This study uses label-free methods to determine the binding interactions of lysophosphatidic acid (LPA) with bovine and human serum albumin (BSA and HSA). LPA is a bioactive lysophospholipid (LysoPL) that signals through a G-protein-coupled receptor (GPCR). Plasma LPAs are primarily carried by albumin; however, their binding interactions with the carrier protein (HSA) are not as well studied as those with fatty acids, drugs, or metal ions. Therefore, the aim of this study is to determine the binding sites of LPA in serum albumin through spectroscopic methods. Intrinsic fluorescence quenching experiments in conjunction with a label-free, free solution light interferometric assay have been employed to determine the binding KDs of LPAs to fatty acid free BSA (KD = 6-191 nM) and HSA (uncertain KD ∼ 84 nM). Our study demonstrates that structurally homologous defatted BSA and HSA behave differently with LPA in contrast to positively charged lipids, neutral lipids or other LysoPLs. LPA interaction with BSA resulted in 20% fluorescence quenching, whereas enhancement of fluorescence emission was observed for HSA, which is in sharp contrast to the reported results for other lysophospholipids (such as LPC or LPE), suggesting a different transport mechanism for LPA in plasma. This study further demonstrates that fatty acids are important in stabilizing HSA to transport these bioactive lipids.

Accurate quantum chemical treatment of covalently bonded biomolecules using fragment-based approaches remains a major challenge as fragmenting across covalent bonds disrupts essential electron correlation and long-range polarization. Importantly, a few of the previously developed fragment-based methods can accurately and efficiently treat both noncovalently and covalently bonded molecular systems, highlighting a significant gap in the field. A key novelty of the grid-adapted many-body analysis (GAMA) framework is that it overcomes this limitation. Building on our earlier work establishing GAMA for noncovalent systems, we extend this framework to covalently bonded biomolecules and develop GAMA2, a fully automated protocol that integrates a simple grid-based fragmentation scheme, many-body expansion with overlapping fragments truncated at two-body order, and a multilayer low-level correction. Across diverse peptides, ranging from flexible bioactive motifs to structured 18-mer helixes, GAMA2 reproduces supersystem MP2/6-311G(d,p) energies with unsigned absolute errors of ∼0.01-4 kcal/mol for flexible small- and medium-size peptide systems using HF as a low level of theory and ∼2-5 kcal/mol for complicated helical-type peptide structures when using M06-2X/6-311G(d,p) as the low-level method, showing substantial improvement over HF using accurate DFT-based methods. In addition to this highly accurate results, GAMA2 also demonstrate a significant computational speedup with HF as a super system low-level method relative to the reference full MP2 calculation, establishing GAMA2 as a scalable, efficient, and systematically improvable route for correlated quantum chemical calculations on biomolecular systems.

Kinases are pivotal in regulating signaling pathways, and their dysregulation is associated with various diseases, including cancers, making them prime therapeutic targets. Bruton's Tyrosine Kinase (BTK) is crucial for B-cell development, and BTK inhibitors have proven effective in treating B-cell malignancies like Chronic Lymphocytic Leukemia (CLL). Non-covalent inhibitors offer a promising therapeutic approach by avoiding covalent bond formation with the protein. However, therapeutic resistance due to BTK mutations in the catalytic domain has led to relapses and refractory cases in CLL, highlighting the need for a deeper understanding of these mutations' impact on treatment outcomes. This study investigates the effects of four prevalent single-point mutations-A428D, T474I, C481S, and L528W-within the catalytic domain of BTK. Using 12.5 microseconds of molecular dynamics simulations and computational drug discovery methods, we examine how these mutations influence the binding affinities and interactions of non-covalent BTK inhibitors. Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) analysis showed that mutant forms of BTK significantly decreased ligand binding free energies compared to the wild types, with a few exceptions. With pocket volume and solvent-accessible surface area analysis, we also show that mutations reduce the binding pocket volume, forcing the inhibitors to move out of the pocket, disrupting the critical non-covalent interactions of the inhibitors with mutant BTK. This confirms the experimental and clinical observations of why these BTK mutations impair inhibitor efficacy fostering drug resistance. Our results offer vital insights for designing next-generation BTK inhibitors to overcome resistance and enhance therapeutic outcomes in B-cell malignancies.

