Bruce Tidor - US grants

Molecular modeling studies are proposed to investigate the structural and thermodynamic effects of mutations on the leucine zipper, a recently discovered sequence motif that mediates dimerization in a large family of eukaryotic transcription factors. The long term objective of this work is to understand the underlying basis of the structure and stability of this dimerization motif. The proposed research will provide insights into structure-function relationships, molecular recognition, helix-packing, and protein stability that are particularly relevant to transcriptional regulation and oncogenesis. The specific goals of the proposed research are to calculate the effect on structure and stability of substitutions of both hydrophobic and polar residues at positions in the dimer interface and of modifications to salt bridges in the GCN4 leucine zipper, with particular emphasis on dissecting the energetics of stability changes into relative contributions due to the hydrophobic effect, packing interactions, helix-forming tendency, buried and surface hydrogen bonds, and other inter- and intramolecular interactions. Free energy computer simulations coupled with a component analysis procedure for analyzing the results will be employed, and results will be carefully compared with experimentally determined crystal structures and measurements of thermodynamic stability. Interpretations from the molecular modeling studies will be used to construct an understanding of the structure and stability of the leucine zipper. This understanding will be tested by proposing new mutations that will be studied experimentally and compared to the theoretical results.

(Adapted from the application): This project will undertake the development of novel methods to analyze the structures of macromolecules that their complexes using energy based models, focusing primarily on electrostatic interactions. One of the fundamental difficulties in understanding the energetics of binding is that most of the interactions are exchange reactions; that is, chemical groups trade interactions with solvent in the unbound state for interactions with the binding partner in the bound state. A novel innovation introduced here is the definition of an electrostatic complement, the optimal tradeoff between unfavorable desolvation energy and favorable interactions in the complex. This essentially inverts the design problem, designing the properties of the optimal based on physical principles, and provides a clear and precise standard that can be compared to trial ligands and that can be used as a template in the modification of existing ligands and the de novo construction of new ligands. The PI has developed methods to solve, for this complement, using simplified geometrics. The aim is to extend those methods to arbitrarily shaped molecules, to develop methods for using the optimal electrostatic complement in ligand discovery, and to extend current methods for the analysis of conformationally flexible ligands. The methods will be tested and validated initially using the well- characterized class II MHC molecule and the HIV protease. Further design efforts will proceed with other targets characterized within this program.

DESCRIPTION: Molecular modeling studies are proposed to investigate DNA binding by two transcriptional regulatory proteins, the CI repressor from bacteriophage 434 and the Arc repressor from bacteriophage P22. The long-term objective of this work is to understand the relationship between the structure of these proteins and both the stability and specificity of their interactions with DNA. The proposed research will provide insight into structure-function relationships, molecular recognition, protein engineering, and drug discovery, that are particularly relevant to transcriptional regulation and oncogenesis. The specific goals of the proposed research are to calculate the contributions to stability and specificity of protein-phosphate contacts, base-specific contacts, and longer-range, non-contacting interactions. Continuum electrostatic and free energy calculations will be employed. The results will be carefully compared with experimentally determined crystal structures and measurements of thermodynamic stability. Interpretations from the molecular modeling studies will be used to construct an understanding of molecular recognition by Arc and 434 repressor. This understanding will be tested by proposing new mutations that will be studied experimentally and compared to theoretical results.

