Solvation energy in protein folding and binding

Molecular Insights into Structural Dynamics and Binding Interactions of Selected Inhibitors Targeting SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease (Mpro, also known as 3CLpro) is a key target for antiviral therapy due to its critical role in viral replication and maturation. This study investigated the inhibitory effects of Bofutrelvir, Nirmatrelvir, and Selinexor on 3CLpro through molecular docking, molecular dynamics (MD) simulations, and free energy calculations. Nirmatrelvir exhibited the strongest binding affinity across docking tools (AutoDock Vina: -8.3 kcal/mol; DiffDock: -7.75 kcal/mol; DynamicBound: 7.59 to 7.89 kcal/mol), outperforming Selinexor and Bofutrelvir. Triplicate 300 ns MD simulations revealed that the Nirmatrelvir-3CLpro complex displayed high conformational stability, reduced root mean square deviation (RMSD), and a modest decrease in solvent-accessible surface area (SASA), indicating enhanced structural rigidity. Gibbs free energy analysis highlighted greater flexibility in unbound 3CLpro, stabilized by Nirmatrelvir binding, supported by stable hydrogen bonds. MolProphet prediction tools, targeting the Cys145 residue, confirmed that Nirmatrelvir exhibited the strongest binding, forming multiple hydrophobic, hydrogen, and π-stacking interactions with key residues, and had the lowest predicted IC50/EC50 (9.18 × 10-8 mol/L), indicating its superior potency. Bofutrelvir and Selinexor showed weaker interactions and higher IC50/EC50 values. MM/PBSA analysis calculated a binding free energy of -100.664 ± 0.691 kJ/mol for the Nirmatrelvir-3CLpro complex, further supporting its stability and binding potency. These results underscore Nirmatrelvir's potential as a promising therapeutic agent for SARS-CoV-2 and provide novel insights into dynamic stabilizing interactions through AI-based docking and long-term MD simulations.

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

Assessing the Interaction between Dodecylphosphocholine and Dodecylmaltoside Mixed Micelles as Drug Carriers with Lipid Membrane: A Coarse-Grained Molecular Dynamics Simulation

Integrating drugs into cellular membranes efficiently is a significant challenge in drug delivery systems. This study aimed to overcome these barriers by utilizing mixed micelles to enhance drug incorporation into cell membranes. We employed coarse-grained molecular dynamics (MD) simulations to investigate the stability and efficacy of micelles composed of dodecylphosphocholine (DPC), a zwitterionic surfactant, and dodecylmaltoside (DDM), a nonionic surfactant, at various mixing ratios. Additionally, we examined the incorporation of a mutated form of Indolicidin (IND) (CP10A), an anti-HIV peptide, into these micelles. This study provides valuable insights for the development of more effective drug delivery systems by optimizing the mixing ratios of DPC and DDM. By balancing stability and penetration efficiency, these mixed micelles can improve the delivery of drugs that face challenges crossing lipid membranes. Such advancements can enhance the efficacy of treatments for various conditions, including viral infections and cancer, by ensuring that therapeutic agents reach their intended cellular targets more effectively.

Selective Chiral Interactions between Hydrophilic/Hydrophobic Amino Acids and Growing Gypsum Crystals

In nature, selective interactions between chiral amino acids and crystals are important for the formation of chiral biominerals and provide insight into the mysterious origin of homochirality. Here, we show that chiral amino acids with different hydrophilicities/hydrophobicities exhibit different chiral selectivity preferences in the dynamically growing gypsum [001] steps. Hydrophilic amino acids show a chiral selectivity preference for their d-isomers, whereas hydrophobic amino acids prefer their l-isomers. These differences in chiral recognition can be attributed to the different stereochemical matching between the hydrophilic and hydrophobic amino acids on the [001] steps of growing gypsum. These different chiral selectivities resulting from the amino acid hydrophilicity/hydrophobicity are confirmed by the experimental crystallization investigations from nano regulation on dynamic steps, to microscopic modification of gypsum morphology, and to macroscopic precipitation. Furthermore, as the hydrophilicity of amino acids increases, the disparity in chiral selection rises; conversely, the increase in the hydrophobicity of amino acids results in a decline in chiral selection. These insights improve our understanding of the interaction mechanism between amino acids and crystals and provide insights into the formation process of chiral biominerals and the origin of homochirality in nature.

Cryo-EM confirms a common fibril fold in the heart of four patients with ATTRwt amyloidosis

ATTR amyloidosis results from the conversion of transthyretin into amyloid fibrils that deposit in tissues causing organ failure and death. This conversion is facilitated by mutations in ATTRv amyloidosis, or aging in ATTRwt amyloidosis. ATTRv amyloidosis exhibits extreme phenotypic variability, whereas ATTRwt amyloidosis presentation is consistent and predictable. Previously, we found unique structural variabilities in cardiac amyloid fibrils from polyneuropathic ATTRv-I84S patients. In contrast, cardiac fibrils from five genotypically different patients with cardiomyopathy or mixed phenotypes are structurally homogeneous. To understand fibril structure's impact on phenotype, it is necessary to study the fibrils from multiple patients sharing genotype and phenotype. Here we show the cryo-electron microscopy structures of fibrils extracted from four cardiomyopathic ATTRwt amyloidosis patients. Our study confirms that they share identical conformations with minimal structural variability, consistent with their homogenous clinical presentation. Our study contributes to the understanding of ATTR amyloidosis biopathology and calls for further studies.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

Exact Analytical Algorithm for the Solvent-Accessible Surface Area and Derivatives in Implicit Solvent Molecular Simulations on GPUs

In this paper, we present differentiable solvent-accessible surface area (dSASA), an exact geometric method to calculate SASA analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedralization adapted for efficient GPU implementation, and the SASA values for atoms and molecules are calculated based on the tetrahedralization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with >98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into AMBER, we can apply dSASA to implicit solvent molecular dynamics simulations with the inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.

