Comparative protein structure modeling using Modeller

Enhancing cryo-enzymatic efficiency in cold-adapted lipase from Psychrobacter sp. C18 via site-directed mutagenesis

As industrial demands for cold-active enzymes have been increased, psychrophilic lipases present a promising solution with potential for innovation and growth in food, pharmaceutical, and detergent industries. Cold-adapted enzymes achieve high catalytic efficiency at low temperatures through their structural flexibility and conformational adaptability. Therefore, in this study, the lipase gene from Psychrobacter sp. C18 was cloned and subjected to site-directed mutagenesis based on computer aided predictions to enhance the enzyme's cold-adapted properties and flexibility. Mutations were strategically selected in loops of the active site to improve the enzyme's accessibility to the substrate under cold conditions. The P163G, L186G, and Q239W mutations were selected for further analysis. Enzyme activity, along with its stability and structural flexibility, was assessed using techniques including UV-Vis spectroscopy, fluorescence, and circular dichroism (CD) spectroscopy. The obtained data revealed that the optimal temperature for the wild-type lipase was 30 °C, which shifted to lower temperatures in the mutants: 15 °C for P163G and L186G, and 20 °C for Q239W. Additionally, the optimal pH of the mutant lipases shifted to more alkaline conditions compared to the wild-type enzyme. While the thermal and pH stability of the mutant enzymes slightly decreased, these findings can be attributed to their enhanced flexibility. Far-UV CD spectroscopy revealed a reduction in α-helical content of the mutant enzymes. Molecular dynamics simulations corroborated these findings, confirming increased structural flexibility in all three mutants compared to the wild-type enzyme. This research underlines the importance of applying engineered cold-adapted enzymes for industrial application.

Molecular mapping and functional validation of GLP-1R cholesterol binding sites in pancreatic beta cells

G protein-coupled receptors (GPCRs) are integral membrane proteins which closely interact with their plasma membrane lipid microenvironment. Cholesterol is a lipid enriched at the plasma membrane with pivotal roles in the control of membrane fluidity and maintenance of membrane microarchitecture, directly impacting on GPCR stability, dynamics, and function. Cholesterol extraction from pancreatic beta cells has previously been shown to disrupt the internalisation, clustering, and cAMP responses of the glucagon-like peptide-1 receptor (GLP-1R), a class B1 GPCR with key roles in the control of blood glucose levels via the potentiation of insulin secretion in beta cells and weight reduction via the modulation of brain appetite control centres. Here, we unveil the detrimental effect of a high cholesterol diet on GLP-1R-dependent glucoregulation in vivo, and the improvement in GLP-1R function that a reduction in cholesterol synthesis using simvastatin exerts in pancreatic islets. We next identify and map sites of cholesterol high occupancy and residence time on active vs inactive GLP-1Rs using coarse-grained molecular dynamics (cgMD) simulations, followed by a screen of key residues selected from these sites and detailed analyses of the effects of mutating one of these, Val229, to alanine on GLP-1R-cholesterol interactions, plasma membrane behaviours, clustering, trafficking and signalling in INS-1 832/3 rat pancreatic beta cells and primary mouse islets, unveiling an improved insulin secretion profile for the V229A mutant receptor. This study (1) highlights the role of cholesterol in regulating GLP-1R responses in vivo; (2) provides a detailed map of GLP-1R - cholesterol binding sites in model membranes; (3) validates their functional relevance in beta cells; and (4) highlights their potential as locations for the rational design of novel allosteric modulators with the capacity to fine-tune GLP-1R responses.

Structural Descriptors for Subunit Interface Regions in Homodimers: Effect of Lipid Membrane and Secondary Structure Type

A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (Cx), surface planarity, and surface roughness. SASA and Cx distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and Cx values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.

Structural basis for channel gating and blockade in tri-heteromeric GluN1-2B-2D NMDA receptor

Discrete activation of N-methyl-D-aspartate receptor (NMDAR) subtypes by glutamate and the co-agonist glycine is fundamental to neuroplasticity. A distinct variant, the tri-heteromeric receptor, comprising glycine-binding GluN1 and two types of glutamate-binding GluN2 subunits, exhibits unique pharmacological characteristics, notably enhanced sensitivity to the anti-depressant channel blocker S-(+)-ketamine. Despite its significance, the structural mechanisms underlying ligand gating and channel blockade of tri-heteromeric NMDARs remain poorly understood. Here, we identify and characterize tri-heteromeric GluN1-2B-2D NMDAR in the adult brain, resolving its structures in the activated, inhibited, and S-(+)-ketamine-blocked states. These structures reveal the ligand-dependent conformational dynamics that modulate the tension between the extracellular domain and transmembrane channels, governing channel gating and blockade. Additionally, we demonstrate that the inhibitor (S)-DQP-997-74 selectively decouples linker tension in GluN2D, offering insights into subtype-selective targeting for cognitive modulation.

Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

In silico evaluation of S-adenosyl-L-homocysteine analogs as inhibitors of nsp14-viral cap N7 methyltranferase and PLpro of SARS-CoV-2: synthesis, molecular docking, physicochemical data, ADMET and molecular dynamics simulations studies

A series of S-adenosyl-L-homosysteine (SAH) analogs, with modification in the base and sugar moiety, have been designed, synthesized and screened as nsp14 and PLpro inhibitors of severe acute respiratory syndrome corona virus (SARS-CoV-2). The outcomes of ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) studies demonstrated that the physicochemical properties of all analogs were permissible for development of these SAH analogs as antiviral agents. All molecules were screened against different SARS-CoV-2 targets using molecular docking. The docking results revealed that the SAH analogs interacted well in the active site of nsp14 protein having H-bond interactions with the amino acid residues Arg289, Val290, Asn388, Arg400, Phe401 and π-alkyl interactions with Arg289, Val290 and Phe426 of Nsp14-MTase site. These analogs also formed stable H-bonds with Leu163, Asp165, Arg167, Ser246, Gln270, Tyr274 and Asp303 residues of PLpro proteins and found to be quite stable complexes therefore behaved as probable nsp14 and PLpro inhibitors. Interestingly, analog 3 showed significant in silico activity against the nsp14 N7 methyltransferase of SARS-CoV-2. The molecular dynamics (MD) and post-MD results of analog 3 unambiguously established the higher stability of the nsp14 (N7 MTase):3 complex and also indicated its behavior as probable nsp14 inhibitor like the reference sinefungin. The docking and MD simulations studies also suggested that sinefungin did act as SARS-CoV-2 PLpro inhibitor as well. This study's findings not only underscore the efficacy of the designed SAH analogs as potent inhibitors against crucial SARS-CoV-2 proteins but also pinpoint analog 3 as a particularly promising candidate. All the study provides valuable insights, paving the way for potential advancements in antiviral drug development against SARS-CoV-2.

A hypomorphic FLVCR2 variant resulting in moderate transport deficiency causes hydranencephaly syndrome with brain calcifications

FLVCR2 is a highly conserved member of the major facilitator superfamily (MFS), the largest superfamily of solute carriers that are involved in the transport of small molecules across lipid bilayers. The loss of the murine ortholog Mfsd7c, an endothelial transporter in brain blood vessels, causes brain angiogenic growth deficiency and lethality. Recessive FLVCR2 variants cause proliferative vasculopathy and hydranencephaly-hydrocephaly syndrome (PVHH), also known as Fowler syndrome. This often-lethal condition features microcephaly, skeletal deformities, and severe cerebrovascular defects. Although a number of cases have been reported, very limited evidence of the pathogenicity of FLVCR2 variants is available. In this study, we thoroughly investigated a new fetal case of Fowler syndrome. Through exome sequencing, we identified two compound heterozygous FLVCR2 variants: the maternal c.1124+3_1124+6del and the paternal p.(Arg492Trp). The effects of the c.1124+3_1124+6del variant were investigated through a minigene assay, which showed impaired splicing of the exon 5 of FLVCR2. To characterize the impact of the p.(Arg492Trp) substitution, we performed protein modeling using Rosetta and DynaMut2, that showed a highly destabilizing effect. Then, based on the very recent evidence that choline is a major FLVCR2 ligand, we performed a radiolabeled-choline or ethanolamine transport assays in HEK 293 cells and found that the p.(Arg492Trp) variant causes a 50-60% reduction of FLVCR2 transport activity, resulting in a net activity of 25-30%. Our findings suggest that FLVCR2 deficiency may be sufficient to cause PVHH even in the absence of a complete loss of transport activity, possibly involving extragenetic factors in the pathophysiology of this complex condition.