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Tidor
An important goal of molecular biochemistry is a detailed understanding of how enzymes bind their ligands with correct affinity and specificity to catalyze appropriate reactions while not accelerating inappropriate ones. While there is clearly a key role for affinity in many molecular binding and recognition events, specificity is also essential in many biochemical contexts. For example, enzymes often must discriminate between correct substrates and closely related molecules. Likewise, there is a compelling literature implicating differential binding of transition state over substrate as a fundamental principle of enzyme function. While tremendous progress has been made in advancing our understanding of the molecular determinants of affinity and specificity, the current state of the art is still significantly incomplete. While rationalization of relative affinities and specificities is possible given high-resolution structural information, the knowledge obtained from such studies has been insufficient to allow for routine rational ligand design or enzyme engineering. New approaches are needed to expand our fundamental understanding in this crucial area. The project involves the study of two tRNA synthetases, the glutaminyl-tRNA synthetase (GlnRS) from Escherichia coli and the aspartyl-tRNA synthetase (AspRS) from the hyperthermophilic archaeon Pyrococcus kodakaraensis. Research activities include the application of current and novel approaches using theoretical and computational techniques to understand more thoroughly the molecular basis for ligand binding affinity and specificity by these highly evolved enzymes. Molecular mechanics, molecular dynamics, and continuum electrostatics will be used to analyze determinants of binding in the enzyme active sites and in the ligands. The research will expand our fundamental understanding of binding affinity and specificity by two tRNA-synthetase enzymes. The principles learned are expected to have broad applicability to enzymes and other molecular catalysts. They will contribute to the growing foundation that is essential to produce enabling technologies in the key areas of molecular design and enzyme engineering.

Abstract:
A grant has been awarded to the BioMicro Center at MIT to acquire high-performance computers for research in biology and biological engineering. The systems will be among the most powerful at MIT and will be dedicated to advancing the research programs of an active group of young faculty. These Assistant and Associate Professors lead research teams working on a wide range of problems in computational biology. The teams are united in their interest in the application of numerical models, computational simulations and large-scale computation to biological problems. The completion of the human genome sequence has emphasized the remarkable complexity of biological processes. While traditional molecular biology and molecular medicine will continue to play an important role in unraveling how these processes work, it seems almost certain that fundamental advances will come from the application of quantitative and rigorous analytic methods.
The research includes Professor Amy Keating's work on protein design. Dr. Keating's goal is to discern the rules governing protein-protein interactions through calculation and simulation and to then apply the rules to actual experiments. Professor Mike Yaffe will use computation to mine the human genome for proteins that play crucial roles in the transmission of signals within cells. Dr. Yaffe hopes to determine how circuits are constructed in cells, and to compare these circuits to electronic and mechanical circuits. Professor Tidor will use large-scale calculations to try to determine how proteins interact with each other and with small molecules (such as drugs). This is a long-standing problem in structural biology, but Dr. Tidor has recently shown that approximations drawn from engineering can be successfully applied to the problems that have proven to be impossible to calculate using conventional methods.
The new facility will not only enhance novel research but also graduate and undergraduate education. A new set of courses have the unusual distinction of being in the curriculum of three different departments: biology, biological engineering, and computer science. The group's intention is to expose students to the very latest computational methods and computer systems. Thus, high-performance computing will not only have a direct impact on world-class science, but also on education and training. With the help of Prof. Amy Keating, the group is working hard to attract additional women to computational biology. In the physical sciences women are significantly under-represented. Computational biology holds great promise as a discipline in which the historically inequitable under-representation of women in physical sciences can be overcome by association with biology.

DESCRIPTION (provided by applicant): The broad long-term objectives of this work are to develop and apply theoretical models to analyze complementary interactions formed during protein folding and binding. The ability of molecules to recognize one another with appropriate affinity and specificity is central to biology and medicine. The clinical activity of pharmaceutical agents is due largely to their ability to recognize and interfere with one or a small number of molecular targets; undesirable side effects are frequently caused by lack of specificity for the correct target. An important area of research involves understanding the design principles of natural protein molecules and developing tools to engineer modified or entirely new molecules by similar principles. The current proposal focuses on (1) further developments in methodology for the study and engineering of molecular structures and binding partners and (2) applications to particular biological molecules of interest. Current algorithms efficiently search side chain rotameric space when the backbone is fixed; we propose new methods to allow incorporation of backbone degrees of freedom or docking degrees of freedom as well. We have previously developed electrostatic optimization techniques that allow computation of idealized complementary binding partners, whose shape and charge distribution lead to high affinity binding. We propose to extend these methods to allow the design of actual ligand molecules that approach the idealized shape and charge distribution. Moreover, while high affinity or stability has been the focus of many design studies, we propose to develop methods that engineer in specificity as well. We will then apply this approach to investigations of the well studied Arc repressor and of proteins involved in phosphate recognition in signal transduction, and we will collaborate with experimentalists who are experts in these systems and will carry out parallel studies.

