Pratul K. Agarwal

Enzymes: An integrated view of structure, dynamics and function

Microbes utilize enzymes to perform a variety of functions. Enzymes are biocatalysts working as highly efficient machines at the molecular level. In the past, enzymes have been viewed as static entities and their function has been explained on the basis of direct structural interactions between the enzyme and the substrate. A variety of experimental and computational techniques, however, continue to reveal that proteins are dynamically active machines, with various parts exhibiting internal motions at a wide range of time-scales. Increasing evidence also indicates that these internal protein motions play a role in promoting protein function such as enzyme catalysis. Moreover, the thermodynamical fluctuations of the solvent, surrounding the protein, have an impact on internal protein motions and, therefore, on enzyme function. In this review, we describe recent biochemical and theoretical investigations of internal protein dynamics linked to enzyme catalysis. In the enzyme cyclophilin A, investigations have lead to the discovery of a network of protein vibrations promoting catalysis. Cyclophilin A catalyzes peptidyl-prolyl cis/trans isomerization in a variety of peptide and protein substrates. Recent studies of cyclophilin A are discussed in detail and other enzymes (dihydrofolate reductase and liver alcohol dehydrogenase) where similar discoveries have been reported are also briefly discussed. The detailed characterization of the discovered networks indicates that protein dynamics plays a role in rate-enhancement achieved by enzymes. An integrated view of enzyme structure, dynamics and function have wide implications in understanding allosteric and co-operative effects, as well as protein engineering of more efficient enzymes and novel drug design.

Evolutionarily Conserved Linkage between Enzyme Fold, Flexibility, and Catalysis

Proteins are intrinsically flexible molecules. The role of internal motions in a proteins designated function is widely debated. The role of protein structure in enzyme catalysis is well established, and conservation of structural features provides vital clues to their role in function. Recently, it has been proposed that the protein function may involve multiple conformations: the observed deviations are not random thermodynamic fluctuations; rather, flexibility may be closely linked to protein function, including enzyme catalysis. We hypothesize that the argument of conservation of important structural features can also be extended to identification of protein flexibility in interconnection with enzyme function. Three classes of enzymes (prolyl-peptidyl isomerase, oxidoreductase, and nuclease) that catalyze diverse chemical reactions have been examined using detailed computational modeling. For each class, the identification and characterization of the internal protein motions coupled to the chemical step in enzyme mechanisms in multiple species show identical enzyme conformational fluctuations. In addition to the active-site residues, motions of protein surface loop regions (>10 Å away) are observed to be identical across species, and networks of conserved interactions/residues connect these highly flexible surface regions to the active-site residues that make direct contact with substrates. More interestingly, examination of reaction-coupled motions in non-homologous enzyme systems (with no structural or sequence similarity) that catalyze the same biochemical reaction shows motions that induce remarkably similar changes in the enzyme–substrate interactions during catalysis. The results indicate that the reaction-coupled flexibility is a conserved aspect of the enzyme molecular architecture. Protein motions in distal areas of homologous and non-homologous enzyme systems mediate similar changes in the active-site enzyme–substrate interactions, thereby impacting the mechanism of catalyzed chemistry. These results have implications for understanding the mechanism of allostery, and for protein engineering and drug design.

Using FPGA Devices to Accelerate Biomolecular Simulations

A field-programmable gate array implementation of a molecular dynamics simulation method reduces the microprocessor time-to-solution by a factor of three while using only high-level languages. The application speedup on FPGA devices increases with the problem size. The authors use a performance model to analyze the potential of simulating large-scale biological systems faster than many cluster-based supercomputing platforms

Cis/trans isomerization in HIV‐1 capsid protein catalyzed by cyclophilin A: Insights from computational and theoretical studies

A network of protein vibrations has recently been identified in the enzyme cyclophilin A (CypA) that is associated with its peptidyl‐prolyl cis/trans isomerization activity of small peptide substrates. It has been suggested that this network may have a role in promoting the catalytic step during the isomerization reaction. This work presents the results from the characterization of this network during the isomerization of the Gly89‐Pro90 peptide bond in the N‐terminal domain of the capsid protein (CAN) from human immunodeficiency virus type 1 (HIV‐1), which is a naturally occurring, biologically relevant protein substrate for CypA. A variety of computational and theoretical studies are utilized to investigate the protein dynamics of the CypA‐CAN complex, at multiple time scales, during the isomerization step. The results provide insights into the detailed mechanism of isomerization and confirm the presence of previously reported network of protein vibrations coupled to the reaction. Conserved CypA residues at the complex interface and at positions distal to the interface form parts of this network. There is HIV‐1 related medical interest in CypA; incorporation of CypA, complexed with the capsid protein, into the virion is required for the infectious activity of HIV‐1. Interaction energy and dynamical cross‐correlation calculations are used for a detailed investigation of the protein–protein interactions in the CypA‐CAN complex. The results show that CAN residues His87‐Ala‐Gly‐Pro‐Ile‐Ala92 form the majority of the interactions with CypA residues. New protein–protein interactions distal to the active site (CypA Arg148‐CAN Gln95 and CypA Arg148‐CAN Asn121) are also identified. Proteins 2004.

