Cryptic pockets, that is, sites on protein targets that only become apparent when drugs bind, provide a promising alternative to classical binding sites for drug development. Here, we investigate the nature and dynamical properties of cryptic sites in four pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. We find that the studied cryptic sites do not correspond to local minima in the computed conformational free energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as Parallel Tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. Our observed mechanism of cryptic site formation is suggestive of an interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, we developed a novel Hamiltonian Replica Exchange-based method "SWISH" (Sampling Water Interfaces through Scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. We also addressed the issue of "false-positives" and propose a simple approach to distinguish them from druggable cryptic pockets. Our simulations, whose cumulative sampling time was more than 200 μs, help in clarifying the molecular mechanism of pocket formation, providing a solid basis for the choice of an efficient computational method.

Protein-protein interactions are at the heart of regulatory and signaling processes in the cell. In many interactions, one or both proteins are disordered before association. However, this disorder in the unbound state does not prevent many of these proteins folding to a well-defined, ordered structure in the bound state. Here we examine a typical system, where a small disordered protein (PUMA, p53 upregulated modulator of apoptosis) folds to an α-helix when bound to a groove on the surface of a folded protein (MCL-1, induced myeloid leukemia cell differentiation protein). We follow the association of these proteins using rapid-mixing stopped flow, and examine how the kinetic behavior is perturbed by denaturant and carefully chosen mutations. We demonstrate the utility of methods developed for the study of monomeric protein folding, including β-Tanford values, Leffler α, Φ-value analysis, and coarse-grained simulations, and propose a self-consistent mechanism for binding. Folding of the disordered protein before binding does not appear to be required and few, if any, specific interactions are required to commit to association. The majority of PUMA folding occurs after the transition state, in the presence of MCL-1. We also examine the role of the side chains of folded MCL-1 that make up the binding groove and find that many favor equilibrium binding but, surprisingly, inhibit the association process.Protein folding | stopped flow | coarse-grained simulation | protein-protein interactions | BCL-2

We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field.

Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

Bovines have evolved a subset of antibodies with ultra-long CDRH3 regions that harbour cysteine-rich knob domains. To produce high affinity peptides, we previously isolated autonomous 3-6 kDa knob domains from bovine antibodies. Here, we show that binding of four knob domain peptides elicits a range of effects on the clinically validated drug target complement C5. Allosteric mechanisms predominated, with one peptide selectively inhibiting C5 cleavage by the alternative pathway C5 convertase, revealing a targetable mechanistic difference between the classical and alternative pathway C5 convertases. Taking a hybrid biophysical approach, we present C5-knob domain co-crystal structures and, by solution methods, observed allosteric effects propagating >50 Å from the binding sites. This study expands the therapeutic scope of C5, presents new inhibitors and introduces knob domains as new, low molecular weight antibody fragments, with therapeutic potential.

Huntington disease is a neurodegenerative disease characterized by a polymorphic tract of polyglutamine repeats in exon 1 of the huntingtin protein, which is thought to be responsible for protein aggregation and neuronal death. The polyglutamine tract is preceded by a 17-residue sequence that is intrinsically disordered. This region is subject to phosphorylation, acetylation and other post-translational modifications in vivo, which modulate its secondary structure, aggregation and, subcellular localization. We used Molecular Dynamics simulations with a novel Hamiltonian-replica-exchange-based enhanced sampling method, SWISH, and an optimal combination of water and protein force fields to study the effects of phosphorylation and acetylation as well as cross-talk between these modifications on the huntingtin N-terminus. The simulations, validated by circular dichroism, were used to formulate a mechanism by which the modifications influence helical conformations. Our findings have implications for understanding the structural basis underlying the effect of PTMs in the aggregation and cellular properties of huntingtin.

Thalidomide and its derivatives are powerful cancer therapeutics that are among the best-understood molecular glue degraders (MGDs). These drugs selectively reprogram the E3 ubiquitin ligase cereblon (CRBN) to commit target proteins for degradation by the ubiquitin-proteasome system. MGDs create novel recognition interfaces on the surface of the E3 ligase that engage in induced protein-protein interactions with neosubstrates. Molecular insight into their mechanism of action opens exciting opportunities to engage a plethora of targets through a specific recognition motif, the G-loop. Our analysis shows that current CRBN-based MGDs can in principle recognize over 2,500 proteins in the human proteome that contain a G-loop. We review recent advances in tuning the specificity between CRBN and its MGD-induced neosubstrates and deduce a set of simple rules that govern these interactions. We conclude that rational MGD design efforts will enable selective degradation of many more proteins, expanding this therapeutic modality to more disease areas. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 64 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

<div><div><div><p>Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.<br></p></div></div></div>