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.     

SARS病毒:非典型肺炎相关病毒

SARS是目前在世界范围内流行的严重呼吸系统疾病。SARS病毒是有包膜的正链RNA病毒,属冠状病毒科,为新近分离鉴定的该疾病相关病原体。初步预测该病毒的复制周期与其他冠状病毒类似,但其细胞膜受体结合蛋白S的S1区、M跨膜糖蛋白等部分则存在较大的变异,可能是该病毒发生宿主改变的原因之一。此外,对SARS病毒的检测、临床诊断等方面也取得了一定的进展。     

Architecture of the SARS coronavirus prefusion spike

The emergence in 2003 of a new coronavirus (CoV) responsible for the atypical pneumonia termed severe acute respiratory syndrome (SARS) was a stark reminder that hitherto unknown viruses have the potential to cross species barriers to become new human pathogens. Here we describe the SARS-CoV 'spike' structure determined by single-particle cryo-EM, along with the docked atomic structures of the receptor-binding domain and prefusion core.     

Identifying inhibitors of the SARS coronavirus proteinase

The Severe Acute Respiratory Syndrome (SARS) is a serious respiratory illness that has recently been reported in parts of Asia and Canada. In this study, we use molecular dynamics (MD) simulations and docking techniques to screen 29 approved and experimental drugs against the theoretical model of the SARS CoV proteinase as well as the experimental structure of the transmissible gastroenteritis virus (TGEV) proteinase. Our predictions indicate that existing HIV-1 protease inhibitors, L-700,417 for instance, have high binding affinities and may provide good starting points for designing SARS CoV proteinase inhibitors.     

Current topics on SARS coronavirus

The serious respiratory disease, SARS (Severe Acute Respiratory Syndrome), outbreaking in winter of 2003 to 2004 remained in a sporadic patient's generating at this winter. However, there is also a possibility that wild animals as the source of infection may not be specified and that it may be much in fashion again. The paper regarding SARS and SARS-CoV is published at one per day now which has passed since fashion of SARS in one or so year. There are many papers which the researchers of other viruses enter into the research field of SARS-CoV using their own technology in addition to the researchers of coronavirus. Topics of the research on the present SARS-research field are development of vaccine, inspecting of medicine and establishment of diagnostic method. Here, the newest information is offered about these researches.     

Principles of protein-protein interactions

In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. Thus experimental investigation of protein-protein interactions has been extensive, including recent large-scale screens using mass spectrometry. The role of computational research on protein-protein interactions encompasses not only prediction, but also understanding the nature of the interactions and their three-dimensional structures. I will discuss properties such as sequence conservation and co-regulation of genes and proteins involved in different types of physical interactions. Given that all proteins consist of their evolutionary units, the domains, all interactions occur between these domains. The interactions between domains belonging to different protein families will be the second topic of my talk.     

Diversity of protein-protein interactions

In this review, we discuss the structural and functional diversity of protein-protein interactions (PPIs) based primarily on protein families for which three-dimensional structural data are available. PPIs play diverse roles in biology and differ based on the composition, affinity and whether the association is permanent or transient. In vivo, the protomer's localization, concentration and local environment can affect the interaction between protomers and are vital to control the composition and oligomeric state of protein complexes. Since a change in quaternary state is often coupled with biological function or activity, transient PPIs are important biological regulators. Structural characteristics of different types of PPIs are discussed and related to their physiological function, specificity and evolution.     

Analysis on multi-domain cooperation for predicting protein-protein interactions

Background  

Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.     

Viral evolution and the emergence of SARS coronavirus

The recent appearance of severe acute respiratory syndrome coronavirus (SARS-CoV) highlights the continual threat to human health posed by emerging viruses. However, the central processes in the evolution of emerging viruses are unclear, particularly the selection pressures faced by viruses in new host species. We outline some of the key evolutionary genetic aspects of viral emergence. We emphasize that, although the high mutation rates of RNA viruses provide them with great adaptability and explain why they are the main cause of emerging diseases, their limited genome size means that they are also subject to major evolutionary constraints. Understanding the mechanistic basis of these constraints, particularly the roles played by epistasis and pleiotropy, is likely to be central in explaining why some RNA viruses are more able than others to cross species boundaries. Viral genetic factors have also been implicated in the emergence of SARS-CoV, with the suggestion that this virus is a recombinant between mammalian and avian coronaviruses. We show, however, that the phylogenetic patterns cited as evidence for recombination are more probably caused by a variation in substitution rate among lineages and that recombination is unlikely to explain the appearance of SARS in humans.     

