Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning

Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning

L2 β-lactamases, serine-based class A β-lactamases expressed by Stenotrophomonas maltophilia, play a pivotal role in antimicrobial resistance (AMR). However, limited studies have been conducted on these important enzymes. To understand the coevolutionary dynamics of L2 β-lactamase, innovative computational methodologies, including adaptive sampling molecular dynamics simulations, and deep learning methods (convolutional variational autoencoders and BindSiteS-CNN) explored conformational changes and correlations within the L2 β-lactamase family together with other representative class A enzymes including SME-1 and KPC-2. This work also investigated the potential role of hydrophobic nodes and binding site residues in facilitating the functional mechanisms. The convergence of analytical approaches utilized in this effort yielded comprehensive insights into the dynamic behavior of the β-lactamases, specifically from an evolutionary standpoint. In addition, this analysis presents a promising approach for understanding how the class A β-lactamases evolve in response to environmental pressure and establishes a theoretical foundation for forthcoming endeavors in drug development aimed at combating AMR.

Phenotypic consequences of GBA1 pathological variant R463C (p.R502C)

Gaucher disease (GD) is an autosomal recessively inherited lysosomal storage disorder caused by biallelic pathological variants in the GBA1 gene. Patients present along a broad clinical spectrum, and phenotypes are often difficult to predict based on genotype alone. The variant R463C (p.Arg502Cys) exemplifies this challenge. To better characterize its different clinical presentations, we examined the records of 25 current and historical patients evaluated at the National Institutes of Health. Nine patients were classified as GD1, 14 were classified as GD3, and two had an ambiguous diagnosis between GD1 and GD3. In addition, we reviewed the published literature in PubMed and Web of Science through December 2023, identifying 62 cases with an R463C variant from 18 countries. Within the NIH cohort, the most common second variants were N370S (p.N409S) and L444P (p.L483P). R463C/L444P was encountered in patients with GD1 and GD3 in both the NIH cohort and worldwide. In the literature, R463C/R463C was also reported in both GD1 and GD3, although sparse phenotypic information was shared. Often the phenotype reflected what might be predicted for the second mutant allele. This diversity of phenotypes emphasizes the need for longitudinal follow-up to assess symptom development and neurological involvement.

Targeting neuronal lysosomal dysfunction caused by β-glucocerebrosidase deficiency with an enzyme-based brain shuttle construct

Mutations in glucocerebrosidase cause the lysosomal storage disorder Gaucher's disease and are the most common risk factor for Parkinson's disease. Therapies to restore the enzyme's function in the brain hold great promise for treating the neurological implications. Thus, we developed blood-brain barrier penetrant therapeutic molecules by fusing transferrin receptor-binding moieties to β-glucocerebrosidase (referred to as GCase-BS). We demonstrate that these fusion proteins show significantly increased uptake and lysosomal efficiency compared to the enzyme alone. In a cellular disease model, GCase-BS rapidly rescues the lysosomal proteome and lipid accumulations beyond known substrates. In a mouse disease model, intravenous injection of GCase-BS leads to a sustained reduction of glucosylsphingosine and can lower neurofilament-light chain plasma levels. Collectively, these findings demonstrate the potential of GCase-BS for treating GBA1-associated lysosomal dysfunction, provide insight into candidate biomarkers, and may ultimately open a promising treatment paradigm for lysosomal storage diseases extending beyond the central nervous system.

Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase

β-Lactam antibiotics are the most important and widely used antibacterial agents across the world. However, the widespread dissemination of β-lactamases among pathogenic bacteria limits the efficacy of β-lactam antibiotics. This has created a major public health crisis. The use of β-lactamase inhibitors has proven useful in restoring the activity of β-lactam antibiotics, yet, effective clinically approved inhibitors against class B metallo-β-lactamases are not available. L1, a class B3 enzyme expressed by Stenotrophomonas maltophilia, is a significant contributor to the β-lactam resistance displayed by this opportunistic pathogen. Structurally, L1 is a tetramer with two elongated loops, α3-β7 and β12-α5, present around the active site of each monomer. Residues in these two loops influence substrate/inhibitor binding. To study how the conformational changes of the elongated loops affect the active site in each monomer, enhanced sampling molecular dynamics simulations were performed, Markov State Models were built, and convolutional variational autoencoder-based deep learning was applied. The key identified residues (D150a, H151, P225, Y227, and R236) were mutated and the activity of the generated L1 variants was evaluated in cell-based experiments. The results demonstrate that there are extremely significant gating interactions between α3-β7 and β12-α5 loops. Taken together, the gating interactions with the conformational changes of the key residues play an important role in the structural remodeling of the active site. These observations offer insights into the potential for novel drug development exploiting these gating interactions.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Clinical Sphingolipids Pathway in Parkinson’s Disease: From GCase to Integrated-Biomarker Discovery

