Tuesday, April 11, 2017

Lecture 18: Jeliazko Jeliazkov (Jeff Gray Lab)

Computational Modeling and Docking of Antibody Structures.

The vertebrate adaptive immune system is capable of promoting cells to degranulate or phagocytose nearly any foreign pathogen by producing immunoglobulin G (IgG) proteins (antibodies) that recognize a specific region (epitope) of a pathogenic molecule (antigen). The ability to bind diverse antigens requires a diverse population of antibodies, which is achieved through complex processes in bone marrow and lymphatic tissues, namely V(D)J recombination and somatic hypermutation. The diversity of antibodies is astonishing; the size of the theoretical naive antibody repertoire is estimated to be >1e13 in humans. In addition to their biological importance, antibodies are routinely used in biotechnology as probes and diagnostics, and dozens of antibodies have been approved as therapeutics.

Therapeutic monoclonal antibodies are a genre of biopharmaceuticals which has benefitted healthcare in various fields from oncology to immune and inflammatory disorders. Development of successful novel therapeutic antibodies requires an understanding of drug and disease mechanisms and the ability to stabilize, affinity mature, and humanize antibodies. Antibody structures can help overcome these challenges by providing atomic-level insights into structure–function relationships and the antibody–antigen interaction [e.g. see refs. (1–4)]. However, experimental techniques for obtaining antibody structures, like X-ray crystallography and nuclear magnetic resonance, are laborious, time-consuming and costly. Computational antibody structure prediction provides a fast and inexpensive route to obtain structures, including those which are not obtainable otherwise.

A schematic[1] of the modeling protocols (full flowcharts
for Rosetta Antibody and Rosetta SnugDock
are available in the original publications).
In their recent Nature Protocols article, Jeliazko Jeliazkov and Jeff Gray from Johns Hopkins Department of Chemical and Biomolecular Engineering and their international collaborators describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from the sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL–VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers. In the figure, the structure on the left shows the FV antibody domains predicted by homology modeling (heavy chain in dark blue with CDR H1 and H2 loops in orange and CDR H3 loop in red; light chain in yellow with its CDR loops in light blue). The structure on the right depicts an antibody–antigen structure output by docking (antigen in green).

[1]Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack Jr RL, Gray JJ. Modeling and docking of antibody structures with Rosetta. Nature Protocols. 2017 Feb 1;12(2):401-16.
[2]Sircar A, Kim ET, Gray JJ. RosettaAntibody: antibody variable region homology modeling server. Nucleic acids research. 2009 May 20:gkp387.
Jeliazko Jeliazkov received his B.S. in Physics from the University of Illinois at Urbana-Champaign. Since graduating, he joined the Program in Molecular Biophysics and is pursuing a Ph.D. under the tutelage of Prof. Jeffrey Gray. His research involves the computational prediction of protein–protein interactions, focusing in particular on antibody–antigen, disordered–ordered protein domain, and crystallographic (non-biological) protein–protein interactions.

Tuesday, April 4, 2017

Lecture 17: Prof. Sagar Khare

A New Weapon in the Fight Against Cancer and Viral Infections: Custom-Designed Enzymes
Arising out of natural selection, the structures of proteins (and their complexes with small molecules, nucleic acids, and other proteins) display exquisitely fine-tuned molecular recognition, which is critical for life to operate. Under selection conditions, accurate molecular recognition must be robust to random perturbations such as mutations. Yet, natural proteins are also evolvable — variation in a few amino acids can lead to profound changes in function, e.g. a new enzymatic activity can arise in an “old” enzyme. In other words, these molecular interactions have the fascinating property of being simultaneously functionally robust and plastic.
Fitness scoring function [1].
Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data. In his talk, Prof. Sagar Khare described a general approach for predicting peptidase substrates de novo using protein structure modeling and biophysical evaluation of enzyme–substrate complexes. His research team constructed atomic resolution models of thousands of candidate substrate–enzyme complexes for each of five model proteases belonging to the four major protease mechanistic classes—serine, cysteine, aspartyl, and metallo-proteases—and develop a discriminatory scoring function using enzyme design modules from Rosetta and AMBER's MMPBSA. They ranked putative substrates based on calculated interaction energy with a modeled near-attack conformation of the enzyme active site. Their results show that the energetic patterns obtained from these simulations can be used to robustly rank and classify known cleaved and uncleaved peptides and that these structural-energetic patterns have greater discriminatory power compared to purely sequence-based statistical inference. Combining sequence and energetic patterns using machine-learning algorithms further improves classification performance, and analysis of structural models provides physical insight into the structural basis for the observed specificities.
Summary of the CPG2 circular permutations [2].
In the second part of his talk, Prof. Khare talked about spatio-temporal design of enzymes. Carboxypeptidase G2 (CPG2) is an Food and Drug Administration (FDA)-approved enzyme drug used to treat methotrexate (MTX) toxicity in cancer patients receiving MTX treatment. It has also been used in directed enzyme-prodrug chemotherapy, but this strategy has been hampered by off-site activation of the prodrug by the circulating enzyme. The development of a tumor protease activatable CPG2, which could be achieved using a circular permutation of CPG2 fused to an inactivating ‘prodomain’, would aid in these applications. The research team reported the development of a protease accessibility-based screen to identify candidate sites for circular permutation in proximity of the CPG2 active site. The resulting six circular permutants showed similar expression, structure, thermal stability, and, in four cases, activity levels compared to the wild-type enzyme. They rationalize these results based on structural models of the permutants obtained using the Rosetta software by developing a cell growth-based selection system, and demonstrated that when fused to periplasm-directing signal peptides, one of the circular permutants confers MTX resistance in Escherichiacoli with equal efficiency as the wild-type enzyme. As the permutants have similar properties to wild-type CPG2, these enzymes are promising starting points for the development of autoinhibited, protease-activatable zymogen forms of CPG2 for use in therapeutic contexts[2]
Related References:
[1] Pethe, M. A., Rubenstein, A. B., & Khare, S. D. (2017). Large-scale Structure-based Prediction and Identification of Novel Protease Substrates using Computational Protein Design. Journal of Molecular Biology, 429(2), 220-236
[2] Yachnin, B. J., & Khare, S. D. (2017). Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme. Protein engineering, design & selection: PEDS, 1.
Sagar Khare is an Assistant Professor at Rutgers University. He teaches Chemistry and Chemical Biology and seeks to understand the structural determinants of enzymatic specificity and reactivity using a combination of computational protein design and experimental characterization. His research team's goal is to develop a quantitative and predictive understanding of specificity at protein-ligand and protein-peptide interfaces; which will inform various therapeutic and synthetic applications. Prior to working at Rutgers, Prof. Sagar Khare was a Postdoctoral Fellow at the University of Washington in Seattle, Washington. He was also a Software Engineer for Affymax Research Institute in Bangalore, India.