Important Residues Research Articles

Structural insights into the activation mechanism of phosphoinositide 3-kinase alpha

Phosphoinositide 3-kinases (PI3Ks) are lipid kinases known to regulate important cellular functions by phosphorylating the inositol ring of inositol-phospholipids (PtdIns) at 3’ position. The PI3Kα is a heterodimer and the activation of the catalytic subunit (p110α) is regulated by its regulatory subunit (p85α). The current work deals with studying the activation mechanism of the PI3Kα using multi micro-second molecular dynamic simulations. Structural changes involved in activation mechanism is studied by gradually releasing the inhibitory effects of different domains of regulatory subunit namely, n-terminal SH2 (nSH2) and inter SH2 (iSH2). The observation shows that even in the presence of n-terminal and inter SH2 domain (niSH2) of regulatory subunit, the catalytic domain has some intrinsic activation activity and the presence of c-terminal SH2 (cSH2) domain may be required for complete inhibition. The release of nSH2 domain leads to loss of interactions between iSH2 domain (regulatory subunit) and C2 and kinase domain (catalytic subunit). The study shows that early events in the activation mechanism involve the movement of the ABD domain of the catalytic subunit along with the linker region between ABD and RBD region which may lead to movement of ABD closer to the CLobe of the kinase domain. This movement is essentially as it triggers the rearrangement of CLobe especially the catalytic loop and activation loop which bring catalytic important residues closer to ATP and PIP2(phosphatidylinositol-4,5-bisphosphate). Water mediated interaction analysis reveal that water may be playing an important role in the transfer of phosphate from ATP to PIP2. The study shows that initial signal for release of inhibitory effect of the regulatory subunit might be propagated through the linker region between ABD and RBD through allosteric effect to different regions of the protein. These understanding of early events during the activation mechanism may help in the design of better therapeutic targeting PI3K.

Towards designing of some potential new autoimmune disorder inhibitors using crystal structures and Hirshfeld surface analyses in combination with molecular docking and molecular dynamics simulations

The emergence of multi-drug-resistant autoimmune diseases poses a significant risk to human health and has garnered global attention. In this study, metformin and sulfasalazine have been used as starting materials or control. This research has successfully designed a hundred compounds, to assess their efficacy against two autoimmune disease pathogens: type 1 diabetes and rheumatoid arthritis. The DFT method was engaged to calculate the vibrational frequencies and Frontier Molecular orbitals (FMOs) of the selected compounds. The reactivity and selectivity of the selected compounds are analyzed using parameters like MEP and global reactivity descriptors, which are calculated and interpreted. The Density of state (DOS) of the molecule has been plotted and interpreted. Furthermore, docking results showed favorable interactions of the designed compound D385 with catalytically important amino acid residues. The interactions of the best active D385 when compared with the template and the standard drugs show similar binding sites. DFT studies further confirm the presence of HOMO orbital centered on the isoxazole ring further highlighting its importance for receptor-ligand hydrogen and hydrophobic interactions. The molecular dynamics simulations and MM/GBSA analysis reveal that the inhibitory nature of the designed compound D385 is a proven inhibitor of diabetes type 1 and rheumatoid arthritis inhibitor activities. Our study suggested that the designed compounds showed comparable results to that of metformin and sulfasalazine and may be used for further experimental studies. It can also be used as a pipeline to search for and design new potential autoimmune disease inhibitors. The most promising candidates from computational trials can be examined in a wet laboratory experiment before moving on to clinical trials.

Metallothionein Protein Modeling from Pseudomonas aeruginosa PAO1 as A Metal Biosorber Candidate