Atomic resolution structure of full-length human insulin fibrils

Patients with type 1 diabetes mellitus who are dependent on an external supply of insulin develop insulin-derived amyloidosis at the sites of insulin injection. A major component of these plaques is identified as full-length insulin consisting of the two chains A and B. While there have been several reports that characterize insulin misfolding and the biophysical properties of the fibrils, atomic-level information on the insulin fibril architecture remains elusive. We present here an atomic resolution structure of a monomorphic insulin amyloid fibril that has been determined using magic angle spinning solid-state NMR spectroscopy. The structure of the insulin monomer yields a U-shaped fold in which the two chains A and B are arranged in parallel to each other and are oriented perpendicular to the fibril axis. Each chain contains two β-strands. We identify two hydrophobic clusters that together with the three preserved disulfide bridges define the amyloid core structure. The surface of the monomeric amyloid unit cell is hydrophobic implicating a potential dimerization and oligomerization interface for the assembly of several protofilaments in the mature fibril. The structure provides a starting point for the development of drugs that bind to the fibril surface and disrupt secondary nucleation as well as for other therapeutic approaches to attenuate insulin aggregation.

Tryptophan residues in TDP-43 and SOD1 modulate the cross-seeding and toxicity of SOD1

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease of motor neurons. Neuronal superoxide dismutase-1 (SOD1) inclusion bodies are characteristic of familial ALS with SOD1 mutations, while a hallmark of sporadic ALS is inclusions containing aggregated WT TAR DNA-binding protein 43 (TDP-43). We show here that co-expression of mutant or WT TDP-43 with SOD1 leads to misfolding of endogenous SOD1 and aggregation of SOD1 reporter protein SOD1G85R-GFP in human cell cultures and promotes synergistic axonopathy in zebrafish. Intriguingly, this pathological interaction is modulated by natively solvent-exposed tryptophans in SOD1 (tryptophan-32) and TDP-43 RNA-recognition motif RRM1 (tryptophan-172), in concert with natively sequestered TDP-43 N-terminal domain tryptophan-68. TDP-43 RRM1 intrabodies reduce WT SOD1 misfolding in human cell cultures, via blocking tryptophan-172. Tryptophan-68 becomes antibody-accessible in aggregated TDP-43 in sporadic ALS motor neurons and cell culture. 5-fluorouridine inhibits TDP-43-induced G85R-GFP SOD1 aggregation in human cell cultures and ameliorates axonopathy in zebrafish, via its interaction with SOD1 tryptophan-32. Collectively, our results establish a novel and potentially druggable tryptophan-mediated mechanism whereby two principal ALS disease effector proteins might directly interact in disease.

Exotic self-assembly of hard spheres in a morphometric solvent

The self-assembly of spheres into geometric structures, under various theoretical conditions, offers valuable insights into complex self-assembly processes in soft systems. Previous studies have utilized pair potentials between spheres to assemble maximum contact clusters in simulations and experiments. The morphometric approach to solvation free energy that we utilize here goes beyond pair potentials; it is a geometry-based theory that incorporates a weighted combination of geometric measures over the solvent accessible surface for solute configurations in a solvent. In this paper, we demonstrate that employing the morphometric model of solvation free energy in simulating the self-assembly of sphere clusters results, under most conditions, in the previously observed maximum contact clusters. Under other conditions, it unveils an assortment of extraordinary sphere configurations, such as double helices and rhombohedra. These exotic structures arise specifically under conditions where the interactions take multibody potentials into account. This investigation establishes a foundation for comprehending the diverse range of geometric forms in self-assembled structures, emphasizing the significance of the morphometric approach in this context.

Cryo-EM confirms a common fibril fold in the heart of four patients with ATTRwt amyloidosis

ATTR amyloidosis results from the conversion of transthyretin into amyloid fibrils that deposit in tissues causing organ failure and death. This conversion is facilitated by mutations in ATTRv amyloidosis, or aging in ATTRwt amyloidosis. ATTRv amyloidosis exhibits extreme phenotypic variability, whereas ATTRwt amyloidosis presentation is consistent and predictable. Previously, we found an unprecedented structural variability in cardiac amyloid fibrils from polyneuropathic ATTRv-I84S patients. In contrast, cardiac fibrils from five genotypically-different patients with cardiomyopathy or mixed phenotypes are structurally homogeneous. To understand fibril structure's impact on phenotype, it is necessary to study the fibrils from multiple patients sharing genotype and phenotype. Here we show the cryo-electron microscopy structures of fibrils extracted from four cardiomyopathic ATTRwt amyloidosis patients. Our study confirms that they share identical conformations with minimal structural variability, consistent with their homogenous clinical presentation. Our study contributes to the understanding of ATTR amyloidosis biopathology and calls for further studies.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

