Insights from molecular dynamics simulations for computational protein design

Mechanistic insights into Alpha-Synuclein binding to P2RX7: A molecular dynamic and docking study

Alpha-synucleinopathies, characterized by extracellular alpha-synuclein (αSyn or SNCA) accumulation and aggregation, have been linked to neurological disorders including Parkinson's disease and multiple system atrophy. P2RX7 is a non-selective cationic transmembrane purinergic receptor activated by elevated levels of extracellular ATP, which typically occurs during inflammatory conditions. Activation of P2RX7 by αSyn is implicated in neuronal degeneration, potentially causing pore dilation and increased inflammation. By integrating the data curation, molecular docking, and molecular dynamics (MD) simulations, along with structural analyses, we attempted to elucidate the molecular mechanisms and binding sites for P2RX7-αSyn interaction. We elucidated interactions between P2RX7 and the N-terminal domain (NTD) of αSyn. Utilizing cryo-EM structures of P2RX7 in ATP-bound and unbound states, we assessed αSyn's effect on P2RX7 structural and functional dynamics. Initially, the analyses revealed that αSyn interactomes are mainly involved in mitochondrial functions, while P2RX7 interactors are linked to receptor internalization and calcium transport. Molecular docking with six tools identified that αSyn-NTD fragments preferentially bind to the proximal region of P2RX7's transmembrane domain. Microsecond all atom MD simulations in a POPS lipid bilayer showed significant atomic fluctuations, particularly in the head region, lower body, and large loop of P2RX7's cytoplasmic domain. Secondary structure analysis indicated unfolding in regions related to pore dilation and receptor desensitization. Further by contact-based and solvent accessibility analyses, along with protein structure network (PSN) studies, we identified crucial residues involved in αSyn-P2RX7 interactions. This understanding enhances the knowledge of how αSyn and P2RX7 interactions take place, potentially contributing to neurodegenerative diseases, and could be instrumental in developing future preventive and therapeutic approaches.

Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (kcat ∼ 1700 s-1, kcat/KM ∼ 4.3 × 105 M-1 s-1) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.

Bioprospection of Hura crepitans metabolites against oxidative stress and inflammation: An in vitro and in silico exploration

Background: Despite the recognized therapeutic potential of Hura crepitans, its mechanistic antioxidant and anti-inflammatory actions remain underexplored. Methods: This study investigates the inhibitory effects, binding stability, and interactions of metabolites from H. crepitans on oxidative and inflammatory biomarkers/targets using in vitro analyses and molecular dynamics (MD) simulations. Results: In vitro experiments revealed significant dose-dependent antioxidant and anti-inflammatory activities. The crude methanolic extract (CMEHC) showed notable half-maximal inhibitory concentration (IC50) values for antioxidant assays, such as diphenyl picrylhydrazine (45.51 µg/mL) and ferric-reducing power (10.86 µg/mL), with comparable performance to standard ascorbic acid. Anti-inflammatory activities, including protein denaturation, proteinase inhibition, and membrane stabilization, demonstrated IC50 values between 77.29-171.30 µg/mL. Liquid chromatography-mass spectrophotometry identified five primary compounds, predominantly phenolics, with rutin as the most abundant. Computational analyses confirmed these compounds' safety profiles, robust binding interactions, and stability against oxidative and inflammatory targets, with rutin forming the most stable interactions. Conclusion: These findings highlight the potential of H. crepitans phenolics as alternative therapies for oxidative stress and inflammation, warranting further drug development studies.

Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition

This study presents a novel computational approach for engineering nanobodies (Nbs) for improved interaction with receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Using Protein Structure Reliability reports, RBD (7VYR_R) was selected and refined for subsequent Nb-RBD interactions. By leveraging electrostatic complementarity (EC) analysis, we engineered and characterized five Electrostatically Complementary Nbs (ECSb1-ECSb5) based on the CeVICA library's SR6c3 Nb. Through targeted modifications in the complementarity-determining regions (CDR) and framework regions (FR), we optimized electrostatic interactions to improve binding affinity and specificity. The engineered Nbs (ECSb3, ECSb4, and ECSb5) demonstrated high binding specificity for AS3, CA1, and CA2 epitopes. Interestingly, ECSb1 and ECSb2 selectively engaged with AS3 and CA1 instead of AS1 and AS2, respectively, due to a preference for residues that conferred superior binding complementarities. Furthermore, ECSbs significantly outperformed SR6c3 Nb in MM/GBSA results, notably, ECSb4 and ECSb3 exhibited superior binding free energies of -182.58 kcal.mol-1 and -119.07 kcal.mol-1, respectively, compared to SR6c3 (-105.50 kcal.mol-1). ECSbs exhibited significantly higher thermostability (100.4-148.3 kcal·mol⁻1) compared to SR6c3 (62.6 kcal·mol⁻1). Similarly, enhanced electrostatic complementarity was also observed for ECSb4-RBD and ECSb3-RBD (0.305 and 0.390, respectively) relative to SR6c3-RBD (0.233). Surface analyses confirmed optimized electrostatic patches and reduced aggregation propensity in the engineered Nb. This integrated EC and structural engineering approach successfully developed engineered Nbs with enhanced binding specificity, increased thermostability, and reduced aggregation, laying the groundwork for novel therapeutic applications targeting the SARS-CoV-2 spike protein.

Molecular dynamics simulations: Insights into protein and protein ligand interactions

Molecular dynamics (MD) simulations are a powerful tool for studying biomolecular systems, offering in-depth insights into the dynamic behaviors of proteins and their interactions with ligands. This chapter delves into the fundamental principles and methodologies of MD simulations, exploring how they contribute to our understanding of protein structures, conformational changes, and the mechanisms underlying protein-ligand interactions. We discuss the computational techniques, force fields, and algorithms that drive MD simulations, highlighting their applications in drug discovery and design. Through case studies and practical examples, we illustrate the capabilities and limitations of MD simulations, emphasizing their role in predicting binding affinities, elucidating binding pathways, and optimizing lead compounds. This chapter offers a thorough understanding of how MD simulations can be leveraged to advance the study of protein-ligand interactions.

