation‐based sequence decomposition, in order to identify sequence motifs. We demonstrate the value of our mechanism in analyzing the Kazal superfamily sequence–reactivity space, providing insights into the underlying biochemistry and suggesting hypotheses for further experimental consideration. In general, our mechanism offers a valuable tool for investigating the available degrees of freedom in protein design within a combined computational–experimental framework. Proteins 2005. © 2004 Wiley‐Liss, Inc.     

Multiple biomarker tissue arrays: A computational approach to identifying protein-protein interactions in the EGFR/ERK signalling pathway

ABSTRACT: BACKGROUND: Many studies have demonstrated genetic and environmental factors that lead to renal cell carcinoma (RCC) and that occur during a protracted period of tumourigenesis. It appears suitable to identify and characterise potential molecular markers that appear during tumourigenesis and that might provide rapid and effective possibilities for the early detection of RCC. EGFR activation induces cell cycle progression, inhibition of apoptosis and angiogenesis, promotion of invasion/metastasis, and other tumour promoting activities. Over-expression of EGFR is thought to play an important role in tumour initiation and progression of RCC because up-regulation of EGFR has been associated with high grade cancers and a worse prognosis. METHODS: Characterisation of the protein profile interacting with EGFR was performed using the following: an immunohistochemical (IHC) study of EGFR, a comprehensive computational study of EGFR protein-protein interactions, an analysis correlating the expression levels of EGFR with other significant markers in the tumourigenicity of RCC, and finally, an analysis of the utility of EGFR for prognosis in a cohort of patients with renal cell carcinoma. RESULTS: The cases that showed a higher level of this protein fell within the clear cell histological subtype (p = 0.001). The EGFR significance statistic was found with respect to a worse prognosis. In vivo significant correlations were found with PDGFR-beta, Flk-1, Hif1-alpha, proteins related to differentiation (such as DLL3 and DLL4 ligands), and certain metabolic proteins such as Glut5. In silico significant associations gave us a panel of 32 EGFR-interacting proteins (EIP) using the APID and STRING databases. CONCLUSIONS: This work summarises the multifaceted role of EGFR in the pathology of RCC, and it identifies EIPs that could help to provide mechanistic explanations for the different behaviours observed in tumours.     

Self-interaction chromatography: a tool for the study of protein-protein interactions in bioprocessing environments

We describe a new protein characterization technique called self-interaction chromatography (SIC), which exploits the specificity of protein-protein interactions that is common to protein aggregates and enables the rapid screening of protein formulation additives as physical stabilizers against aggregation. This technique also enables the identification of specific interaction sites and the determination of their relative importance for self-association. Mannitol, glycine, and dextran 40 were tested for their stabilizing effect toward the model protein lysozyme. Dextran 40 exhibited a poor stabilizing effect. While mannitol stabilized both the native and acid-denatured forms of lysozyme, glycine stabilized the native form with respect to the denatured species. These results are in good agreement with findings in the formulation literature. The SIC shows tremendous potential as a rapid formulation development tool. We also screened two putative interaction sites for involvement in the self-association of lysozyme and estimated the associated binding energies using a binding isotherm model that we developed. The sites screened consisted of residues 41-48 and 125-128 and were selected based on their apparent importance in forming crystal contacts in several different crystal forms of lysozyme. Of the two sites, only residues 125-128 were found to influence self-association under the conditions we employed. Because the success of this technique depends on the exploitation of self-interactions between native species, several important applications are also suggested such as separating native from misfolded or variant species and probing site utilization in aggregation versus crystallization phenomena. (c) 1996 John Wiley & Sons, Inc.     