Computational Development of Allosteric Peptide Inhibitors Targeting LIM Kinases as a Novel Therapeutic Intervention

LIM Kinases (LIMKs) have emerged as critical therapeutic targets in cancer research due to their central role in regulating cytoskeletal dynamics and cell motility via cofilin phosphorylation. Allosteric inhibitors, which bind outside the ATP-binding pocket, offer distinct advantages over ATP-competitive inhibitors, such as increased specificity, reduced off-target effects, and the ability to overcome resistance. This study investigates a series of novel tetrapeptides mimicking the binding mode of TH470, an allosteric LIMK inhibitor, using in silico docking and molecular dynamics simulations to identify potential lead compounds with high specificity, binding affinity, and favorable pharmacokinetic properties. Structural analyses revealed critical interactions between TH470 and LIMKs, particularly with conserved residues such as Thr405 (gatekeeper residue), Ile408 (hinge region), and Asp469 (XDFG motif), which are essential for stabilizing inhibitor binding. Molecular dynamics simulations confirmed the stability of TH470-LIMK1 and TH470-LIMK2 complexes, with lower RMS deviations and robust interaction patterns enhancing binding affinity. From the set of tetrapeptides mimicking TH470 binding mode, only YFYW, WPHW, and YWFP for LIMK1, and PYWG, FYWV, and WFVW for LIMK2 demonstrated high binding affinities, non-toxic profiles, and promising anti-cancer, anti-angiogenic, and anti-inflammatory properties. Among the studied peptides, LIMK1-YFYW and LIMK2-WFVW exhibited the most substantial binding affinities, supported by high hydrogen bond occupancy with key residues such as Ile416 and Thr405. The findings highlight the therapeutic potential of allosteric peptide inhibitors targeting LIMK-mediated pathways in cancer progression. The study underscores the importance of specific interactions with conserved LIMK residues, providing a foundation for further developing selective inhibitors to modulate actin dynamics and combat cancer-related processes.

Biosynthesis of poly(ester amide)s in engineered Escherichia coli

The development of biobased polymers to substitute their current petroleum-based counterparts is crucial for fostering a sustainable plastic industry. Here we report the biosynthesis and characterization of a group of biopolymers, poly(ester amide)s (PEAs), in Escherichia coli. PEAs are biosynthesized by constructing a new-to-nature amino acid polymerization pathway, comprising amino acid activation by β-alanine CoA transferase and subsequent polymerization of amino acyl-CoA by polyhydroxyalkanoate synthase. The engineered E. coli strains harboring this pathway are capable of biosynthesizing various PEAs, each incorporating different amino acid monomers in varying fractions. Examination of the physical, thermal and mechanical properties reveals a dependence of molecular weight on the type of polyhydroxyalkanoate synthase, a decrease in melting temperature and crystallinity as the 3-aminopropionate monomer fraction increases and enhanced elongation at break compared to its polyester analog. The engineered bacterial system will prove beneficial for the biobased production of various PEAs using renewable resources.

Leveraging AlphaFold models to predict androgenic effects of endocrine-disrupting chemicals through zebrafish androgen receptor analysis

The androgen receptor (AR) activation by androgens is vital for tissue development, sexual differentiation, and reproductive attributes in zebrafish (Danio rerio). However, our understanding of the molecular mechanisms behind their activation remains limited. In this study, we employed both ab initio (AlphaFold) and homology (SWISS-MODEL) structure models of zebrafish androgen receptor ligand-binding domain (zAR-LBD) to explore the binding specificity, binding affinity, and molecular interactions of endogenous hormones (testosterone (T), 11-ketotestosterone (11-KT), and dihydrotestosterone (DHT)) in a computational simulation. Molecular docking analysis showed that both structures formed the same interactions and similar patterns of binding energy with androgens. Molecular Dynamics (MD) simulation analysis revealed that hydrogen bond occupancy aligned with in vitro findings related to androgenic effect. When comparing complexes modeled by SWISS-MODEL and AlphaFold, significant differences were observed in root mean square deviation (RMSD) and root mean square fluctuations (RMSF). The AlphaFold structures also exhibited a clear separation between ligands in principal component analysis. Further correlation analysis between in silico features and in vitro EC50 values identified MMPBSA energies as the most significant contributors to ligand-specific variance in the in silico complexes (p < 0.05). Overall, this integrative approach offers significant insights into the molecular mechanisms underlying zebrafish AR activity.

In Silico Investigation of Mineralocorticoid Receptor Antagonists: Insights into Binding Mechanisms and Structural Dynamics

The mineralocorticoid receptor (MR) is a steroid hormone receptor that plays a key role in regulating sodium and water homeostasis and blood pressure. MR antagonists are a guideline recommended for therapy for the treatment of hypertension and cardiovascular disease but can cause hyperkalaemia. Modelling was performed for binding of the endogenous ligands aldosterone and cortisol and MR antagonist spironolactone to the ligand binding domain (LBD) of the MR. A molecular docking screen of compounds that were structurally similar to known antagonists was performed, leading to the identification of two novel compounds, C79 and E67. Molecular dynamics (MD) assessed the dynamic interactions with C79, E76, endogenous ligands, and spironolactone with the MR ligand binding domain (LBD). Analysis of the protein backbone showed modest changes in the overall structure of the MR LBD in response to binding of antagonists, with movement in helix 12 consistent with previous observations. All ligands tested maintained stable binding within the MR LBD throughout the simulations. Hydrogen bond formation played a more prominent role in the binding of endogenous ligands compared to antagonists. MM-PBSA binding free energy calculations showed that all ligands had similar binding affinities, with binding facilitated by key residues within the binding site. The novel antagonists demonstrated similar binding properties to spironolactone, warranting further evaluation. This study provides insights into the molecular mechanisms of MR activation and inhibition, which can aid in the development of novel therapeutic strategies for cardiovascular diseases.

Computational drug discovery of an inhibitor of APOBEC3B as a treatment for epithelial cancers

Cancer is one of the leading causes of death in the U.S., and tumorous cancers such as cervical, lung, breast, and ovarian cancers are the most common types. APOBEC3B is a nonessential cytidine deaminase found in humans and theorized to defend against viral infection. However, overexpression of APOBEC3B is linked to cancer in humans, which makes APOBEC3B a potential cancer treatment target through competitive inhibition for several tumorous cancers. Computational studies can help reveal a small molecule inhibitor using high-throughput virtual screening of millions of candidates with relatively little cost. This study aims to narrow the field of potential APOBEC3B inhibition candidates for future in vitro assays and provide an effective scaffold for drug design studies. Another goal of this project is to provide critical amino acid targets in the active site for future drug design studies. This study simulated 7.8 million drug candidates using high-throughput virtual screening and further processed the top scoring 241 molecules from AutoDock Vina, DOCK 6, and de novo design. Using virtual screening, de novo design, and molecular dynamics simulations, a competitive inhibitor candidate was discovered with an average binding free energy score of -46.03 kcal/mol, more than 10 kcal/mol better than the substrate control (dCMP). These results indicate that this molecule (or a structural derivative) may be an effective inhibitor of APOBEC3B and prevent host genome mutagenesis resulting from protein overexpression. Another important finding is the confirmation of essential amino acid targets, such as Tyr250 and Gln213 within the active site of APOBEC3B. Therefore, study used novel computational methods to provide a theoretical scaffold for future drug design studies that may prove useful as a treatment for epithelial cancers.Communicated by Ramaswamy H. Sarma.