Conformational Space of the Translocation Domain of Botulinum Toxin: Atomistic Modeling and Mesoscopic Description of the Coiled-Coil Helix Bundle

The toxicity of botulinum multi-domain neurotoxins (BoNTs) arises from a sequence of molecular events, in which the translocation of the catalytic domain through the membrane of a neurotransmitter vesicle plays a key role. A recent structural study of the translocation domain of BoNTs suggests that the interaction with the membrane is driven by the transition of an α helical switch towards a β hairpin. Atomistic simulations in conjunction with the mesoscopic Twister model are used to investigate the consequences of this proposition for the toxin-membrane interaction. The conformational mobilities of the domain, as well as the effect of the membrane, implicitly examined by comparing water and water-ethanol solvents, lead to the conclusion that the transition of the switch modifies the internal dynamics and the effect of membrane hydrophobicity on the whole protein. The central two α helices, helix 1 and helix 2, forming two coiled-coil motifs, are analyzed using the Twister model, in which the initial deformation of the membrane by the protein is caused by the presence of local torques arising from asymmetric positions of hydrophobic residues. Different torque distributions are observed depending on the switch conformations and permit an origin for the mechanism opening the membrane to be proposed.

Structural polymorphism of amyloid fibrils in ATTR amyloidosis revealed by cryo-electron microscopy

ATTR amyloidosis is caused by the deposition of transthyretin in the form of amyloid fibrils in virtually every organ of the body, including the heart. This systemic deposition leads to a phenotypic variability that has not been molecularly explained yet. In brain amyloid conditions, previous studies suggest an association between clinical phenotype and the molecular structures of their amyloid fibrils. Here we investigate whether there is such an association in ATTRv amyloidosis patients carrying the mutation I84S. Using cryo-electron microscopy, we determined the structures of cardiac fibrils extracted from three ATTR amyloidosis patients carrying the ATTRv-I84S mutation, associated with a consistent clinical phenotype. We found that in each ATTRv-I84S patient, the cardiac fibrils exhibited different local conformations, and these variations can co-exist within the same fibril. Our finding suggests that one amyloid disease may associate with multiple fibril structures in systemic amyloidoses, calling for further studies.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Structural evolution of fibril polymorphs during amyloid assembly

Cryoelectron microscopy (cryo-EM) has provided unprecedented insights into amyloid fibril structures, including those associated with disease. However, these structures represent the endpoints of long assembly processes, and their relationship to fibrils formed early in assembly is unknown. Consequently, whether different fibril architectures, with potentially different pathological properties, form during assembly remains unknown. Here, we used cryo-EM to determine structures of amyloid fibrils at different times during in vitro fibrillation of a disease-related variant of human islet amyloid polypeptide (IAPP-S20G). Strikingly, the fibrils formed in the lag, growth, and plateau phases have different structures, with new forms appearing and others disappearing as fibrillation proceeds. A time course with wild-type hIAPP also shows fibrils changing with time, suggesting that this is a general property of IAPP amyloid assembly. The observation of transiently populated fibril structures has implications for understanding amyloid assembly mechanisms with potential new insights into amyloid progression in disease.

Fibril structures of TFG protein mutants validate the identification of TFG as a disease-related amyloid protein by the IMPAcT method

We previously presented a bioinformatic method for identifying diseases that arise from a mutation in a protein's low-complexity domain that drives the protein into pathogenic amyloid fibrils. One protein so identified was the tropomyosin-receptor kinase-fused gene protein (TRK-fused gene protein or TFG). Mutations in TFG are associated with degenerative neurological conditions. Here, we present experimental evidence that confirms our prediction that these conditions are amyloid-related. We find that the low-complexity domain of TFG containing the disease-related mutations G269V or P285L forms amyloid fibrils, and we determine their structures using cryo-electron microscopy (cryo-EM). These structures are unmistakably amyloid in nature and confirm the propensity of the mutant TFG low-complexity domain to form amyloid fibrils. Also, despite resulting from a pathogenic mutation, the fibril structures bear some similarities to other amyloid structures that are thought to be nonpathogenic and even functional, but there are other factors that support these structures' relevance to disease, including an increased propensity to form amyloid compared with the wild-type sequence, structure-stabilizing influence from the mutant residues themselves, and double-protofilament amyloid cores. Our findings elucidate two potentially disease-relevant structures of a previously unknown amyloid and also show how the structural features of pathogenic amyloid fibrils may not conform to the features commonly associated with pathogenicity.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted. However, many other forms, such as the G13D mutant, are yet to be addressed. Here, we used a receptor-based pharmacophore modeling technique to generate potential inhibitors of the KRAS-G13D oncogenic mutant. Using a comprehensive virtual screening workflow model, top hits were selected, out of which CSC01 was identified as a promising inhibitor of the oncogenic KRAS mutant (G13D). The stability of CSC01 upon binding the switch II pocket was evaluated through an exhaustive molecular dynamics simulation study. The several post-simulation analyses conducted suggest that CSC01 formed a stable complex with KRAS-G13D. CSC01, through a dynamic protein-ligand interaction profiling analysis, was also shown to maintain strong interactions with the mutated aspartic acid residue throughout the simulation. Although binding free energy analysis through the umbrella sampling approach suggested that the affinity of CSC01 with the switch II pocket of KRAS-G13D is moderate, our DFT analysis showed that the stable interaction of the compound might be facilitated by the existence of favorable molecular electrostatic potentials. Furthermore, based on ADMET predictions, CSC01 demonstrated a satisfactory drug likeness and toxicity profile, making it an exemplary candidate for consideration as a potential KRAS-G13D inhibitor.