The regulatory effects of mitragynine on P-glycoprotein transporter

OBJECTIVES

Kratom preparation containing Mitragyna speciosa Korth plant is frequently used as a recreational drug. Mitragynine, a major alkaloid isolated from M. speciosa, is often detected concurrently with other drugs during forensic analysis, indicating a safety concern. P-glycoprotein (P-gp) is a multidrug transporter. Modulation of P-gp transport activity by drugs or herbal compounds in the brain may lead to drug-herb interactions, resulting in neurotoxicity. We aim to determine the effects of mitragynine on the P-gp regulation and possible neurotoxicity.

METHODS

The effects of mitragynine on the P-gp regulation were investigated in human brain capillary endothelial cells (hCMEC/D3) using molecular docking and dynamic simulation and an optimized bidirectional transport assay, respectively. Repeated-dose treatment and neurotoxicity assessment were carried out using a blood-brain barrier model and polimerase chain reaction (PCR) array.

KEY FINDINGS

Mitragynine inhibits the P-gp transport activity via binding onto the nucleotide-binding domain site and forms a stable interaction with the P-gp protein complex. Nontoxic concentrations of mitragynine (<4 μM) and substrate drugs (0.001 μM) in the cells significantly enhanced endothelial cell permeability and elicited signs of neurotoxicity in PC-12 cells.

CONCLUSIONS

Mitragynine is likely a P-gp inhibitor, hence concurrent administration of kratom products with P-gp substrates may lead to clinically significant interactions and neurotoxicity.

In Silico Hybridization and Molecular Dynamics Simulations for the Identification of Candidate Human MicroRNAs for Inhibition of Virulent Proteins' Expression in Staphylococcus aureus

Staphylococcus aureus is a major threat to human health, causing infections that range in severity from moderate to fatal. The rising rates of antibiotic resistance highlight the critical need for new therapeutic techniques to combat this infection. It has been recently discovered that microRNAs (miRNAs) are essential for cross-kingdom communication, especially when it comes to host-pathogen interactions. It has been demonstrated that these short noncoding RNAs control gene expression in the gut microbiota, maintaining homeostasis; dysbiosis in this system has been linked to several diseases, including cancer. Our research attempts to use this understanding to target specific bacterial species and prevent severe diseases. In particular, we look for putative human miRNAs that can attach to virulent bacterial proteins' mRNA and prevent them from being expressed. In-silico hybridization experiments were performed between 100 human miRNA sequences with varied expression levels in gram-positive bacterial infections and five virulence factor genes. In addition, these miRNAs' binding properties were investigated using molecular dynamics (MD) simulations. Our findings demonstrate that human miRNAs can target and inhibit the expression of bacterial virulent genes, thereby opening up new paths for developing innovative miRNA-based therapeutics. The implementation of MD simulations in our study not only improves the validity of our findings but also proposes a new method for constructing miRNA-based therapies against life-threatening bacterial infections.

Use of genomics & proteomics in studying lipase producing microorganisms & its application

In biotechnological applications, lipases are recognized as the most widely utilized and versatile enzymes, pivotal in biocatalytic processes, predominantly produced by various microbial species. Utilizing omics technology, natural sources can be meticulously screened to find microbial flora which are responsible for oil production. Lipases are versatile biocatalysts. They are used in a variety of bioconversion reactions and are receiving a lot of attention because of the quick development of enzyme technology and its usefulness in industrial operations. This article offers recent insights into microbial lipase sources, including fungi, bacteria, and yeast, alongside traditional and modern methods of purification such as precipitation, immunopurification and chromatographic separation. Additionally, it explores innovative methods like the reversed micellar system, aqueous two-phase system (ATPS), and aqueous two-phase flotation (ATPF). The article deals with the use of microbial lipases in a variety of sectors, including the food, textile, leather, cosmetics, paper, detergent, while also critically analyzing lipase-producing microbes. Moreover, it highlights the role of lipases in biosensors, biodiesel production, tea processing, bioremediation, and racemization. This review provides the concept of the use of omics technique in the mechanism of screening of microbial species those are capable of producing lipase and also find the potential applications.

Helichrysum populifolium Compounds Inhibit MtrCDE Efflux Pump Transport Protein for the Potential Management of Gonorrhoea Infection

