Lecture 5: Dr. Julia Köhler Leman (Jeff Gray Lab)

Computational membrane protein structure prediction

Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. As we also learned in Lecture 4, the determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs targetMPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated.

Computational tools are instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. In their recent article, Dr. Julia Kohler Leman and her colleagues describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, the researchers developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. The preliminary results show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

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Dr. Julia Koehler Leman received her Bachelors and Masters of Science degrees in physics from the University of Leipzig, Germany. Her doctoral research revolved around membrane protein structure determination using NMR spectroscopy and computational techniques and she received her Ph.D. in Chemical and Physical Biology in 2012 while working with Drs. Jens Meiler and Charles Sanders at Vanderbilt University. After graduation, she worked as a postdoctoral fellow under the supervision of Dr. Jeff Gray at Hopkins on the projects that involve development and application of Rosetta software, focusing on creating and improving applications for structure prediction, docking, and design. Very recently, she started working as a research fellow at Simons Foundation. 

Dr. Koehler Leman has published 7 first-author papers and co-authored 6 papers in peer-reviewed international journals. Her current research interests lie in computational methods development in modeling membrane proteins with and without restraints from NMR spectroscopy.

Lecture 4: Dr. Sirish Kaushik Lakkaraju (Alex MacKerell Lab at UMB)

In search for a better drug against asthma (... and many others)

The seven-transmembrane α-helix structure
of a G protein–coupled receptor (wikipedia).

Occluded ligand binding pockets (LBP) in proteins with minimal or no accessibility to the surrounding environment represent a significant, yet challenging opportunity for structure-based and computer-aided drug design approaches. LBPs of more than half of all clinical drug targets, including the G-protein coupled receptors (GPCR) and nuclear receptors (NR) are either partially or fully occluded. As the efficacies of ligands of both GPCRs and NRs are known to be coupled to small conformational changes in their binding sites, accurate modeling of these sites is critical for future development of therapeutic agents.

To determine free energy maps of functional groups of LBPs, Dr. Kaushik Lakkaraju and his colleagues from the University of Maryland Baltimore combined a Grand-Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) strategy [1] with the Site Identification by Ligand Competitive Saturation (SILCS) methodology. In this method, the inclusion of protein flexibility helps identify regions of the binding pockets not accessible in crystal conformations and allows for better quantitative estimates of relative ligand binding affinities in all the proteins tested. The research group’s approach successfully recapitulates locations of functional groups across diverse classes of ligands in the LBPs of many important NR and GPCR proteins. In their study, the differences in functional group requirements of the active and inactive states of the β2AR LBP were used in virtual drug screening to identify high-efficacy agonists targeting β2AR in Airway Smooth Muscle (ASM) cells which are very important drug targets in patients with severe asthma. The experimental tests showed that 7 of the 15 selected ligands effect ASM relaxation, representing a very high hit rate (46%) [2].

FragMaps overlaid on the LBP of AR [2]

The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in the environment, ligand binding, and interactions with other macromolecules, among others. Therefore, there is always a need for faster extraction and comparison of functionally relevant conformational dynamics information from extended molecular simulations of large and complex macromolecular systems. Dr. Lakkaraju and his colleagues also have developed DIRECT-ID, a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the method calculates the norms of the differences in covariance matrices for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. As a demonstration of its applicability to bio-macromolecular systems, DIRECT-ID was used to identify relevant ligand-modulated structural variations in the β2 -adrenergic (β2 AR) G-protein coupled receptor. Micro-second MD simulations of the β2 AR in an explicit lipid bilayer were run in the apo-state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist), or carazolol (inverse agonist). Each ligand was found to modulate the conformational dynamics of β2 AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2 AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR [3]. DIRECT-ID is accessible from here.

References:

[1] Lakkaraju, S. K.; Raman, E. P.; Yu, W.; MacKerell, A. D.Sampling of Organic Solutes in Aqueous and Heterogeneous Environments using Oscillating Excess Chemical Potentials in Grand Canonical-Like Monte Carlo-Molecular Dynamics Simulations J. Chem. Theory Comput. 2014,10, 2281– 2290

[2] Lakkaraju, Sirish Kaushik, et al. "Mapping Functional Group Free Energy Patterns at Protein Occluded Sites: Nuclear Receptors and G-Protein Coupled Receptors." Journal of chemical information and modeling 55.3 (2015): 700-708.

[3] Lakkaraju, Sirish Kaushik, et al. "DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β2‐adrenergic GPCR." Journal of computational chemistry 37.4 (2016): 416-425.

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Dr. Sirish Kaushik Lakkaraju received his Bachelor of Engineering degree in electronics and instrumentation from the University of Madras at Chennai, India in 2003. He received his Master of Science degree in biomedical engineering from Texas A&M University in 2008 and his Ph.D. in December 2011 with Dr. Wonmuk Hwang. After graduation, he worked as a postdoctoral fellow under the supervision of Dr. Alex MacKerell at University of Maryland, Baltimore on the projects that involve method development and in-silico drug design. Recently, he started his second postdoc at Pfizer. 

Dr. Lakkaraju has published 8 first-author papers and co-authored 2 papers in peer-reviewed international journals. His current research interests lie in studying the role of dynamics of biological macromolecules and macromolecular assemblies on their function and also computational drug design.