Protein Conformational Populations and Functionally Relevant Substates

Functioning proteins do not remain fixed in a unique structure, but instead they sample a range of conformations facilitated by motions within the protein. Even in the native state, a protein exists as a collection of interconverting conformations driven by thermodynamic fluctuations. Motions on the fast time scale allow a protein to sample conformations in the nearby area of its conformational landscape, while motions on slower time scales give it access to conformations in distal areas of the landscape. Emerging evidence indicates that protein landscapes contain conformational substates with dynamic and structural features that support the designated function of the protein. Nuclear magnetic resonance (NMR) experiments provide information about conformational ensembles of proteins. X-ray crystallography allows researchers to identify the most populated states along the landscape, and computational simulations give atom-level information about the conformational substates of different proteins. This ability to characterize and obtain quantitative information about the conformational substates and the populations of proteins within them is allowing researchers to better understand the relationship between protein structure and dynamics and the mechanisms of protein function. In this Account, we discuss recent developments and challenges in the characterization of functionally relevant conformational populations and substates of proteins. In some enzymes, the sampling of functionally relevant conformational substates is connected to promoting the overall mechanism of catalysis. For example, the conformational landscape of the enzyme dihydrofolate reductase has multiple substates, which facilitate the binding and the release of the cofactor and substrate and catalyze the hydride transfer. For the enzyme cyclophilin A, computational simulations reveal that the long time scale conformational fluctuations enable the enzyme to access conformational substates that allow it to attain the transition state, therefore promoting the reaction mechanism. In the long term, this emerging view of proteins with conformational substates has broad implications for improving our understanding of enzymes, enzyme engineering, and better drug design. Researchers have already used photoactivation to modulate protein conformations as a strategy to develop a hypercatalytic enzyme. In addition, the alteration of the conformational substates through binding of ligands at locations other than the active site provides the basis for the design of new medicines through allosteric modulation.

Computational Identification of Slow Conformational Fluctuations in Proteins

Conformational flexibility of proteins has been linked to their designated functions. Slow conformational fluctuations occurring at the microsecond to millisecond time scale, in particular, have recently attracted considerable interest in connection to the mechanism of enzyme catalysis. Computational methods are providing valuable insights into the connection between protein structure, flexibility, and function. In this report, we present studies on identification and characterization of microsecond flexibility of ubiquitin, based on quasi-harmonic analysis (QHA) and normal-mode analysis (NMA). The results indicate that the slowest 10 QHA modes, computed from the 0.5 mus molecular dynamics ensemble, contribute over 78% of all motions. The identified slow movements show over 75% similarity with the conformational fluctuations observed in nuclear magnetic resonance ensemble and also agree with displacements in the set of X-ray structures. The slowest modes show high flexibility in the beta1-beta2, alpha1-beta3, and beta3-beta4 loop regions, with functional implications in the mechanism of binding other proteins. NMA of ubiquitin structures was not able to reproduce the long time scale fluctuations, as they were found to strongly depend on the reference structures. Further, conformational fluctuations coupled to the cis/trans isomerization reaction catalyzed by the enzyme cyclophilin A (CypA), occurring at the microsecond to millisecond time scale, have also been identified and characterized on the basis of QHA of conformations sampled along the reaction pathway. The results indicate that QHA covers the same conformational landscape as the experimentally observed CypA flexibility. Overall, the identified slow conformational fluctuations in ubiquitin and CypA indicate that the intrinsic flexibility of these proteins is closely linked to their designated functions.