Metallothionein is a protein that is well known to play a role in metal metabolism in bacterial cells. Metallothionein is a multifunctional protein that has the potential to be used as a metal adsorbing agent. Pseudomonas aeruginosa is a ubiquitous gram-negative and rapid-growth bacterium. In addition, the complete genome of Pseudomonas aeruginosa has been largely known. Pseudomonas aeruginosa PAO1 is a strain of Pseudomonas aeruginosa that the complete genome of this strain is easily accessible in NCBI. These features make Pseudomonas aeruginosa PAO1 become a common model in bacterial studies. This research aimed to find and model the putative metallothionein of Pseudomonas aeruginosa PAO1. This research was carried out by bioinformatic and protein homology methods. Based on the results, the putative metallothionein of Pseudomonas aeruginosa PAO1 was found in the bacterial genome at base sequence of 2355918 to 2356157. The putative metallothionein-encoding gene of Pseudomonas aeruginosa PAO1 has a size of 240 bp. The translation result of the gene showed that the putative metallothionein of Pseudomonas aeruginosa PAO1 has 79 amino acids. The modeling result showed the 3D structure of the putative metallothionein of Pseudomonas aeruginosa PAO1 is similar to the metallothionein 3D structure of Pseudomonas fluorescens Q2-87. The 3D structure of the putative metallothionein of Pseudomonas aeruginosa PAO1 was dominated by turn and coil, but contained 1 α-helix structure and 2 β-sheet structures. Based on protein analysis, it was found that the putative metallothionein of Pseudomonas aeruginosa PAO1 has 1 metal-binding cluster with 10 amino acids and the most important amino acid residue is Cysteine . Even though, there was 1 Histidine amino acid residue on the metal-binding cluster.

Prevalence, Domain Specificity, and Residue Specificity of IgG, IgM, and IgA Anti-β2GPI Antibodies in Tetra-Positive Antiphospholipid Syndrome Patients: Identification of Two Non-Overlapping Epitopes in Domain I of β2GPI

Background. Risk stratification of antiphospholipid syndrome (APS) patients involves several laboratory tests, including lupus anticoagulant (LAC), anti-cardiolipin antibodies (anti-CL), anti-β2GPI antibodies (anti-β2GPI), and, more recently, anti-phosphatidylserine/prothrombin antibodies (anti-PS/PT). Patients with positive results for all four tests, known as tetra-positive APS patients, are considered at high risk for thrombotic recurrence. β2GPI is a protein composed of five domains: DI, DII, DIII, DIV, and DV, arranged in a flexible elongated structure. Previous studies have demonstrated that DI is frequently targeted by IgG in thrombotic APS patients. However, the prevalence, domain specificity, and residue specificity of the three different isotypes of anti-β2GPI antibodies in tetra-positive APS patients remain unknown. Objective. To establish the prevalence, domain specificity, and residue specificity of IgG, IgM, and IgA anti-β2GPI antibodies in tetra-positive APS patients. Methods. Plasma samples from 32 clinically confirmed tetra-positive APS patients and 8 healthy controls were analyzed for levels of IgG, IgM, and IgA using an enzyme-linked immunosorbent assay (ELISA) with immobilized β2GPI as the antigen. Domain and residue mapping were investigated using five deleted domain variants and eight single-point mutations. Mutants lacking specific disulfide bonds were used to assess whether the epitopes targeted by the antibodies are linear or conformational. Results. Among the 32 patients tested, 31 (96%) had IgG, 18 (56%) had IgM, and 18 (56%) had IgA anti-β2GPI antibodies. Ten patients (31%) had all three isotypes, 7 (22%) had IgG and IgM, and 8 (25%) had IgG and IgA. Six patients (19%) had only IgG, 1 had only IgM, and none had only IgA. The removal of DI from the β2GPI molecule led to an almost complete loss of binding (~80%) for the majority of IgG antibodies. There was a lesser but still significant reduction in binding when DII and DV were removed. However, no loss of binding was observed with variants lacking DIII and DIV. Notably, IgA antibodies behaved similarly to IgG but differently from IgM, as their reactivity towards DI and DV was significantly attenuated. To investigate the contribution of specific residues to binding, charged residues K19, E23, E26, E27, R39, R43, K44, and K59 in DI were individually replaced with alanine (A). The most important residue for IgG and IgA antibodies was R43, followed by R39 and K44. In contrast, most residues contributed to optimal binding for IgM antibodies, suggesting a less specific epitope definition. Interestingly, the mutations R39A and K44A often led to loss of binding in conjunction with the mutation R43A, but rarely simultaneously, suggesting the existence of two distinct epitopes for anti-DI antibodies-one located north and one south of R43. Finally, IgG, IgM, and IgA antibodies did not react against the mutant C32S/C60S but showed good reactivity against the mutant C288S/C326S, indicating that epitopes in DI are conformational and require the integrity of the disulfide bond 32-60. Conclusions. In tetra-positive APS patients, IgG anti-β2GPI antibodies are the most prevalent isotype, followed by IgA and IgM. The IgG and IgA isotypes exhibit a pronounced preference for DI, while the IgM isotype demonstrates lower specificity, which aligns with IgM's role as initial responders in infections. Notably, our research revealed that anti-DI antibodies are diverse, as they recognize epitopes within DI that are closely related but physically distinct. Ongoing studies aim to further characterize these antibody subtypes and understand their significance in APS. This knowledge may inform improved diagnostic and therapeutic approaches for APS patients.