Cryo-EM structure of a human LECT2 amyloid fibril reveals a network of polar ladders at its core

ALECT2 systemic amyloidosis is associated with deposition of the leukocyte cell-derived chemotaxin-2 (LECT2) protein in the form of fibrils. In ALECT2 amyloidosis, ALECT2 fibrils deposit in the glomerulus, resulting in renal failure. Patients lack effective treatment options outside of renal transplant or dialysis. The structure of globular LECT2 has been determined but structures of ALECT2 amyloid fibrils remain unknown. Using single-particle cryo-EM, we find that recombinant human LECT2 forms robust twisting fibrils with canonical amyloid features. ALECT2 fibrils contain two mating protofilaments spanning residues 55-75 of the LECT2 sequence. The geometry of the ALECT2 fibril displays features in line with other pathogenic amyloids. Its core is tightly packed and stabilized by both hydrophobic contacts and hydrogen-bonded uncharged polar residues. The robustness of ALECT2 fibril cores is illustrated by their resistance to denaturants and proteases. This ALECT2 fibril structure presents a potential new target for treatments against ALECT2 systemic amyloidosis.

Disease-associated patterns of acetylation stabilize tau fibril formation

Assembly of tau into beta-sheet-rich amyloids dictates the pathology of a diversity of diseases. Lysine acetylation has been proposed to drive tau amyloid assembly, but no direct mechanism has emerged. Using tau fragments, we identify patterns of acetylation that flank amyloidogenic motifs on the tau fragments that promote rapid fibril assembly. We determined a 3.9 Å cryo-EM amyloid fibril structure assembled from an acetylated tau fragment uncovering how lysine acetylation can mediate gain-of-function interactions. Comparison of the structure to an ex vivo tauopathy fibril reveals regions of structural similarity. Finally, we show that fibrils encoding disease-associated patterns of acetylation are active in cell-based tau aggregation assays. Our data uncover the dual role of lysine residues in limiting tau aggregation while their acetylation leads to stabilizing pro-aggregation interactions. Design of tau sequence with specific acetylation patterns may lead to controllable tau aggregation to direct folding of tau into distinct amyloid folds.

An updated dataset and a structure-based prediction model for protein-RNA binding affinity

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach

Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.

Analyzing the Geometry and Dynamics of Viral Structures: A Review of Computational Approaches Based on Alpha Shape Theory, Normal Mode Analysis, and Poisson-Boltzmann Theories

The current SARS-CoV-2 pandemic highlights our fragility when we are exposed to emergent viruses either directly or through zoonotic diseases. Fortunately, our knowledge of the biology of those viruses is improving. In particular, we have more and more structural information on virions, i.e., the infective form of a virus that includes its genomic material and surrounding protective capsid, and on their gene products. It is important to have methods that enable the analyses of structural information on such large macromolecular systems. We review some of those methods in this paper. We focus on understanding the geometry of virions and viral structural proteins, their dynamics, and their energetics, with the ambition that this understanding can help design antiviral agents. We discuss those methods in light of the specificities of those structures, mainly that they are huge. We focus on three of our own methods based on the alpha shape theory for computing geometry, normal mode analyses to study dynamics, and modified Poisson-Boltzmann theories to study the organization of ions and co-solvent and solvent molecules around biomacromolecules. The corresponding software has computing times that are compatible with the use of regular desktop computers. We show examples of their applications on some outer shells and structural proteins of the West Nile Virus.

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors

Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Ligand-based 3D pharmacophore modeling, virtual screening, and molecular dynamic simulation of potential smoothened inhibitors

CONTEXT

The Hedgehog (Hh) signaling pathway is a crucial regulator of various cellular processes. Dysregulated activation of the Smoothened (SMO) oncoprotein, a key component of the Hh pathway, has been implicated in several types of cancer. Although SMO inhibitors are important anti-cancer therapeutics, drug-resistant SMO mutants have emerged, limiting their efficacy. This study aimed to discover stable SMO inhibitors for both wild-type and mutant SMOs, using a 12-feature pharmacophore model validated for virtual screening. One lead compound, LCT10312, was identified with high affinity to SMO and showed a significant conformational change in the SMO structure upon binding. Molecular dynamic simulation revealed stable interaction of LCT10312 with SMO and large atom motions, indicating SMO structural fluctuation. The lead compound showed high predicted binding scores to several clinically relevant SMO mutants.