As a fundamental component of MITs new Computational and Systems Biology Initiative (CSBi;), a new interdisciplinary predoctoral graduate training program in computational and systems biology is proposed. Program faculty are concentrated in the three founding academic units ? Biology, Biological Engineering (BE), and Electrical Engineering & Computer Science (EECS), with additional involvement of faculty from other departments, such as Chemical Engineering, Mechanical Engineering, and Brain and Cognitive Sciences. Faculty from these departments are actively involved in research programs spanning a broad set of topics in computational and systems biology, including gene and protein networks, cell and tissue engineering, imaging and image informatics, predictive toxicology and metabolic engineering, genomics and proteomics, nanobiology and microsystems, molecular biophysics, and cancer biology. Students will apply directly to the Ph.D. program from their undergraduate or Master's institution and receive interdisciplinary training in the emerging field of computational and systems biology. Unique aspects of the program include: (a) close association with the multi- and inter-disciplinary research agenda of CSBi, (b) a unique core formed from newly developed, interdisciplinary classroom subjects that combine biology, engineering, and computation, (c) a seminar program focusing on a broad range of research both within and outside of MIT and that includes student participation, (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research, (e) the annual CSBi symposium, which is an exceptional conference bringing leaders in the field to MIT each year, and (f) multi-disciplinary thesis committees.

DESCRIPTION (provided by applicant): As a fundamental component of MIT's new Computational and Systems Biology Initiative (CSBi;), a new interdisciplinary pre-doctoral graduate training program in computational and systems biology is proposed. Program faculty are concentrated in the three founding academic units - Biology, Biological Engineering (BE), and Electrical Engineering & Computer Science (EECS), with additional involvement of faculty from other departments, such as Chemical Engineering, Mechanical Engineering, and Brain and Cognitive Sciences. Faculty from these departments are actively involved in research programs spanning a broad set of topics in computational and systems biology, including gene and protein networks, cell and tissue engineering, imaging and image informatics, predictive toxicology and metabolic engineering, genomics and proteomics, nanobiology and microsystems, molecular biophysics, and cancer biology. Students will apply directly to the Ph.D. program from their undergraduate or Master's institution and receive interdisciplinary training in the emerging field of computational and systems biology. Unique aspects of the program include: (a) close association with the multi- and inter-disciplinary research agenda of CSBi, (b) a unique core formed from newly developed, interdisciplinary classroom subjects that combine biology, engineering, and computation, (c) a seminar program focusing on a broad range of research both within and outside of MIT and that includes student participation, (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research, (e) the annual CSBi symposium, which is an exceptional conference bringing leaders in the field to MIT each year, and (f) multi-disciplinary thesis committees.

DESCRIPTION (provided by applicant): As a fundamental component of MIT's new Computational and Systems Biology Initiative (CSBi;), a new interdisciplinary pre-doctoral graduate training program in computational and systems biology is proposed. Program faculty are concentrated in the three founding academic units - Biology, Biological Engineering (BE), and Electrical Engineering & Computer Science (EECS), with additional involvement of faculty from other departments, such as Chemical Engineering, Mechanical Engineering, and Brain and Cognitive Sciences. Faculty from these departments are actively involved in research programs spanning a broad set of topics in computational and systems biology, including gene and protein networks, cell and tissue engineering, imaging and image informatics, predictive toxicology and metabolic engineering, genomics and proteomics, nanobiology and microsystems, molecular biophysics, and cancer biology. Students will apply directly to the Ph.D. program from their undergraduate or Master's institution and receive interdisciplinary training in the emerging field of computational and systems biology. Unique aspects of the program include: (a) close association with the multi- and inter-disciplinary research agenda of CSBi, (b) a unique core formed from newly developed, interdisciplinary classroom subjects that combine biology, engineering, and computation, (c) a seminar program focusing on a broad range of research both within and outside of MIT and that includes student participation, (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research, (e) the annual CSBi symposium, which is an exceptional conference bringing leaders in the field to MIT each year, and (f) multi-disciplinary thesis committees.