A co-localization assay for the analysis of protein-protein interactions

The understanding and analysis of protein associations in living cells is a major goal of molecular biology. Here, we describe an assay for the analysis of protein-protein interactions based on the co-localization of a fused site-specific protease with a cleavable reporter in close proximity to the interaction partner under examination. We exemplified this scheme in the temperature-sensitive Saccharomyces cerevisiae cdc25-2 mutant strain using the nuclear inclusion protease of tobacco etch virus fused to the adaptor protein growth factor receptor binding protein 2 (Grb2). The growth-defective phenotype of cdc25-2 was complemented by expression of a membrane-targeted constitutively active Ras protein, which contained a TEV protease substrate sequence allowing for release from the membrane upon proteolysis. Interaction of Grb2 with the membrane-targeted intracellular domain of the oncogene vErbB resulted in co-localization of the TEV protease with its substrate, release of Ras from the membrane and restoration of the temperature-sensitive phenotype of cdc25-2. The flexibility of the general scheme of this approach may allow for its application in many different assay scenarios and may represent a suitable alternative in cases where other approaches fail.     

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

With the decline in productivity of drug‐development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence‐based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure‐based approach, in which protein‐peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 505–513, 2009. This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

CoreGenes: A computational tool for identifying and cataloging "core" genes in a set of small genomes

Background  

Improvements in DNA sequencing technology and methodology have led to the rapid expansion of databases comprising DNA sequence, gene and genome data. Lower operational costs and heightened interest resulting from initial intriguing novel discoveries from genomics are also contributing to the accumulation of these data sets. A major challenge is to analyze and to mine data from these databases, especially whole genomes. There is a need for computational tools that look globally at genomes for data mining.     

The Bead blot: a method for identifying ligand-protein and protein-protein interactions using combinatorial libraries of peptide ligands

Small molecules that bind proteins can be used as ligands for protein purification and for investigating protein-protein and protein-drug interactions. Unfortunately, many methods used to identify new ligands to desired proteins suffer from common shortcomings, including the requirement that the target protein be purified and/or the requirement that the ligands be selected under conditions different from those under which it will be used. We have developed a new method called the Bead blot that can (i) select ligands to unpurified proteins, including trace proteins, present in complex materials (e.g., unfractionated plasma); (ii) select ligands to multiple proteins under a variety of conditions in a single experiment; and (iii) be used with libraries of different types of ligands. In the Bead blot, a library of ligands, synthesized on chromatography resin beads, is incubated with a starting material containing a target protein for which a ligand is sought. The proteins in the material bind to their complementary ligands according to specific affinity interactions. Then the protein-loaded beads are immobilized in a porous matrix, and the proteins are directionally eluted from the beads and captured on a membrane superimposed on the beads. The location of the target protein on the membrane is determined, and because the position of the protein(s) on the membrane reflects the position of the bead(s) in the matrix, the bead that originally bound the protein is identified, with subsequent elucidation of the ligand sequence. Ligands to several targets can be identified in one experiment. Here we demonstrate the broad utility of this method by the selection of ligands that purify plasma protein complexes or that remove pathogens from whole blood with very high affinity constants. We also select ligands to a protein based on competitive elution.     

A miniaturized sandwich immunoassay platform for the detection of protein-protein interactions

Background  

Analysis of protein-protein interactions (PPIs) is a valuable approach for the characterization of huge networks of protein complexes or proteins of unknown function. Co-immunoprecipitation (coIP) using affinity resins coupled to protein A/G is the most widely used method for PPI detection. However, this traditional large scale resin-based coIP is too laborious and time consuming. To overcome this problem, we developed a miniaturized sandwich immunoassay platform (MSIP) by combining antibody array technology and coIP methods.     

Implementation of an experimental and computational tool set to study protein-mAb interactions