Alterations in the sphingolipid metabolism of Parkinson’s Disease (PD) could be a potential diagnostic feature. Only around 10–15% of PD cases can be diagnosed through genetic alterations, while the remaining population, idiopathic PD (iPD), manifest without validated and specific biomarkers either before or after motor symptoms appear. Therefore, clinical diagnosis is reliant on the skills of the clinician, which can lead to misdiagnosis. IPD cases present with a spectrum of non-specific symptoms (e.g., constipation and loss of the sense of smell) that can occur up to 20 years before motor function loss (prodromal stage) and formal clinical diagnosis. Prodromal alterations in metabolites and proteins from the pathways underlying these symptoms could act as biomarkers if they could be differentiated from the broad values seen in a healthy age-matched control population. Additionally, these shifts in metabolites could be integrated with other emerging biomarkers/diagnostic tests to give a PD-specific signature. Here we provide an up-to-date review of the diagnostic value of the alterations in sphingolipids pathway in PD by focusing on the changes in definitive PD (postmortem confirmed brain data) and their representation in “probable PD” cerebrospinal fluid (CSF) and blood. We conclude that the trend of holistic changes in the sphingolipid pathway in the PD brain seems partly consistent in CSF and blood, and could be one of the most promising pathways in differentiating PD cases from healthy controls, with the potential to improve early-stage iPD diagnosis and distinguish iPD from other Parkinsonism when combined with other pathological markers.

Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.

Are Lysosomes Potential Therapeutic Targets for Parkinson's Disease?

Parkinson´s Disease (PD) is the second most common neurodegenerative disorder, affecting ~2-3% of the population over 65 years old. In addition to progressive degeneration of nigrostriatal neurons, the histopathological feature of PD is the accumulation of misfolded α-synuclein protein in abnormal cytoplasmatic inclusions, known as Lewy Bodies (LBs). Recently, Genome-Wide Association Studies (GWAS) have indicated a clear association of variants within several lysosomal genes with risk for PD. Newly evolving data have been shedding light on the relationship between lysosomal dysfunction and alpha-synuclein aggregation. Defects in lysosomal enzymes could lead to the insufficient clearance of neurotoxic protein materials, possibly leading to selective degeneration of dopaminergic neurons. Specific modulation of lysosomal pathways and their components could be considered a novel opportunity for therapeutic intervention for PD. The purpose of this review is to illustrate lysosomal biology and describe the role of lysosomal dysfunction in PD pathogenesis. Finally, the most promising novel therapeutic approaches designed to modulate lysosomal activity, as a potential disease-modifying treatment for PD will be highlighted.

Mapping Sphingolipid Metabolism Pathways during Phagosomal Maturation

Phagocytosis is an important physiological process, which, in higher organisms, is a means of fighting infections and clearing cellular debris. During phagocytosis, detrimental foreign particles (e.g. pathogens and apoptotic cells) are engulfed by phagocytes (e.g. macrophages), enclosed in membrane-bound vesicles called phagosomes, and transported to the lysosome for eventual detoxification. During this well-choreographed process, the nascent phagosome (also called early phagosome, EP) undergoes a series of spatiotemporally regulated changes in its protein and lipid composition and matures into a late phagosome (LP), which subsequently fuses with the lysosomal membrane to form the phagolysosome. While several elegant proteomic studies have identified the role of unique proteins during phagosomal maturation, the corresponding lipidomic studies are sparse. Recently, we reported a comparative lipidomic analysis between EPs and LPs and showed that ceramides are enriched on the LPs. Further, we found that this ceramide accumulation on LPs was orchestrated by ceramide synthase 2, inhibition of which hampers phagosomal maturation. Following up on this study, here, using biochemical assays, we first show that the increased ceramidase activity on EPs also significantly contributes to the accumulation of ceramides on LPs. Next, leveraging lipidomics, we show that de novo ceramide synthesis does not significantly contribute to the ceramide accumulation on LPs, while concomitant to increased ceramides, glucosylceramides are substantially elevated on LPs. We validate this interesting finding using biochemical assays and show that LPs indeed have heightened glucosylceramide synthase activity. Taken together, our studies provide interesting insights and possible new roles of sphingolipid metabolism during phagosomal maturation.

