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Background: Helicobacter pylori is considered a true human pathogen for which rising drug resistance constitutes a drastic concern globally. The present study aimed to reconstruct a genome-scale metabolic model (GSMM) to decipher the metabolic capability of H. pylori strains in response to clarithromycin and rifampicin along with identification of novel drug targets., Materials and Methods: The iIT341 model of H. pylori was updated based on genome annotation data, and biochemical knowledge from literature and databases. Context-specific models were generated by integrating the transcriptomic data of clarithromycin and rifampicin resistance into the model. Flux balance analysis was employed for identifying essential genes in each strain, which were further prioritized upon being nonhomologs to humans, virulence factor analysis, druggability, and broad-spectrum analysis. Additionally, metabolic differences between sensitive and resistant strains were also investigated based on flux variability analysis and pathway enrichment analysis of transcriptomic data., Results: The reconstructed GSMM was named as HpM485 model. Pathway enrichment and flux variability analyses demonstrated reduced activity in the ribosomal pathway in both clarithromycin- and rifampicin-resistant strains. Also, a significant decrease was detected in the activity of metabolic pathways of clarithromycin-resistant strain. Moreover, 23 and 16 essential genes were exclusively detected in clarithromycin- and rifampicin-resistant strains, respectively. Based on prioritization analysis, cyclopropane fatty acid synthase and phosphoenolpyruvate synthase were identified as putative drug targets in clarithromycin- and rifampicin-resistant strains, respectively., Conclusions: We present a robust and reliable metabolic model of H. pylori. This model can predict novel drug targets to combat drug resistance and explore the metabolic capability of H. pylori in various conditions., (© 2024 John Wiley & Sons Ltd.)

Purpose: The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia., Methods: The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls., Results: Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia., Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01384-9., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s), under exclusive licence to Tehran University of Medical Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Parizi, Marzella, Ramakrishnan, ‘t Hoen, Karimi-Jafari and Xue.)

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The assembly of the amyloid- β peptide (A β ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the A β monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of A β , standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric A β 42 . It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to A β 42 . Additionally, it has been found that all the studied ligands, especially EGCG , can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of A β . Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against A β .

Anti-VEGF therapies are common for the treatment of cancer. Carboxypeptidase G (CPG-2) enzyme is a zinc-dependent metalloenzyme that metabolizes non-toxic synthetic 'benzoic mustard prodrugs' to cytotoxic moieties in tumor cells. In this study, we designed a dual-activity agent by combining a designed anti-VEGF- and CPG-2 enzyme to convert methotrexate (MTX). VEGF-A was docked against a set of scaffolds, and suitable inverse rotamers were made. Rosetta design was used for the interface design. The top 1200 binders were chosen by flow cytometry and displayed in yeast. The activity of CPG-2 enzyme was analyzed at different temperature conditions and in the presence of the substrate, MTX. Optimal binders were selected and protein was eluted using immobilized metal affinity chromatography and size-exclusion chromatography. Both, native PAGE and on-yeast flow cytometry confirmed the binding of the binder to VEGF-A. The activity of truncated enzymes was slightly lower than that of full-length enzymes linked to VEGF-A. The method should be generally useful as a dual-activity agent for targeting VEGF-A and combination therapy with the enzyme CPG-2 for metabolizing non-toxic prodrugs to cytotoxic moieties.Communicated by Ramaswamy H. Sarma.

Triple negative breast cancer (TNBC) metastases are assumed to exhibit similar functions in different organs as in the original primary tumor. However, studies of metastasis are often limited to a comparison of metastatic tumors with primary tumors of their origin, and little is known about the adaptation to the local environment of the metastatic sites. We therefore used transcriptomic data and metabolic network analyses to investigate whether metastatic tumors adapt their metabolism to the metastatic site and found that metastatic tumors adopt a metabolic signature with some similarity to primary tumors of their destinations. The extent of adaptation, however, varies across different organs, and metastatic tumors retain metabolic signatures associated with TNBC. Our findings suggest that a combination of anti-metastatic approaches and metabolic inhibitors selected specifically for different metastatic sites, rather than solely targeting TNBC primary tumors, may constitute a more effective treatment approach.