METHODS

A ligand-based pharmacophore model was developed from 25 structurally clustered SMO inhibitors using LigandScout v3.12 software and virtually screened for hit identification from a library of 511,878 chemicals. Molecular docking was employed to identify potential leads based on SMO affinities. Molecular dynamic simulation (MDS) with GROMACS v5.1.4 was performed to analyze the structural changes of SMO oncoprotein upon binding lead compound(s) and cyclopamine as the control for 100 ns. The binding affinity of lead compound(s) was predicted on clinical and laboratory SMO mutants.

Dipole Cooperativity and Polarization Frustration Determine the Secondary Structure Distribution of Short Alanine Peptides in Water

The physical driving forces for secondary structure preferences of hydrated alanine peptide are investigated with B3LYP-D3(BJ) and the adaptive force matching (AFM) method. The AFM fit to the DFT surface, ALA2022, provides excellent agreement with the nuclear magnetic resonance scalar coupling constants from experiments. In turn, the model is used to gain insight into the physical driving forces behind secondary structure preferences of hydrated peptides. DFT calculations with and without the Conductor-like Screening Model (COSMO) show that the α helix is stabilized by solvent polarization due to dipole cooperativity. The two adjacent amide groups in β strand form a near-planar trapezoid that is not much larger than the size of water molecules. When the finite size of a water molecule is considered, the stabilization from solvent polarization for such a trapezoid is frustrated. Water molecules cannot find orientations to properly stabilize all four polar regions close to each other with such an awkward arrangement. This leads to quite substantial reduction in polarization stabilization. Although the polyproline II (PP-II) conformation is very similar to the β strand, the small twist in the backbone angles allowed much improved polarization stabilization. The improved polarization, when combined with favorable intrapeptide interactions, leads to the PP-II to be lowest in free energy. Other factors, such as the entropic TΔS and the ϕ, ψ coupling terms, are also studied but are found to play only a minor role. The insight shown in this work helps to better understand the structure of globular and intrinsic disordered proteins and facilitate future force field development.

Multi-order graph attention network for water solubility prediction and interpretation

The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.

Cryo-EM Structure of a Human LECT2 Amyloid Fibril Reveals a Network of Polar Ladders at its Core

ALECT2 is a type of systemic amyloidosis caused by deposition of the leukocyte cell-derived chemotaxin-2 (LECT2) protein in the form of fibrils. In ALECT2, LECT2 fibril deposits can be found in the glomerulus, resulting in renal failure. Affected patients lack effective treatment options outside of renal transplant or dialysis. While the structure of LECT2 in its globular form has been determined by X-ray crystallography, structures of LECT2 amyloid fibrils remain unknown. Using single particle cryo-EM, we now find that human LECT2 forms robust twisting fibrils with canonical amyloid features. At their core, LECT2 fibrils contain two mating protofilaments, the ordered core of each protofilament spans residues 55-75 of the LECT2 sequence. The overall geometry of the LECT2 fibril displays features in line with other pathogenic amyloids. Its core is tightly packed and stabilized by a network of hydrophobic contacts and hydrogen-bonded uncharged polar residues, while its outer surface displays several charged residues. The robustness of LECT2 fibril cores is illustrated by their limited dissolution in 3M urea and their persistence after treatment with proteinase K. As such, the LECT2 fibril structure presents a potential new target for treatments against ALECT2.

In silico protein engineering shows that novel mutations affecting NAD+ binding sites may improve phosphite dehydrogenase stability and activity

Pseudomonas stutzeri phosphite dehydrogenase (PTDH) catalyzes the oxidation of phosphite to phosphate in the presence of NAD, resulting in the formation of NADH. The regeneration of NADH by PTDH is greater than any other enzyme due to the substantial change in the free energy of reaction (G°' = - 63.3 kJ/mol). Presently, improving the stability of PTDH is for a great importance to ensure an economically viable reaction process to produce phosphite as a byproduct for agronomic applications. The binding site of NAD⁺ with PTDH includes thirty-four residues; eight of which have been previously mutated and characterized for their roles in catalysis. In the present study, the unexplored twenty-six key residues involved in the binding of NAD⁺ were subjected to in silico mutagenesis based on the physicochemical properties of the amino acids. The effects of these mutations on the structure, stability, activity, and interaction of PTDH with NAD⁺ were investigated using molecular docking, molecular dynamics simulations, free energy calculations, and secondary structure analysis. We identified seven novel mutations, A155I, G157I, L217I, P235A, V262I, I293A, and I293L, that reduce the compactness of the protein while improving PTDH stability and binding to NAD⁺.

Computing the Volume, Surface Area, Mean, and Gaussian Curvatures of Molecules and Their Derivatives

Geometry is crucial in our efforts to comprehend the structures and dynamics of biomolecules. For example, volume, surface area, and integrated mean and Gaussian curvature of the union of balls representing a molecule are used to quantify its interactions with the water surrounding it in the morphometric implicit solvent models. The Alpha Shape theory provides an accurate and reliable method for computing these geometric measures. In this paper, we derive homogeneous formulas for the expressions of these measures and their derivatives with respect to the atomic coordinates, and we provide algorithms that implement them into a new software package, AlphaMol. The only variables in these formulas are the interatomic distances, making them insensitive to translations and rotations. AlphaMol includes a sequential algorithm and a parallel algorithm. In the parallel version, we partition the atoms of the molecule of interest into 3D rectangular blocks, using a kd-tree algorithm. We then apply the sequential algorithm of AlphaMol to each block, augmented by a buffer zone to account for atoms whose ball representations may partially cover the block. The current parallel version of AlphaMol leads to a 20-fold speed-up compared to an independent serial implementation when using 32 processors. For instance, it takes 31 s to compute the geometric measures and derivatives of each atom in a viral capsid with more than 26 million atoms on 32 Intel processors running at 2.7 GHz. The presence of the buffer zones, however, leads to redundant computations, which ultimately limit the impact of using multiple processors. AlphaMol is available as an OpenSource software.

