Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multi-temperature crystallography could therefore augment the structure-based drug design toolbox.

11The spindle generates force to segregate chromosomes at cell division. In mammalian 12 cells, kinetochore-fibers connect chromosomes to the spindle. The dynamic spindle 13 anchors kinetochore-fibers in space and time to coordinate chromosome movement. 14 Yet, how it does so remains poorly understood as we lack tools to directly challenge this 15 anchorage. Here, we adapt microneedle manipulation to exert local forces on the 16 spindle with spatiotemporal control. Pulling on kinetochore-fibers reveals that the 17 spindle retains local architecture in its center on the seconds timescale. Upon pulling, 18 sister, but not neighbor, kinetochore-fibers remain tightly coupled, restricting 19 chromosome stretching. Further, pulled kinetochore-fibers freely pivot around poles but 20 not around chromosomes, retaining their orientation within 3 µm of chromosomes. This 21 local reinforcement has a 20 s lifetime, and requires the microtubule crosslinker PRC1. 22Together, these observations indicate short-lived, specialized reinforcement of the 23 kinetochore-fiber in the spindle center. This could help the spindle protect local structure 24 near chromosomes from transient forces while allowing its remodeling over longer 25 timescales, thereby supporting robust chromosome attachments and movements. 26 27 48 spindle. K-fibers make contacts along their length with a dense network of non-49 kinetochore microtubules (non-kMTs) (Mastronarde et al., 1993;McDonald et al., 1992), 50 likely through both motor and non-motor microtubule binding proteins (Elting et al., 51 2017;Kajtez et al., 2016;Vladimirou et al., 2013). We know that the non-kMT network 52 bridges sister k-fibers together (Kajtez et al., 2016;Mastronarde et al., 1993; Witt, Ris & 53 Borisy, 1981), and that it can locally anchor k-fibers and bear load in the spindle's 54 longitudinal (pole-pole) axis (Elting et al., 2017). Yet, how the dynamic spindle 55 mechanically anchors k-fibers in space and time remains poorly mapped and 56 understood. Specifically, we do not know if k-fibers are anchored uniformly along their 57 length, to what structures they are anchored to, over what timescale this anchorage 58 persists before remodeling is allowed, or more broadly how local forces propagate 59 through the spindle's longitudinal and lateral axes. These questions are central to the 60spindle's ability to robustly maintain its structure, respond to force and ultimately move 61 chromosomes. 62We currently lack tools to apply forces with both spatial and temporal control to 63 mammalian spindles. For example, laser ablation, commonly used to alter forces in the 64 spindle, can locally perturb spindle structure, but lacks control over the duration and 65 direction of ensuing force changes. Further, mammalian spindles cannot yet be 66 reconstituted in vitro. To understand how the dynamic spindle robustly anchors k-fibers, 67 and to ultimately map mammalian spindle mechanics to function, we need approaches 68 to apply local and reproducible forces inside cells, with spatiotemp...

The spindle generates force to segregate chromosomes at cell division. In mammalian cells, kinetochore-fibers connect chromosomes to the spindle. The dynamic spindle anchors kinetochore-fibers in space and time to move chromosomes. Yet, how it does so remains poorly understood as we lack tools to directly challenge this anchorage. Here, we adapt microneedle manipulation to exert local forces on the spindle with spatiotemporal control. Pulling on kinetochore-fibers reveals the preservation of local architecture in the spindle-center over seconds. Sister, but not neighbor, kinetochore-fibers remain tightly coupled, restricting chromosome stretching. Further, pulled kinetochore-fibers pivot around poles but not chromosomes, retaining their orientation within 3 μm of chromosomes. This local reinforcement has a 20 s lifetime, and requires the microtubule crosslinker PRC1. Together, these observations indicate short-lived, specialized reinforcement in the spindle center. This could help protect chromosome attachments from transient forces while allowing spindle remodeling, and chromosome movements, over longer timescales.

The spindle segregates chromosomes at cell division, and its task is a mechanical one. While we have a nearly complete list of spindle components, how their molecular-scale mechanics give rise to cellular-scale spindle architecture, mechanics, and function is not yet clear. Recent in vitro and in vivo measurements bring new levels of molecular and physical control and shed light on this question. Highlighting recent findings and open questions, we introduce the molecular force generators of the spindle, and discuss how they organize microtubules into diverse architectural modules and give rise to the emergent mechanics of the mammalian spindle. Throughout, we emphasize the breadth of space and time scales at play, and the feedback between spindle architecture, dynamics, and mechanics that drives robust function.