The progressive development of resistance in Neisseria gonorrhoeae to almost all available antibiotics has made it crucial to develop novel approaches to tackling multi-drug resistance (MDR). One of the primary causes of antibiotic resistance is the over-expression of the MtrCDE efflux pump protein, making this protein a vital target for fighting against antimicrobial resistance (AMR) in N. gonorrhoeae. This study was aimed at evaluating the potential MtrCDE efflux pump inhibitors (EPIs) and their stability in treating gonorrhoea infection. This is significant because finding novel EPIs would allow for the longer maintenance of antibiotics at therapeutic levels, thereby prolonging the susceptibility of currently available antibiotics. A virtual screening of the selected Helichrysum populifolium compounds (4,5-dicaffeoylquinic acid, apigeninin-7-glucoside, and carvacrol) was conducted to evaluate their potential EPI activity. An integrated computational framework consisting of molecular docking (MD), molecular mechanics generalized born, and surface area solvation (MMGBSA) analysis, molecular dynamics simulations (MDS), and absorption, distribution, metabolism, and excretion (ADME) properties calculations were conducted. Of the tested compounds, 4,5-dicaffeoylquinic acid revealed the highest molecular docking binding energies (-8.8 kcal/mol), equivalent MMGBSA binding free energy (-54.82 kcal/mol), indicative of consistent binding affinity with the MtrD protein, reduced deviations and flexibility (root mean square deviation (RMSD) of 5.65 Å) and, given by root mean square fluctuation (RMSF) of 1.877 Å. Carvacrol revealed a docking score of -6.0 kcal/mol and a MMGBSA computed BFE of -16.69 kcal/mol, demonstrating the lowest binding affinity to the MtrD efflux pump compared to the remaining test compounds. However, the average RMSD (4.45 Å) and RMSF (1.638 Å) of carvacrol-bound MtrD protein showed no significant difference from the unbound MtrD protein, except for the reference compounds, implying consistent MtrD conformation throughout simulations and indicates a desirable feature during drug design. Additionally, carvacrol obeyed the Lipinski rule of five which confirmed the compound's drug-likeness properties making it the most promising EPI candidate based on its combined attributes of a reasonable binding affinity, sustained stability during MDS, its obedience to the Lipinski rule of five and compliance with drug-likeness criteria. An in vitro validation of the potential EPI activities of H. populifolium compounds confirmed that 4,5-dicaffeoylquinic acid reduced the expulsion of the bis-benzimide dye by MtrCDE pump, while carvacrol showed low accumulation compared to other compounds. While 4,5-dicaffeoylquinic acid demonstrated the highest binding affinity in computational analysis and an EPI activity in vitro, it showed lower stability compared to the other compounds, as indicated in MDS. This leaves carvacrol, as a better EPI candidate for the management of gonorrhoea infection.

Similarity-Based Machine Learning for Small Data Sets: Predicting Biolubricant Base Oil Viscosities

Machine learning (ML) has been successfully applied to learn patterns in experimental chemical data to predict molecular properties. However, experimental data can be time-consuming and expensive to obtain and, as a result, it is often scarce. Several ML methods face challenges when trained with limited data. Here, we introduce a similarity-based machine learning approach that enables precise model training on small data sets while requiring fewer features and enhancing prediction accuracy. We group molecules with similar structures, represented by molecular fingerprints, and use these groups to train separate ML models for each group. We first validate our method on larger data sets of dynamic viscosity and aqueous solubility, demonstrating comparable or better performance than traditional approaches while requiring fewer features. We then apply the validated methodology to predict the kinematic viscosity of biolubricant base oil molecules at 40 °C (KV40), where experimental data is particularly limited. Our method shows noticeable model performance improvement for KV40 prediction compared to transfer learning and the standard Random Forest. This approach provides a robust framework for limited data that can be readily generalized to a diverse range of molecular data sets especially when clear structural patterns exist in the data set.

In-silico and in-vitro studies to identify potential inhibitors of SARS-CoV-2 spike protein from Omani medicinal plants

In the quest for novel therapeutic agents against SARS-CoV-2, the proposed study explores the potential of traditional Omani medicinal plants, focusing on the efficacy of natural ligands against the virus's Spike protein. Among 437 identified medicinal plants across Oman, 47 species that are documented for their traditional use in treating respiratory infections, with 30 species' ligands available were chosen for analysis. Molecular docking was performed using Autodock Vina on these ligands, yielding 406 unique ligands post-duplication removal. The binding affinities of target-ligand complexes were precisely determined, ranking them by interaction strength. This process identified Corilagin, a phytochemical from the Acalypha indica plant (locally known as Aeyan Al Aqrada), as the most promising inhibitor. Subsequent analyses using GROMACS for molecular dynamics simulation confirmed its binding stability and interaction dynamics of the Corilagin-protein complex. The in-vitro studies further validated Corilagin's inhibitory effect on SARS-CoV-2, demonstrating a remarkable 92 % inhibition at 0.5 mM concentration. Dilution studies to ascertain the IC50 value revealed Corilagin's high potency at a micromolar level (IC50 = 2.15 ± 0.13 μM), underscoring its potential as a drug candidate for SARS-CoV-2 treatment. These findings highlight the significance of ethnomedicine and in-silico methodologies in drug discovery, offering promising directions for future antiviral research.

Spiers Memorial Lecture: Engineering biocatalysts

Enzymes are being engineered to catalyze chemical reactions for many practical applications in chemistry and biotechnology. The approaches used are surveyed in this short review, emphasizing methods for accessing reactivities not expressed by native protein scaffolds. The successful generation of completely de novo enzymes that rival the rates and selectivities of their natural counterparts highlights the potential role that designer enzymes may play in the coming years in research, industry, and medicine. Some challenges that need to be addressed to realize this ambitious dream are considered together with possible solutions.

Mechanistic study of inhibitory peptides with SHP-1 in hypertonic environment for infection model

Cutaneous Leishmaniasis, an infectious disease is globally the most prevalent form of leishmaniasis accounting for approximately 1 million cases every year as per world health organization. Infected individuals develop skin lesion which has been reported to be infiltrated by immune cells and parasite with high sodium accumulation creating hypertonic environment. In our work, we tried to mimic the hypertonic environment in virtual environment to study dynamicity of SHP-1 and NFAT5 along with their interactions through molecular dynamics simulation. We validated the SHP-1 and NFAT5 dynamics in infection and HSD conditions to study the impact of hypertonicity derived NFAT5 mediated response to L.major infection. We also evaluated our therapeutic peptides for their binding to SHP-1 and to form stable complex. Membrane stability with the peptides was analyzed to understand their ability to sustain mammalian membrane. We identified PepA to be a potential candidate to interact with SHP-1. Inhibition of SHP-1 through PepA to discern IL-10 and IL-12 reciprocity may be assessed in future and furnish us with a potential therapeutic molecule. HSD mice exhibited high pro-inflammatory response to L.major infection which resulted in reduced lesion size. Contrary to observations in HSD mice, infection model exhibited low pro-inflammatory response and increased lesion size with high parasite load. Thus, increase in NFAT5 expression and reduced SHP-1 expression may result in disease resolving effect which can be further studied through incorporation of synthetic circuit using PepA to modulate IL-10 and IL-12 reciprocity.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF

Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites

In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery of the model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or "coordinate-lock" approach is commonly employed but leads to undesirable artifacts in the form of small imaginary frequencies. These preclude evaluation of finite-temperature free-energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials. Here, we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While the latter strategy does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy while introducing artificial rigidity. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct active-site models that can be used in unconstrained geometry relaxations, affording better convergence of reaction energies and barrier heights with respect to the model size, as compared to models with fixed-atom constraints.