Intratype variants and high genotypic diversity of human papillomavirus with polymorphisms in the antigenic hypervariable loops of the L1 protein from women living with human immunodeficiency virus in Northeastern Brazil

Introduction. The human papillomavirus (HPV) is one of the main oncogenic viruses. High-risk HPV types are associated with the development of cervical cancers. In addition, it is known that some mutations in HPV genes, or variant viral lineages, have been associated with greater oncogenic risk.Gap statement. The L1 protein is the major component of the viral capsid and is therefore used in currently available vaccines. However, the characterization of mutations in the L1 gene, which is relevant to increasing the knowledge of the immune escape mechanisms used by the virus, is still incipient.Aim. This study aimed to characterize mutations associated with antigenic domains in the L1 protein of HPVs isolated from cervical samples of women living with HIV in Northeastern Brazil.Methodology. L1 gene sequences were obtained from the samples, and the mutations and the viral variants were characterized. Phylogenetic and functional analyses of the structure of the L1 protein were carried out.Results. A total of 41 HPV variant isolates were obtained, distributed among 16 different viral types. Of this, 25 non-synonymous mutations were evaluated regarding the stability of the L1 protein. It was observed that 10 of these mutations were predicted to increase, and 14 to decrease, the stability of the L1 protein and that most of them occurred in the FG hypervariable antigenic loop.Conclusion. These results add useful knowledge to understanding the biological and immunological aspects of HPV variants and the impact of these mutations on the development of vaccine strategies.

Specific Rosetta-based protein-peptide prediction protocol allows the design of novel cholinesterase inhibitor peptides

The search for novel cholinesterase inhibitors is essential for advancing treatments for neurodegenerative disorders such as Alzheimer's disease (AD). In this study, we employed the Rosetta pepspec module, originally developed for designing peptides targeting protein-protein interactions, to design de novo peptides targeting the peripheral aromatic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). A total of nine peptides were designed for human AChE (hAChE), T. californica AChE (TcAChE), and human BChE (hBChE). These peptides were synthesized using Fmoc-SPPS and tested in vitro using Ellman's reaction to evaluate their inhibitory potency. Peptide 11tA, designed for TcAChE, exhibited potent inhibition of hAChE (IC50 = 1.21 ± 0.25 µM) and demonstrated strong antioxidant activity against DPPH radicals and lipid peroxidation, making it a promising multitherapeutic candidate for AD. Peptide 11hB, designed for hBChE, showed the highest inhibitory activity against hBChE, with a Ki of 12.69 ± 1.27 µM, making it the most potent natural amino acid peptide reported against hBChE. The computational protocol effectively distinguished the specific characteristics of each enzyme target. Toxicity assessments, including hemolysis tests and A. salina lethality assays, revealed no toxic effects at low concentrations, further supporting the potential of these peptides for peptide-based drug development in AD. This study underscores the growing potential of peptides as alternatives to small-molecule drugs. It demonstrates that computational protocols for protein-protein interactions can be successfully adapted to design high-affinity peptide inhibitors.

Enhancement of the structure and biochemical function of cyclomaltodextrinase from the Anoxybacillus flavithermus ZNU-NGA with site-directed mutagenesis

This study was conducted to examine the role of the central domain of cyclomaltodextrinase in terms of stability, substrate specificity, becoming dodecameric form, and enzyme activity. To this end, H403R/L309V double-point mutation and T280Q single-point mutation were performed at the central domain and (β/α)8-barrel. The results indicated that the activity of the H403R/L309V mutant at the optimal pH and temperature increased by about 25% and 40%, respectively. Plus, the irreversible thermal inactivation of the H403R/L309V mutant at 60 °C and 160 min was approximately twice of the enzyme without mutation. Both mutants underwent significant structural change relative to the wild enzyme and subsequently a significant catalytic activity. However, the catalytic efficiency (kcat/Km) of the H403R/L309V mutant increased in the presence of beta- and gamma-cyclomaltodextrin substrates compared to the wild enzyme and T280Q mutant. As a result, by applying the L309V mutant and given the smaller size of the valine, leucine spatial inhibition in the wild protein seems to decline, and also it facilitates the substrate access to active site amino acids. Moreover, as gamma substrate is larger, eliminating the effect of spatial inhibition on this substrate has a greater effect on improving the catalytic activity of this enzyme.

How a disordered linker in the Polycomb protein Polyhomeotic tunes phase separation and oligomerization

Biomolecular condensates are increasingly appreciated for their function in organizing and regulating biochemical processes in cells, including chromatin function. Condensate formation and properties are encoded in protein sequence but the mechanisms linking sequence to macroscale properties are incompletely understood. Cross species comparisons can reveal mechanisms either because they identify conserved functions or because they point to important differences. Here we use in vitro reconstitution and molecular dynamics simulations to compare Drosophila and human sequences that regulate condensate formation driven by the sterile alpha motif (SAM) oligomerization domain in the Polyhomeotic (Ph) subunit of the chromatin regulatory complex PRC1. We discover evolutionarily diverged contacts between the conserved SAM and the disordered linker that connects it to the rest of Ph. Linker-SAM interactions increase oligomerization and regulate formation and properties of reconstituted condensates. Oligomerization affects condensate dynamics but, in most cases, has little effect on their formation. Linker-SAM interactions also affect condensate formation in Drosophila and human cells, and growth in Drosophila imaginal discs. Our data show how evolutionary sequence changes in linkers connecting conserved structured domains can alter condensate properties.

Advancements in Nanobody Epitope Prediction: A Comparative Study of AlphaFold2Multimer vs AlphaFold3

Nanobodies have emerged as a versatile class of biologics with promising therapeutic applications, driving the need for robust tools to predict their epitopes, a critical step for in silico affinity maturation and epitope-targeted design. While molecular docking has long been employed for epitope identification, it requires substantial expertise. With the advent of AI driven tools, epitope identification has become more accessible to a broader community increasing the risk of models' misinterpretation. In this study, we critically evaluate the nanobody epitope prediction performance of two leading models: AlphaFold3 and AlphaFold2-Multimer (v.2.3.2), highlighting their strengths and limitations. Our analysis revealed that the overall success rate remains below 50% for both tools, with AlphaFold3 achieving a modest overall improvement. Interestingly, a significant improvement in AlphaFold3's performance was observed within a specific nanobody class. To address this discrepancy, we explored factors influencing epitope identification, demonstrating that accuracy heavily depends on CDR3 characteristics, such as its 3D spatial conformation and length, which drive binding interactions with the antigen. Additionally, we assessed the robustness of AlphaFold3's confidence metrics, highlighting their potential for broader applications. Finally, we evaluated different strategies aimed at improving the prediction success rate. This study can be extended to assess the accuracy of emerging deep learning models adopting an approach similar to that of AlphaFold3.

