Computational Structural Biology

Computational Structural Biology aims at establishing biomolecular sequence-structure-function relations using fundamental principles of physical sciences in theoretical models and simulations of structure and dynamics.
Computational Structural Biology aims at establishing biomolecular sequence-structure-function relations using fundamental principles of physical sciences in theoretical models and simulations of structure and dynamics. After the advances in complete genomes sequencing, it became evident that structural information is needed for understanding the origin and mechanisms of biological interactions, and designing/controlling function. Computational Structural Biology emerged as a tool for efficient identification of structure and dynamics in many applications. Major research topics include protein folding, protein dynamics with emphasis on large complexes and assemblies, protein-protein, protein-ligand and protein-DNA interactions and their functional implications. Drug design and protein engineering represent applications of note.

 

Faculty

Department of Computational & Systems Biology, Chair

Research interests: Biomolecular systems dynamics at multiple scales; evolution of proteins’ sequence, structure, dynamics and function; computer-aided drug discovery and polypharmacology; network models for protein-protein interactions, supramolecular machinery and allostery; modeling and simulations of membrane proteins dynamics and mechanisms of interactions.

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Department of Computational & Systems Biology

A striking set of specific and non-specific interactions encoded in the protein structure tolerates binding only to a unique substrate. My main research interests focus on modeling the physical interactions responsible for molecular recognition, and in the development of new technologies for structural prediction, their substrates and supramolecular assemblies. Any progress in these fundamental problems is bound to bring about a better understanding of how proteins work cooperatively in a cell, promoting breakthroughs in every aspect of the biological sciences.

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Language Technologies Institute (LTI), Lane Center for Computational Biology, and Department of Computer Science

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Department of Computational & Systems Biology

We investigate the molecular and cellular origins of human epithelial malignancies (e.g., breast cancer, Barrett’s) through computational models. We pursue two interrelated approaches:

Computational Pathology and Bioimaging We develop algorithms to analyze intratumor phenotypic heterogeneity from in situ fluorescent imaging of tissue sections or tissue microarrays.
Computational Biophysics We develop models based on anharmonic fluctuations to discern short-lived and rare intermediate conformations that proteins access to fold, bind, and function.

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Departments of Chemistry and Computational & Systems Biology

Computational Biophysics; Biomolecular simulations

Research in the Chong lab involves the development and application of molecular simulation approaches to model a variety of biophysical processes. A summary of some of our research directions is provided below.

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CAREER

 

Department of Biomedical Engineering

We study mechanical and rheological properties of the nucleus. In deciphering the structural and mechanical elements of the cell’s nucleus we hope to determine roles of epigenetic regulation, stem cell differentiation, aging pathologies and cancer metastases. Mechanical regulation of cell and tissue function is poorly understood but is a fascinating area of study. Our research focuses on molecular, organelle, cellular and multicellular length scales over time, and we use a combination of spectroscopic, imaging, image informatics, biophysics and computational approaches.

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CAREER

 

Department of Biomedical Informatics and Intelligent Systems Program

Dr. Ganapathiraju’s primary area of research is in Systems Biology, specifically on protein-protein interaction prediction at the system level. The outcomes of this research will subsequently be applied to translational bioinformatics. A second core area is in Sequence Analysis, for pattern mining in whole-genome and whole-proteome sequences, with application of suffix array data structures for preprocessing the genome sequences.

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BRAINS

 

Department of Chemistry, Center for Simulation and Modeling

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Department of Computational & Systems Biology

We develop novel computational algorithms and build full-scale systems to support rapid and inexpensive drug discovery while simultaneously applying these methods to develop novel therapeutics. We are particularly interested in the application of machine learning and high performance computing to computational drug discovery.

Lab Website: http://bits.csb.pitt.edu/

Department of Computer Science and Lane Center for Computational Biology

My research spans two areas: Personalized Medicine and Computational Structural Biology.

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CAREER

 

Department of Computational & Systems Biology

Cellular behavior emerges from a complex network of chemical interactions, the details of which remain largely unknown. Many pathways within the cell are redundant or interdependent, hindering our ability to experimentally delineate their in vivo activity. Further complicating matters are slight and unmeasurable differences between cells within a population and the interplay between cells and their environment. Thus, seemingly identical cells may respond differently to the same environmental perturbation. This cellular heterogeneity is particularly problematic in cancer, where slight differences determine whether or not a cell will go on to produce a tumor. Our lab has several on-going projects modeling disease emergence and progression from sub-cellular to multi-cellular systems.

