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PrePPI inputs
PrePPI predicts the likelihood that two proteins A and B are capable of interacting based on their similarities to other proteins that are known to interact. This requires integrating structural data (green) as well as other kinds of information (blue), such as evidence of protein co-activity in other species as well as involvement in similar cellular functions. PrePPI now offers a searchable database of unprecedented scope, constituting a virtual interactome of all proteins in human cells. (Image courtesy of eLife.) 

The molecular machinery within every living cell includes enormous numbers of components functioning at many different levels. Features like genome sequence, gene expression, proteomic profiles, and chromatin state are all critical in this complex system, but studying a single level is often not enough to explain why cells behave the way they do. For this reason, systems biology strives to integrate different types of data, developing holistic models that more comprehensively describe networks of interactions that give rise to biological traits. 

Although the concept of an interaction network can seem abstract, at its foundation each interaction is a physical event that takes place when two proteins encounter one another, bind, and cause a change that affects a cell’s activity. In order for this to take place, however, they need to have compatible shapes and physical properties. Being able to predict the entire universe of possible pairwise protein-protein interactions could therefore be immensely valuable to systems biology, as it could both offer a framework for interpreting the feasibility of interactions proposed by other methods and potentially reveal unique features of networks that other approaches might miss. 

In a 2012 paper in Nature, scientists in the laboratory of Barry Honig first presented a landmark algorithm and database they call PrePPI (Predicting Protein-Protein Interactions). At the time, PrePPI used a novel computational strategy that deploys concepts from structural biology to predict approximately 300,000 protein-protein interactions, a dramatic increase in the number of available interactions when compared with experimentally generated resources.

Since then, the Honig Lab has been working hard to improve PrePPI’s scope and usefulness. In a paper recently published in eLife they now report on some impressive developments. With enhancements to their algorithm and the incorporation several new types of data into its analysis, the PrePPI database now contains more than 1.35 million predictions of protein-protein interactions, covering about 85% of the entire human proteome. This makes it the largest resource of its kind. In parallel with these improvements, the investigators have also begun to apply PrePPI in new ways, using the information it contains to provide new kinds of insights into the organization and function of protein interaction networks.

Andrew Anzalone and Sakellarios ZairisMD/PhD students Andrew Anzalone and Sakellarios Zairis combined approaches based in chemical biology, synthetic biology, and computational biology to develop a new method for protein engineering.

The ribosome is a reliable machine in the cell, precisely translating the nucleotide code carried by messenger RNAs (mRNAs) into the polypeptide chains that form proteins. But although the ribosome typically reads this code with uncanny accuracy, translation has some unusual quirks. One is a phenomenon called -1 programmed ribosomal frameshifting (-1 PRF), in which the ribosome begins reading an mRNA one nucleotide before it should. This hiccup bumps translation “out of frame,” creating a different sequence of three-nucleotide-long codons. In essence, -1 PRF thus gives a single gene the unexpected ability to code for two completely different proteins.

Recently Andrew Anzalone, an MD/PhD student in the laboratory of Virginia Cornish, set out to explore whether he could take advantage of -1 PRF to engineer cells capable of producing alternate proteins. Together with Sakellarios Zairis, another MD/PhD student in the Columbia University Department of Systems Biology, the two developed a pipeline for identifying RNA motifs capable of producing this effect, as well as a method for rationally designing -1 PRF “switches.” These switches, made up of carefully tuned strands of RNA bound to ligand-sensing aptamers, can react to the presence of a specific small molecule and reliably modulate the ratio in the production of two distinct proteins from a single mRNA. The technology, they anticipate, could offer a variety of exciting new applications for synthetic biology. A paper describing their approach and findings has been published in Nature Methods.

February 24, 2016

Barry Honig Named ISCB Fellow

Barry Honig The International Society for Computational Biology has elected Professor Barry Honig to its 2016 ISCB Class of Fellows. The award recognizes distinguished ISCB members who shown excellence in research and/or service to the computational biology community. Dr. Honig’s award acknowledges his “seminal contributions to protein structure prediction and molecular electrostatics, and his more recent work on protein function prediction, protein-DNA recognition, and cell-cell adhesion.”

The International Society for Computational Biology is the largest professional society for scientists working in the fields of computational biology and bioinformatics. The 2016 Class of Fellows will be presented at its annual Intelligent Systems for Molecular Biology (ISMB) conference, to be held July 8-12, 2016 in Orlando, Florida.

We are pleased to announce that Columbia University Medical Center professors Oliver Hobert, Richard Mann, and Rodney Rothstein have been named to interdisciplinary appointments in the Department of Systems Biology. The addition of this new expertise will expand the breadth of science currently being explored in the Department, enhance educational opportunities for students, facilitate new collaborations, and promote the integration of systems biology perspectives and methods into research being conducted elsewhere in the university.

Barry Honig

When Columbia University founded the Center for Multiscale Analysis of Genomic and Cellular Networks (MAGNet) in 2005, one of its goals was to integrate the methods of structural biology with those of systems biology. Considering protein structure within the context of computational models of cellular networks, researchers hoped, would not only improve the predictive value of their models by giving another layer of evidence, but also lead to new types of predictions that could not be made using other methods.

In a new paper published in Nature magazine, Barry Honig, Andrea Califano, and other members of the Columbia Initiative in Systems Biology, including first authors Qiangfeng Cliff Zhang and Donald Petrey, report that this goal has now been realized. For the first time, the researchers have shown that information about protein structure can be used to make predictions about protein-protein interactions on a genome-wide scale. Their approach capitalizes on innovative techniques in computational structural biology that the Honig lab has developed over the last 15 years, culminating in the development of a new algorithm called Predicting Protein-Protein Interactions (PrePPI). In this interview, Honig describes the evolution of this new approach, and what it could mean for the future of systems biology.

Barry Honig, Professor of Biochemistry & Molecular Biophysics, Howard Hughes Medical Institute investigator, and Director of the Center for Computational Biology and Bioinformatics, was honored by The Protein Society with the Christian B. Anfinsen Award. The award, sponsored by The Aviv Family Foundation, recognizes significant technical achievements in the field of protein science. The following is an excerpt from the award citation: Dr. Honig is the recipient of the 2012 award for his contributions to our understanding of the electrostatic properties of proteins and the development of DelPhi and GRASP, which are among the most widely used programs in structural biology. These and other computational tools from his group have enabled numerous discoveries related to protein molecular recognition, protein-membrane interactions, and protein structural stability. Honig's own recent discoveries related to cell-cell adhesion and sequence-dependent protein-DNA recognition are outstanding examples.

Minor Groove Insertion of Scr Residues His−12 and Arg3 in fkh250

Minor Groove Insertion of Scr Residues His−12 and Arg3 in fkh250. (A) Electron densities for Arg3 and His−12 in the fkh250 complex. (B) Details of the His−12–Arg3 interaction and water-mediated interactions with Thy14, Thy29, and Thy30 of fkh250

Although the basic structure of the double helix has been known since the classic work of Watson and Crick. it has become increasingly clear that the helix is not regular and that its shape depends on nucleotide sequence. In two recent papers in Cell and Nature, Barry Honig, Richard Mann and their colleagues in C2B2 and the Department of Biochemistry and Molecular Biophysics have shown that sequence-dependent variations in the helix shape allow DNA-binding proteins to recognize their specific binding sites.