The main protease (Mpro) is essential for the replication of SARS-CoV-2, making it one of the major therapeutic targets for COVID-19 treatment. Here, we explored the conformational dynamics and energetics of the catalytic residue His41 in Mpro, as revealed by a rare conformational shift observed in the cocrystal structures of Mpro bound by certain inhibitors. Using steered molecular dynamics combined with umbrella sampling, we demonstrated that π-cation interactions between these inhibitors and the ionized catalytic dyad significantly reduced the energy barrier for the conformational flip of the His41 side chain. To further investigate the structure-activity relationship linked to this conformational change, we designed and synthesized a series of covalent inhibitors that control His41 flipping. Among these, compound H102-7 exhibited remarkable inhibitory activity with an IC50 of 5 nM. Drug resistance studies revealed that these inhibitors displayed improved resistance profiles compared to the clinically approved Mpro covalent inhibitor, Nirmatrelvir. This study integrates computational simulations, medicinal chemistry, and molecular biology to uncover an interesting allosteric effect of a key catalytic residue of SARS-CoV-2 Mpro and yields new promising molecules for the further development of Mpro-targeted therapeutic intervention.

Performing ab initio calculations on large molecular systems is challenging. Fragment-based quantum chemistry (FBQC) provides an efficient alternative. We introduce GAMA(Grid-Adapted-Manybody Analysis), a many-body based expansion (MBE) method that fragments a molecular system into grid-based monomers. Additionally, GAMA employs an ONIOM-style multilevel framework to capture long-range interactions. We applied GAMA to water clusters, demonstrating that increasing monomer size improves accuracy in reproducing unfragmented MP2 energies across various basis sets. Counterpoise correction was found essential for mitigating Basis Set Superposition Error (BSSE) and ensuring accuracy. Compared to direct MP2 calculations, GAMA significantly reduces computational cost while maintaining high accuracy. These results highlight GAMA's potential for accurate and efficient high-level quantum chemistry calculations on water clusters towards enabling quantum mechanics for large molecular systems.

Protein-protein interactions (PPIs) have become increasingly attractive as therapeutic targets due to their central role in regulating cellular functions. Despite computational advancements, accurately estimating binding free energies for PPIs remains challenging due to the dynamic and critically solvent-exposed nature of their interfaces. In this study, we present MnM-W-MMGBSA (i.e., MM-GBSA with Mix-and-Match sampling and water inclusion), a method that addresses these challenges by incorporating both conformational flexibility and interfacial solvation effects. We thoroughly demonstrated the applicability of MnM-W-MMGBSA across a diverse set of 20 PPI systems and validated its robustness using two distinct and rigorous simulation schemes. We demonstrate that our protocol improves correlation with experimental binding affinities─from 47% to 70% with MnM sampling alone for standard MM-GBSA, and up to 89% when interfacial water molecules are included. Our approach underscores the pivotal role of individual protein dynamics in validating the concept of "destabilization of individual proteins in the unbound form." Specifically, we show that in explicit solvent, such destabilization leads to a loss of native structure, suggesting that excessive conformational sampling may compromise the accuracy of binding affinity predictions. Furthermore, the critical role of intrinsically disordered regions in the interface of PPIs, as well as the impact of the MnM approach in the pairwise per-residue energy decomposition, were also investigated. Finally, our implementation overcomes the limitations of the gmx_MMPBSA tool for incorporating explicit solvent molecules from MD simulation trajectories into the complex/receptor during MM-GBSA calculation, providing an automated and reproducible workflow using GROMACS with AmberTools to enable efficient high-throughput screening of protein-protein complexes. The protocol is robust, computationally efficient, and applicable to a broad range of PPIs. Overall, our protocol offers a practical and physically meaningful alternative for estimating the binding affinities of PPIs and provides a valuable tool for advancing peptide-based drug discovery.