Inhibitor Action of Unsaturated Fatty Acids on Equine Serum Butyrylcholinesterase

Butyrylcholinesterase (BChE; EC 3.1.1.8), a serine hydrolase found in various tissues, hydrolyses choline esters such as acetylcholine and succinylcholine, as well as other esters such as heroin and acetylsalicylic acid. It is considered to play a role in lipid metabolism as it belongs to the same enzyme group as lipases and its catalytic subunits are similar. In this study, the effects of unsaturated fatty acids, namely arachidonic (AA), linoleic (LA), alpha-linolenic (ALA) and oleic acid (OA), on equine serum BChE (EqBChE) were investigated. Enzyme activity was measured by the modified Ellman method. When the activity results were evaluated, the IC50 values were found 45.49, 8.465, 1556, and 56.57 μM; while the Ki values were 63.92, 11.46, 1800, and 15.24 μM for AA, ALA, LA, and OA, respectively. Analysis of the kinetic results showed that ALA was compatible with mixed inhibition and other fatty acids were compatible with non-competitive inhibition, a special type of mixed inhibition. Molecular docking predicted binding of the fatty acids to the active site, as well as to predicted allosteric sites. The results of this study provide another support to the hypothesis that cholinesterases are associated with lipid metabolism.

Salicylaldehyde Benzoylhydrazones with Anticancer Activity and Selectivity: Design, Synthesis, and In Vitro Evaluation

Dimethoxy derivatives of salicylaldehyde benzoylhydrazone containing a methoxy group on both aromatic rings were designed and synthesized. The compounds were obtained in high yields, and their structures were confirmed by elemental analysis and various spectral techniques. In vitro evaluation of dimethoxy hydrazones demonstrated potent activity against the leukemic cell lines at low micro- and nanomolar concentrations. Remarkably, two dimethoxy analogs showed exceptional antileukemic selectivity, with no toxicity observed in normal human embryonic kidney HEK-293 cells. In silico modeling identified likely interactions with the target, human cAbl kinase, and suggested a possible mechanism for their antileukemic activity.

Effects of F53 hotspot mutations on the molecular dynamics of MEK1 protein and the binding of its FDA-approved inhibitors

MEK1 (MAP2K1) could emerge as an oncogenic protein in the presence of certain mutations, and could be inhibited by FDA-approved drugs (trametinib, cobimetinib, binimetinib and selumetinib). However, how the mutations on MEK1's hotspot residue F53 affect the bindings of these therapeutic molecules remained largely unexplored. We performed molecular dynamics (MD) simulations with wild-type and mutated (F53L/V/C/I/Y) MEK1 structures in the presence and absence of these drugs and observed changes on the motion of MEK1. A longer duration in the lowest energy state conformation was observed during the simulations in the presence of F53 mutations. This was complemented by cross-correlated motions of amino acids of MEK1. More importantly, the binding affinities of inhibitors were affected. There was a marked reduction in the calculated binding affinity of trametinib in the presence of F53C mutation. On the other hand, the binding affinities of cobimetinib and selumetinib could overcome F53 mutations on MEK1. Binimetinib interestingly exhibited an increased binding affinity when F53C/I mutations were present. Taken together, our results provide a comprehensive perspective on the structural and drug-binding effects of observed mutations on the hotspot residue F53 within MEK1; warranting further research in stratifying F53 hotspot mutations for effective drug binding.

Harnessing allosteric inhibition: prioritizing LIMK2 inhibitors for targeted cancer therapy through pharmacophore-based virtual screening and essential molecular dynamics

The therapeutic potential of small molecule kinase inhibitors in cancer treatment is well recognized. However, achieving selectivity remains a formidable challenge, primarily due to the structural similarity of ATP binding pockets among kinases. Allosteric inhibition, which involves targeting binding pockets beyond the ATP-binding site, provides a promising alternative to overcome this challenge. In this study, a meticulous approach was implemented to prioritize type 3 inhibitors for LIMK2, employing a range of techniques including Molecular Dynamics (MD) simulations, e-pharmacophore-guided High Throughput Virtual Screening (HTVS), MM/GBSA and ADMETox analyses, Density Functional Theory (DFT) calculations, and MM/PBSA investigations. The e-pharmacophore model identifies a hypothesis featuring five essential pharmacophoric elements (RRRAH). Through virtual screening of the ZINC compound database, we identified only five compounds that align with all four pharmacophoric features: ZINC1044382792, ZINC1433610865, ZINC1044109145, ZINC952869440, and ZINC490621334. These compounds not only exhibit higher binding affinity but also demonstrate favorable ADME/Tox profiles. Molecular dynamics simulations underscore the stability of hydrogen bond interactions with critical cryptic LIMK2 pocket residues, Asp469 and Arg474, only for two compounds: ZINC143361086 and ZINC1044382792. These compounds also exhibit superior occupancy interactions, as indicated by HOMO-LUMO analysis. Additionally, binding free energy calculations highlight the significant affinities of these two compounds when complexed with LIMK2: -83.491 ± 1.230 kJ/mol and -90.122 ± 1.248 kJ/mol for ZINC1044382792 and ZINC1433610862, respectively. Hence, this comprehensive investigation identifies ZINC1433610862 and ZINC1044382792 as prospective hits, representing promising leads for targeting LIMK2 in cancer therapeutics.Communicated by Ramaswamy H. Sarma.

Molecular Prediction and Correlation of the Structure and Function of Universal Stress Protein A (UspA) from Salmonella Typhimurium

Salmonella Typhimurium (ST) is a zoonotic pathogen that can cause gastroenteritis in humans when they consume contaminated food or water. When exposed to various stressors, both from living organisms (biotic) and the environment (abiotic), Salmonella Typhimurium produces Universal Stress Proteins (USPs). These proteins are gaining recognition for their crucial role in bacterial stress resistance and the ability to enter a prolonged state of growth arrest. Additionally, USPs exhibit diverse structures due to the fusion of the USP domain with different catalytic motifs, enabling them to participate in various reactions and cellular activities during stressful conditions. In this particular study, researchers cloned and analyzed the uspA gene obtained from poultry-derived strains of Salmonella Typhimurium. The gene comprises 435 base pairs, encoding a USP family protein consisting of 144 amino acids. Phylogenetic analysis demonstrated a close relationship between the uspA genes of Salmonella Typhimurium and those found in other bacterial species. We used molecular dynamics simulations and 3D structure prediction to ensure that the USPA protein was stable. Furthermore, we also carried out motif search and network analysis of protein-protein interactions. The findings from this study offer valuable insights for the development of inhibitors targeted against Salmonella Typhimurium.

Engineering Protein Dynamics through Mutational Energy Landscape Traps

Protein dynamics is essential for various biological processes, influencing functions such as enzyme activity, molecular recognition, and signal transduction. However, traditional protein engineering methods often focus on static structures, lacking tools to precisely manipulate dynamic behaviors. Here, we developed Mutational Energy Landscape Trap (MELT), a novel method designed to control protein dynamics by combining Normal Mode Analysis (NMA) and in silico mutagenesis. MELT works by displacing protein structures along low-frequency normal modes and introducing mutations to either lock proteins in these conformations or increase dynamics along the chosen normal modes. We tested MELT using hen-egg lysozyme as a model system. The method was validated by monitoring relevant collective coordinates during molecular dynamics simulations and evaluation of the collective movements of each construct. Our experiments showed that MELT was able to consistently create new protein sequences with the desired dynamical behavior in simulations. It demonstrates its potential for applications in the field of protein engineering, being an unprecedented way of manipulating protein features.

Systemic antihyperalgesic effect of a novel conotoxin from Californiconus californicus in an inflammatory pain model

Introduction

This study explores the analgesic potential of the novel conotoxin O1_cal6.4b, derived from Californiconus californicus, as a candidate for pain management in a model of inflammatory pain.

Methods

O1_cal6.4b was systemically administered to Wistar rats, and its effects on thermal hyperalgesia and motor coordination were evaluated. Comparative analyses were conducted against O1_cal6.4d, ω-MVIIA, and standard analgesics (morphine, dexamethasone, and diclofenac). Structural differences between O1_cal6.4b and O1_cal6.4d were examined using in silico modeling and molecular dynamics simulations.