Inter-Subunit Dynamics Controls Tunnel Formation During the Oxygenation Process in Hemocyanin Hexamers

Hemocyanin from horseshoe crab in its active form is a homo-hexameric protein. It exists in open and closed conformations when transitioning between deoxygenated and oxygenated states. Here, we present a detailed dynamic atomistic investigation of the oxygenated and deoxygenated states of the hexameric hemocyanin using explicit solvent molecular dynamics simulations. We focus on the variation in solvent cavities and the formation of tunnels in the two conformational states. By employing principal component analysis and CVAE-based deep learning, we are able to differentiate between the dynamics of the deoxy- and oxygenated states of hemocyanin. Finally, our results identify the deoxygenated open conformation, which adopts a stable, closed conformation after the oxygenation process.

The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases

Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A β-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A β-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A β-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A β-lactamases.

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Longitudinal clinical, cognitive, and neuroanatomical changes over 5 years in GBA-positive Parkinson's disease patients

OBJECTIVE

To study the longitudinal disease course of Parkinson's disease (PD) patients with glucocerebrosidase (GBA) mutation (GBA-positive) compared to PD non-carriers (GBA-negative) along a 5-year follow-up, evaluating changes in clinical and cognitive outcomes, cortical thickness, and gray-matter (GM) volumes.

METHODS

Ten GBA-positive and 20 GBA-negative PD patients underwent clinical, neuropsychological, and MRI assessments (cortical thickness and subcortical, hippocampal, and amygdala volumes) at study entry and once a year for 5 years. At baseline and at the last visit, each group of patients was compared with 22 age-matched healthy controls. Clinical, cognitive, and MRI features were compared between groups at baseline and over time.

RESULTS

At baseline, GBA-positive and GBA-negative PD patients had similar clinical and cognitive profiles. Compared to GBA-negative and controls, GBA-positive patients showed cortical thinning of left temporal, parietal, and occipital gyri. Over time, compared to GBA-negative, GBA-positive PD patients progressed significantly in motor and cognitive symptoms, and showed a greater pattern of cortical thinning of posterior regions, and frontal and orbito-frontal cortices. After 5 years, compared to controls, GBA-negative PD patients showed a pattern of cortical thinning similar to that showed by GBA-positive cases at baseline. The two groups of patients showed similar patterns of subcortical, hippocampal, and amygdala volume loss over time.

CONCLUSIONS

Compared to GBA-negative PD, GBA-positive patients experienced a more rapid motor and cognitive decline together with a greater, earlier and faster cortical thinning. Cortical thickness measures may be a useful tool for monitoring and predicting PD progression in accordance with the genetic background.

Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites

β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.

Engineering of Saposin C Protein Chimeras for Enhanced Cytotoxicity and Optimized Liposome Binding Capability

Saposin C (sapC) is a lysosomal, peripheral-membrane protein displaying liposome fusogenic capabilities. Proteoliposomes of sapC and phosphatidylserine have been shown to be toxic for cancer cells and are currently on clinical trial to treat glioblastoma. As proof-of-concept, we show two strategies to enhance the applications of sapC proteoliposomes: (1) Engineering chimeras composed of sapC to modulate proteoliposome function; (2) Engineering sapC to modify its lipid binding capabilities. In the chimera design, sapC is linked to a cell death-inducing peptide: the BH3 domain of the Bcl-2 protein PUMA. We show by solution NMR and dynamic light scattering that the chimera is functional at the molecular level by fusing liposomes and by interacting with prosurvival Bcl-xL, which is PUMA’s known mechanism to induce cell death. Furthermore, sapC-PUMA proteoliposomes enhance cytotoxicity in glioblastoma cells compared to sapC. Finally, the sapC domain of the chimera has been engineered to optimize liposome binding at pH close to physiological values as protein–lipid interactions are favored at acidic pH in the native protein. Altogether, our results indicate that the properties of sapC proteoliposomes can be modified by engineering the protein surface and by the addition of small peptides as fusion constructs.

Using GANs with adaptive training data to search for new molecules

The process of drug discovery involves a search over the space of all possible chemical compounds. Generative Adversarial Networks (GANs) provide a valuable tool towards exploring chemical space and optimizing known compounds for a desired functionality. Standard approaches to training GANs, however, can result in mode collapse, in which the generator primarily produces samples closely related to a small subset of the training data. In contrast, the search for novel compounds necessitates exploration beyond the original data. Here, we present an approach to training GANs that promotes incremental exploration and limits the impacts of mode collapse using concepts from Genetic Algorithms. In our approach, valid samples from the generator are used to replace samples from the training data. We consider both random and guided selection along with recombination during replacement. By tracking the number of novel compounds produced during training, we show that updates to the training data drastically outperform the traditional approach, increasing potential applications for GANs in drug discovery.