Disease-associated patterns of acetylation stabilize tau fibril formation

Assembly of the microtubule-associated protein into tauopathy fibril conformations dictates the pathology of a diversity of diseases. Recent cryogenic Electron Microscopy (cryo-EM) structures have uncovered distinct fibril conformations in different tauopathies but it remains unknown how these structures fold from a single protein sequence. It has been proposed that post-translational modifications may drive tau assembly but no direct mechanism for how modifications drive assembly has emerged. Leveraging established aggregation-regulating tau fragments that are normally inert, we tested the effect of chemical modification of lysines with acetyl groups on tau fragment conversion into amyloid aggregates. We identify specific patterns of acetylation that flank amyloidogenic motifs on the tau fragments that drive rapid fibril assembly. To understand how this pattern of acetylation may drive assembly, we determined a 3.9 Å cryo-EM structure of an amyloid fibril assembled from an acetylated tau fragment. The structure uncovers how lysine acetylation patterns mediate gain-of-function interactions to promote amyloid assembly. Comparison of the structure to an ex vivo tau fibril conformation from Pick's Disease reveals regions of high structural similarity. Finally, we show that our lysine- acetylated sequences exhibit fibril assembly activity in cell-based tau aggregation assays. Our data uncover the dual role of lysine residues in limiting aggregation while their acetylation leads to stabilizing pro-aggregation interactions. Design of tau sequence with specific acetylation patterns may lead to controllable tau aggregation to direct folding of tau into distinct folds.

Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method

Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has generated significant interest and desire in recent years and extensive research due to its low cost and speed of operation. As a result, it is critical to develop an intelligent classification system for drug proteins. This study proposes two distinct models for the prediction of druggable protein classes based on the deep learning method. The translation of drug-protein sequences is based on six physicochemical properties of amino acids. Following the application of the autocovariance method, converted sequences are used as fixed-length input vectors in deep stacked sparse auto-encoders (DSSAEs) network. The coded protein sequences are also considered and utilized as a six-channel input vector for the deep convolutional neural network model. The experimental results contributing to the deep convolution model are more efficient than previous studies for classifying druggable proteins. The proposed approach achieved a sensitivity of 96.92%, a specificity of 99.51%, and an accuracy of 98.29%.

Impact of SARS-CoV-2 ORF6 and its variant polymorphisms on host responses and viral pathogenesis

We and others have previously shown that the SARS-CoV-2 accessory protein ORF6 is a powerful antagonist of the interferon (IFN) signaling pathway by directly interacting with Nup98-Rae1 at the nuclear pore complex (NPC) and disrupting bidirectional nucleo-cytoplasmic trafficking. In this study, we further assessed the role of ORF6 during infection using recombinant SARS-CoV-2 viruses carrying either a deletion or a well characterized M58R loss-of-function mutation in ORF6. We show that ORF6 plays a key role in the antagonism of IFN signaling and in viral pathogenesis by interfering with karyopherin(importin)-mediated nuclear import during SARS-CoV-2 infection both in vitro , and in the Syrian golden hamster model in vivo . In addition, we found that ORF6-Nup98 interaction also contributes to inhibition of cellular mRNA export during SARS-CoV-2 infection. As a result, ORF6 expression significantly remodels the host cell proteome upon infection. Importantly, we also unravel a previously unrecognized function of ORF6 in the modulation of viral protein expression, which is independent of its function at the nuclear pore. Lastly, we characterized the ORF6 D61L mutation that recently emerged in Omicron BA.2 and BA.4 and demonstrated that it is able to disrupt ORF6 protein functions at the NPC and to impair SARS-CoV-2 innate immune evasion strategies. Importantly, the now more abundant Omicron BA.5 lacks this loss-of-function polymorphism in ORF6. Altogether, our findings not only further highlight the key role of ORF6 in the antagonism of the antiviral innate immune response, but also emphasize the importance of studying the role of non-spike mutations to better understand the mechanisms governing differential pathogenicity and immune evasion strategies of SARS-CoV-2 and its evolving variants.

ONE SENTENCE SUMMARY

SARS-CoV-2 ORF6 subverts bidirectional nucleo-cytoplasmic trafficking to inhibit host gene expression and contribute to viral pathogenesis.