An attractive drug target to combat COVID-19 is the main protease (M pro ) because of its key role in the viral life cycle by processing the polyproteins translated from the viral RNA. Studying the crystal structures of the protease is important to enhance our understanding of its mechanism of action at the atomic-level resolution, and consequently may provide crucial structural insights for structure-based drug discovery. In the current study, we report a comparative structural analysis of the M pro substrate binding site for both apo and holo forms to identify key interacting residues and conserved water molecules during the ligand-binding process. It is shown that in addition to the catalytic dyad residues (His41 and Cys145), the oxyanion hole residues (Asn142-Ser144) and residues His164-Glu166 form essential parts of the substrate-binding pocket of the protease in the binding process. Furthermore, we address the issue of the substrate-binding pocket flexibility and show that two adjacent loops in the M pro structures (residues Thr45-Met49 and Arg188-Ala191) with high flexibility can regulate the binding cavity' accessibility for different ligand sizes. Moreover, we discuss in detail the various structural and functional roles of several important conserved and mobile water molecules within and around the binding site in the proper enzymatic function of M pro . We also present a new docking protocol in the framework of the ensemble docking strategy. The performance of the docking protocol has been evaluated in predicting ligand binding pose and affinity ranking for two popular docking programs; AutoDock4 and AutoDock Vina. Our docking results suggest that the top-ranked poses of the most populated clusters obtained by AutoDock Vina are the most important representative docking runs that show a very good performance in estimating experimental binding poses and affinity ranking., (© 2022 Wiley Periodicals LLC.)

Introduction: The COVID-19 pandemic is now affecting all people around the world and getting worse. New antiviral medications are desperately needed other than the few approved medications that have shown no promising efficacy so far., Methods: Here we report three blocking binders for targeting SARS-CoV-2 spike protein to block the interaction between the spike protein on the SARS-CoV-2 and the angiotensin-converting enzyme 2 (ACE2) receptors, responsible for viral homing into the alveolar epithelium type II cells (AECII)., Results: The design process is based on the collected natural scaffolds and using Rosetta interface for designing the binders., Conclusion: Based on the structural analysis, three binders were selected, and the results showed that they might be promising as new therapeutic targets for blocking COVID-19., Competing Interests: None., (© 2021 Elsevier Inc. All rights reserved.)

pK a values of homorepeat hexapeptides with a 2,3-diazabicyclo[2.2.2]oct-2-ene (DBO) chromophore attached at the peptide C termini, through an asparagine derivative (Dbo), namely His 6 -Dbo (H 6 ), Lys 6 -Dbo (K 6 ), and Arg 6 -Dbo (R 6 ), were determined by a novel fluorescence-based method. The fluorescence lifetime of Dbo in the peptides (τ) was measured as a function of pH. The side chains collide with Dbo intramolecularly and quench it efficiently only when they are deprotonated (i.e., pH ≥ side chain pK a ). The pK a values of the H 6 , K 6 , and R 6 peptides, attributable to side chain ionization, were found to be depressed compared to the pK a values of the His, Lys, and Arg residues in their free amino acid forms. We further looked into the structural changes of the peptides by molecular dynamics (MD) simulations; the peptides were structurally more expanded when their side chains are protonated. The structural expansion of the peptides reflects an electrostatic repulsion between the protonated side chain residues, which also accounts for the observed decrease in pK a values, which corresponds to a facilitated deprotonation, assisted by electrostatic repulsion., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein-ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies., (© 2022. The Author(s).)

Background: Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed., Results: In this study, by using an up-to-date dataset, a machine learning model has been trained successfully to predict the toxicity of antimicrobial peptides. The comprehensive set of features of both physico-chemical and linguistic-based with local and global essences have undergone feature selection to identify key properties behind toxicity of antimicrobial peptides. After feature selection, the hybrid model showed the best performance with a recall of 0. 876 and a F1 score of 0. 849., Conclusions: The obtained model can be useful in extracting AMPs with low toxicity from AMP libraries in clinical applications. On the other hand, several properties with local nature including positions of strand forming and hydrophobic residues in final selected features show that these properties are critical definer of peptide properties and should be considered in developing models for activity prediction of peptides. The executable code is available at https://git.io/JRZaT ., (© 2021. The Author(s).)