Ultra-weak reversible protein-protein interactions

Ultra-weak interactions (K(d)>100μM) between proteins have in the last decade become an increasing focus of attention in cell biology, especially in relation to cell-cell interactions and signalling processes. Methods for their quantitative definition are reviewed. NMR spectroscopy plays a major role in this area, as it not only can define interactions as weak or weaker than 3mM, but in favourable cases structural information concerning the complex can be yielded. Free solution technologies mostly fail when addressed to such systems. The AUC has the highest practical capability, but evaluation of the data to yield K(a) values is complicated by the presence of thermodynamic/hydrodynamic effects of a comparable order of magnitude. These effects can however be computationally removed by means of suitable algorithms, and K(d) values of up to 50mM can be characterised. The relative merits of velocity and equilibrium approaches are discussed, and both are shown to have particular advantages.     

Non-additivity in protein-protein interactions

The energy of binding between proteins may be seen as the sum of the contributions of the individual amino acid residues. These contributions are additive when the binding energy, due to different amino acid residues, is independent of the interactions between amino acids in the same polypeptide chain. A measure of non-additivity is the coupling free energy. In this communication it is shown that: (1) the coupling free energy is the sum of intramolecular and intermolecular contributions; and (2), when additivity exists, experimentally determined values for the free energy of transfer of amino acids from water to the hydrophobic protein-protein interface are a very good approximation of their contribution to the energy of binding. Additivity cycles can be useful in determining the precise conditions where this approximation holds.     

The intraviral protein-protein interaction of SARS-CoV-2 reveals the key role of N protein in virus-like particle assembly

Intraviral protein-protein interactions (PPIs) of SARS-CoV-2 in host cells may provide useful information for deep understanding of virology of SARS-CoV-2. In this study, 22 of 55 interactions of the structural and accessory proteins of SARS-CoV-2 were identified by biomolecular fluorescence complementation (BiFC) assay. The nucleocapsid (N) protein was found to have the most interactions among the structural and accessory proteins of SARS-CoV-2, and also specifically interacted with the putative packaging signal (PS) of SARS-CoV-2. We also demonstrated that the PS core containing PS576 RNA bears a functional PS, important for the assembly of the viral RNA into virus like particles (VLPs), and the packaging of SARS-CoV-2 RNA was N dependent.     

Prediction-based fingerprints of protein-protein interactions

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein-protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction-based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure-derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein-protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction-based fingerprints. The new method is available at http://sppider.cchmc.org.     

Mean-field analysis of protein-protein interactions

Calculations were performed on the D1.3-E5.2 antibody-antibody complex estimating the binding affinities of the wild-type and 16 alanine substitutions. Analyzed were structural models of the interfacial region containing a zinc ion and crystallographic waters. A continuum approach was used to evaluate the electrostatic free energies and the hydrophobic effect was calculated by employing a buried molecular surface area relationship. Estimates of the absolute binding affinity reproduced the experimental value within the uncertainty of assessing entropic and strain energy contributions. The best correlation for mutants with experimental data was achieved when the hydrophilicity of created cavities were considered, and yielded a correlation coefficient of 0.7 and an average error of +/-1.4 kcal/mol. Empirically fitting the free energy function produced a smaller error of +/-1.0 kcal/mol. Depending on the electrical potential and electrostatic reorganization, scaling the 'protein dielectric constant' to approximately 10 may improve the accuracy of continuum models for evaluating amino acid substitutions.     

Ontological visualization of protein-protein interactions

Background  

Cellular processes require the interaction of many proteins across several cellular compartments. Determining the collective network of such interactions is an important aspect of understanding the role and regulation of individual proteins. The Gene Ontology (GO) is used by model organism databases and other bioinformatics resources to provide functional annotation of proteins. The annotation process provides a mechanism to document the binding of one protein with another. We have constructed protein interaction networks for mouse proteins utilizing the information encoded in the GO annotations. The work reported here presents a methodology for integrating and visualizing information on protein-protein interactions.