Current Pharmaceutical Biotechnology

ISSN: 1389-2010

Current Pharmaceutical Biotechnology
Volume 9, Number 2, April 2008

Contents

Protein-Protein Interactions
Guest Editor: Emil Alexov


Editorial Pp. 55-56


Predicting 3D Structures of Protein-Protein Complexes Pp. 57-66
Ilya A. Vakser and Petras Kundrotas
[Abstract]


Characterization and Prediction of Protein Interfaces to Infer Protein- Protein Interaction Networks Pp. 67-76
Ozlem Keskin, Nurcan Tuncbag and Attila Gursoy
[Abstract]


Recognition-induced Conformational Changes in Protein Protein Docking Pp. 77-86
M.F. Lensink and R. Méndez
[Abstract]


Molecular Recognition and Binding Free Energy Calculations in Drug Development Pp. 87-95
B.N. Dominy
[Abstract]


Calculating pH and Salt Dependence of Protein-Protein Binding Pp. 96-102
Jan H. Jensen
[Abstract]


In Silico-In Vitro Screening of Protein-Protein Interactions: Towards the Next Generation of Therapeutics Pp. 103-122
Bruno O. Villoutreix, Karine Bastard, Olivier Sperandio, Robin Fahraeus, Jean-Luc Poyet, Fabien Calvo, Benoit Déprez and Maria A. Miteva
[Abstract]


Approaches and Resources for Prediction of the Effects of Non- Synonymous Single Nucleotide Polymorphism on Protein Function and Interactions Pp. 123-133
S. Teng, E. Michonova-Alexova and E. Alexov
[Abstract]




Abstracts

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Editorial

The ultimate goal of pharmacology and biotechnology is to develop drugs that could prevent or cure human diseases. Despite of the enormous progress made in experimental techniques, still discovering a new drug is an expensive and lengthy procedure. Structure-based drug discovery techniques offer fast and efficient alternative to the experimental approaches. Since protein-protein interactions are essential for the function of the living cell, they are one of the primary subjects of pharmaceutical investigations. However, the success of structure-based drug discovery depends on the availability of 3D structures of the proteins and protein-protein complexes being targeted. Apparently vast majority of these structures have to be modeled in silico. This special issue describes the current state-of-art and the progress made in developing computational approaches in two major directions: (A) Predicting structural features such as 3D structures and interfaces of protein-protein complexes and the conformational changes induced by the binding; and (B) Using the 3D structures to calculate biophysical characteristics such as the binding affinity and the effect of pH and salt concentration, to design inhibitors and to evaluate the effect of disease-associated single nucleoside polymorphism.

(A). Predicting Structural Features Such as 3D Structures and Interfaces of Protein-Protein Complexes and the Conformational Changes Induced by the Binding

The recent success of the human genome project and the progress in sequencing other genomes has enormously increased the universe of known proteins at amino acid sequences level. However, the biological function and the molecular basis of protein-protein interactions cannot easily be revealed from the sequence alone. Specifically, for the aims of structure-based drug design, 3D structures of the proteins and their complexes are needed. It is unlikely that all these structures will be determined experimentally. To bridge this gap, Structural Genomics Initiatives (SGI) are intended to experimentally determine the 3D structures of carefully selected targets so they can later serve as templates for the maximum number of protein sequences with unknown 3D structures. Currently SGIs are in stage 2, the production phase, and it has been projected that the 3D structures of all monomeric proteins of interest will be predicted in feasible time. The next level of these initiatives is naturally extended toward predicting the 3D structure of the corresponding protein-protein complexes. There are two distinctive approaches of predicting the 3D structures of protein-protein complexes: ab-initio docking and template-based docking. The first one uses physical methods to dock the experimentally determined 3D structures or high quality models of the monomers. The second one, that does not require a priori knowledge of the monomeric structures, predicts the 3D structure of a complex based on the homology relations to another complex with known 3D structure. The last approach, perhaps will require Structural Proteomic Initiatives (SPI), such that significant carefully selected number of representative 3D structures of protein complexes will be experimentally determined and will be further used as templates to generate models for maximal number of complexes with unknown 3D structures. The achievements and perspectives in this important area are outlined in the manuscript “Predicting 3D structures of protein-protein complexes” by Ilya A. Vakser and Petras Kundrotas.