Spermatogenesis is a complex process of cellular division and differentiation that begins with spermatogonia stem cells and leads to functional spermatozoa production. However, many of the molecular mechanisms underlying this process remain unclear. Single-cell RNA sequencing (scRNA-seq) is used to sequence the entire transcriptome at the single-cell level to assess cell-to-cell variability. In this study, more than 33,000 testicular cells from different scRNA-seq datasets with normal spermatogenesis were integrated to identify single-cell heterogeneity on a more comprehensive scale. Clustering, cell type assignments, differential expressed genes and pseudotime analysis characterized 5 spermatogonia, 4 spermatocyte, and 4 spermatid cell types during the spermatogenesis process. The UTF1 and ID4 genes were introduced as the most specific markers that can differentiate two undifferentiated spermatogonia stem cell sub-cellules. The C7orf61 and TNP can differentiate two round spermatid sub-cellules. The topological analysis of the weighted gene co-expression network along with the integrated scRNA-seq data revealed some bridge genes between spermatogenesis's main stages such as DNAJC5B, C1orf194, HSP90AB1, BST2, EEF1A1, CRISP2, PTMS, NFKBIA, CDKN3, and HLA-DRA. The importance of these key genes is confirmed by their role in male infertility in previous studies. It can be stated that, this integrated scRNA-seq of spermatogenic cells offers novel insights into cell-to-cell heterogeneity and suggests a list of key players with a pivotal role in male infertility from the fertile spermatogenesis datasets. These key functional genes can be introduced as candidates for filtering and prioritizing genotype-to-phenotype association in male infertility., (© 2021. The Author(s).)

Acetylcholinesterase (AChE), with a rigid structure and buried active site at the end of a deep narrow gorge, is interesting enough to solve the paradox between high catalytic activity and unavailability of the active site in treatment of Alzheimer's disease (AD). In this way, the blind docking process is performed on an ensemble of AChE structures created with molecular dynamics (MD) simulations to survey the whole space of AChE to find multiple access pathways to the active site and ranking them based on their affinity scores. Our results show that there are other allosteric binding sites in the protein structure whose inhibition, can affect protein function by disrupting the release of the Acetylcholine (AC) degradation products. In this study, inhibitory activities of Hybride14 and two natural compounds (Papaverine and Palmatine) were evaluated for all possible allosteric sites via docking method. The results confirmed the non-competitive inhibition mechanism. The best binding mode for these inhibitors and efficacy of hydrogen bonds and hydrophobic interactions on inhibitory activities of ligands were also disclosed. Furthermore, our studies provide significant molecular insight for AChE inhibition that could aid in the development of new drugs for AD's treatment., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning algorithms in the development of scoring functions has led to a significant improvement in docking performance. In this work, we introduce a new machine learning (ML) based scoring function called ET-Score, which employs the distance-weighted interatomic contacts between atom type pairs of the ligand and the protein for featurizing protein-ligand complexes and Extremely Randomized Trees algorithm for the training process. The performance of ET-Score is compared with some successful ML-based scoring functions and several popular classical scoring functions on the PDBbind 2016v core set. It is shown that our ET-Score model (with Pearson's correlation of 0.827 and RMSE of 1.332) achieves very good performance in comparison with most of the ML-based scoring functions and all classical scoring functions despite its extremely low computational cost. ET-Score's codes are freely available on the web at https://github.com/miladrayka/ET_Score., (© 2021 Wiley-VCH GmbH.)

In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6-D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin-dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry-corrected heavy-atom RMSD and the fraction of recovered ligand-protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Since the first in silico generation of a genome-scale metabolic (GSM) model for Haemophilus influenzae in 1999, the GSM models have been reconstructed for various organisms including human and mouse. There are two important strategies for generating a GSM model: in the bottom-up approach, individual genomic and biochemical components are integrated to build a GSM model. Alternatively, the orthology-based strategy uses a previously reconstructed model of a reference organism to infer a GSM model of a target organism. Following the update and development of the metabolic network of reference organism, the model of the target organism can also be updated to eliminate defects. Here, we presented iMM1865 model as an orthology-based reconstruction of a GSM model for Mus musculus based on the last flux-consistent version of the human metabolic network, Recon3D. We proposed two versions of the new mouse model, iMM1865 and min-iMM1865, with the same number of gene-associated reactions but different subsets of non-gene-associated reactions. A third extended but flux-inconsistent model (iMM3254) was also created based on the extended version of Recon3D. Compared to the previously published mouse models, both versions of iMM1865 include more comprehensive annotations of metabolites and reactions with no dead-end metabolites and blocked reactions. We evaluated functionality of the models using 431 metabolic objective functions. iMM1865 and min-iMM1865 passed 93% and 87% of the tests, respectively, while iMM1415 and MMR (another available mouse GSM) passed 80% and 84% of the tests, respectively. Three versions of tissue-specific embryo heart models were also reconstructed from each of iMM1865 and min-iMM1865 using mCADRE algorithm with different thresholds on expression-based scores. The ability of corresponding GSM and embryo heart models to predict essential genes was assessed across experimentally derived lethal and viable gene sets. Our analysis revealed that tissue-specific models render much better predictions than GSM models.