Incomplete-penetrant hypertrophic cardiomyopathy MYH7 G256E mutation causes hypercontractility and elevated mitochondrial respiration

Determining the pathogenicity of hypertrophic cardiomyopathy-associated mutations in the β-myosin heavy chain (MYH7) can be challenging due to its variable penetrance and clinical severity. This study investigates the early pathogenic effects of the incomplete-penetrant MYH7 G256E mutation on myosin function that may trigger pathogenic adaptations and hypertrophy. We hypothesized that the G256E mutation would alter myosin biomechanical function, leading to changes in cellular functions. We developed a collaborative pipeline to characterize myosin function across protein, myofibril, cell, and tissue levels to determine the multiscale effects on structure-function of the contractile apparatus and its implications for gene regulation and metabolic state. The G256E mutation disrupts the transducer region of the S1 head and reduces the fraction of myosin in the folded-back state by 33%, resulting in more myosin heads available for contraction. Myofibrils from gene-edited MYH7WT/G256E human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) exhibited greater and faster tension development. This hypercontractile phenotype persisted in single-cell hiPSC-CMs and engineered heart tissues. We demonstrated consistent hypercontractile myosin function as a primary consequence of the MYH7 G256E mutation across scales, highlighting the pathogenicity of this gene variant. Single-cell transcriptomic and metabolic profiling demonstrated upregulated mitochondrial genes and increased mitochondrial respiration, indicating early bioenergetic alterations. This work highlights the benefit of our multiscale platform to systematically evaluate the pathogenicity of gene variants at the protein and contractile organelle level and their early consequences on cellular and tissue function. We believe this platform can help elucidate the genotype-phenotype relationships underlying other genetic cardiovascular diseases.

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r =  - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

A Bibliometric Analysis: Current Perspectives and Potential Trends of Enzyme Thermostability from 1991-2022

Thermostability is considered a crucial parameter to evaluate the viability of enzymes in industrial applications. Over the past 31 years, many studies have been reported on the thermostability of enzymes. However, there is no systematic bibliometric analysis of publications on the thermostability of enzymes. In this study, 16,035 publications related to the thermostability of enzymes were searched and collected, showing an increasing annual trend. China contributed the most publications, while the United States had the highest citation count. International Journal of Biological Macromolecules is the most productive journal in the research field. Moreover, Chinese acad sci and Khosro Khajeh are the most active institutions and prolific authors in the field, respectively. Analysis of references with the strongest citation bursts and keyword co-occurrences, magnetic nanoparticles, metal-organic frameworks, molecular dynamics, and rational design are current hot spots and significant future research directions. This study is the first comprehensive bibliometric analysis summarizing trends and developments in enzyme thermostability research. Our findings could provide scholars with an understanding of the fundamental knowledge framework of the field and identify recent potential hotspots and research trends that could facilitate the discovery of collaboration opportunities.

PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Assessment of Kaistella jeonii esterase conformational dynamics in response to poly(ethylene terephthalate) binding

The pervasive presence of plastic in the environment has reached a concerning scale, being identified in many ecosystems. Bioremediation is the cheapest and most eco-friendly alternative to remove this polymer from affected areas. Recent work described that a novel cold-active esterase enzyme extracted from the bacteria Kaistella jeonii could promiscuously degrade PET. Compared to the well-known PETase from Ideonella sakaiensis, this novel esterase presents a low sequence identity yet has a remarkably similar folding. However, enzymatic assays demonstrated a lower catalytic efficiency. In this work, we employed a strict computational approach to investigate the binding mechanism between the esterase and PET. Understanding the underlying mechanism of binding can shed light on the evolutive mechanism of how enzymes have been evolving to degrade these artificial molecules and help develop rational engineering approaches to improve PETase-like enzymes. Our results indicate that this esterase misses a disulfide bridge, keeping the catalytic residues closer and possibly influencing its catalytic efficiency. Moreover, we describe the structural response to the interaction between enzyme and PET, indicating local and global effects. Our results aid in deepening the knowledge behind the mechanism of biological catalysis of PET degradation and as a base for the engineering of novel PETases.

The origin and structural evolution of de novo genes in Drosophila

Recent studies reveal that de novo gene origination from previously non-genic sequences is a common mechanism for gene innovation. These young genes provide an opportunity to study the structural and functional origins of proteins. Here, we combine high-quality base-level whole-genome alignments and computational structural modeling to study the origination, evolution, and protein structures of lineage-specific de novo genes. We identify 555 de novo gene candidates in D. melanogaster that originated within the Drosophilinae lineage. Sequence composition, evolutionary rates, and expression patterns indicate possible gradual functional or adaptive shifts with their gene ages. Surprisingly, we find little overall protein structural changes in candidates from the Drosophilinae lineage. We identify several candidates with potentially well-folded protein structures. Ancestral sequence reconstruction analysis reveals that most potentially well-folded candidates are often born well-folded. Single-cell RNA-seq analysis in testis shows that although most de novo gene candidates are enriched in spermatocytes, several young candidates are biased towards the early spermatogenesis stage, indicating potentially important but less emphasized roles of early germline cells in the de novo gene origination in testis. This study provides a systematic overview of the origin, evolution, and protein structural changes of Drosophilinae-specific de novo genes.