Accurate quantum mechanical modelling of large molecular systems remains a formidable challenge due to the steep computational scaling of conventional methods. Fragment-based quantum chemistry approaches offer a more efficient alternative, but their accuracy is often limited by the neglect of long-range inter-fragment electrostatic interactions. To overcome this, we introduce Electrostatically Embedded Grid-Adapted Many-Body Analysis (EE-GAMA), a fragment-based quantum chemistry method that combines systematic many-body energy decomposition with electrostatic embedding based on overlapping spatial grid-based fragments. In EE-GAMA, electrostatic charge embedding is implemented within the Many-Overlapping Body (MOB) expansion framework, an approach rarely explored in previous studies. Each fragment is embedded in the electrostatic field generated by background point charges derived from Mulliken, Hirshfeld, or CM5 charge models, and both Geometry Dependent (GD) and Geometry Independent (GI) embedding schemes are investigated. Our primary goal is to accurately reproduce full-system energies at the MP2/6-311G(d,p) level. Benchmark calculations on neutral water clusters and hydronium clusters show that EE-GAMA achieves near full-system MP2 accuracy and significantly outperforms the non-embedded GAMA method. In charged systems, EE-GAMA effectively captures long-range electrostatics and consistently demonstrates that GD embedding offers greater accuracy than GI, underscoring the importance of environment-specific charge representation in highly polarized systems. While GI embedding offers computational simplicity, GD embedding provides enhanced reliability in such contexts. Overall, EE-GAMA offers an excellent balance between computational efficiency and accuracy and lays a strong foundation for the future development of advanced embedding schemes incorporating polarization and charge-transfer effects in large-scale quantum calculations.

Abstract Introduction The central nervous system (CNS) has a specialized barrier, the blood brain barrier (BBB), that tightly regulates the entry of circulating blood cells and molecules. The BBB is compromised in many neurodegenerative diseases, thus understanding BBB dysfunction is critical to treating neurodegenerative diseases, like multiple sclerosis (MS). CNS macrophages regulate the BBB and have potential for development as a cell therapy. Objectives In this study, we tested if umbilical cord blood derived macrophages (DUOC-01) could prevent BBB dysfunction and suppress the progression of disease in as mouse model of MS. Methods We utilized the experimental autoimmune encephalomyelitis (EAE) mouse model for MS. To initiate EAE, we immunized C57BL/6 mice with myelin oligodendrocyte glycoprotein peptide (MOG35-55) in complete Freund’s adjuvant. We manufactured DUOC-01 from cord blood as previously described and injected into the cerebrospinal fluid at the onset of EAE symptoms. We measured clinical paralysis, immune cell infiltration, and BBB integrity. Results DUOC-01 suppressed clinical scores over 21 days of EAE accompanied by decreased infiltration of leukocytes induced by the disease. Mice treated with DUOC-01 had less infiltrating neutrophils, and Ly6Chi and Ly6Clo macrophages in the lumbar spinal cord. Myeloid cell attracting chemokines: CCL2, CXCL1, and CCL4 were decreased in DUOC-01 treated group. Using immunohistochemistry, we determined tight junction proteins, ZO-1 and Claudin-5, on endothelial cells of BBB decreased in EAE and this was blocked by DUOC-01. To study the direct effects of DUOC-01 on brain endothelial cells, we set up primary mouse brain endothelial cell culture. In co-cultures, DUOC-01 suppressed secretion of CCL2, CXCL1, and CCL4 from activated endothelial cells. Using a transwell migration assay, we found DUOC-01 suppressed myeloid cell migration through activated endothelial cells. Discussion In this study, we determined DUOC-01 alleviated EAE, at least in part by suppressing the activation of brain endothelial cells. This restored BBB function, suppressed chemokine expression and immune cell infiltration. Overall, our data suggest that DUOC-01 could be beneficial in reducing neuroinflammation in neurodegenerative diseases, like MS.

Neuropsychiatric disorders such as major depressive disorders and schizophrenia are often associated with disruptions to the normal 24 h sleep wake cycle. Casein kinase 1 (CK1δ) is an integral part of the molecular machinery that regulates circadian rhythms. Starting from a cluster of bicyclic pyrazoles identified from a virtual screening effort, we utilized structure-based drug design to identify and reinforce a unique "hinge-flip" binding mode that provides a high degree of selectivity for CK1δ versus the kinome. Pharmacokinetics, brain exposure, and target engagement as measured by ex vivo autoradiography are described for advanced analogs.