Ambroxol increases glucocerebrosidase (GCase) activity and restores GCase translocation in primary patient-derived macrophages in Gaucher disease and Parkinsonism

Mutations in the glucocerebrosidase gene (GBA) encoding the lysosomal enzyme glucocerebrosidase (GCase) cause Gaucher disease (GD) and are the most commonly known genetic risk factor for Parkinson disease (PD). Ambroxol is one of the most effective pharmacological chaperones of GCase. Fourteen GD patients, six PD patients with mutations in the GBA gene (GBA-PD), and thirty controls were enrolled. GCase activity and hexosylsphingosine (HexSph) concentration were measured in dried blood and macrophage spots using liquid chromatography coupled with tandem mass spectrometry. The effect of ambroxol on GCase translocation to lysosomes was assessed using confocal microscopy. The results showed that ambroxol treatment significantly increased GCase activity in cultured macrophages derived from patient blood monocytic cell (PBMC) of GD (by 3.3-fold) and GBA-PD patients (by 3.5-fold) compared to untreated cells (p < 0.0001 and p < 0.0001, respectively) four days after cultivation. Ambroxol treatment significantly reduced HexSph concentration in GD (by 2.1-fold) and GBA-PD patients (by 1.6-fold) (p < 0.0001 and p < 0.0001, respectively). GD macrophage treatment resulted in increased GCase level and increased enzyme colocalization with the lysosomal marker LAMP2. The possible binding modes of ambroxol to mutant GCase carrying N370S amino acid substitution at pH 4.7 were examined using molecular docking and molecular dynamics simulations. The ambroxol position characterized by minimal binding free energy was observed in close vicinity to the residue, at position 370. Taken together, these data showed that PBMC-derived macrophages could be used for assessing ambroxol therapy response for GD patients and also for GBA-PD patients.

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships-however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

The interplay between Glucocerebrosidase, α-synuclein and lipids in human models of Parkinson's disease

Mutations in the gene GBA, encoding glucocerebrosidase (GCase), are the highest genetic risk factor for Parkinson's disease (PD). GCase is a lysosomal glycoprotein responsible for the hydrolysis of glucosylceramide into glucose and ceramide. Mutations in GBA cause a decrease in GCase activity, stability and protein levels which in turn lead to the accumulation of GCase lipid substrates as well as α-synuclein (αS) in vitro and in vivo. αS is the main constituent of Lewy bodies found in the brain of PD patients and an increase in its levels was found to be associated with a decrease in GCase activity/protein levels in vitro and in vivo. In this review, we describe the reported biophysical and biochemical changes that GBA mutations can induce in GCase activity and stability as well as the current overview of the levels of GCase protein/activity, αS and lipids measured in patient-derived samples including post-mortem brains, stem cell-derived neurons, cerebrospinal fluid, blood and fibroblasts as well as in SH-SY5Y cells. In particular, we report how the levels of αS and lipids are affected by/correlated to significant changes in GCase activity/protein levels and which cellular pathways are activated or disrupted by these changes in each model. Finally, we review the current strategies used to revert the changes in the levels of GCase activity/protein, αS and lipids in the context of PD.

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

The role of disulfide bonds in a Solanum tuberosum saposin-like protein investigated using molecular dynamics

The Solanum tuberosum plant specific insert (StPSI) has a defensive role in potato plants, with the requirements of acidic pH and anionic lipids. The StPSI contains a set of three highly conserved disulfide bonds that bridge the protein’s helical domains. Removal of these bonds leads to enhanced membrane interactions. This work examined the effects of their sequential removal, both individually and in combination, using all-atom molecular dynamics to elucidate the role of disulfide linkages in maintaining overall protein tertiary structure. The tertiary structure was found to remain stable at both acidic (active) and neutral (inactive) pH despite the removal of disulfide linkages. The findings include how the dimer structure is stabilized and the impact on secondary structure on a residue-basis as a function of disulfide bond removal. The StPSI possesses an extensive network of inter-monomer hydrophobic interactions and intra-monomer hydrogen bonds, which is likely the key to the stability of the StPSI by stabilizing local secondary structure and the tertiary saposin-fold, leading to a robust association between monomers, regardless of the disulfide bond state. Removal of disulfide bonds did not significantly impact secondary structure, nor lead to quaternary structural changes. Instead, disulfide bond removal induces regions of amino acids with relatively higher or lower variation in secondary structure, relative to when all the disulfide bonds are intact. Although disulfide bonds are not required to preserve overall secondary structure, they may have an important role in maintaining a less plastic structure within plant cells in order to regulate membrane affinity or targeting.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