Results

Systemic administration of O1_cal6.4b significantly reduced thermal hyperalgesia in a dose-dependent manner without impairing motor coordination. The analgesic effect of O1_cal6.4b was superior to that of O1_cal6.4d, ω-MVIIA, and standard analgesics. Structural analyses revealed notable differences between O1_cal6.4b and O1_cal6.4d, suggesting unique functional properties.

Discussion

The findings indicate that O1_cal6.4b exhibits a promising analgesic profile with advantages over traditional opioid-based therapies. These results underscore the molecular diversity of conotoxins and highlight their potential as innovative analgesic treatments. Further research is needed to elucidate the mechanism of action of this novel conotoxin.

Data-driven equation-free dynamics applied to many-protein complexes: The microtubule tip relaxation

Microtubules (MTs) constitute the largest components of the eukaryotic cytoskeleton and play crucial roles in various cellular processes, including mitosis and intracellular transport. The property allowing MTs to cater to such diverse roles is attributed to dynamic instability, which is coupled to the hydrolysis of guanosine-5'-triphosphate (GTP) to guanosine-5'-diphosphate (GDP) within the β-tubulin monomers. Understanding the dynamics and structural features of both GDP- and GTP-complexed MT tips, especially at an all-atom level, remains challenging for both experimental and computational methods because of their dynamic nature and the prohibitive computational demands of simulating large, many-protein systems. This study employs the "equation-free" multiscale computational method to accelerate the relaxation of all-atom simulations of MT tips toward their putative equilibrium conformation. Using large MT lattice systems (14 protofilaments × 8 heterodimers) comprising ∼21-38 million atoms, we applied this multiscale approach to leapfrog through time and nearly double the computational efficiency in realizing relaxed all-atom conformations of GDP- and GTP-complexed MT tips. Commencing from an initial 4 μs unbiased all-atom simulation, we interleave coarse-projective equation-free jumps with short bursts of all-atom molecular dynamics simulation to realize an additional effective simulation time of 1.875 μs. Our 5.875 μs of effective simulation trajectories for each system expose the subtle yet essential differences in the structures of MT tips as a function of whether β-tubulin monomer is complexed with GDP or GTP, as well as the lateral interactions within the MT tip, offering a refined understanding of features underlying MT dynamic instability. The approach presents a robust and generalizable framework for future explorations of large biomolecular systems at atomic resolution.

In silico drug repurposing at the cytoplasmic surface of human aquaporin 1

Aquaporin 1 (AQP1) is a key channel for water transport in peritoneal dialysis. Inhibition of AQP1 could therefore impair water transport during peritoneal dialysis. It is not known whether inhibition of AQP1 occurs unintentionally due to off-target interactions of administered medications. A high-throughput virtual screening study has been performed to investigate the possible binding of licensed medications to the water pore of human AQP1. A complete model of human AQP1 based on its canonical sequence was assembled using I-TASSER and MODELLER. The model was refined via the incorporation of pore water molecules from a high-resolution yeast aquaporin structure. Docking studies were conducted for the cytoplasmic domain of the AQP1 monomer against a library of all compounds listed in the British National Formulary (BNF), using the PLANTS software with the ChemPLP scoring function. The stability of the best docked conformations within the intrinsic water pore was assessed via short 15 nanosecond molecular dynamics (MD) simulations using the GROMACS-on-Colab utility. Of the 1512 compounds tested, 1002 docking results were obtained, and 198 of these conformations occupied a position within the intrinsic water pore. 30 compounds with promising docking scores were assessed by MD. The docked conformations for dopamine, gabapentin, pregabalin, and methyldopa were stable in these short MD studies. For furosemide and pravastatin, the MD trajectory suggested a binding mode different to the docking result. A small set of compounds which could impede water transport through human AQP1 have been identified in this computational screening study.

Exploring CRISPR-Cas9 HNH-Domain-Catalyzed DNA Cleavage Using Accelerated Quantum Mechanical Molecular Mechanical Free Energy Simulation

The target DNA (tDNA) cleavage catalyzed by the CRISPR Cas9 enzyme is a critical step in the Cas9-based genome editing technologies. Previously, the tDNA cleavage from an active SpyCas9 enzyme conformation was modeled by Palermo and co-workers (Nierzwicki et al., Nat. Catal. 2022 5, 912) using ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulations, where the free energy barrier was found to be more favorable than that from a pseudoactive enzyme conformation. In this work, we performed ai-QM/MM simulations based on another catalytically active conformation (PDB 7Z4J) of the Cas9 HNH domain from cryo-electron microscopy experiments. For the wildtype enzyme, we acquired a free energy profile for the tDNA cleavage that is largely consistent with the previous report. Furthermore, we explored the role of the active-site K866 residue on the catalytic efficiency by modeling the K866A mutant and found that the K866A mutation increased the reaction free energy barrier, which is consistent with the experimentally observed reduction in the enzyme activity.

Screening and Identification of Natural Compounds as Potential Inhibitors of Glutamate Racemase, an Emerging Drug Target of Food Pathogen E. coli O157:H7: An In-silico Approach to Combat Increasing Drug Resistance

BACKGROUND

Shiga Toxin-Producing Escherichia coli (E. coli) O157:H7, capable of causing serious food-borne illnesses, is extensively studied and is known to be transmitted through animal reservoirs or person-to-person contact, leading to severe disease outbreaks. The emergence of antibiotic resistance in these strains, coupled with increased adverse effects of existing therapeutics, underscores the urgent need for alternative therapeutic strategies.

OBJECTIVE

This study aims to evaluate Glutamate Racemase (MurI protein) of the food-pathogenic E. coli O157:H7 (EC MurI) as a novel drug target. Furthermore, the study seeks to identify new compounds with potential inhibitory effects against this protein.

METHODS

Using computational tools, the study identified inhibitor binding sites on EC MurI and identified relevant inhibitors capable of binding to these sites. Molecular docking techniques were employed to assess potential hits, and selected compounds were further analyzed for their structural activity and binding affinity to the protein.

RESULTS

The results of the study revealed that Frigocyclinone and Deslanoside, exhibited the best binding affinity with EC-MurI. Subsequent molecular dynamic (MD) simulations of the selected complexes indicated that both compounds were stable. This suggests that Frigocyclinone and Deslanoside have the potential to serve as potent inhibitors of EC-MurI.

CONCLUSION

In summary, this study highlights the urgent need for alternative therapies against food-pathogenic E. coli, focusing on E. coli O157:H7. Evaluation of Glutamate Racemase as a drug target identified Frigocyclinone and Deslanoside as promising inhibitors. MD simulations indicated their stability, suggesting their potential as lead molecules for further research and treatment development.

Visual Support for the Loop Grafting Workflow on Proteins

In understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on exploring regions in proteins called loops. Analyzing various characteristics of these regions helps the experts design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. We designed a set of interactive visualizations that provide experts with visual support through all the loop grafting pipeline steps. The workflow is divided into several phases, reflecting the steps of the pipeline. Each phase is supported by a specific set of abstracted 2D visual representations of proteins and their loops that are interactively linked with the 3D View of proteins. By sequentially passing through the individual phases, the user shapes the list of loops that are potential candidates for loop grafting. Finally, the actual in-silico insertion of the loop candidates from one protein to the other is performed, and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. We showcase the contribution of our visual support design on a real case scenario changing the enantiomer selectivity of the engineered enzyme. Moreover, we provide the readers with the experts' feedback.