An atomistic scale simulation study of structural properties in the silk-fibrohexamerin complex

The use of Bombyx mori silk fibroin in composite materials has been extensively explored in many studies, owing to its remarkable mechanical properties. Recently, the N-glycan-engineered P25 protein was utilized to improve the mechanical properties of silk. However, the mechanism by which N-glycan-engineered P25 protein enhances the mechanical properties of silk remains unclear. This study analyzed the interaction between the P25 protein and silkworm silk using quantum mechanics/molecular mechanics multiscale simulations and discovered stronger hydrogen bonding between the amorphous domain and the P25 protein. The results confirmed that glycoengineering of the mannose molecule in N-glycan in orders of three, five, and seven increased the hydrogen bonding of the amorphous structures. However, P25 has fewer binding interactions with the crystalline domain. Silk amino acids and mannose molecules were analyzed using QM simulations, and hydroxyl and charged amino acids in the amorphous domains were found to have relatively higher reactivity with mannose molecules in N-glycans than basic and aliphatic amino acids in the crystalline domain. This study demonstrates how the N-glycan-engineered P25 protein can improve the mechanical properties of silk fibroin and identifies a key factor for N-glycan-engineered proteins.

Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?

Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.

Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation

Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Cheminformatics identification of modulators of key carbohydrate-metabolizing enzymes from C. cujete for type-2 diabetes mellitus intervention

Purpose

The therapeutic use of oral hypoglycaemic agents in the management of type-2 diabetes mellitus (T2DM) is without adverse effects; thus, calls for alternative and novel candidates from natural products in medicinal plants.

Method

The study explored molecular docking and molecular dynamics (MD) simulation approaches to identify key antidiabetic metabolites from Crescentia cujete.

Results

Molecular docking results identified four and/or five best compounds against each target enzyme (alpha-glucosidase, dipeptidyl peptidase-IV, aldose reductase, and protein tyrosine phosphatase-1B (PTP-1B)) implicated in diabetes. The resulting complexes (except against PTP-1B) had higher docking scores above respective standards (acarbose, Diprotin A, ranirestat). The MD simulation results revealed compounds such as benzoic acid (-48.414 kcal/mol) and phytol (-45.112 kcal/mol) as well as chlorogenic acid (-42.978 kcal/mol) and naringenin (-31.292 kcal/mol) had higher binding affinities than the standards [acarbose (-28.248 kcal/mol), ranirestat (-21.042 kcal/mol)] against alpha-glucosidase and aldose reductase, respectively while Diprotin A (-45.112 kcal/mol) and ursolic acid (-18.740 kcal/mol) presented superior binding affinities than the compounds [luteolin (-41.957 kcal/mol and naringenin (-16.518 kcal/mol)] against DPP-IV and PTP-1B respectively.

Conclusion

While isoflavone (alpha-glucosidase), xylocaine (DPP-IV), luteolin (aldose reductase,) and chlorogenic acid (PTP-1B) were affirmed as the best inhibitors of respective enzyme targets, luteolin, and chlorogenic acid may be suggested and proposed as probable candidates against T2DM and related retinopathy complication based on their structural stability, compactness and affinity for three (DPP-IV, aldose reductase, and PTP-1B) of the four targets investigated. Further studies are warranted in vitro and in vivo on the antihyperglycaemic effects of these drug candidates.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01249-7.

Improvement of α-amino Ester Hydrolase Stability via Computational Protein Design

Amino ester hydrolases (AEHs) are capable of rapid synthesis of cephalexin but suffer from rapid deactivation even at low temperatures. Previous efforts to engineer AEH have generated several improved variants but have been limited in scope in part due to limitations in activity assay throughput for β-lactam synthesis reactions. Rational design of 'whole variants' was explored to rapidly improve AEH thermostability by mutating between 3-15% of residues. Most variants were found to be inactive due to a mutated calcium binding site, the function of which has not previously been described. Four active variants, all with improved melting temperatures, were characterized in terms of synthesis and hydrolysis activity, melting temperature, and deactivation at 25°C. Two variants were found to have improved total turnover numbers relative to the initial AEH variant; however, a clear tradeoff exists between improved stability and overall activity of each variant.

Improved catalytic performance and molecular insight for lipoxygenase from Enterovibrio norvegicus via directed evolution

Lipoxygenase (LOX) holds significant promise for food and pharmaceutical industries. However, albeit its application has been hampered by low catalytic activity and suboptimal thermostability. To address the drawbacks, a directed evolution strategy was explored to enhance the catalytic activity and thermostability of LOX from Enterovibrio norvegicus (EnLOX) for the first time. After two rounds of error-prone polymerase chain reaction (error-prone PCR) and one generations of sequential DNA shuffling, all of four different mutants showed a significant increase in the specific activity of EnLOX, ranging from 132.07 ± 9.34 to 330.17 ± 18.54 U/mg. Among these mutants, D95E/T99A/A121H/S142N/N444W/S613G (EAHNWG) exhibited the highest specific activity, which was 8.25-fold higher than the wild-type enzyme (WT). Meanwhile, the catalytic efficiency (K cat /K m) of EAHNWG was also improved, which was 13.61 ± 1.67 s-1 μM-1, in comparison to that of WT (4.83 ± 0.38 s-1 μM-1). In addition, mutant EAHNWG had a satisfied thermostability with the t 1/2,50 °C value of 6.44 ± 0.24 h, which was 0.4 h longer than that of the WT. Furthermore, the molecular dynamics simulation and structural analysis demonstrated that the reduction of hydrogen bonds number, the enhancement of hydrophobic interactions in the catalytic pocket, and the improvement of flexibility of the lid domain facilitated structural stability and the strength of substrate binding capacity for improved thermal stability and catalytic efficiency of mutant LOX after directed evolution. Overall, these results could provide the guidance for further enzymatic modification of LOX with high catalytic performance for industrial application.