Milk casein is regarded as source to release potential sleep-enhancing peptides. Although various casein hydrolysates exhibited sleep-enhancing activity, the underlying reason remains unclear. This study firstly revealed the structural features of potential sleep-enhancing peptides from casein hydrolysates analyzed through peptidomics and multivariate analysis. Additionally, a random forest model and a potential Tyr-based peptide library were established, and then those peptides were quantified to facilitate rapidly-screening. Our findings indicated that YP-, YI/L, and YQ-type peptides with 4-10 amino acids contributed more to higher sleep-enhancing activity of casein hydrolysates, due to their crucial structural features and abundant numbers. Furthermore, three novel strong sleep-enhancing peptides, YQKFPQY, YPFPGPIPN, and YIPIQY were screened, and their activities were validated in vivo. Molecular docking results elucidated the importance of the YP/I/L/Q- structure at the N-terminus of casein peptides in forming crucial hydrogen bond and it-alkyl interactions with His-102 and Asn-60, respectively in the GABAA receptor for activation.

Vitamin B12 (B12) deficiency causes neurological manifestations resembling multiple sclerosis (MS); however, a molecular explanation for the similarity is unknown. FTY720 (fingolimod) is a sphingosine 1-phosphate (S1P) receptor modulator and sphingosine analog approved for MS therapy that can functionally antagonize S1P1. Here, we report that FTY720 suppresses neuroinflammation by functionally and physically regulating the B12 pathways. Genetic and pharmacological S1P1 inhibition upregulates a transcobalamin 2 (TCN2)-B12 receptor, CD320, in immediate-early astrocytes (ieAstrocytes; a c-Fos-activated astrocyte subset that tracks with experimental autoimmune encephalomyelitis [EAE] severity). CD320 is also reduced in MS plaques. Deficiency of CD320 or dietary B12 restriction worsens EAE and eliminates FTY720's efficacy while concomitantly downregulating type I interferon signaling. TCN2 functions as a chaperone for FTY720 and sphingosine, whose complex induces astrocytic CD320 internalization, suggesting a delivery mechanism of FTY720/sphingosine via the TCN2-CD320 pathway. Taken together, the B12-TCN2-CD320 pathway is essential for the mechanism of action of FTY720.

The 20S proteasome is an attractive drug target for the development of anticancer agents because it plays an important role in cellular protein degradation. It has a threonine residue that can act as a nucleophile to attack inhibitors with an electrophilic warhead, forming a covalent adduct. Fundamental understanding of the reaction mechanism between covalent inhibitors and the proteasome may assist the design and refinement of compounds with the desired activity. In this study, we investigated the covalent inhibition mechanism of an α-keto phenylamide inhibitor of the proteasome. We calculated the noncovalent binding free energy using the PDLD/S-LRA/β method and the reaction free energy through the empirical valence bond method (EVB). Several possible reaction pathways were explored. Subsequently, we validated the calculated activation and reaction free energies of the most plausible pathways by performing kinetic experiments. Furthermore, the effects of different ionization states of Asp17 on the activation energy at each step were also discussed. The results revealed that the ionization states of Asp17 remarkably affect the activation energies and there is an electrostatic reorganization of Asp17 during the course of the reaction. Our results demonstrate the critical electrostatic effect of Asp17 in the active site of the 20S proteasome.

G-protein-coupled receptors (GPCRs) contribute to numerous physiological processes via complex network mechanisms. While indirect signaling assays (Ca2+ mobilization, cAMP production, and GTP gamma S binding) have been useful in identifying and characterizing downstream signaling mechanisms of GPCRs, these methods lack measurements of direct binding affinities, kinetics, binding specificity, and selectivity that are important parameters in GPCR drug discovery. In comparison to existing direct methods that use radio- or fluorescent labels, label-free techniques can closely emulate the native interactions around binding partners. Surface plasmon resonance (SPR) is a label-free technique that utilizes the refractive index (RI) property and is applied widely in quantitative GPCR-ligand binding kinetics measurement including small molecules screening. However, purified GPCRs are further embedded in a synthetic lipid environment which is immobilized through different tags to the SPR sensor surface, resulting in a non-native environment. Here, we introduced a methodology that also uses the RI property to measure binding interactions in a label-free, immobilization-free arrangement. The free-solution technique is successfully applied in quantifying the interaction of bioactive lipids to cognate lipid GPCRs, which is not purified but rather present in near-native conditions, i.e., in milieu of other cytoplasmic lipids and proteins. To further consider the wide applicability of these free-solution approaches in biomolecular interaction research, additional applications on a variety of receptor-ligand pairs are imperative.