Computational analyses of drug resistance mutations in katG and emb complexes in Mycobacterium tuberculosis

The number of antibiotic resistant pathogens is increasing rapidly, and with this comes a substantial socioeconomic cost that threatens much of the world. To alleviate this problem, we must use antibiotics in a more responsible and informed way, further our understanding of the molecular basis of drug resistance, and design new antibiotics. Here, we focus on a key drug-resistant pathogen, Mycobacterium tuberculosis, and computationally analyze trends in drug-resistant mutations in genes of the proteins embA, embB, embC, and katG, which play essential roles in the action of the first-line drugs ethambutol and isoniazid. We use docking to predict binding modes of isoniazid to katG that agree with suggested binding sites found in our laboratory using cryo-EM. Using mutant stability predictions, we recapitulate the idea that resistance occurs when katG's heme cofactor is destabilized rather than due to a decrease in affinity to isoniazid. Conversely, we have identified resistance mutations that affect the affinity of ethambutol more drastically than the affinity of the natural substrate of embB. With this, we illustrate that we can distinguish between the two types of drug resistance-cofactor destabilization and drug affinity reduction-suggesting potential uses in the prediction of novel drug-resistant mutations.

Generating Multistate Conformations of P-type ATPases with a Conditional Diffusion Model

Understanding and predicting the diverse conformational states of membrane proteins is essential for elucidating their biological functions. Despite advancements in computational methods, accurately capturing these complex structural changes remains a significant challenge. Here, we introduce a computational approach to generate diverse and biologically relevant conformations of membrane proteins using a conditional diffusion model. Our approach integrates forward and backward diffusion processes, incorporating state classifiers and additional conditioners to control the generation gradient of conformational states. We specifically targeted the P-type ATPases, a critical family of membrane transporters, and constructed a comprehensive data set through a combination of experimental structures and molecular dynamics simulations. Our model, incorporating a graph neural network with specialized membrane constraints, demonstrates exceptional accuracy in generating a wide range of P-type ATPase conformations associated with different functional states. This approach represents a meaningful step forward in the computational generation of membrane protein conformations using AI and holds promise for studying the dynamics of other membrane proteins.

The Application of Machine Learning on Antibody Discovery and Optimization

Antibodies play critical roles in modern medicine, serving as diagnostics and therapeutics for various diseases due to their ability to specifically bind to target antigens. Traditional antibody discovery and optimization methods are time-consuming and resource-intensive, though they have successfully generated antibodies for diagnosing and treating diseases. The advancements in protein data, computational hardware, and machine learning (ML) models have the opportunity to disrupt antibody discovery and optimization research. Machine learning models have demonstrated their abilities in antibody design. These machine learning models enable rapid in silico design of antibody candidates within a few days, achieving approximately a 60% reduction in time and a 50% reduction in cost compared to traditional methods. This review focuses on the latest machine learning-based antibody discovery and optimization developments. We briefly discuss the limitations of traditional methods and then explore the machine learning-based antibody discovery and optimization methodologies. We also focus on future research directions, including developing Antibody Design AI Agents and data foundries, alongside the ethical and regulatory considerations essential for successfully adopting machine learning-driven antibody designs.

Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based self-supervised machine learning framework, MutComputeX, with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher expression yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40 % improvement in the TTN (218±3) as compared to WT LDO (TTN=160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models

The binding of peptides to class-I Major Histocompability Complex (MHC) receptors and their subsequent recognition downstream by T-cell receptors are crucial processes for most multicellular organisms to be able to fight various diseases. Thus, the identification of peptide antigens that can elicit an immune response is of immense importance for developing successful therapies for bacterial and viral infections, even cancer. Recently, studies have demonstrated the importance of peptide-MHC (pMHC) structural analysis, with pMHC structural modeling methods gradually becoming more popular in peptide antigen identification workflows. Most of the pMHC structural modeling tools provide an ensemble of candidate peptide poses in the MHC-I cleft, each associated with a score stemming from a scoring function, with the top scoring pose assumed to be the most representative of the ensemble. However, identifying the binding mode, that is, the peptide pose from the ensemble that is closer to an unavailable native structure, is not trivial. Oftentimes, the peptide poses characterized as best by a protein-ligand scoring function are not the ones that are the most representative of the actual structure. In this work, we frame the peptide binding pose identification problem as a Learning-to-Rank (LTR) problem. We present RankMHC, an LTR-based pMHC binding mode identification predictor, which is specifically trained to predict the most accurate ranking of an ensemble of pMHC conformations. RankMHC outperforms classical peptide-ligand scoring functions, as well as previous Machine Learning (ML)-based binding pose predictors. We further demonstrate that RankMHC can be used with many pMHC structural modeling tools that use different structural modeling protocols.

Bioguided Identification of Polymethoxyflavones as Novel Vascular CaV1.2 Channel Blockers from Citrus Peel

The huge amount of citrus peel produced worldwide represents an economic burden for society. However, this agricultural by-product is a rich source of natural molecules, potentially endowed with interesting pharmacological activities. In this regard, we decided to investigate if the polymethoxyflavones contained in citrus peel waste could be exploited as novel vasorelaxant agents. A hydroalcoholic blond orange (Citrus sinensis) peel extract, obtained by ultrasonication, was partitioned in dichloromethane. Column chromatography allowed for the isolation of four polymethoxyflavones, namely, scutellarein tetramethyl ether, nobiletin, tangeretin, and sinensetin, identified by nuclear magnetic resonance (NMR) spectroscopy and UPLC-HRMS/MS and confirmed by multivariate curve resolution of NMR fractional spectra. The four molecules showed interesting in vitro vasorelaxant activity, at least, in part, due to the blockade of smooth muscle CaV1.2 channels. Molecular modeling and docking analysis elucidated the binding mode of the polymethoxyflavones at the homology model of the rat CaV1.2c subunit and provided the structural basis to rationalise the highest activity of scutellarein tetramethyl ether in the set and the dramatic effect of the additional methoxy group occurring in nobiletin and sinensetin. In conclusion, citrus peel can be considered a freely available, valuable source of vasoactive compounds worthy of pharmaceutical and/or nutraceutical exploitation.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

Influence of Cataract Causing Mutations on αA-Crystallin: A Computational Approach

The αA-crystallin protein plays a vital role in maintaining the refractive index and transparency of the eye lens. Significant clinical studies have emerged as the αA-crystallin is prone to aggregation, resulting in the formation of cataracts with varied etiologies due to mutations. This work aims to comprehend the structural and functional role of cataract-causing mutations in αA-crystallin, particularly at N-Terminal and α-Crystallin Domains, using in-silico approaches including molecular dynamics simulation. About 19 mutants of αA-crystallin along with native structure were simulated for 100 ns and the post-simulations analyses reveal pronounced dynamics of αA-crystallin due to the enhanced structure flexibility as its native compactness was lost and is witnessed mainly by the mutants R12L, R21L, R21Q, R54L, R65Q, R116C and R116H. It is observed that αA-crystallin discloses the NTD motions as the dominant one and the same was endorsed by the linear variation between Rg and the center-of-mass of αA-crystallin. Interestingly, such enhanced dynamics of αA-crystallin mutants associated with the structure flexibility is internally modulated by the dynamic exchange of secondary structure elements β-sheets and coils (R2 = 0.619) during simulation. Besides, the observed pronounced dynamics of dimer interface region (β3-L6-β4 segment) of ACD along with CTD dynamics also gains importance. Particularly, the highly dynamic mutants are also characterized by enhanced non-covalent and hydrophobic interactions which renders detrimental effects towards its stability, and favours possible protein unfolding mechanisms. Overall, this study highlights the mutation-mediated structural distortions in αA-crystallin and demands the need for further potential development of inhibitors against cataract formation.