Hydration Free Energies of Polypeptides from Popular Implicit Solvent Models versus All-Atom Simulation Results Based on Molecular Quasichemical Theory

Calculating the hydration free energy of a macromolecule in all-atom simulations has long remained a challenge, necessitating the use of models wherein the effect of the solvent is captured without explicit account of solvent degrees of freedom. This situation has changed with developments in the molecular quasi-chemical theory (QCT)─an approach that enables calculation of the hydration free energy of macromolecules within all-atom simulations at the same resolution as is possible for small molecular solutes. The theory also provides a rigorous and physically transparent framework to conceptualize and model interactions in molecular solutions and thus provides a convenient framework to investigate the assumptions in implicit solvent models. In this study, we compare the results using molecular QCT versus predictions from EEF1, ABSINTH, and GB/SA implicit solvent models for polyglycine and polyalanine solutes covering a range of chain lengths and conformations. The hydration free energies or the differences in hydration free energies between conformers obtained from the implicit solvent models do not agree with explicit solvent results, with the deviations being largest for the group additive EEF1 and ABSINTH models. GB/SA does better in capturing the qualitative trends seen in explicit solvent results. Analysis founded on QCT reveals the critical importance of the cooperativity of hydration that is inherent in the hydrophilic and hydrophobic contributions to hydration─physics that is not well captured in additive models but somewhat better accounted for by means of a dielectric in the GB/SA approach.

Molecular dynamics study of the interactions between a hydrophilic polymer brush on graphene and amino acid side chain analogues in water

We perform all-atom molecular dynamics simulations of poly(2-hydroxyethyl methacrylate) (PHEMA) brushes in aqueous solutions of isobutane, propionamide, and sodium propionate. These solutes are side chain analogues to leucine, glutamine, and glutamic acid, respectively. We compute the Gibbs energy profile of the solute's adsorption to the polymer brush and decompose it into the contributions from the steric repulsion, van der Waals interaction, and Coulomb interaction to reveal the energetic origin of repulsion or attraction of the solute by the polymer brush. The Henry adsorption constant is the amount of adsorption normalized by the concentration in aqueous solution. We examine the dependence of this quantity on the grafting density and chain length. Our results suggest that the concurrent primary and ternary adsorption mechanism may be more important than previously expected when the solute is hydrophobic.

Molecular interactions of FG nucleoporin repeats at high resolution

Proteins that contain repeat phenylalanine-glycine (FG) residues phase separate into oncogenic transcription factor condensates in malignant leukaemias, form the permeability barrier of the nuclear pore complex and mislocalize in neurodegenerative diseases. Insights into the molecular interactions of FG-repeat nucleoporins have, however, remained largely elusive. Using a combination of NMR spectroscopy and cryoelectron microscopy, we have identified uniformly spaced segments of transient β-structure and a stable preformed α-helix recognized by messenger RNA export factors in the FG-repeat domain of human nucleoporin 98 (Nup98). In addition, we have determined at high resolution the molecular organization of reversible FG–FG interactions in amyloid fibrils formed by a highly aggregation-prone segment in Nup98. We have further demonstrated that amyloid-like aggregates of the FG-repeat domain of Nup98 have low stability and are reversible. Our results provide critical insights into the molecular interactions underlying the self-association and phase separation of FG-repeat nucleoporins in physiological and pathological cell activities.

Proteins rich in phenylalanine-glycine (FG) repeats can phase separate through FG–FG interactions. The molecular interactions of an important FG-repeat protein, nucleoporin 98, have now been studied in liquid-like transient and amyloid-like cohesive states. These interactions underlie the behaviour of FG-repeat proteins and their function in physiological and pathological cell activities.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Comparison of hydrophobicity scales for predicting biophysical properties of antibodies

While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.

Crustwater: Modeling Hydrophobic Solvation

We describe Crustwater, a statistical mechanical model of nonpolar solvation in water. It treats bulk water using the Cage Water model and introduces a crust, i.e., a solvation shell of coordinated partially structured waters. Crustwater is analytical and fast to compute. We compute here solvation vs temperature over the liquid range, and vs pressure and solute size. Its thermal predictions are as accurate as much more costly explicit models such as TIP4P/2005. This modeling gives new insights into the hydrophobic effect: (1) that oil-water insolubility in cold water is due to solute-water (SW) translational entropy and not water-water (WW) orientations, even while hot water is dominated by WW cage breaking, and (2) that a size transition at the Angstrom scale, not the nanometer scale, takes place as previously predicted.

Micro-electron diffraction structure of the aggregation-driving N-terminus of Drosophila neuronal protein Orb2A reveals amyloid-like β-sheets

Amyloid protein aggregation is commonly associated with progressive neurodegenerative diseases, however not all amyloid fibrils are pathogenic. The neuronal cytoplasmic polyadenylation element binding (CPEB) protein is a regulator of synaptic mRNA translation, and has been shown to form functional amyloid aggregates that stabilize long-term memory. In adult Drosophila neurons, the CPEB homolog Orb2 is expressed as two isoforms, of which the Orb2B isoform is far more abundant, but the rarer Orb2A isoform is required to initiate Orb2 aggregation. The N-terminus is a distinctive feature of the Orb2A isoform and is critical for its aggregation. Intriguingly, replacement of phenylalanine in the 5^th position of Orb2A with tyrosine (F5Y) in Drosophila impairs stabilization of long-term memory. The structure of endogenous Orb2B fibers was recently determined by cryo-EM, but the structure adopted by fibrillar Orb2A is less certain. Here we use micro-electron diffraction to determine the structure of the first nine N-terminal residues of Orb2A, at a resolution of 1.05 Å. We find that this segment (which we term M9I) forms an amyloid-like array of parallel in-register β-sheets, which interact through side chain interdigitation of aromatic and hydrophobic residues. Our structure provides an explanation for the decreased aggregation observed for the F5Y mutant, and offers a hypothesis for how the addition of a single atom (the tyrosyl oxygen) affects long-term memory. We also propose a structural model of Orb2A that integrates our structure of the M9I segment with the published Orb2B cryo-EM structure.