Putative staphylococcal enterotoxin possesses two common structural motifs for MHC-II binding

Staphylococcus aureus has become a significant cause of health risks in humankind. Staphylococcal superantigens (SAgs) or enterotoxins are the key virulent factors that can exhibit acute diseases to severe life-threatening conditions. Recent literature reports S. aureus has steadily gained new enterotoxin genes over the past few decades. In spite of current knowledge of the established SAgs, several questions on putative enterotoxins are still remaining unanswered. Keeping that in mind, this study sheds light on a putative enterotoxin SEl26 to characterize its structural and functional properties. In-silico analyses indicate its close relation with the conventional SAgs, especially the zinc-binding SAgs. Additionally, important residues that are vital for the T-cell receptor (TcR) and major histocompatibility complex class II (MHC-II) interaction were predicted and compared with established SAgs. Besides, our biochemical analyses exhibited the binding of this putative enterotoxin with MHC-II, followed by regulating pro-inflammatory and anti-inflammatory cytokines.

Network Pharmacology Integrated Molecular Docking and Dynamics to Elucidate Saffron Compounds Targeting Human COX-2 Protein.

Background and Objectives: Cyclooxygenase-2 (COX-2) is mostly linked to inflammation and has been validated as a molecular target for treating inflammatory diseases. The present study aimed to identify novel compounds that could inhibit COX-2, which is associated with various diseases including inflammation, and in such a scenario, plant-derived biomolecules have been considered as attractive candidates. Materials and Methods: In the present study, physiochemical properties and toxicity of natural compounds/drugs were determined by SWISSADME and ProTox-II. In the present study, the molecular docking binding features of saffron derivatives (crocetin, picrocrocin, quercetin, safranal, crocin, rutin, and dimethylcrocetin) against human COX-2 protein were assessed. Moreover, protein-protein interactions, topographic properties, gene enrichment analysis and molecular dynamics simulation were also determined. Results: The present study revealed that picrocrocin showed the highest binding affinity of -8.1 kcal/mol when docked against the COX-2 protein. PROCHECK analysis revealed that 90.3% of the protein residues were found in the most favored region. Compartmentalized Protein-Protein Interaction identified 90 interactions with an average interaction score of 0.62, and the highest localization score of 0.99 found in secretory pathways. The Computed Atlas of Surface Topography of Proteins was used to identify binding pockets and important residues that could serve as drug targets. Use of WEBnmα revealed protein dynamics by using normal mode analysis. Ligand and Receptor Dynamics used the Molecular Generalized Born Surface Area approach to determine the binding free energy of the protein. Gene enrichment analysis revealed that ovarian steroidogenesis, was the most significant enrichment pathway. Molecular dynamic simulations were executed for the best docked (COX-2-picrocrocin) complex, and the results displayed conformational alterations with more pronounced surface residue fluctuations in COX-2 with loss of the intra-protein hydrogen bonding network. The direct interaction of picrocrocin with various crucial amino-acid residues like GLN203, TYR385, HIS386 and 388, ASN382, and TRP387 causes modifications in these residues, which ultimately attenuates the activity of COX-2 protein. Conclusions: The present study revealed that picrocrocin was the most effective biomolecule and could be repurposed via computational approaches. However, various in vivo and in vitro observations are still needed.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.

Identification, Characterization, and Receptor Binding Mechanism of New Umami Peptides from Traditional Fermented Soybean Paste (Dajiang).