The performance of both ab-initio and template-based docking methods can be significantly improved if the binding interfaces are successfully predicted. With respect to the ab-initio docking, this will reduce the sampling and will avoid wrong binding modes. With regard to template-based docking, especially if the interfaces are predicted on sequence level, this will allow for applying profile-to-profile alignment methods that emphasize on the alignment of interfacial residues and thus will contribute to better detection of appropriate templates. From point of view of drug discovery, the ability to predict putative binding interfaces on the 3D structures of monomeric proteins is of special interest. It has the potential to reveal protein-protein interaction networks and the interfacial residues, which can be targeted by inhibitors to alter the corresponding protein-protein interactions. The progress made in developing computational methods to predict protein-protein interfaces and corresponding interaction networks is reviewed in the manuscript “Characterization and prediction of protein interfaces to infer protein-protein interaction networks” by Ozlem Keskin, Nurcan Tuncbag and Attila Gursoy.

Upon formation of the protein-protein complex, the monomers undergo conformational change. This is well established experimental observation made by comparing the 3D structures of bound and unbound proteins. The magnitude of the conformational change varies from relatively small displacements of a fraction of Angstroms (all heavy atoms RMSD) to large motions of more than several Angstroms. These conformational changes are the main bottleneck of the ab-initio docking methods, since they alter the shape of the unbound monomers and thus affect the detection of shape complementarity and scoring of the alternative modes. In addition, the ability to predict recognition-induced conformational changes has important applications in the pharmacological research. This will make possible to target these important for binding flexible regions and to alter their flexibility by drugs, which will provide alternative way of regulating protein-protein interactions. The current progress made in predicting conformational changes important for binding is reviewed in the manuscript “Recognition-induced Conformational Changes in Protein-Protein Docking” by M.F. Lensink and R. Méndez.

(B). Using the 3D Structures to Calculate Biophysical Characteristics as Binding Affinity and the Effect of pH and Salt Concentration, to Design Inhibitors and to Evaluate the Effect of Disease-Associated Single Nucleoside Polymorphism

Armed with the arsenal of experimentally determined 3D structures and high quality models of protein-protein complexes, the pharmaceutical researchers will have the opportunity to apply structure-based drug design methods to discover new drugs. An essential component of such a procedure is the calculation of the binding free energy. The magnitude of the binding energy can be used to discriminate permanent versus transient complexes and to apply appropriate techniques for each specific case. In the case of designing competitive inhibitors, the correct calculation of the binding affinity of the wild type complex and the corresponding protein-inhibitor complex is crucial. The current methods of computing the binding free energy and biological aspects of molecular recognitions are reviewed in the manuscript “Molecular Recognition and Binding Free Energy Calculations in Drug Development” by Brian Dominy.

All biological interactions occur in specific environment in the cell. Two main characteristics of the environment are the local pH and the concentration of mobile ions. The pH varies in different cellular compartments from the typical pH=7.0 in the cytosol to as low as 4.0 in the lysosome. The pH varies in different organs in human body, the lowest being in the stomach (pH=3.0-4.0). Apparently protein-protein interactions taking place in stomach or lysosome should be able to tolerate low pH and the drugs targeting these interactions should behave similarly. In addition, the binding affinity is strongly affected by the local pH, and hence the ability to correctly calculate the pH-dependence of the binding free energy is crucial for effective drug design. The salt concentration is also very important characteristic of the cellular environment. The value of the “physiological” salt concentration varies in different human organs and thus affects the binding affinity. The local concentration of specific ions may deviate significantly from the “standard” value near to the membrane surface and close to a corresponding ion pump protein. The current state-of-art in developing methods for computing the pH and salt dependence of the binding free energy is reviewed in the manuscript “Calculating pH and Salt Dependence of Protein-Protein Binding” by Jan H. Jensen.