Background aims White matter diseases are commonly associated with microglial activation and neuroinflammation. Mesenchymal stromal cells (MSCs) have immunomodulatory properties and thus have the potential to be developed as cell therapy for white matter disease. MSCs interact with resident macrophages to alter the trajectory of inflammation; however, the impact MSCs have on central nervous system macrophages and the effect this has on the progression of white matter disease are unclear. Methods In this study, we utilized numerous assays of varying complexity to model different aspects of white matter disease. These assays ranged from an in vivo spinal cord acute demyelination model to a simple microglial cell line activation assay. Our goal was to investigate the influence of human umbilical cord tissue MSCs on the activation of microglia. Results MSCs reduced the production of tumor necrosis factor (TNF) by microglia and decreased demyelinated lesions in the spinal cord after acute focal injury. To determine if MSCs could directly suppress the activation of microglia and to develop an efficient potency assay, we utilized isolated primary microglia from mouse brains and the Immortalized MicroGlial Cell Line (IMG). MSCs suppressed the activation of microglia and the release of TNF after stimulation with lipopolysaccharide, a toll-like receptor agonist. Conclusions In this study, we demonstrated that MSCs altered the immune response after acute injury in the spinal cord. In numerous assays, MSCs suppressed activation of microglia and release of the pro-inflammatory cytokine TNF. Of these assays, IMG could be standardized and used as an effective potency assay to determine the efficacy of MSCs for treating white matter disease or other neuroinflammatory conditions associated with microglial activation.

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Abstract Introduction DUOC-01 is a macrophage-like cell therapy product manufactured by culturing banked human umbilical cord blood cells under GMP conditions. Currently, the safety of DUOC-01 is being tested as a bridging therapy in children with demyelinating leukodystrophies undergoing unrelated donor umbilical cord blood transplantation after myeloablative conditioning. DUOC-01 protects against loss of function in several preclinical models with demyelinating conditions of the central nervous system, making it an attractive therapy for patients with multiple sclerosis (MS) who experience destruction of myelin sheaths as pathology of their disease. The mechanism by which DUOC-01 promotes remyelination and if it directly influences oligodendrocyte lineage cells is untested. Objective Using multiple systems (primary oligodendrocyte precursor cell [OPC] cultures, in vitro cerebellar slice cultures, and experimental autoimmune encephalomyelitis [EAE], a mouse model of MS), we examined how DUOC-01 influences numerous steps of pathology and recovery. Methods Using a brain slice culture, we added DUOC-01 to the lysophosphatidylcholine (LPC)-treated slices. We quantified myelinated axons by assessing percent co-localization of myelin basic protein and neurofilament in the control, LPC, and LPC+DUOC-01 groups. To test the DUOC-01 effect in the EAE model, we immunized C57BL/6 mice with myelin oligodendrocyte glycoprotein peptide (MOG35-55) in complete Freund’s adjuvant. To match clinical protocols, we incubated DUOC-01 in Ringer’s lactate with hydrocortisone (HC) for 2 hours at room temperature. At the onset of EAE disease symptoms, we injected DUOC-01 into the cerebrospinal fluid by a single intra-cisterna magna injection, then recorded clinical scores daily for 2 weeks. To test if DUOC-01 could directly affect OPCs, we set up a primary OPC culture isolated from neonatal mice and added DUOC-01 treatment to the culture. Results In the cerebellar slice model, we demonstrated a higher number of myelinated neuron fibers in the DUOC-treated group compared with the LPC-treated group. In the EAE model, compared with mice injected with Ringer’s or HC+Ringer’s, mice injected with DUOC-01 derived clinical benefit with lower clinical scores. In the primary OPC culture, the DUOC-01 treatment drove the maturation of OPC to become myelin producing oligodendrocytes. Discussion Our data suggest that DUOC-01 could be beneficial in treating MS and other diverse neurological demyelinating conditions.