Dajiang, a traditional Chinese condiment, is made from fermented soybeans. It is highly popular among consumers as a result of its delicious umami flavor, which mainly originates from umami peptides. To examine the mechanism of umami taste in Dajiang, we selected Dajiang samples with strong umami taste and subjected them to purification and identification analysis using ethanol precipitation, gel chromatography, reversed-phase high-performance liquid chromatography, and ultraperformance liquid chromatography-tandem mass spectrometry. Subsequently, on the basis of toxicity and umami prediction analysis, we screened, synthesized, and characterized three novel bean umami peptides in Dajiang: TLGGPTTL, 758.4174 Da; GALEQILQ, 870.4811 Da; and HSISDLQ, 911.4713 Da. Their sensory threshold values were 0.25, 0.40, and 0.17 mmol/L, respectively. Furthermore, molecular docking results showed that hydrogen-bonding and hydrophobic interactions are important interaction forces in the binding of umami peptide to taste receptors. Ser147 and Glu148 of the T1R3 taste receptor are important amino acid residues for binding of the three umami peptides. This study uncovers the mechanism of umami-peptide-driven flavor in fermented soybean products.

Synthesis of new non-natural L-glycosidic flavonoid derivatives and their evaluation as inhibitors of Trypanosoma cruzi ecto-nucleoside triphosphate diphosphohydrolase 1 (TcNTPDase1).

Trypanosoma cruzi is the pathogen of Chagas disease, a neglected tropical disease that affects more than 6 million people worldwide. There are no vaccines to prevent infection, and the therapeutic arsenal is very minimal and toxic. The unique E-NTPDase of T. cruzi (TcNTPDase1) plays essential roles in adhesion and infection and is a virulence factor. Quercetin is a flavonoid with antimicrobial, antiviral, and antitumor activities. Its potential as a partial inhibitor of NTPDases has also been demonstrated. In this work, we synthesized the non-natural L-glycoside derivatives of quercetin and evaluated them as inhibitors of recombinant TcNTPDase1 (rTcNTPDase1). These compounds, and quercetin and miquelianin, a natural quercetin derivative, were also tested. Compound 16 showed the most significant inhibitory effect (94%). Quercetin, miquelianin, and compound 14 showed inhibition close to 50%. We thoroughly investigated the inhibitory effect of 16. Our data suggested a competitive inhibition with a Ki of 8.39 μM (± 0.90). To better understand the interaction of compound 16 and rTcNTPDase1, we performed molecular dynamics simulations of the enzyme and docking analyses with the compounds. Our predictions show that compound 16 binds to the enzyme's catalytic site and interacts with important residues for NTPDase activity. As an inhibitor of a critical T. cruzi enzyme, (16) could be helpful as a starting point in the developing of a future treatment for Chagas disease. Furthermore, the discovery of (16) as an inhibitor of TcNTPDase1 may open new avenues in the study and development of new inhibitors of E-NTPDases.

Antibreast cancer activities of phytochemicals from Anonna muricata using computer-aided drug design (CADD) approach

Abstract Antibreast cancer activities of 131 phytochemicals from Annona muricata (Soursop) were investigated against human placental aromatase (PDB ID: 3S7S), a prominent target receptor in breast cancer therapy using computer aided-drug design approach. An antibreast cancer drug (tamoxifen) was used for comparison. The result of this work flourishes caffeoquinic acid (−8.4 kcal/mol), roseoside (−8.3 kcal/mol), chlorogenic acid (−8.2 kcal/mol), feruloylglycoside (−8.1 kcal/mol), citroside A (−8.0 kcal/mol), and coreximine (−7.8 kcal/mol), as probable inhibitors of human placental aromatase. This is due to their excellent binding affinities (ΔG), coupled with outstanding druglike, absorption, distribution, metabolism, excretion, and toxicity profiles, bioavailability and oral-bioavailability properties, and the interactions of important residues with the active pocket of human placental aromatase. All the results obtained were similar to that of the standards tamoxifen (−8.0 kcal/mol) but could be better when optimized. Thus, lead optimization, molecular dynamics, and in vivo investigations are thereby recommended on the identified potent compounds in the quest of developing new therapeutic agents against breast cancer.