Obstructive azoospermia (OA), defined as an obstruction in any region of the male genital tract, accounts for 40% of all azoospermia cases. Of all OA cases, ~30% are thought to have a genetic origin, however, hitherto, the underlying genetic etiology of the majority of these cases remain unknown. To address this, we took a family-based whole-exome sequencing approach to identify causal variants of OA in a multiplex family with epidydimal obstruction. A novel gain-of-function missense variant in CLDN2 (c.481G>C; p.Gly161Arg) was found to co-segregate with the phenotype, consistent with the X-linked inheritance pattern observed in the pedigree. To assess the pathogenicity of this variant, the wild and mutant protein structures were modeled and their potential for strand formation in multimeric form was assessed and compared. The results showed that dimeric and tetrameric arrangements of Claudin-2 were not only reduced, but were also significantly altered by this single residue change. We, therefore, envisage that this amino acid change likely forms a polymeric discontinuous strand, which may lead to the disruption of tight junctions among epithelial cells. This missense variant is thus likely to be responsible for the disruption of the blood-epididymis barrier, causing dislodged epithelial cells to clog the genital tract, hence causing OA. This study not only sheds light on the underlying pathobiology of OA, but also provides a basis for more efficient diagnosis in the clinical setting.

Polymyxin B and E (colistin), are a group of cationic charged cyclic antibiotic lipopeptides that are frequently used in the clinics to treat infections caused by the multidrug-resistant gram-negative bacteria. Since the interactions with the blood plasma drug-transport proteins may play a critical role in determining their pharmacological and pharmacokinetic profiles, we studied the binding properties of polymyxins to the human serum albumin (HSA) under simulated physiological conditions by the combination of biophysical approaches, such as isothermal titration calorimetry (ITC), fluorescence anisotropy, circular dichroism (CD) buttressed by computational studies. The HSA binding to the polymyxins was relatively strong (K a  ≈ 1.0 × 10 7  M -1 ). Molecular docking indicated that polymyxins bind to the cleft of HSA between domains I and III via the electrostatic interactions. This evidence was further confirmed by the entropy-driven interaction for the polymyxins bound HSA. Far UV-CD experiments showed that the secondary structure of HSA doesn't alter and its stable structure is preserved. Collectively, these investigations revealed that the polymyxins bind preferentially to the partially unfolded intermediate forms of the protein structure; however, HSA molecule does not undergo any significant conformational changes upon binding. This is promising as it may limit the unfavorable side effects of the medicine. On the whole, the results provide quantitative and qualitative insight of the binding interaction between HSA and polymyxins, which is important in understanding their effect as therapeutic agents., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Structural characterization of intrinsically disordered proteins (IDPs) is paramount and challenging in structural biology. In this regard, a de novo computational protocol is introduced to build heterogeneous structural libraries for amyloid-β (Aβ) as a critical IDP. This method combines the strength of the simulated annealing - in jumping over energy barriers and escaping from traps - with short conventional molecular dynamics simulations to quickly explore local regions of the conformational space. The protocol efficiency and reliability in building Aβ conformational library is compared with two widely used simulation methods, replica exchange molecular dynamics and multiple trajectory sampling. The probability distribution functions of various structural and energetic features are constructed for each library, and also the diversity and convergence rates in these protocols were compared. Our results show that the suggested protocol is a successful computational method in the generation of a diverse conformational library of the Aβ monomer in agreement with experimental data. This method focuses on visiting more conformations in less computational time without paying attention to the statistical weight of each state in the library. We believe that the suggested computational technique can be used for generating a reasonable starting pool for subsequent reweighting with experimental data to obtain a statistical ensemble., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Humanized monoclonal antibodies (mAbs) against HER2 including trastuzumab and pertuzumab are widely used to treat HER2 overexpressing metastatic breast cancers. These two mAbs recognize distinct epitopes on HER2 and their combination induces a more potent blockade of HER2 signaling than trastuzumab alone. Recently, we have reported characterization of a new chimeric mAb (c-1T0) which binds to an epitope different from that recognized by trastuzumab and significantly inhibits proliferation of HER2 overexpressing tumor cells. Here, we describe humanization of this mAb by grafting all six complementarity determining regions (CDRs) onto human variable germline genes. Humanized VH and VL sequences were synthesized and ligated to human γ1 and κ constant region genes using splice overlap extension (SOE) PCR. Subsequently, the humanized antibody designated hersintuzumab was expressed and characterized by ELISA, Western blot and flow cytometry. The purified humanized mAb binds to recombinant HER2 and HER2-overexpressing tumor cells with an affinity comparable with the chimeric and parental mouse mAbs. It recognizes an epitope distinct from those recognized by trastuzumab and pertuzumab. Binding of hersintuzumab to HER2 overexpressing tumor cells induces G1 cell cycle arrest, inhibition of ERK and AKT signaling pathways and growth inhibition. Moreover, hersintuzumab could induce antibody-dependent cell cytotoxicity (ADCC) on BT-474 cells. This new humanized mAb is a potentially valuable tool for single or combination breast cancer therapy.