Large protein complex interfaces have evolved to promote cotranslational assembly

Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromeric subunits in bacterial, yeast, and human complexes. When considering all together, we observe the N-terminal interface to be larger than the C-terminal interface 54% of the time, increasing to 64% when we exclude subunits with only small interfaces, which are unlikely to cotranslationally assemble. This strongly suggests that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.

Digging for the discovery of SARS-CoV-2 nsp12 inhibitors: a pharmacophore-based and molecular dynamics simulation study

Aim: COVID-19 is a global health threat. Therapeutics are urgently needed to cure patients severely infected with COVID-19. Objective: to investigate potential candidates of nsp12 inhibitors by searching for druggable cavity pockets within the viral protein and drug discovery. Methods: A virtual screening of ZINC natural products on SARS-CoV-2 nsp12's druggable cavity was performed. A lead compound with the highest affinity to nsp12 was simulated dynamically for 10 ns. Results: ZINC03977803 was nominated as the lead compound. The results showed stable interaction between ZINC03977803 and nsp12 during 10 ns. Discussion: ZINC03977803 showed stable interaction with the catalytic subunit of SARS-CoV-2, nsp12. It could inhibit the SARS-CoV-2 life cycle by direct interaction with nsp12 and inhibit RdRp complex formation.

Molecular dynamics simulation or structure refinement of proteins: are solvent molecules required? A case study using hen lysozyme

In protein simulation or structure refinement based on values of observable quantities measured in (aqueous) solution, solvent (water) molecules may be explicitly treated, omitted, or represented by a potential of mean-solvation-force term, depending on protein coordinates only, in the force field used. These three approaches are compared for hen egg white lysozyme (HEWL). This 129-residue non-spherical protein contains a variety of secondary-structure elements, and ample experimental data are available: 1630 atom–atom Nuclear Overhauser Enhancement (NOE) upper distance bounds, 213 ³ J-couplings and 200 S² order parameters. These data are used to compare the performance of the three approaches. It is found that a molecular dynamics (MD) simulation in explicit water approximates the experimental data much better than stochastic dynamics (SD) simulation in vacuo without or with a solvent-accessible-surface-area (SASA) implicit-solvation term added to the force field. This is due to the missing energetic and entropic contributions and hydrogen-bonding capacities of the water molecules and the missing dielectric screening effect of this high-permittivity solvent. Omission of explicit water molecules leads to compaction of the protein, an increased internal strain, distortion of exposed loop and turn regions and excessive intra-protein hydrogen bonding. As a consequence, the conformation and dynamics of groups on the surface of the protein, which may play a key role in protein–protein interactions or ligand or substrate binding, may be incorrectly modelled. It is thus recommended to include water molecules explicitly in structure refinement of proteins in aqueous solution based on nuclear magnetic resonance (NMR) or other experimentally measured data.

Computational Tools for Accurate Binding Free-Energy Prediction

A quantitative thermodynamic understanding of the noncovalent association of (bio)molecules is of central importance in molecular life sciences. An important quantity characterizing (bio)molecular association is the binding affinity or absolute binding free energy. In recent years, the computational prediction of absolute binding free energies has evolved considerably in terms of accuracy, computational speed, and user-friendliness. In this chapter, we first give an overview of how absolute free energies are defined and how they can be determined with computational means. We proceed with an outline of the theoretical basis of the two most reliable methods, potential of mean force, and double decoupling calculations. In particular, we describe how the sampling problem can be alleviated by application of restraints. Finally, we provide step-by-step instructions of how to set up corresponding molecular simulations with a commonly employed molecular dynamics simulation engine.

Data set and fitting dependencies when estimating protein mutant stability: Toward simple, balanced, and interpretable models

Accurate prediction of protein stability changes upon mutation (ΔΔG) is increasingly important to evolution studies, protein engineering, and screening of disease-causing gene variants but is challenged by biases in training data. We investigated 45 linear regression models trained on data sets that account systematically for destabilization bias and mutation-type bias B_M . The models were externally validated on three test data sets probing different pathologies and for internal consistency (symmetry and neutrality). Model structure and performance substantially depended on training data and even fitting method. We developed two final models: SimBa-IB for typical natural mutations and SimBa-SYM for situations where stabilizing and destabilizing mutations occur to a similar extent. SimBa-SYM, despite is simplicity, is essentially non-biased (vs. the S^sym data set) while still performing well for all data sets (R ~ 0.46-0.54, MAE = 1.16-1.24 kcal/mol). The simple models provide advantage in terms of interpretability, use and future improvement, and are freely available on GitHub.

The Therapeutic Antibody Profiler for Computational Developability Assessment

The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.