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Department of Biological Sciences

Gene expression varies between individuals and species, and this variation is largely responsible for phenotypic diversity and disease. Research in the McManus lab focuses on understanding the genetic causes of variation in gene expression. Gene expression involves transcription of DNA into mRNA, alternative splicing of mRNA, translation of mRNA into proteins, and regulation of mRNA and protein levels through turnover pathways. Differences in the regulatory networks controlling these processes lead to gene expression variation. Our lab uses high-throughput sequencing and bioinformatics to compare regulation of alternative splicing and mRNA translation in closely related species of fruit flies and yeast. We also use these tools to investigate the structure of large RNAs genome wide. RNA structures play important roles in gene expression, yet very little is known about the structures of most large RNAs.

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Department of Biological Sciences

My research is directed at understanding inter-molecular interactions in biological systems. Our research efforts have been directed at enzyme-substrate interactions, protein-lipid interactions, antibody-antigen interactions, RNA structure, and protein-nucleic acid interactions.

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Department of Chemical Engineering and Lane Center for Computational Biology

Professor Sahinidis concentrates on optimization in biology, chemistry, medicine, and engineering.

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Departments of Anesthesiology, Pharmacology and Computational & Systems Biology

Determination of high-resolution domain structures of neuronal ion channels, such as nicotinic acetylcholine and glycine receptors

Characterization at the molecular level of how low-affinity drugs, particularly general anesthetics and alcohols, affect the functions of transmembrane ion channels. The approaches used in Dr. Tang’s laboratory include high-resolution NMR and large-scale molecular dynamic simulations.

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Department of Physics

Current and Recent Projects

  • Metal alloys
  • Atomistic modeling and statistical mechanics of quasicrystals, metal alloys with noncrystalline quasiperiodic structures of high symmetry (description)
  • Structure, formation and properties of bulk metallic glass-forming alloys (sample publication)
  • Intermetallic structures and their enthalpies of formation (cohesive energy database)
  • Biological Physics
  • Buckling instability of viral capsids (reprint)
  • Normal mode analysis of spherical and icosahedral shells (descriptionreprint)
  • Secondary structure of RNA (description)

 

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Department of Pharmaceutical Sciences

Dr. Xie’s research group focuses on development and application of GPCRs chemical genomics-based drug design and discovery approach for osteoporosis, multiple myeloma and breast cancer research. His group has established a drug discovery research platform with the integrated 3D pharmacophore database search, in-silico design and in-vitro bioassay validation as well as medicinal chemistry modification syntheses. This technology was developed through screening and identifying novel CB2 drug-like molecules with new chemical scaffolds and high CB2 specificity (US patents WO 2009058377). It has also been successfully applied to identify p18-based drugs for hematopoietic stem cell self-renewal, and novel chemical agents for multiple myeloma and osteoporosis as well as other targets.

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Departments of Anesthesiology and Pharmacology

Dr. Xu and his research group are interested in (1) rational design of new therapeutic strategies for treatment of brain injury during and after cardiac arrest, (2) the molecular mechanisms underlying the actions of nonspecific drugs on the ligand-gated neuronal receptors in their membrane environment, and (3) functional mapping of neural activities in drug-induced unconscious state. In the first project, research activities are particularly directed at evaluating therapeutic potentials of umbilical cord matrix stem cells. The second project involves the 3-D structure determination of functional segments of human glycine receptor. The current focus of the third project is on differentiating receiving and perceiving external stimulations in the visual and olfactory systems.

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Department of Computational & Systems Biology

The Zuckerman group develops and applies computer simulation methods for studying biological systems. A primary focus is the deployment of sampling algorithms based on statistical physics that can be used to study (i) large-scale, potentially allosteric motions in proteins, (ii) signaling processes encoded in interaction networks, (iii) protein binding, and (iv) protein folding. Among the strategies used in the group are approaches that can yield super-linear parallel performance – estimation of observables using N processors that is more than N times faster than an estimate based on a single-processor simulation. Prof. Zuckerman also has a strong interest in biophysics education, which has led to a textbook and a new online book.

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CAREER