Molecular analysis of the type III interferon complex and its applications in protein engineering

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.

In Silico Exploration and Experimental Validation of Camellia sinensis Extract against Rhipicephalus microplus and Sarcoptes scabiei: An Integrated Approach

Sarcoptes scabiei is an ectoparasite of humans and animals that causes scabies. The Rhipicephalus (Boophilus) microplus is a blood-sucking ectoparasite that transmits various pathogens. These two parasites have caused great losses to a country's dairy and agriculture sectors. The aim of this study was to determine the in vitro and in silico efficacy of Camellia sinensis plant extracts. Different concentrations of C. sinensis ethanolic plant extracts were prepared using the maceration method and were used against mites and ticks (in adult immersion test AIT and larval packet test LPT) to evaluate their in vitro acaricidal activity. Additionally, in silico molecular docking was performed to investigate the inhibitory interactions between the phytochemicals of the plant and S. scabiei and R. microplus glutathione transferase proteins (SsGST and RmGST). This study observed that the plant extract showed high efficacy in vitro against mites and different tick stages in adult immersion and larval packet tests. Additionally, the in silico study revealed a strong binding interaction between ellagic acid and SsGST protein, with a binding energy of -7.3 kcal/mol, with respect to permethrin (-6.7 kcal/mol), whereas quercetin and RmGST resulted in a docking score of -8.6 kcal/mol compared to deltamethrin (-8.2 kcal/mol). Overall, this study explored the potential of C. sinensis as a natural alternative for controlling tick and mite infestations and provided insights into the inhibitory mechanisms of its phytochemicals.

LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

Biocomputational characterisation of MBO_200107 protein of Mycobacterium tuberculosis variant caprae: a molecular docking and simulation study

The principal objective of this study was to delineate the potentiality of the MBO_200107 protein from the Mycobacterium tuberculosis variant caprae in cancer research. It is a cytoplasmic protein, comprised of a 354-long amino acid chain, alkaline, had a molecular weight of 39089.37 Da, an isoelectric point of 9.62 and a grand average of hydropathicity of -0.345. One of the functional domains was predicted as Gammaglutamylcyclotransferase (GGCT). Among tertiary structures, the Modeller and Phyre2 model satisfied all the quality parameters, though they are truncated; contrarily, the I-TASSER model is full length and contains the sequence for the GGCT domain, though it did not meet all the quality parameters. It also has significant sequence similarities (47.5% by EMBOSS Water and 72.4% by EMBOSS Matcher) with a human GGCT, and the conserved sequences are confined to the GGCT domain of the MBO_200107. According to molecular docking analyses, the protein has a binding affinity of -4.8 kcal/mol by Autodock Vina and -56.465 kcal/mol by HPEPDOCK to the human glutathione (GSH), an essential metabolite for GGCT metabolism. The Molecular dynamic simulation of the docked complex showed the binding efficiency of the GSH to MBO_200107 with a minimal structural alteration. The in silico findings mentioned above revealed that the protein could be used as a supplementary tool in cancer research, such as designing vaccines or drugs where the role of GGCT has been implicated. Further, we recommend fully characterising the protein and conducting essential in vitro and in vivo experiments to determine its detailed usefulness.Communicated by Ramaswamy H. Sarma.

Learned Reconstruction of Protein Folding Trajectories from Noisy Single-Molecule Time Series

Single-molecule Förster resonance energy transfer (smFRET) is an experimental methodology to track the real-time dynamics of molecules using fluorescent probes to follow one or more intramolecular distances. These distances provide a low-dimensional representation of the full atomistic dynamics. Under mild technical conditions, Takens' Delay Embedding Theorem guarantees that the full three-dimensional atomistic dynamics of a system are diffeomorphic (i.e., related by a smooth and invertible transformation) to a time-delayed embedding of one or more scalar observables. Appealing to these theoretical guarantees, we employ manifold learning, artificial neural networks, and statistical mechanics to learn from molecular simulation training data the a priori unknown transformation between the atomic coordinates and delay-embedded intramolecular distances accessible to smFRET. This learned transformation may then be used to reconstruct atomistic coordinates from smFRET time series data. We term this approach Single-molecule TAkens Reconstruction (STAR). We have previously applied STAR to reconstruct molecular configurations of a C24H50 polymer chain and the mini-protein Chignolin with accuracies better than 0.2 nm from simulated smFRET data under noise free and high time resolution conditions. In the present work, we investigate the role of signal-to-noise ratio, data volume, and time resolution in simulated smFRET data to assess the performance of STAR under conditions more representative of experimental realities. We show that STAR can reconstruct the Chignolin and Villin mini-proteins to accuracies of 0.12 and 0.42 nm, respectively, and place bounds on these conditions for accurate reconstructions. These results demonstrate that it is possible to reconstruct dynamical trajectories of protein folding from time series in noisy, time binned, experimentally measurable observables and lay the foundations for the application of STAR to real experimental data.

