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New: The book that Amit Kessel and Nir Ben-Tal wrote: Introduction to Proteins: Structure, Function, and Motion

The Ben-Tal Lab of Computational Structural Biology

Welcome to Nir Ben-Tal's lab at the Department of Biochemistry and Molecular Biology, The George S Wise Faculty of Life Sciences , Tel Aviv University.

Our research is focused on studying the interplay between protein sequence, structure, motion and function using computational tools. The understanding of these relations provides a molecular dimension to our understanding of protein functions and their involvement in genetic disorders and other diseases. Within the broad fields of structural-bioinformatics and phylogeny, we limit our research to specific niches where structure and motion are difficult to obtain experimentally, and the computations provide data beyond our current knowledge:

Pandemic Simulation using Facebook

Social networks such as Facebook "connect" between people and allow them to interact in an online environment. We argue that at least in the qualitative sense these online interactions mimic real-life interactions. For example, a user has a hundred or even a thousand friends on Facebook, which is not the norm in real life. However, a standard Facebook user only interacts with very few of the people in this large group of friends, much like in real life where a person is aware of a large number of acquaintances but is unlikely to have epidemiologically relevant interactions with people not in some sort of direct contact. Thus we reason that we may treat Facebook society as an actual society and as such we may use this society, with its stochastic contacts and well-defined geography to model the epidemiology of Influenza and other viruses. The advantages in this approach are significant and include the ease with which models may be formulated (there is no need for differential equations and Monte-Carlo stochastic algorithms), the ability to easily run multiple models simultaneously without the need for heavy computing power and the freedom to define the model assumptions without the need to adhere to mathematic simplicity.

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Transmembrane proteins

Fig. 1 The low resolution structure of hCTR1 (shown as grey mesh) revealed a central pore implicated in ion translocation, but could not unravel the precise molecular details of the structure. The model predicted in this study is shown as ribbons and colored by evolutionary conservation according to the scale below, with spheres indicating the specific location of each residue. Disclosing the molecular details of the structure, the model predicts that evolutionarily conserved residues face the ion pathway or form close interactions between the different structural elements (ribbons), thus stabilizing the structural scaffold.

Fig. 2 Ion translocation mechanism. The elements important for ion transport, located at the entrance to the central pore (gray stars), are responsible for selecting copper over all other ions and compounds. After a copper ion enters the selection filter, the entrance to the pore is blocked, so that no additional ions can enter at the same time. When – and only when - the ion has been released inside the cell, the cycle is repeated. This allows close regulation of the translocation of this essential yet highly toxic ion. The chemotherapeutic drug, cisplatin, may enter the cell using the same pathway.

The human copper transporter (hCTR1) is a critical link in a chain of proteins ensuring the proper transport of potentially toxic copper ions to various intra-cell enzymes, that need them in order to fulfill many crucial functions. Failure in copper transport may result in serious illnesses, such as hemophilia, anemia, diabetes and cardiac disorders. However, due to the inherent difficulty of investigating the 3D structure and motion of proteins located in the cell membrane, little was known previously about hCTR1, and the way it actually carries the copper ions into the cell (Fig. 1, mesh).

Applying an evolution-based approach, we found elements of the protein preserved in different animals, including humans, and used this information to create a conjectured Ca-trace model of hCTR1’s transmembrane domain (Fig. 1, spirals). We then proceeded to calculate the protein’s main modes of motion (Movie 1). Finally, based on the model-structure and predicted motion, a mechanism by which the protein transports the copper ions into the cell was suggested. According to the proposed mechanism, hCTR1 lets the ions through one by one, keeping them under close control (Fig. 2). Careful ion selection and regulation are essential because free copper ions are highly reactive, potentially forming harmful free radicals. Since hCTR1 also appears to transport cisplatin into human cells, the new insight into this critical protein’s transport mechanism has significant implications for the understanding, and eventually also the improvement, of chemotherapy treatments for cancer patients.

Movie 1. An illustration of one of the main modes of motions predicted to mediate copper transport.

A ConSurf analysis of the evolutionary conservation profile of the platelet glycoprotein alpha-IIb subunit (displayed in CPK models, colored by conservation according to the bar at the bottom), and its interaction with the integrin beta-3 subunit (green ribbon; PDB-2VDL). The interface between the alpha and beta subunits, which is important for proper function of the protein, is evolutionary conserved.

Structural-genomics initiatives

Structural-genomics initiatives aim to produce crystal structures of myriad proteins. This outpouring of structural data is not matched by a similar experimental effort to understand function, and we try to fill this growing gap using phylogenetic analysis. Patches of evolutionarily conserved amino acids that are located in close proximity to each other on the protein surface (see picture on the right) are often important for biological functions; these patches may mediate the association of the protein with other proteins, RNA, DNA or ligand molecules, and may be involved in enzymatic catalysis. We continually develop the Rate4Site algorithm and ConSurf web-server for computation of the evolutionary conservation of the amino acids in proteins.

We develop other computational tools to identify key amino acids that are functionally important, and use them to investigate specific protein families. For example, we develop methodology to look for amino acids that determine specific traits (slight function alterations) within homologous proteins. We also develop methods to search for pairs of amino acids with similar evolutionary history, i.e. pairs of residues that change in tandem and manifest correlated (or compensatory) mutation behavior. We use these methods to investigate selected protein families. As an example, by the use of these and other computational tools, we studied the EGF receptors (also called ErbB and HER), which are involved in breast and lung cancers (EGFR), and suggested a new mechanism of regulation by these proteins. The model provides an explanation, at the molecular level, for the effects of cancer-causing EGFR mutations, and suggests a novel therapeutic venue for EGFR-related cancer.

Systems biology and protein-interaction networks

Ample data on inter-protein interactions are available from high-throughput studies, using, e.g., the yeast two-hybrid method and complex-purification techniques using mass-spectrometry. These relatively recent data are attractive in that they provide vast amounts of interaction data. However, they suffer from a high frequency of false positive interactions and systematic omissions. We aim to bridge the gap between this and structural data as a means to improve the quality of protein networks.