This work focused on the development of a combined experimental and computational tool set to study protein-mAb interactions. A model protein library was first screened using cross interaction chromatography to identify proteins with the strongest retention. Fluorescence polarization was then employed to study the interactions and thermodynamics of the selected proteins—lactoferrin, pyruvate kinase, and ribonuclease B with the mAb. Binding affinities of lactoferrin and pyruvate kinase to the mAb were seen to be relatively salt insensitive in the range examined. Further, a strong entropic contribution was observed, suggesting the importance of hydrophobic interactions. On the other hand, ribonuclease B-mAb binding was seen to be enthalpically driven and salt sensitive, indicating the importance of electrostatic interactions. Protein–protein docking was then carried out and the results identified the CDR region on the mAb as an important binding site for all three proteins. The binding interfaces identified for the mAb-lactoferrin and mAb-pyruvate kinase systems were found to contain complementary hydrophobic and oppositely charged clusters on the interacting regions which were indicative of both hydrophobic and electrostatic interactions. On the other hand, the binding site on ribonuclease B was predominantly positively charged with minimal hydrophobicity. This resulted in an alignment with negatively charged clusters on the mAb, supporting the contention that these interactions were primarily electrostatic in nature. Importantly, these computational results were found to be consistent with the fluorescence polarization data and this combined approach may have utility in examining mAb-HCP interactions which can often complicate the downstream processing of biologics. © 2019 American Institute of Chemical Engineers     

Experimental and computational approaches for the study of calmodulin interactions

Ca²⁺, a universal messenger in eukaryotes, plays a major role in signaling pathways that control many growth and developmental processes in plants as well as their responses to various biotic and abiotic stresses. Cellular changes in Ca²⁺ in response to diverse signals are recognized by protein sensors that either have their activity modulated or that interact with other proteins and modulate their activity. Calmodulins (CaMs) and CaM-like proteins (CMLs) are Ca²⁺ sensors that have no enzymatic activity of their own but upon binding Ca²⁺ interact and modulate the activity of other proteins involved in a large number of plant processes. Protein-protein interactions play a key role in Ca²⁺/CaM-mediated in signaling pathways. In this review, using CaM as an example, we discuss various experimental approaches and computational tools to identify protein-protein interactions. During the last two decades hundreds of CaM-binding proteins in plants have been identified using a variety of approaches ranging from simple screening of expression libraries with labeled CaM to high-throughput screens using protein chips. However, the high-throughput methods have not been applied to the entire proteome of any plant system. Nevertheless, the data provided by these screens allows the development of computational tools to predict CaM-interacting proteins. Using all known binding sites of CaM, we developed a computational method that predicted over 700 high confidence CaM interactors in the Arabidopsis proteome. Most (>600) of these are not known to bind calmodulin, suggesting that there are likely many more CaM targets than previously known. Functional analyses of some of the experimentally identified Ca²⁺ sensor target proteins have uncovered their precise role in Ca²⁺-mediated processes. Further studies on identifying novel targets of CaM and CMLs and generating their interaction network - “calcium sensor interactome” - will help us in understanding how Ca²⁺ regulates a myriad of cellular and physiological processes.     

Absolute binding free energy calculations: On the accuracy of computational scoring of protein–ligand interactions

Calculating the absolute binding free energies is a challenging task. Reliable estimates of binding free energies should provide a guide for rational drug design. It should also provide us with deeper understanding of the correlation between protein structure and its function. Further applications may include identifying novel molecular scaffolds and optimizing lead compounds in computer‐aided drug design. Available options to evaluate the absolute binding free energies range from the rigorous but expensive free energy perturbation to the microscopic linear response approximation (LRA/β version) and related approaches including the linear interaction energy (LIE) to the more approximated and considerably faster scaled protein dipoles Langevin dipoles (PDLD/S‐LRA version) as well as the less rigorous molecular mechanics Poisson–Boltzmann/surface area (MM/PBSA) and generalized born/surface area (MM/GBSA) to the less accurate scoring functions. There is a need for an assessment of the performance of different approaches in terms of computer time and reliability. We present a comparative study of the LRA/β, the LIE, the PDLD/S‐LRA/β, and the more widely used MM/PBSA and assess their abilities to estimate the absolute binding energies. The LRA and LIE methods perform reasonably well but require specialized parameterization for the nonelectrostatic term. The PDLD/S‐LRA/β performs effectively without the need of reparameterization. Our assessment of the MM/PBSA is less optimistic. This approach appears to provide erroneous estimates of the absolute binding energies because of its incorrect entropies and the problematic treatment of electrostatic energies. Overall, the PDLD/S‐LRA/β appears to offer an appealing option for the final stages of massive screening approaches. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