In Silico Functional Characterization of a Hypothetical Protein From Pasteurella Multocida Reveals a Novel S-Adenosylmethionine-Dependent Methyltransferase Activity

Genomes may now be sequenced in a matter of weeks, leading to an influx of "hypothetical" proteins (HP) whose activities remain a mystery in GenBank. The information included inside these genes has quickly grown in prominence. Thus, we selected to look closely at the structure and function of an HP (AFF25514.1; 246 residues) from Pasteurella multocida (PM) subsp. multocida str. HN06. Possible insights into bacterial adaptation to new environments and metabolic changes might be gained by studying the functions of this protein. The PM HN06 2293 gene encodes an alkaline cytoplasmic protein with a molecular weight of 28352.60 Da, an isoelectric point (pI) of 9.18, and an overall average hydropathicity of around -0.565. One of its functional domains, tRNA (adenine (37)-N6)-methyltransferase TrmO, is a S-adenosylmethionine (SAM)-dependent methyltransferase (MTase), suggesting that it belongs to the Class VIII SAM-dependent MTase family. The tertiary structures represented by HHpred and I-TASSER models were found to be flawless. We predicted the model's active site using the Computed Atlas of Surface Topography of Proteins (CASTp) and FTSite servers, and then displayed it in 3 dimensional (3D) using PyMOL and BIOVIA Discovery Studio. Based on molecular docking (MD) results, we know that HP interacts with SAM and S-adenosylhomocysteine (SAH), 2 crucial metabolites in the tRNA methylation process, with binding affinities of 7.4 and 7.5 kcal/mol, respectively. Molecular dynamic simulations (MDS) of the docked complex, which included only modest structural adjustments, corroborated the strong binding affinity of SAM and SAH to the HP. Evidence for HP's possible role as an SAM-dependent MTase was therefore given by the findings of Multiple sequence alignment (MSA), MD, and molecular dynamic modeling. These in silico data suggest that the investigated HP might be used as a useful adjunct in the investigation of Pasteurella infections and the development of drugs to treat zoonotic pasteurellosis.

The origin and structural evolution of de novo genes in Drosophila

Although previously thought to be unlikely, recent studies have shown that de novo gene origination from previously non-genic sequences is a relatively common mechanism for gene innovation in many species and taxa. These young genes provide a unique set of candidates to study the structural and functional origination of proteins. However, our understanding of their protein structures and how these structures originate and evolve are still limited, due to a lack of systematic studies. Here, we combined high-quality base-level whole genome alignments, bioinformatic analysis, and computational structure modeling to study the origination, evolution, and protein structure of lineage-specific de novo genes. We identified 555 de novo gene candidates in D. melanogaster that originated within the Drosophilinae lineage. We found a gradual shift in sequence composition, evolutionary rates, and expression patterns with their gene ages, which indicates possible gradual shifts or adaptations of their functions. Surprisingly, we found little overall protein structural changes for de novo genes in the Drosophilinae lineage. Using Alphafold2, ESMFold, and molecular dynamics, we identified a number of de novo gene candidates with protein products that are potentially well-folded, many of which are more likely to contain transmembrane and signal proteins compared to other annotated protein-coding genes. Using ancestral sequence reconstruction, we found that most potentially well-folded proteins are often born folded. Interestingly, we observed one case where disordered ancestral proteins become ordered within a relatively short evolutionary time. Single-cell RNA-seq analysis in testis showed that although most de novo genes are enriched in spermatocytes, several young de novo genes are biased in the early spermatogenesis stage, indicating potentially important but less emphasized roles of early germline cells in the de novo gene origination in testis. This study provides a systematic overview of the origin, evolution, and structural changes of Drosophilinae-specific de novo genes.

Bioprospection of Selected Plant Secondary Metabolites as Modulators of the Proteolytic Activity of Plasmodium falciparum Plasmepsin V

Malaria is a devastating disease, and its management is only achieved through chemotherapy. However, resistance to available medication is still a challenge; therefore, there is an urgent need for the discovery and development of therapeutics with a novel mechanism of action to counter the resistance scourge consistent with the currently available antimalarials. Recently, plasmepsin V was validated as a therapeutic target for the treatment of malaria. The pepsin-like aspartic protease anchored in the endoplasmic reticulum is responsible for the trafficking of parasite-derived proteins to the erythrocytic surface of the host cells. In this study, a small library of compounds was preliminarily screened in vitro to identify novel modulators of Plasmodium falciparum plasmepsin V (PfPMV). The results obtained revealed kaempferol, quercetin, and shikonin as possible PfPMV inhibitors, and these compounds were subsequently probed for their inhibitory potentials using in vitro and in silico methods. Kaempferol and shikonin noncompetitively and competitively inhibited the specific activity of PfPMV in vitro with IC₅₀ values of 22.4 and 43.34 μM, respectively, relative to 62.6 μM obtained for pepstatin, a known aspartic protease inhibitor. Further insight into the structure-activity relationship of the compounds through a 100 ns molecular dynamic (MD) simulation showed that all the test compounds had a significant affinity for PfPMV, with quercetin (-36.56 kcal/mol) being the most prominent metabolite displaying comparable activity to pepstatin (-35.72 kcal/mol). This observation was further supported by the compactness and flexibility of the resulting complexes where the compounds do not compromise the structural integrity of PfPMV but rather stabilized and interacted with the active site amino acid residues critical to PfPMV modulation. Considering the findings in this study, quercetin, kaempferol, and shikonin could be proposed as novel aspartic protease inhibitors worthy of further investigation in the treatment of malaria.

Multi-scale models reveal hypertrophic cardiomyopathy MYH7 G256E mutation drives hypercontractility and elevated mitochondrial respiration

Rationale

Over 200 mutations in the sarcomeric protein β-myosin heavy chain (MYH7) have been linked to hypertrophic cardiomyopathy (HCM). However, different mutations in MYH7 lead to variable penetrance and clinical severity, and alter myosin function to varying degrees, making it difficult to determine genotype-phenotype relationships, especially when caused by rare gene variants such as the G256E mutation.

Objective

This study aims to determine the effects of low penetrant MYH7 G256E mutation on myosin function. We hypothesize that the G256E mutation would alter myosin function, precipitating compensatory responses in cellular functions.

Methods

We developed a collaborative pipeline to characterize myosin function at multiple scales (protein to myofibril to cell to tissue). We also used our previously published data on other mutations to compare the degree to which myosin function was altered.