Unveiling local and global conformational changes and allosteric communications in SOD1 systems using molecular dynamics simulation and network analyses

BackgroundAmyotrophic lateral sclerosis (ALS) is a serious neurodegenerative disorder affecting nerve cells in the brain and spinal cord that is caused by mutations in the superoxide dismutase 1 (SOD1) enzyme. ALS-related mutations cause misfolding, dimerisation instability, and increased formation of aggregates. The underlying allosteric mechanisms, however, remain obscure as far as details of their fundamental atomistic structure are concerned. Hence, this gap in knowledge limits the development of novel SOD1 inhibitors and the understanding of how disease-associated mutations in distal sites affect enzyme activity.Methods: We combined microsecond-scale based unbiased molecular dynamics (MD) simulation with network analysis to elucidate the local and global conformational changes and allosteric communications in SOD1 Apo (unmetallated form), Holo, Apo_CallA (mutant and unmetallated form), and Holo_CallA (mutant form) systems. To identify hotspot residues involved in SOD1 signalling and allosteric communications, we performed network centrality, community network, and path analyses.Results: Structural analyses showed that unmetallated SOD1 systems and cysteine mutations displayed large structural variations in the catalytic sites, affecting structural stability. Inter- and intra H-bond analyses identified several important residues crucial for maintaining interfacial stability, structural stability, and enzyme catalysis. Dynamic motion analysis demonstrated more balanced atomic displacement and highly correlated motions in the Holo system. The rationale for structural disparity observed in the disulfide bond formation and R143 configuration in Apo and Holo systems were elucidated using distance and dihedral probability distribution analyses.Conclusion: Our study highlights the efficiency of combining extensive MD simulations with network analyses to unravel the features of protein allostery.

Molecular characterization of genes involved in tolerance of cadmium in Triticum aestivum (L.) under Cd stress

The NRAMPs (natural resistance-associated macrophage proteins) are major transporters for the absorption and transport of metals like Pb, Zn, Mn, Fe, and Cd in plants. While NRAMP gene family members have been extensively studied as metal transporters in model and other plants, little information has been reported on their role in Triticum aestivum, particularly in response to Cd stress. Current study reported 13 NRAMP candidates in the genome of T. aestivum. Phylogenetic analysis divided these into three clades. Motif and gene structure study showed that members in the same clades shared the same location and pattern, which further supported the phylogenetic analysis. The analysis of cis-acting elements in promoter sequences of NRAMP genes in wheat identified stress-responsive transcription factor binding sites. Multiple sequence alignment identified the conservation of important residues. Based on RNA-seq and qRT-PCR analysis, Cd stress-responsive variations of TaNRAMP gene expression were reported. This study provides comprehensive data to understand the TaNRAMP gene family, its features, and its expression, which will be a helpful framework for functional research.

Multiplexed assay of variant effect reveals residues of functional importance in the BRCA1 coiled-coil and serine cluster domains.

Delineating functionally normal variants from functionally abnormal variants in tumor suppressor proteins is critical for cancer surveillance, prognosis, and treatment options. BRCA1 is a protein that has many variants of uncertain significance which are not yet classified as functionally normal or abnormal. In vitro functional assays can be used to identify the functional impact of a variant when the variant has not yet been categorized through clinical observation. Here we employ a homology-directed repair (HDR) reporter assay to evaluate over 300 missense and nonsense BRCA1 variants between amino acid residues 1280 and 1576, which encompasses the coiled-coil and serine cluster domains. Functionally abnormal variants tended to cluster in residues known to interact with PALB2, which is critical for homology-directed repair. Multiplexed results were confirmed by singleton assay and by ClinVar database variant interpretations. Comparison of multiplexed results to designated benign or likely benign or pathogenic or likely pathogenic variants in the ClinVar database yielded 100% specificity and 100% sensitivity of the multiplexed assay. Clinicians can reference the results of this functional assay for help in guiding cancer treatment and surveillance options. These results are the first to evaluate this domain of BRCA1 using a multiplexed approach and indicate the importance of this domain in the DNA repair process.