As a fundamental component of MITs new Computational and Systems Biology Initiative (CSBi;), a new interdisciplinary predoctoral graduate training program in computational and systems biology is proposed. Program faculty are concentrated in the three founding academic units ? Biology, Biological Engineering (BE), and Electrical Engineering & Computer Science (EECS), with additional involvement of faculty from other departments, such as Chemical Engineering, Mechanical Engineering, and Brain and Cognitive Sciences. Faculty from these departments are actively involved in research programs spanning a broad set of topics in computational and systems biology, including gene and protein networks, cell and tissue engineering, imaging and image informatics, predictive toxicology and metabolic engineering, genomics and proteomics, nanobiology and microsystems, molecular biophysics, and cancer biology. Students will apply directly to the Ph.D. program from their undergraduate or Master's institution and receive interdisciplinary training in the emerging field of computational and systems biology. Unique aspects of the program include: (a) close association with the multi- and inter-disciplinary research agenda of CSBi, (b) a unique core formed from newly developed, interdisciplinary classroom subjects that combine biology, engineering, and computation, (c) a seminar program focusing on a broad range of research both within and outside of MIT and that includes student participation, (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research, (e) the annual CSBi symposium, which is an exceptional conference bringing leaders in the field to MIT each year, and (f) multi-disciplinary thesis committees.

DESCRIPTION (provided by applicant): As a fundamental component of MIT's new Computational and Systems Biology Initiative (CSBi;), a new interdisciplinary pre-doctoral graduate training program in computational and systems biology is proposed. Program faculty are concentrated in the three founding academic units - Biology, Biological Engineering (BE), and Electrical Engineering & Computer Science (EECS), with additional involvement of faculty from other departments, such as Chemical Engineering, Mechanical Engineering, and Brain and Cognitive Sciences. Faculty from these departments are actively involved in research programs spanning a broad set of topics in computational and systems biology, including gene and protein networks, cell and tissue engineering, imaging and image informatics, predictive toxicology and metabolic engineering, genomics and proteomics, nanobiology and microsystems, molecular biophysics, and cancer biology. Students will apply directly to the Ph.D. program from their undergraduate or Master's institution and receive interdisciplinary training in the emerging field of computational and systems biology. Unique aspects of the program include: (a) close association with the multi- and inter-disciplinary research agenda of CSBi, (b) a unique core formed from newly developed, interdisciplinary classroom subjects that combine biology, engineering, and computation, (c) a seminar program focusing on a broad range of research both within and outside of MIT and that includes student participation, (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research, (e) the annual CSBi symposium, which is an exceptional conference bringing leaders in the field to MIT each year, and (f) multi-disciplinary thesis committees.

DESCRIPTION (provided by applicant): As a fundamental component of MIT's new Computational and Systems Biology Initiative (CSBi;), a new interdisciplinary pre-doctoral graduate training program in computational and systems biology is proposed. Program faculty are concentrated in the three founding academic units - Biology, Biological Engineering (BE), and Electrical Engineering & Computer Science (EECS), with additional involvement of faculty from other departments, such as Chemical Engineering, Mechanical Engineering, and Brain and Cognitive Sciences. Faculty from these departments are actively involved in research programs spanning a broad set of topics in computational and systems biology, including gene and protein networks, cell and tissue engineering, imaging and image informatics, predictive toxicology and metabolic engineering, genomics and proteomics, nanobiology and microsystems, molecular biophysics, and cancer biology. Students will apply directly to the Ph.D. program from their undergraduate or Master's institution and receive interdisciplinary training in the emerging field of computational and systems biology. Unique aspects of the program include: (a) close association with the multi- and inter-disciplinary research agenda of CSBi, (b) a unique core formed from newly developed, interdisciplinary classroom subjects that combine biology, engineering, and computation, (c) a seminar program focusing on a broad range of research both within and outside of MIT and that includes student participation, (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research, (e) the annual CSBi symposium, which is an exceptional conference bringing leaders in the field to MIT each year, and (f) multi-disciplinary thesis committees.