Results

At the protein level, the G256E mutation disrupts the transducer region of the S1 head and reduces the fraction of myosin in the folded-back state by 50.9%, suggesting more myosins available for contraction. Myofibrils isolated from hiPSC-CMs CRISPR-edited with G256E (MYH7 WT/G256E ) generated greater tension, had faster tension development and slower early phase relaxation, suggesting altered myosin-actin crossbridge cycling kinetics. This hypercontractile phenotype persisted in single-cell hiPSC-CMs and engineered heart tissues. Single-cell transcriptomic and metabolic profiling demonstrated upregulation of mitochondrial genes and increased mitochondrial respiration, suggesting altered bioenergetics as an early feature of HCM.

Conclusions

MYH7 G256E mutation causes structural instability in the transducer region, leading to hypercontractility across scales, perhaps from increased myosin recruitment and altered crossbridge cycling. Hypercontractile function of the mutant myosin was accompanied by increased mitochondrial respiration, while cellular hypertrophy was modest in the physiological stiffness environment. We believe that this multi-scale platform will be useful to elucidate genotype-phenotype relationships underlying other genetic cardiovascular diseases.

Application of Metabolite-Responsive Biosensors for Plant Natural Products Biosynthesis

Plant natural products (PNPs) have shown various pharmaceutical activities, possessing great potential in global markets. Microbial cell factories (MCFs) provide an economical and sustainable alternative for the synthesis of valuable PNPs compared with traditional approaches. However, the heterologous synthetic pathways always lack native regulatory systems, bringing extra burden to PNPs production. To overcome the challenges, biosensors have been exploited and engineered as powerful tools for establishing artificial regulatory networks to control enzyme expression in response to environments. Here, we reviewed the recent progress involved in the application of biosensors that are responsive to PNPs and their precursors. Specifically, the key roles these biosensors played in PNP synthesis pathways, including isoprenoids, flavonoids, stilbenoids and alkaloids, were discussed in detail.

Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations

Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.

Triazine Herbicides Risk Management Strategies on Environmental and Human Health Aspects Using In-Silico Methods

As an effective herbicide, 1, 3, 5-Triazine herbicides (S-THs) are used widely in the pesticide market. However, due to their chemical properties, S-THs severely threaten the environment and human health (e.g., human lung cytotoxicity). In this study, molecular docking, Analytic Hierarchy Process—Technique for Order Preference by Similarity to the Ideal Solution (AHP-TOPSIS), and a three-dimensional quantitative structure-active relationship (3D-QSAR) model were used to design S-TH substitutes with high herbicidal functionality, high microbial degradability, and low human lung cytotoxicity. We discovered a substitute, Derivative-5, with excellent overall performance. Furthermore, Taguchi orthogonal experiments, full factorial design of experiments, and the molecular dynamics method were used to identify three chemicals (namely, the coexistence of aspartic acid, alanine, and glycine) that could promote the degradation of S-THs in maize cropping fields. Finally, density functional theory (DFT), Estimation Programs Interface (EPI), pharmacokinetic, and toxicokinetic methods were used to further verify the high microbial degradability, favorable aquatic environment, and human health friendliness of Derivative 5. This study provided a new direction for further optimizations of novel pesticide chemicals.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

Insights Into The Effects of Amino Acid Substitutions on The Stability of Reteplase Structure: A Molecular Dynamics Simulation Study

Background

Reteplase (recombinant plasminogen activator, r-PA) is a recombinant protein designed to imitate the endogenous tissue plasminogen activator and catalyze the plasmin production. It is known that the application of reteplase is limited by the complex production processes and protein's stability challenges. Computational redesign of proteins has gained momentum in recent years, particularly as a powerful tool for improving protein stability and consequently its production efficiency. Hence, in the current study, we implemented computational approaches to improve r-PA conformational stability, which fairly correlates with protein's resistance to proteolysis.

Objectives

The current study was developed in order to evaluate the effect of amino acid substitutions on the stability of reteplase structure using molecular dynamic simulations and computational predictions.

Materials and Methods

Several web servers designed for mutation analysis were utilized to select appropriate mutations. Additionally, the experimentally reported mutation, R103S, converting wild type r-PA into non-cleavable form, was also employed. Firstly, mutant collection, consisting of 15 structures, was constructed based on the combinations of four designated mutations. Then, 3D structures were generated using MODELLER. Finally, 17 independent 20-ns molecular dynamics (MD) simulations were conducted and different analysis were performed like root-mean-square deviation (RMSD), root-mean-square fluctuations (RMSF), secondary structure analysis, number of hydrogen bonds, principal components analysis (PCA), eigenvector projection, and density analysis.

Results

Predicted mutations successfully compensated the more flexible conformation caused by R103S substitution, so, improved conformational stability was analyzed from MD simulations. In particular, R103S/A286I/G322I indicated the best results and remarkably enhanced the protein stability.

Conclusion

The conformational stability conferred by these mutations will probably lead to more protection of r-PA in protease-rich environments in various recombinant systems and potentially enhance its production and expression level.

Thermal Stability Estimation of Single Domain Antibodies Using Molecular Dynamics Simulations

In this chapter, we describe a protocol to estimate the thermal stability of single domain antibodies (sdAbs) using molecular dynamics (MD) simulations. This method measures the Q-value, the fraction of the native contacts, along the trajectory of high-temperature MD simulations starting from the experimental X-ray structure. We show a good correlation between the Q-value and the experimental melting temperature (T_m) in seven sdAbs. Assessing the Q-value on a per-residue level enabled us to identify residues that contribute to the instability and thus demonstrate which residues could be mutated to improve the stability and have later been validated by experiments. Our protocol extends beyond the application on sdAbs, as it is also suitable for other proteins and to determine the interfacial stability between protein and ligand.

Molecular Dynamics Methods for Antibody Design

Complex and coordinated dynamics are closely connected with protein functions, including the binding of antibodies to antigens. Knowledge of such dynamics could improve the design of antibodies. Molecular dynamics (MD) simulations provide a "computational microscope" that can resolve atomic motions and inform antibody design efforts.