The COVID-19 pandemic has been a public health emergency with continuously evolving deadly variants around the globe. Among many preventive and therapeutic strategies, the design of covalent inhibitors targeting the main protease (Mpro) of SARS-CoV-2 that causes COVID-19 has been one of the hotly pursued areas. Currently, about 30% of marketed drugs that target enzymes are covalent inhibitors. Such inhibitors have been shown in recent years to have many advantages that counteract past reservation of their potential off-target activities, which can be minimized by modulation of the electrophilic warhead and simultaneous optimization of nearby noncovalent interactions. This process can be greatly accelerated by exploration of binding affinities using computational models, which are not well-established yet due to the requirement of capturing the chemical nature of covalent bond formation. Here, we present a robust computational method for effective prediction of absolute binding free energies (ABFEs) of covalent inhibitors. This is done by integrating the protein dipoles Langevin dipoles method (in the PDLD/S-LRA/β version) with quantum mechanical calculations of the energetics of the reaction of the warhead and its amino acid target, in water. This approach evaluates the combined effects of the covalent and noncovalent contributions. The applicability of the method is illustrated by predicting the ABFEs of covalent inhibitors of SARS-CoV-2 Mpro and the 20S proteasome. Our results are found to be reliable in predicting ABFEs for cases where the warheads are significantly different. This computational protocol might be a powerful tool for designing effective covalent inhibitors especially for SARS-CoV-2 Mpro and for targeted protein degradation.

We report the application of our fragment-based quantum chemistry model MIM (Molecules-In-Molecules) with electrostatic embedding. The method is termed "EE-MIM (Electrostatically Embedded Molecules-In-Molecules)" and accounts for the missing electrostatic interactions in the subsystems resulting from fragmentation. Point charges placed at the atomic positions are used to represent the interaction of each subsystem with the rest of the molecule with minimal increase in the computational cost. We have carefully calibrated this model on a range of different sizes of clusters containing up to 57 water molecules. The fragmentation methods have been applied with the goal of reproducing the unfragmented total energy at the MP2/6-311G(d,p) level. Comparative analysis has been carried out between MIM and EE-MIM to gauge the impact of electrostatic embedding. Performance of several different parameters such as the type of charge and levels of fragmentation are analyzed for the prediction of absolute energies. The use of background charges in subsystem calculations improves the performance of both one- and two-layer MIM while it is noticeably important in the case of one-layer MIM. Embedded charges for two-layer MIM are obtained from a full system calculation at the low-level. For one-layer MIM, in the absence of a full system calculation, two different types of embedded charges, namely, Geometry dependent (GD) and geometry independent (GI) charges, are used. A self-consistent procedure is employed to obtain GD charges. We have further tested our method on challenging charged systems with stronger intermolecular interactions, namely, protonated ammonia clusters (containing up to 30 ammonia molecules). The observations are similar to water clusters with improved performance using embedded charges. Overall, the performance of NPA charges as embedded charges is found to be the best.

The pandemic caused by SARS-CoV-2 has cost millions of lives and tremendous social/financial loss. The virus continues to evolve and mutate. In particular, the recently emerged "UK", "South Africa", and Delta variants show higher infectivity and spreading speed. Thus, the relationship between the mutations of certain amino acids and the spreading speed of the virus is a problem of great importance. In this respect, understanding the mutational mechanism is crucial for surveillance and prediction of future mutations as well as antibody/vaccine development. In this work, we used a coarse-grained model (that was used previously in predicting the importance of mutations of N501) to calculate the free energy change of various types of single-site or combined-site mutations. This was done for the UK, South Africa, and Delta mutants. We investigated the underlying mechanisms of the binding affinity changes for mutations at different spike protein domains of SARS-CoV-2 and provided the energy basis for the resistance of the E484 mutant to the antibody m396. Other potential mutation sites were also predicted. Furthermore, the in silico predictions were assessed by functional experiments. The results establish that the faster spreading of recently observed mutants is strongly correlated with the binding-affinity enhancement between virus and human receptor as well as with the reduction of the binding to the m396 antibody. Significantly, the current approach offers a way to predict new variants and to assess the effectiveness of different antibodies toward such variants.

This work explored the molecular origin of substrate translocation by the AAA+ motor of the 26S proteasome. This exploration was performed by combining different simulation approaches including calculations of binding free energies, coarse-grained simulations, and considerations of the ATP hydrolysis energy. The simulations were used to construct the free energy landscape for the translocation process. This included the evaluation of the conformational barriers in different translocation steps. Our simulation reveals that the substrate translocation by the AAA+ motor is guided in part by electrostatic interactions. We also validated the experimental observation that bulkier residues in pore loop 1 are responsible for substrate translocation. However, our calculation also reveals that the lysine residues prior to the bulkier residues (conserved along pore loop 1) are also important for the translocation process. We believe that this computational study can help in guiding the ongoing research of the proteasome.