Through a grant from the National Science Foundation to the Massachusetts Institute of Technology, six faculty members will collaborate in the purchase and use of high-performance computing equipment for research and education in computational and systems biology. The advent of high-throughput technologies in the life sciences has provided many genome sequences, protein structures and biological interaction networks. The amount of such data will continue to grow, compelling the development of rigorous and quantitative approaches to decipher and understand it. Simultaneously, advances in computing technology are enabling new ways of attacking complex biological problems using modeling and simulation. The projects to be supported cover a wide range of exciting areas, including the study of gene and organism evolution, transcriptional and post-transcriptional gene regulation, molecular signaling, protein conformational modeling, protein design, and the analysis of complex networks. The work will lead to advances in computational methods and provide basic biological insights.

This award will support MIT?s active role in developing computational and systems biology in the United States. The university is establishing novel programs and curricula to train students at the interface of the life sciences, engineering and the physical sciences. The investigators on this award are deeply involved in these activities. Shared computing resources will help attract talented students and provide them with modern, cross-disciplinary training. These students, who come from diverse backgrounds, will assume leadership positions in American universities and companies.

DESCRIPTION (provided by applicant): The development of new approaches to treat rapidly evolving viral infections is an important area of research. This project will pursue general methods for the development of drug therapies that lead to inhibitors that avoid resistance in the context of AIDS, yet broadly applicable to infectious disease and cancer. We will continue to develop and improve the substrate envelope hypothesis, will develop and apply inverse computational design methods for small-molecule ligands, will study failures of the substrate envelope hypothesis to improve the approach, and will develop alternative approaches related to the substrate envelope hypothesis in the context of HIV-1 protease through a collaborative effort with experimental groups expert in organic and medicinal chemistry, enzyme assays, protein crystallography, and virology. Because essentially all therapies developed for infectious disease and cancer are limited by the selection of resistant variants, strategies for avoiding or a least delaying resistance that are generally applicable would have tremendous impact on drug development. This project will understand and improve the substrate envelope and related approaches in the well studied HIV-1 protease, but the methods developed will be broadly applicable to target-based infectious disease and cancer drug resistance. The substrate envelope hypothesis maintains that inhibitors that reside within the volume shared by substrates are less susceptible to resistance mutations, because such mutants must still process substrates. Preliminary work has demonstrated some success and shown some limitations of the substrate envelope hypothesis, and the proposed project will further test and develop the substrate envelope hypothesis in the context of HIV-1 protease. Extensions of the substrate envelope hypothesis include other modes of being substrate-like besides the geometric criteria of occupying the common substrate volume. Our previous work includes successes and failures of the substrate envelope hypothesis. The failures are molecules we designed that when synthesized bind wholly within the substrate envelope but fail to bind robustly across a panel of drug-resistant variants. By studying these molecules and substrates bound to wild type and drug-resistant variants, we will seek a mechanistic understanding of failures of the substrate envelope hypothesis, which will use to develop improved versions of the substrate envelope hypothesis. Finally, advances in deep sequencing technology make it possible to consider mapping the functional mutational space of candidate targets, and using the functional mutational space as a guide to the development of inhibitors that are not susceptible to resistance mutations. This project will develop and study methodology for using the functional mutational space as a basis for the design of inhibitors that avoid resistance. We will compare the success of this approach to the substrate envelope approach, noting that the new approach is applicable to targets whether they are enzymes or not, whereas the current state of the substrate envelope hypothesis is applicable to enzymes only.