Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Summary Dengue virus belongs to the family Flaviviridae and is a major emerging pathogen for which the development of vaccines and antiviral therapy has seen little success. The NS3 viral protease is a potential target for antiviral drugs since it is required for virus replication. The goal of this study was to identify novel dengue virus (type 2; DEN2V) protease inhibitors for eventual development as effective anti-flaviviral drugs. The EUDOC docking program was used to computationally screen a small-molecule library for compounds that dock into the P1 pocket and the catalytic site of the DEN2V NS3 protease domain apo-structure (Murthy et al., 1999) and the Bowman-Birk inhibitor-bound structure (Murthy et al., 2000). The top 20 computer–identified hits that demonstrated the most favorable scoring “energies” were selected for in vitro assessment of protease inhibition. Preliminary protease activity assays demonstrated that more than half of the tested compounds were soluble and exhibited in vitro inhibition of the DEN2V protease. Two of these compounds also inhibited viral replication in cell culture experiments, and thus are promising compounds for further development.

Ligand-induced protein allostery plays a central role in modulating cellular signaling pathways. Here, using the conserved cyclic-nucleotide binding domain of protein kinase A’s (PKA) regulatory subunit as a prototype signaling unit, we combine long-timescale, all-atom molecular dynamics simulations with Markov state models to elucidate the conformational ensembles of PKA’s cyclic-nucleotide binding domain A for the cAMP-free (apo) and cAMP-bound states. We find that both systems exhibit shallow free-energy landscapes that link functional states through multiple transition pathways. This observation suggests conformational selection as the general mechanism of allostery in this canonical signaling domain. Further, we expose the propagation of the allosteric signal through key structural motifs in the cyclic-nucleotide binding domain and explore the role of kinetics in its function. Our approach integrates disparate lines of experimental data into one cohesive framework to understand structure, dynamics, and function in complex biological systems.

Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.

The alphavirus nsP2 protease is essential for correct processing of the alphavirus nonstructural polyprotein (nsP1234) and replication of the viral genome. We have combined molecular dynamics simulations with our structural studies to reveal features of the nsP2 protease catalytic site and S1'-S4 subsites that regulate the specificity of the protease. The catalytic mechanism of the nsP2 protease appears similar to the papain-like cysteine proteases, with the conserved catalytic dyad forming a thiolate-imidazolium ion pair in the nsP2-activated state. Substrate binding likely stabilizes this ion pair. Analysis of bimolecular complexes of Venezuelan equine encephalitis virus (VEEV) nsP2 protease with each of the nsP1234 cleavage sites identified protease residues His 510 , Ser 511 , His 546 , and Lys 706 as critical for cleavage site recognition. Homology modelling and molecular dynamics simulations of diverse alphaviruses and their cognate cleavage site sequences revealed general features of substrate recognition that operate across alphavirus strains as well as strain specific covariance between binding site and cleavage site residues. For instance, compensatory changes occurred in the P3 and S3 subsite residues to maintain energetically favourable complementary binding surfaces. These results help explain how alphavirus nsP2 proteases recognize different cleavage sites within the non-structural polyprotein and discriminate between closely related cleavage targets.