Given the 3D structure of a protein-protein complex, in silico methods can be applied to design modulators of protein-protein interactions. Even in case that only 3D structures of the free monomeric proteins, known to form a complex, are available, in silico methods can be used to predict interacting interfaces and to search for druggable pockets at these interfaces. As outlined by Bruno O. Villoutreix, Karine Bastard, Olivier Sperandio, Robin Fahraeus, Jean-Luc Poyet, Fabien Calvo, Benoit Déprez, Maria A. Miteva in their excellent contribution entitled “In silico-in vitro screening of protein-protein interactions: towards the next generation of therapeutics”, the “protein-protein interactions have a pivotal role in many biological processes suggesting that targeting macromolecular complexes will open new avenues for the design of the next generation of therapeutics”. In their review article they summarize the current developments in the filed of virtual ligand screening, protein-protein docking, structural predictions and druggable pockets predictions. The authors also discuss four successful projects aiming at developing protein-protein interactions modulators through the combination of in silico and in vitro screening experiments.

Another important source of valuable information for drug discovery are the databases describing disease-causing single nucleotide polymorphism (SNP). Combined with the progress made in modeling structures of protein-protein complexes, the amino acid changes associated with the disease SNPs can be mapped onto the structures of the corresponding protein-protein complexes and their effects on stability, selectivity and specificity could be investigated. Such approaches and resources are reviewed in the manuscript “Approaches and Resources for Prediction of the Effects of Non-Synonymous Single Nucleotide Polymorphism on Protein Function and Interactions” by Saolei Teng, Ekaterina Michonova-Alexova and Emil Alexov. It should be pointed out that while nsSNPs resulting in amino acid substitution in the core of a protein may affect protein stability irreversibly, the effect of an nsSNP resulting to a mutation at the surface of a protein or at the interface of protein-protein complexes, could in principle be subject of drug therapy. Thus, combining the knowledge of disease-causing SNPs, the models of the corresponding structures of protein-protein complexes and the structure-based ligand screening, one could envision new drug generations that could alter the effects, and thus to prevent the diseases before they occur.


Emil Alexov
Guest Editor
Associate Professor
Computational Biophysics and Bioinformatics
Department of Physics
Clemson University
Clemson, SC 29634, USA


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Predicting 3D Structures of Protein-Protein Complexes
Ilya A. Vakser and Petras Kundrotas

The protein-protein docking problem is one of the focal points of activity in computational structural biology. Adequate computational techniques for structural modeling of protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. The protein docking methodology offers tools for fundamental studies of protein interactions and provides structural basis for drug design. The paper presents a critical review of the existing protein-protein docking approaches in view of the fundamental principles of protein recognition.


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Characterization and Prediction of Protein Interfaces to Infer Protein- Protein Interaction Networks
Ozlem Keskin, Nurcan Tuncbag and Attila Gursoy

Complex protein-protein interaction networks govern biological processes in cells. Protein interfaces are the sites where proteins physically interact. Identification and characterization of protein interfaces will lead to understanding how proteins interact with each other and how they are involved in protein-protein interaction networks. What makes a given interface bind to different proteins; how similar/different the interactions in proteins are some key questions to be answered. Enormous amount of protein structures and experimental protein-protein interactions data necessitate advanced computational methods for analyzing and inferring new knowledge. Interface prediction methods use a wide range of sequence, structural and physico-chemical characteristics that distinguish interface residues from non-interface surface residues. Here, we present a review focusing on the characteristics of interfaces and the current status of interface prediction methods.