A signaling role for the uncleaved form of alpha 6 integrin in differentiating lens fiber cells

Many alpha integrin subunits are cleaved during their processing to yield heavy and light chains, which remain associated by disulfide bonds. While uncleaved alpha integrin subunits can form functional receptors that sometimes have distinct signaling roles from their better-characterized endoproteolytically cleaved counterparts, their expression at the cell surface and their association with signaling complexes have yet to be determined in vivo. In this study, we demonstrate that, in differentiating lens fiber cells, the uncleaved form of alpha 6 integrin was expressed at the cell surface. This form of alpha 6 integrin coimmunoprecipitated with both the signaling adaptor molecule Shc and its downstream effector Grb2, suggesting that, in lens fiber cells, uncleaved alpha 6 integrin was associated with a Shc-mediated signaling complex. We show that expression of the cleaved form of alpha 6 integrin progressively decreased relative to uncleaved alpha 6 integrin as the state of lens cell differentiation increased, resulting in the predominance of uncleaved alpha 6 integrin in the lens fiber cell zones. Interestingly, we previously have shown that alpha 6 integrin is localized principally along the extensive cell-cell interfaces of these lens fiber cells, in the absence of its extracellular matrix ligand laminin. While we found that the cleaved form of alpha 6 integrin contained both high mannose and complex sugars, the uncleaved form of alpha 6 integrin contained only high mannose sugars. These properties suggest that the uncleaved form of alpha 6 integrin may have a unique role in the embryonic lens. Its high association with Shc and Grb2 in the differentiating cortical fiber cell zone indicates that alpha 6 integrin may provide a cell survival signal in the presence of the apoptotic-like processes that are initiated in this region of the embryonic lens to clear the lens cells of their organelles.     

ECMIS: computational approach for the identification of hotspots at protein-protein interfaces

Background

Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation.

Results

ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions.

Conclusion

Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-303) contains supplementary material, which is available to authorized users.     

牵出技术在蛋白质相互作用研究中的应用

蛋白质与蛋白质的相互作用参与生命体内的许多生物学过程,关于蛋白质相互作用的研究是人们了解蛋白质功能、揭开生命奥秘的关键所在。牵出(pull-down)技术作为一种简单、经济、行之有效的一种体外验证蛋白质与蛋白质之间的相互作用的实验技术,近几年受到了科研人员的青睐。本文阐述了该技术的基本原理和技术特点,总结了近年来牵出技术在生命科学领域中的应用情况,以及由此衍生的新技术的研究进展。     

Structural basis for differential recognition of tyrosine-phosphorylated sites in the linker for activation of T cells (LAT) by the adaptor Gads

The transmembrane protein, linker for activation of T cells (LAT), is essential for T-cell activation and development. Phosphorylation of LAT at multiple tyrosines creates binding sites for the adaptors Gads and Grb2, leading to nucleation of multiprotein signaling complexes. Human LAT contains five potential binding sites for Gads, of which only those at Tyr171 and Tyr191 appear necessary for T-cell function. We asked whether Gads binds preferentially to these sites, as differential recognition could assist in assembling defined LAT-based complexes. Measured calorimetrically, Gads-SH2 binds LAT tyrosine phosphorylation sites 171 and 191 with higher affinities than the other sites, with the differences ranging from only several fold weaker binding to no detectable interaction. Crystal structures of Gads-SH2 complexed with phosphopeptides representing sites 171, 191 and 226 were determined to 1.8-1.9 A resolutions. The structures reveal the basis for preferential recognition of specific LAT sites by Gads, as well as for the relatively greater promiscuity of the related adaptor Grb2, whose binding also requires asparagine at position +2 C-terminal to the phosphorylated tyrosine.     

Assessing the accuracy of contact predictions in CASP13

The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.