Three-state dynamics of zinc(II) complexes yielding significant antidiabetic targets

Protein Tyrosine Phosphatase 1B (PTP1B), being negative regulator of insulin signaling pathways is considered as potential medicinal target. Selective and targeted inhibitors for PTP1B can impact the therapeutic options available to cure chronic illness such as diabetes. Significant research evidence including computational studies on the role of Zn2+ in binding and inhibiting the catalytic pocket have been reported along with experimental exploration of zinc(II) complexes as potent inhibitors of the enzyme. The current study has employed advanced computational methods to explore the binding and conformational orientation of zinc(II) complexes in the active site of apoenzyme, phosphoenzyme, and TSA 2 of PTP1B. Metal ion modeling was performed for zinc metal center (Zn-OOOO) utilizing a Python based Metal Center Parameter Builder (MCPB.py). The findings of the study suggest that zinc(II) complex binds to structurally and functionally important residues in open and closed conformation as well as in the phosphorylated state of the enzyme. It was observed that when the catalytic cysteine is phosphorylated in a closed conformation, the zinc(II) complex forms significant interactions with PHE182, VAL184, GLY183, and PRO180 while pushing away Q-loop GLN262 which is crucial for the hydrolysis of phosphoenzyme. Subsequently, the reported inhibitor has also demonstrated its potential to function as allosteric modulator of the enzyme occupying catalytic WPD loop residues. The study uncovers putative binding sites of zinc-containing drugs and gives insight into the size and design of such compounds which keeps them accessible and anchored in the vicinity of active site residues. Reported inhibitor offers enhanced selectivity and inhibition in all three states of the enzyme in contrast to zinc ions which can only impede enzyme in the phosphorylated state. In addition to this, investigation of ASP265→GLU265 mutation reveals the role of GLU265 in affecting the flexibility of WPD loop residues highlighting it as loss-of-function mutation. Our results hints towards a metallodrug approach that builds on the research evidence of inhibition effects of Zn2+ in the binding pocket of PTP1B. The findings presented are noteworthy, not just due to their significant relevance for clinical application, but also for the design and synthesis of novel zinc(II) complexes.

Developing similarity matrices for antibody-protein binding interactions.

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

Leveraging QM/MM and Molecular Dynamics Simulations to Decipher the Reaction Mechanism of the Cas9 HNH Domain to Investigate Off-Target Effects.

The clustered regularly interspaced short palindromic repeats (CRISPR) technology is an RNA-guided targeted genome-editing tool using Cas family proteins. Two magnesium-dependent nuclease domains of the Cas9 enzyme, termed HNH and RuvC, are responsible for cleaving the target DNA (t-DNA) and nontarget DNA strands, respectively. The HNH domain is believed to determine the DNA cleavage activity of both endonuclease domains and is sensitive to complementary RNA-DNA base pairing. However, the underlying molecular mechanisms of CRISPR-Cas9, by which it rebukes or accepts mismatches, are poorly understood. Thus, investigation of the structure and dynamics of the catalytic state of Cas9 with either matched or mismatched t-DNA can provide insights into improving its specificity by reducing off-target cleavages. Here, we focus on a recently discovered catalytic-active form of the Streptococcus pyogenes Cas9 (SpCas9) and employ classical molecular dynamics and coupled quantum mechanics/molecular mechanics simulations to study two possible mechanisms of t-DNA cleavage reaction catalyzed by the HNH domain. Moreover, by designing a mismatched t-DNA structure called MM5 (C to G at the fifth position from the protospacer adjacent motif region), the impact of single-guide RNA (sgRNA) and t-DNA complementarity on the catalysis process was investigated. Based on these simulations, our calculated binding affinities, minimum energy paths, and analysis of catalytically important residues provide atomic-level details of the differences between matched and mismatched cleavage reactions. In addition, several residues exhibit significant differences in their catalytic roles for the two studied systems, including K253, K263, R820, K896, and K913.

Targeted mutations in IFNα2 improve its antiviral activity against various viruses.

The potency of interferon (IFN)α to restrict viruses was already discovered in 1957. However, until today, only IFNα2 out of the 12 distinct human IFNα subtypes has been therapeutically used against chronic viral infections. There is convincing evidence that other IFNα subtypes are far more efficient than IFNα2 against many viruses. In order to identify critical antiviral residues within the IFNα subtype sequence, we designed hybrid molecules based on the IFNα2 backbone with individual sequence motifs from the more potent subtypes IFNα6 and IFNα14. In different antiviral assays with HIV or HBV, residues binding to IFNAR1 as well as combinations of residues in the IFNAR1 binding region, the putative tunable anchor, and residues outside these regions were identified to be crucial for the antiviral activity of IFNα. Thus, we designed artificial IFNα molecules, based on the clinically approved IFNα2 backbone, but with highly improved antiviral activity against several viruses.