Intrinsic Disorder and Other Malleable Arsenals of Evolved Protein Multifunctionality

Microscopic evolution at the functional biomolecular level is an ongoing process. Leveraging functional and high-throughput assays, along with computational data mining, has led to a remarkable expansion of our understanding of multifunctional protein (and gene) families over the past few decades. Various molecular and intermolecular mechanisms are now known that collectively meet the cumulative multifunctional demands in higher organisms along an evolutionary path. This multitasking ability is attributed to a certain degree of intrinsic or adapted flexibility at the structure-function level. Evolutionary diversification of structure-function relationships in proteins highlights the functional importance of intrinsically disordered proteins/regions (IDPs/IDRs) which are highly dynamic biological soft matter. Multifunctionality is favorably supported by the fluid-like shapes of IDPs/IDRs, enabling them to undergo disorder-to-order transitions upon binding to different molecular partners. Other new malleable members of the protein superfamily, such as those involved in fold-switching, also undergo structural transitions. This new insight diverges from all traditional notions of functional singularity in enzyme classes and emphasizes a far more complex, multi-layered diversification of protein functionality. However, a thorough review in this line, focusing on flexibility and function-driven structural transitions related to evolved multifunctionality in proteins, is currently missing. This review attempts to address this gap while broadening the scope of multifunctionality beyond single protein sequences. It argues that protein intrinsic disorder is likely the most striking mechanism for expressing multifunctionality in proteins. A phenomenological analogy has also been drawn to illustrate the increasingly complex nature of modern digital life, driven by the need for multitasking, particularly involving media.

Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies

In recent years, the advent of computational techniques to predict the potential activity of a drug interacting with a receptor or to predict the structure of unidentified proteins with aberrant characteristics has significantly impacted the field of drug design. We provide a comprehensive review of the current state of in silico approaches and software for investigating the effects of receptor mutations associated with human diseases, focusing on both frequent and rare mutations. The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. This review clearly shows that it is common for successful studies to integrate different techniques in drug design, with docking and molecular dynamics being the most frequently used techniques. This trend reflects the current emphasis on developing novel therapies for diseases resulting from receptor mutations with the recently discovered AlphaFold algorithm as the driving force.

Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review

Significant populations in tropical and sub-tropical locations all over the world are severely impacted by a group of neglected tropical diseases called leishmaniases. This disease is caused by roughly 20 species of the protozoan parasite from the Leishmania genus. Disease prevention strategies that include early detection, vector control, treatment of affected individuals, and vaccination are all essential. The diagnosis is critical for selecting methods of therapy, preventing transmission of the disease, and minimizing symptoms so that the affected individual can have a better quality of life. Nevertheless, the diagnostic methods do eventually have limitations, and there is no established gold standard. Some disadvantages include the existence of cross-reactions with other species, and limited sensitivity and specificity, which are mostly determined by the type of antigen used to perform the tests. A viable alternative for a more precise diagnosis is the application of recombinant antigens, which have been generated using bioinformatics approaches and have shown increased diagnostic accuracy. This approach proves valuable as it spans from epitope selection to predicting the interactions within the antibody-antigen complex through docking analysis. As a result, identifying potential new antigens using bioinformatics resources becomes an effective technique since it may result in an earlier and more accurate diagnosis. Consequently, the primary aim of this review is to conduct a comprehensive overview of the most significant in silico tools developed over time, with a focus on evaluating their efficacy and exploring their potential applications in optimizing the selection of highly specific molecules for a more effective diagnosis of leishmaniasis.

MARK2 variants cause autism spectrum disorder via the downregulation of WNT/β-catenin signaling pathway

Microtubule affinity-regulating kinase 2 (MARK2) contributes to establishing neuronal polarity and developing dendritic spines. Although large-scale sequencing studies have associated MARK2 variants with autism spectrum disorder (ASD), the clinical features and variant spectrum in affected individuals with MARK2 variants, early developmental phenotypes in mutant human neurons, and the pathogenic mechanism underlying effects on neuronal development have remained unclear. Here, we report 31 individuals with MARK2 variants and presenting with ASD, other neurodevelopmental disorders, and distinctive facial features. Loss-of-function (LoF) variants predominate (81%) in affected individuals, while computational analysis and in vitro expression assay of missense variants supported the effect of MARK2 loss. Using proband-derived and CRISPR-engineered isogenic induced pluripotent stem cells (iPSCs), we show that MARK2 loss leads to early neuronal developmental and functional deficits, including anomalous polarity and dis-organization in neural rosettes, as well as imbalanced proliferation and differentiation in neural progenitor cells (NPCs). Mark2+/- mice showed abnormal cortical formation and partition and ASD-like behavior. Through the use of RNA sequencing (RNA-seq) and lithium treatment, we link MARK2 loss to downregulation of the WNT/β-catenin signaling pathway and identify lithium as a potential drug for treating MARK2-associated ASD.

A molecular dynamics simulations analysis of repurposing drugs for COVID-19 using bioinformatics methods

A number of multidisciplinary methods have piqued the interest of researchers as means to accelerate and lower the cost of medication creation. The goal of this research was to find target proteins and then select a lead drug against SARS-CoV-2. The three-dimensional structure is taken from the RCSB PDB using its specific PDB ID 6lu7. Virtual screening based on pharmacophores is performed using Molecular Operating Environment software. We looked for a potent inhibitor in the FDA-approved database. For docking, AutoDock Vina uses Pyrx. The compound-target protein binding interactions were tested using BIOVIA Discovery Studio. The stability of protein and inhibitor complexes in a physiological setting was investigated using Desmond's Molecular Dynamics Simulation (MD simulation). According to our findings, we repurpose the FDA-approved drugs ZINC000169677008 and ZINC000169289767, which inhibit the activity of the virus's main protease (6lu7). The scientific community will gain from this finding, which might create new medicine. The novel repurposed chemicals were promising inhibitors with increased efficacy and fewer side effects.Communicated by Ramaswamy H. Sarma.

PlmCas12e Utilizes Glu662 to Prevent Cleavage Site Occupation by Positively Charged Residues Before Target Strand Cleavage

CRISPR-Cas12e is a recently identified gene-editing tool mainly known because its relatively small size benefits cell delivery. Drastically different from Cas9, it creates a blunt-end double-strand breakage of the DNA via two cleavage sites; Cas12e produces a sticky-end double-strand breakage of the DNA through only one cleavage site in its RuvC domain, meaning two consecutive cleavage events first on the non-target strand (ntsDNA) and then the target strand (tsDNA). Though crucial for Cas12e's cleavage efficiency, the mechanism by which Cas12e loads tsDNA for the second cleavage remains elusive. Through molecular dynamics simulations and our recently matured traveling-salesman-based automated path-searching (TAPS) algorithm, we identified a series of positively charged residues (Arg856TSL, Arg768RuvC, Lys898TSL, Arg904TSL, Arg764RuvC) that guide the tsDNA backbone toward the cleavage site of wild-type PlmCas12e. Further simulations of the R856L and R904L mutants supported such observations. More interestingly, we found the key role of Glu662RuvC in coordinating Arg764RuvC, preventing its occupation of the cleavage site, and facilitating tsDNA cleavage. Additional simulations confirmed that mutating Glu662RuvC to valine disabled such coordination and created a stable intermediate state with Arg764RuvC occupying the cleavage site before tsDNA loading. These insights, revealing an elaborate mechanism of cleavage facilitation, offer essential guiding principles for future rational engineering of Cas12e into more efficient gene-editing tools.

TCRcost: a deep learning model utilizing TCR 3D structure for enhanced of TCR-peptide binding

Introduction

Predicting TCR-peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR-peptide binding from amino acid sequences. However, the performance of sequence-based methods appears to have hit a bottleneck. Considering the 3D structures of TCR-peptide complexes, which provide much more information, could potentially lead to better prediction outcomes.

Methods

In this study, we developed TCRcost, a deep learning method, to predict TCR-peptide binding by incorporating 3D structures. TCRcost overcomes two significant challenges: acquiring a sufficient number of high-quality TCR-peptide structures and effectively extracting information from these structures for binding prediction. TCRcost corrects TCR 3D structures generated by protein structure tools, significantly extending the available datasets. The main and side chains of a TCR structure are separately corrected using a long short-term memory (LSTM) model. This approach prevents interference between the chains and accurately extracts interactions among both adjacent and global atoms. A 3D convolutional neural network (CNN) is designed to extract the atomic features relevant to TCR-peptide binding. The spatial features extracted by the 3DCNN are then processed through a fully connected layer to estimate the probability of TCR-peptide binding.