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Recognition-induced Conformational Changes in Protein-Protein Docking
M.F. Lensink and R. Méndez

The ability to predict the three-dimensional structure of a protein complex starting from the isolated binding partners is becoming increasingly relevant. As our understanding of the molecular mechanisms behind protein-protein binding improves, so do the docking methods, however, it remains a challenge to adequately predict the unbound to bound transition. Side-chain flexibility is routinely handled and most docking methods allow for a certain degree of backbone flexibility, but systems undergoing moderate to large conformational changes can at present not correctly be modeled. The docking community is therefore putting an increased effort in the treatment of protein flexibility. Here we present a survey of the existing computational techniques to model protein flexibility in the context of protein-protein docking.


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Molecular Recognition and Binding Free Energy Calculations in Drug Development
B.N. Dominy

The functional capabilities of biological systems, such as enzyme catalysis, nutrient import, and cell signaling, depend crucially on specific molecular interactions. In addition, the effects of common drugs also act through a mechanism of binding to specific biomolecular targets. Models for the prediction of binding affinity are used in basic research to study the molecular basis of biological function as well as in applied research to study the development of new drugs. This review will address the biological importance of molecular recognition as well as its influence on the development of pharmaceuticals. Further, a broad overview of computational approaches used for the prediction of biological activity and specifically binding free energy will be presented.


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Calculating pH and Salt Dependence of Protein-Protein Binding
Jan H. Jensen

Ionic strength- (or salt-) effects on the protein-protein binding free energy has been included in many computational studies, while comparatively fewer computational studies have looked at the corresponding effect of pH. The pH dependence can be very complex if several groups change protonation state, while the ionic strength dependence usually scales as ln(I), and the main challenge is to predict the magnitude of the correlation. However, there is now very strong indication that pH effects due to binding induced changes in protonation states make a non-negligible contribution to the binding energy of most protein-protein complexes. This observation, together with more efficient pKa prediction methods and the emergence of constant pH molecular dynamics simulations to model the protonation-dependent structural changes will spark more experimental and theoretical work in pH effects on protein-protein binding.


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In Silico-In Vitro Screening of Protein-Protein Interactions: Towards the Next Generation of Therapeutics
Bruno O. Villoutreix, Karine Bastard, Olivier Sperandio, Robin Fahraeus, Jean-Luc Poyet, Fabien Calvo, Benoit Déprez and Maria A. Miteva

Protein-protein interactions (PPIs) have a pivotal role in many biological processes suggesting that targeting macromolecular complexes will open new avenues for the design of the next generation of therapeutics. A wide range of “in silico methods” can be used to facilitate the design of protein-protein modulators. Among these methods, virtual ligand screening, protein-protein docking, structural predictions and druggable pocket predictions have become established techniques for hit discovery and optimization. In this review, we first summarize some key data about protein-protein interfaces and introduce some recently reported computer methods pertaining to the field. URLs for several recent free packages or servers are also provided. Then, we discuss four studies aiming at developing PPI modulators through the combination of in silico and in vitro screening experiments.


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Approaches and Resources for Prediction of the Effects of Non- Synonymous Single Nucleotide Polymorphism on Protein Function and Interactions
S. Teng, E. Michonova-Alexova and E. Alexov

Almost all (99.9%) nucleotide bases are exactly the same in all people, however, the remaining 0.1% account for about 1.4 million locations where single-base DNA differences/polymorphisms (SNPs) occur in humans. Some of these SNPs, called non-synonymous SNPs (nsSNPs), result in a change of the amino acid sequences of the corresponding proteins affecting protein functions and interactions. This review summarizes the plausible mechanisms that nsSNPs may affect the normal cellular function. It outlines the approaches that have been developed in the past to predict the effects caused by nsSNPs with special emphasis on the methods that use structural information. The review provides systematic information on the available resources for predicting the effects of nsSNPs and includes a comprehensive list of existing SNP databases and their features. While nsSNPs resulting in amino acid substitution in the core of a protein may affect protein stability irreversibly, the effect of an nsSNP resulting to a mutation at the surface of a protein or at the interface of protein-protein complexes, could, in principle be, subject of drug therapy. The importance of understanding the effects caused by nsSNP mutations at the protein-protein and protein-DNA interfaces is outlined.