Performance characterization of molecular dynamics techniques for biomolecular simulations

Large-scale simulations and computational modeling using molecular dynamics (MD) continues to make significant impacts in the field of biology. It is well known that simulations of biological events at native time and length scales requires computing power several orders of magnitude beyond todays commonly available systems. Supercomputers, such as IBM Blue Gene/L and Cray XT3, will soon make tens to hundreds of teraFLOP/s of computing power available by utilizing thousands of processors. The popular algorithms and MD applications, however, were not initially designed to run on thousands of processors. In this paper, we present detailed investigations of the performance issues, which are crucial for improving the scalability of the MD-related algorithms and applications on massively parallel processing (MPP) architectures. Due to the varying characteristics of biological input problems, we study two prototypical biological complexes that use the MD algorithm: an explicit solvent and an implicit solvent. In particular, we study the AMBER application, which supports a variety of these types of input problems. For the explicit solvent problem, we focused on the particle mesh Ewald (PME) method for calculating the electrostatic energy, and for the implicit solvent model, we targeted the Generalized Born (GB) calculation. We uncovered and subsequently modified a limitation in AMBER that restricted the scaling beyond 128 processors. We collected performance data for experiments on up to 2048 Blue Gene/L and XT3 processors and subsequently identified that the scaling is largely limited by the underlying algorithmic characteristics and also by the implementation of the algorithms. Furthermore, we found that the input problem size of biological system is constrained by memory available per node. In conclusion, our results indicate that MD codes can significantly benefit from the current generation architectures with relatively modest optimization efforts. Nevertheless, the key for enabling scientific breakthroughs lies in exploiting the full potential of these new architectures.

An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

Collective behavior involving distally separate regions in a protein is known to widely affect its function. In this article, we present an online approach to study and characterize collective behavior in proteins as molecular dynamics (MD) simulations progress. Our representation of MD simulations as a stream of continuously evolving data allows us to succinctly capture spatial and temporal dependencies that may exist and analyze them efficiently using data mining techniques. By using tensor analysis we identify (a) collective motions (i.e., dynamic couplings) and (b) time-points during the simulation where the collective motions suddenly change. We demonstrate the applicability of this method on two different protein simulations for barnase and cyclophilin A. We characterize the collective motions in these proteins using our method and analyze sudden changes in these motions. Taken together, our results indicate that tensor analysis is well suited to extracting information from MD trajectories in an online fashion.

Optimal Utilization of Heterogeneous Resources for Biomolecular Simulations

Biomolecular simulations have traditionally benefited from increases in the processor clock speed and coarse-grain inter-node parallelism on large-scale clusters. With stagnating clock frequencies, the evolutionary path for performance of microprocessors is maintained by virtue of core multiplication. Graphical processing units (GPUs) offer revolutionary performance potential at the cost of increased programming complexity. Furthermore, it has been extremely challenging to effectively utilize heterogeneous resources (host processor and GPU cores) for scientific simulations, as underlying systems, programming models and tools are continually evolving. In this paper, we present a parametric study demonstrating approaches to exploit resources of heterogeneous systems to reduce time-to-solution of a production-level application for biological simulations. By overlapping and pipelining computation and communication, we observe up to 10-fold application acceleration in multi-core and multi-GPU environments illustrating significant performance improvements over code acceleration approaches, where the host-to-accelerator ratio is static, and is constrained by a given algorithmic implementation.

Event detection and sub‐state discovery from biomolecular simulations using higher‐order statistics: Application to enzyme adenylate kinase

Biomolecular simulations at millisecond and longer time‐scales can provide vital insights into functional mechanisms. Because post‐simulation analyses of such large trajectory datasets can be a limiting factor in obtaining biological insights, there is an emerging need to identify key dynamical events and relating these events to the biological function online, that is, as simulations are progressing. Recently, we have introduced a novel computational technique, quasi‐anharmonic analysis (QAA) (Ramanathan et al., PLoS One 2011;6:e15827), for partitioning the conformational landscape into a hierarchy of functionally relevant sub‐states. The unique capabilities of QAA are enabled by exploiting anharmonicity in the form of fourth‐order statistics for characterizing atomic fluctuations. In this article, we extend QAA for analyzing long time‐scale simulations online. In particular, we present HOST4MD—a higher‐order statistical toolbox for molecular dynamics simulations, which (1) identifies key dynamical events as simulations are in progress, (2) explores potential sub‐states, and (3) identifies conformational transitions that enable the protein to access those sub‐states. We demonstrate HOST4MD on microsecond timescale simulations of the enzyme adenylate kinase in its apo state. HOST4MD identifies several conformational events in these simulations, revealing how the intrinsic coupling between the three subdomains (LID, CORE, and NMP) changes during the simulations. Further, it also identifies an inherent asymmetry in the opening/closing of the two binding sites. We anticipate that HOST4MD will provide a powerful and extensible framework for detecting biophysically relevant conformational coordinates from long time‐scale simulations. Proteins 2012.