The Glycine N-Methyltransferase Case Study: Another Challenge for QM-Cluster Models?

The methyl transfer reaction between SAM and glycine catalyzed by glycine N-methyltransferase (GNMT) was examined using QM-cluster models generated by Residue Interaction Network ResidUe Selector (RINRUS). RINRUS is a Python-based tool that can build QM-cluster models with rules-based processing of the active site residue interaction network. This way of enzyme model-building allows quantitative analysis of residue and fragment contributions to kinetic and thermodynamic properties of the enzyme. Many residue fragments are important for the GNMT catalytic reaction, such as Gly137, Asn138, and Arg175, which interact with the glycine substrate, and Trp30, Asp85, and Tyr242, which interact with the SAM cofactor. Our study shows that active site fragments that interact with the glycine substrate and the SAM cofactor must both be included in the QM-cluster models. Even though the proposed mechanism is a simple one-step reaction, GNMT may be a rather challenging case study for QM-cluster models because convergence in energetics requires models with >350 atoms. "Maximal" QM-cluster models built with either qualitative contact count ranking or quantitative interaction energies from functional group symmetry adapted perturbation theory provide acceptable results. Hence, important residue fragments that contribute to the energetics of the methyl-transfer reaction in GNMT are correctly identified in the RIN. Observations from this work suggest new directions to better establish an effective approach for constructing atomic-level enzyme models.

Investigative on the molecular mechanism of novel antioxidation peptides by 3D-QSAR, in vitro assay and MD simulations

Three novel antioxidant peptides MHW, YHW and FHW were designed by the analysis of 3D-QSAR. Two accurate CoMFA (Q2 = 0.886 and R2 = 0.988) and CoMSIA (Q2 = 0.737 and R2 = 0.955) models were established. The FTC assay showed that activities of the new peptides on lipid peroxidation is significantly better than that of the template molecule LHW. The results of erythrocyte hemolysis assay demonstrated that MHW, YHW and FHW significantly inhibited AAPH-induced erythrocyte hemolysis by scavenging free radicals at the cellular level. It caused the restoration of intracellular antioxidant enzyme (GSH, CAT and SOD) activities at an acceptable pace and the silencing of intracellular MDA formation. Furthermore, MTT toxicity assay showed that the novel antioxidant peptides had good biocompatibility and no cytotoxicity. Molecular docking and molecular dynamics simulations were used to investigate the molecular mechanism. The results showed that Ser363, Arg380, Asn414 and Arg415 residues played key roles in the antioxidant activity of MHW, YHW and FHW. And they mainly binded to the important residues of Keap1 protein stably by electrostatic force, van der Waals force, H-bonds and hydrophobic interactions, which can better fit the Kelch pocket structural domain of Keap1. Therefore, MHW, YHW and FHW had strong potential to interfere with Keap1-Nrf2 interaction and promoted the enzymatic expression of Nrf2-ARE downstream target pathway.

Predicting enzymatic function of protein sequences with attention.

There is a growing number of available protein sequences, but only a limited amount has been manually annotated. For example, only 0.25% of all entries of UniProtKB are reviewed by human annotators. Further developing automatic tools to infer protein function from sequence alone can alleviate part of this gap. In this article, we investigate the potential of Transformer deep neural networks on a specific case of functional sequence annotation: the prediction of enzymatic classes. We show that our EnzBert transformer models, trained to predict Enzyme Commission (EC) numbers by specialization of a protein language model, outperforms state-of-the-art tools for monofunctional enzyme class prediction based on sequences only. Accuracy is improved from 84% to 95% on the prediction of EC numbers at level two on the EC40 benchmark. To evaluate the prediction quality at level four, the most detailed level of EC numbers, we built two new time-based benchmarks for comparison with state-of-the-art methods ECPred and DeepEC: the macro-F1 score is respectively improved from 41% to 54% and from 20% to 26%. Finally, we also show that using a simple combination of attention maps is on par with, or better than, other classical interpretability methods on the EC prediction task. More specifically, important residues identified by attention maps tend to correspond to known catalytic sites. Quantitatively, we report a max F-Gain score of 96.05%, while classical interpretability methods reach 91.44% at best. Source code and datasets are respectively available at https://gitlab.inria.fr/nbuton/tfpc and https://doi.org/10.5281/zenodo.7253910.