Using molecular dynamics and enhanced sampling techniques to find cryptic druggable pockets in proteins of pharmaceutical interest

Cryptic pockets are sites on protein targets that are hidden in the unliganded form and only become apparent when drugs bind. These sites provide a promising alternative to classical substrate binding sites for drug development, especially when the latter are not druggable. In this thesis I investigate the nature and dynamical properties of cryptic sites in a number of pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. I found 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. The observed mechanism of cryptic site formation is suggestive of interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, I 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. In addition, we revisit the rather ill-defined concept of the cryptic pockets in order to propose an alternative measurable interpretation. I outline how the new practical definition can be applied to the ligandable targets reported in the PDB, in order to provide a consistent data-driven view on crypticity and how it may impact the drug discovery. This thesis presents a comprehensive study of the cryptic pocket phenomenon: from understanding the nature of their formation to novel detection methodology, and towards understanding their global significance in drug discovery

Interplay between partner and ligand facilitates the folding and binding of an intrinsically disordered protein.

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.This is the final version. It was first published online by PNAS via http://dx.doi.org/10.1073/pnas.140912211

Assessment of the model refinement category in CASP12

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

Unveiling a novel transient druggable pocket in BACE-1 through molecular simulations: conformational analysis and binding mode of multisite inhibitors

The critical role of BACE-1 in the formation of neurotoxic ß-amyloid peptides in the brain makes it an attractive target for an efficacious treatment of Alzheimer’s disease. However, the development of clinically useful BACE-1 inhibitors has proven to be extremely challeng- ing. In this study we examine the binding mode of a novel potent inhibitor (compound 1, with IC50 80 nM) designed by synergistic combination of two fragments—huprine and rhein— that individually are endowed with very low activity against BACE-1. Examination of crystal structures reveals no appropriate binding site large enough to accommodate 1. Therefore we have examined the conformational flexibility of BACE-1 through extended molecular dynamics simulations, paying attention to the highly flexible region shaped by loops 8–14, 154–169 and 307–318. The analysis of the protein dynamics, together with studies of pocket druggability, has allowed us to detect the transient formation of a secondary binding site, which contains Arg307 as a key residue for the interaction with small molecules, at the edge of the catalytic cleft. The formation of this druggable “floppy” pocket would enable the bind- ing of multisite inhibitors targeting both catalytic and secondary sites. Molecular dynamics simulations of BACE-1 bound to huprine-rhein hybrid compounds support the feasibility of this hypothesis. The results provide a basis to explain the high inhibitory potency of the two enantiomeric forms of 1, together with the large dependence on the length of the oligo- methylenic linker. Furthermore, the multisite hypothesis has allowed us to rationalize the inhibitory potency of a series of tacrine-chromene hybrid compounds, specifically regarding the apparent lack of sensitivity of the inhibition constant to the chemical modifications intro- duced in the chromene unit. Overall, these findings pave the way for the exploration of novel functionalities in the design of optimized BACE-1 multisite inhibitors

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

Kidney failure is frequently observed during and after COVID-19, but it remains elusive whether this is a direct effect of the virus. Here, we report that SARS-CoV-2 directly infects kidney cells and is associated with increased tubule-interstitial kidney fibrosis in patient autopsy samples. To study direct effects of the virus on the kidney independent of systemic effects of COVID-19, we infected human-induced pluripotent stem-cell-derived kidney organoids with SARS-CoV-2. Single-cell RNA sequencing indicated injury and dedifferentiation of infected cells with activation of profibrotic signaling pathways. Importantly, SARS-CoV-2 infection also led to increased collagen 1 protein expression in organoids. A SARS-CoV-2 protease inhibitor was able to ameliorate the infection of kidney cells by SARS-CoV-2. Our results suggest that SARS-CoV-2 can directly infect kidney cells and induce cell injury with subsequent fibrosis. These data could explain both acute kidney injury in COVID-19 patients and the development of chronic kidney disease in long COVID

An empirical analysis of futures pricing in the Nordic electricity market

The aim of this paper is to study the pricing of futures contracts relative to expected future spot prices in the Nordic electricity market. Data set of 1–6 weeks ahead weekly and 1–6 months ahead monthly futures is used to identify whether futures premium is present in the Nordic market. The findings reveal that short term futures contracts are unbiased predictors of the future spot prices. However, at longer maturities (5–6 weeks for weekly contracts and 2–6 months for monthly ones) significant time-varying futures premium exists. This premium is positive on average and doesn’t vary significantly by seasons. Furthermore, the magnitude of the premium is found to be substantially higher than in other commodity markets. Finally, a link between the futures premium and measures of risk (reservoir level, variance and skewness of the spot prices) is tested to find out whether the premium exists as a compensation for risk or mispricing in the market. The futures premium is found to be positively related to the reservoir level, while relationship with variance and skewness of the spot prices is mostly insignificant. As the magnitude of the premium is too high to be explained by this sole risk factor, it is concluded that mispricing exists in futures contracts with maturities longer than 4 weeks in the Nordic electricity market.nhhma

Assessment of the model refinement category in CASP12.

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

Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations

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

Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations

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