Empirical profile Bayesian estimation for extrapolation of historical adult data to pediatric drug development

For pediatric drug development, the clinical effectiveness of the study medication for the adult population has already been demonstrated. Given the fact that it is usually not feasible to enroll a large number of pediatric patients, appropriately leveraging historical adult data into pediatric evaluation may be critical to success of pediatric drug development. In this manuscript, we propose a new empirical Bayesian approach, profile Bayesian estimation, to dynamically borrow adult information to the evaluation of treatment effect in pediatric patients. The new approach demonstrates an attractive balance between type I error control and power gain under the transfer-ability assumption that the pediatric treatment effect size may differ from the adult treatment effect size. The decision making boundary mimics the real-world practice in pediatric drug development. In addition, the posterior mean of the proposed empirical profile Bayesian is an unbiased estimator of the true pediatric treatment effect. We compare our approach to robust mixture prior with prior weight for informative borrowing set to 0.5 or 0.9, regular Bayesian approach, and frequentist for both type I error and power.

Long-Term Outcomes of Genetic Parkinson's Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects 1–2% of people by the age of 70 years. Age is the most important risk factor, and most cases are sporadic without any known environmental or genetic causes. Since the late 1990s, mutations in the genes SNCA, PRKN, LRRK2, PINK1, DJ-1, VPS35, and GBA have been shown to be important risk factors for PD. In addition, common variants with small effect sizes are now recognized to modulate the risk for PD. Most studies in genetic PD have focused on finding new genes, but few have studied the long-term outcome of patients with the specific genetic PD forms. Patients with known genetic PD have now been followed for more than 20 years, and we see that they may have distinct and different prognoses. New therapeutic possibilities are emerging based on the genetic cause underlying the disease. Future medication may be based on the pathophysiology individualized to the patient’s genetic background. The challenge is to find the biological consequences of different genetic variants. In this review, the clinical patterns and long-term prognoses of the most common genetic PD variants are presented.

Simple and Complex Sugars in Parkinson's Disease: a Bittersweet Taste

Neuronal homeostasis depends on both simple and complex sugars (the glycoconjugates), and derangement of their metabolism is liable to impair neural function and lead to neurodegeneration. Glucose levels boost glycation phenomena, a wide series of non-enzymatic reactions that give rise to various intermediates and end-products that are potentially dangerous in neurons. Glycoconjugates, including glycoproteins, glycolipids, and glycosaminoglycans, contribute to the constitution of the unique features of neuron membranes and extracellular matrix in the nervous system. Glycosylation defects are indeed frequently associated with nervous system disturbances and neurodegeneration. Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms associated with the loss of dopaminergic neurons in the pars compacta of the substantia nigra. Neurons present intracytoplasmic inclusions of α-synuclein aggregates involved in the disease pathogenesis together with the impairment of the autophagy-lysosome function, oxidative stress, and defective traffic and turnover of membrane components. In the present review, we selected relevant recent contributions concerning the direct involvement of glycation and glycosylation in α-synuclein stability, impaired autophagy and lysosomal function in PD, focusing on potential models of PD pathogenesis provided by genetic variants of glycosphingolipid processing enzymes, especially glucocerebrosidase (GBA). Moreover, we collected data aimed at defining the glycomic profile of PD patients as a tool to help in diagnosis and patient subtyping, as well as those pointing to sugar-related compounds with potential therapeutic applications in PD.

The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

The biochemical basis of interactions between Glucocerebrosidase and alpha-synuclein in GBA1 mutation carriers

The discovery of genes involved in familial as well as sporadic forms of Parkinson disease (PD) constitutes an important milestone in understanding this disorder's pathophysiology and potential treatment. Among these genes, GBA1 is one of the most common and well-studied, but it is still unclear how mutations in GBA1 translate into an increased risk for developing PD. In this review, we provide an overview of the biochemical and structural relationship between GBA1 and PD to help understand the recent advances in the development of PD therapies intended to target this pathway.

Videira P, Valadão G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.