Results

Test results demonstrated that predicting TCR-peptide binding from 3D TCR structures is both efficient and highly accurate with an average accuracy of 0.974 on precise structures. Furthermore, the average accuracy on corrected structures was 0.762, significantly higher than the average accuracy of 0.375 on uncorrected original structures. Additionally, the average root mean square distance (RMSD) to precise structures was significantly reduced from 12.753 Å for predicted structures to 8.785 Å for corrected structures.

Discussion

Thus, utilizing structural information of TCR-peptide complexes is a promising approach to improve the accuracy of binding predictions.

Sanggenol B, a plant bioactive, as a safer alternative to tackle cancer by antagonising human FGFR

Fibroblast Growth Receptor Factor (FGFR) are a family of proteins which are, in addition to their biological role, are involved in various pathological functions, such as cancer cellular proliferation, and metastasis. Deregulation of FGFRs at various points could result in malignancy. A conformational transition of the DFG (Asp-Phe-Gly) motif can switch the enzyme from a catalytically active (DFG-in) to an inactive (DFG-out) state. There are a few FDFR inhibitors which have received approval from the FDA, but these have adverse side effects. Hence, there is a demand for safer alternatives. With this aim, Ligand and Structure based virtual screening was carried to identify suitable lead molecule. In this process, Four Featured atom-based 3D Pharmacophore with quantitative structure-activity relationship analysis (3D-QSAR) was developed. The External validation of the hypothesis was carried invoking criteria such as Area under the ROC curve. Natural plant compound databases such as the Traditional Chinese medicine, NPACT and the ZINC Natural databases were chosen for pharmacophore filtering, which was followed by virtual screening against FGFR isoforms. The compound Sanggenol B was identified as the most suitable lead molecule. Structural stability of the protein-ligand complex and interactions of the ligand (Sanggenol B & the reference compound Ponatinib) with FGFR were analysed for 1000 ns (triplicate) by means of molecular simulation and the binding free energy was calculated using MMGBSA. Sanggenol B (PubChem CID: 15233694) binds effectively at the active site with favourable energies and is proposed as a safe alternative from a natural source.Communicated by Ramaswamy H. Sarma.

Cyclopamine modulates smoothened receptor activity in a binding position dependent manner

Cyclopamine, a natural alkaloid, can act as an agonist when it binds to the Cysteine-Rich Domain (CRD) of Smoothened receptor and as an antagonist when it binds to the Transmembrane Domain (TMD). To study the effect of cyclopamine binding to each site experimentally, mutations in the other site are required. Hence, simulations are critical for understanding the WT activity due to binding at different sites. Using multi-milliseconds long aggregate MD simulations combined with Markov state models and machine learning, we explore the dynamic behavior of cyclopamine's interactions with different domains of WT SMO. A higher population of the active state at equilibrium, a lower free energy barrier of ~2 kcal/mol, and expansion of hydrophobic tunnel to facilitate cholesterol transport agrees with cyclopamine's agonistic behavior when bound to CRD. A higher population of the inactive state at equilibrium, a higher free energy barrier of ~4 kcal/mol and restricted hydrophobic tunnel shows cyclopamine's antagonistic behavior when bound to TMD. With cyclopamine bound to both sites, there is a slightly larger inactive population at equilibrium and an increased free energy barrier (~3.5 kcal/mol) exhibiting an overall weak antagonistic effect. These findings show cyclopamine's domain-specific modulation of SMO regulates Hedgehog signaling and cholesterol transport.

Deep generative models of protein structure uncover distant relationships across a continuous fold space

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generative modeling framework, termed DeepUrfold, for analyzing protein relationships at scale. DeepUrfold's learned embeddings occupy high-dimensional latent spaces that can be distilled for a given protein in terms of an amalgamated representation uniting sequence, structure and biophysical properties. This approach is structure-guided, versus being purely structure-based, and DeepUrfold learns representations that, in a sense, define superfamilies. Deploying DeepUrfold with CATH reveals evolutionarily-remote relationships that evade existing methodologies, and suggests a mostly-continuous view of fold space-a view that extends beyond simple geometric similarity, towards the realm of integrated sequence ↔ structure ↔ function properties.

Utilization of the EpiMed Coronabank Chemical Collection to identify potential SARS-CoV-2 antivirals: in silico studies targeting the nsp14 ExoN domain and PLpro naphthalene binding site

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome encodes 29 proteins including four structural, 16 nonstructural (nsps), and nine accessory proteins (https://epimedlab.org/sars-cov-2-proteome/). Many of these proteins contain potential targetable sites for the development of antivirals. Despite the widespread use of vaccinations, the emergence of variants necessitates the investigation of new therapeutics and antivirals. Here, the EpiMed Coronabank Chemical Collection (https://epimedlab.org/crl/) was utilized to investigate potential antivirals against the nsp14 exoribonuclease (ExoN) domain. Molecular docking was performed to evaluate the binding characteristics of our chemical library against the nsp14 ExoN site. Based on the initial screen, trisjuglone, ararobinol, corilagin, and naphthofluorescein were identified as potential lead compounds. Molecular dynamics (MD) simulations were subsequently performed, with the results highlighting the stability of the lead compounds in the nsp14 ExoN site. Protein-RNA docking revealed the potential for the lead compounds to disrupt the interaction with RNA when bound to the ExoN site. Moreover, hypericin, cyanidin-3-O-glucoside, and rutin were previously identified as lead compounds targeting the papain-like protease (PLpro) naphthalene binding site. Through performing MD simulations, the stability and interactions of lead compounds with PLpro were further examined. Overall, given the critical role of the exonuclease activity of nsp14 in ensuring viral fidelity and the multifunctional role of PLpro in viral pathobiology and replication, these nsps represent important targets for antiviral drug development. Our databases can be utilized for in silico studies, such as the ones performed here, and this approach can be applied to other potentially druggable SARS-CoV-2 protein targets.

AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.

Prediction of the essential intermolecular contacts for side-binding of VASP on F-actin

Vasodilator-stimulated phosphoprotein (VASP) family proteins play a crucial role in mediating the actin network architecture in the cytoskeleton. The Ena/VASP homology 2 (EVH2) domain in each of the four identical arms of the tetrameric VASP consists of a loading poly-Pro region, a G-actin-binding domain (GAB), and an F-actin-binding domain (FAB). Together, the poly-Pro, GAB, and FAB domains allow VASP to bind to sides of actin filaments in a bundle, and recruit profilin-G-actin to processively elongate the filaments. The atomic resolution structure of the ternary complex, consisting of the loading poly-Pro region and GAB domain of VASP with profilin-actin, has been solved over a decade ago; however, a detailed structure of the FAB-F-actin complex has not been resolved to date. Experimental insights, based on homology of the FAB domain with the C region of WASP, have been used to hypothesize that the FAB domain binds to the cleft between subdomains 1 and 3 of F-actin. Here, in order to develop our understanding of the VASP-actin complex, we first augment known structural information about the GAB domain binding to actin with the missing FAB domain-actin structure, which we predict using homology modeling and docking simulations. In earlier work, we used mutagenesis and kinetic modeling to study the role of domain-level binding-unbinding kinetics of Ena/VASP on actin filaments in a bundle, specifically on the side of actin filaments. We further look at the nature of the side-binding of the FAB domain of VASP at the atomistic level using our predicted structure, and tabulate effective mutation sites on the FAB domain that would disrupt the VASP-actin complex. We test the binding affinity of Ena with mutated FAB domain using total internal reflection fluorescence microscopy experiments. The binding affinity of VASP is affected significantly for the mutant, providing additional support for our predicted structure.