Molecular dynamics (MD) at two temperatures of 300 and 340 K identified two histidine residues, His461 and His489, in the most flexible regions of firefly luciferase, a light emitting enzyme. We therefore designed four protein mutants H461D, H489K, H489D and H489M to investigate their enzyme kinetic and thermodynamic stability changes. Substitution of His461 by aspartate (H461D) decreased ATP binding affinity, reduced the melting temperature of protein by around 25 °C and shifted its optimum temperature of activity to 10 °C. In line with the common feature of psychrophilic enzymes, the MD data showed that the overall flexibility of H461D was relatively high at low temperature, probably due to a decrease in the number of salt bridges around the mutation site. On the other hand, substitution of His489 by aspartate (H489D) introduced a new salt bridge between the C-terminal and N-terminal domains and increased protein rigidity but only slightly improved its thermal stability. Similar changes were observed for H489K and, to a lesser degree, H489M mutations. Based on our results we conclude that the MD simulation-based rational substitution of histidines by salt-bridge forming residues can modulate conformational dynamics in luciferase and shift its optimal temperature activity., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.

Ligand binding studies on human serum albumin (HSA) are crucial in determining the pharmacological properties of drug candidates. Here, two representatives of coumarin-chalcone hybrids were selected and their binding mechanism was identified via thermodynamics techniques, curve resolution analysis and computational methods at molecular levels. The binding parameters were derived using spectroscopic approaches and the results point to only one pocket located near the Trp214 residue in subdomain IIA of HSA. The protein tertiary structure was altered during ligand binding and formed an intermediate structure to create stronger ligand binding interactions. The best binding mode of the ligand was initially estimated by docking on an ensemble of HSA crystallographic structures and by molecular dynamics (MD) simulations. Per residue interaction energies were calculated over the MD trajectories as well. Reasonable agreement was found between experimental and theoretical results about the nature of binding, which was dominated by hydrogen bonding and van der Waals contributions.

Due to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors including sulfonamides, polyamines, coumarins and dithiocarbamates (DTCs). However, many of them hardly act as a selective inhibitor against a specific isoform. Therefore, finding highly selective inhibitors for different isoforms of CA is still an ongoing project. Proteochemometrics modeling (PCM) is able to model the bioactivity of multiple compounds against different isoforms of a protein. Therefore, it would be extremely applicable when investigating the selectivity of different ligands towards different receptors. Given the facts, we applied PCM to investigate the interaction space and structural properties that lead to the selective inhibition of CA isoforms by some dithiocarbamates. Our models have provided interesting structural information that can be considered to design compounds capable of inhibiting different isoforms of CA in an improved selective manner. Validity and predictivity of the models were confirmed by both internal and external validation methods; while Y-scrambling approach was applied to assess the robustness of the models. To prove the reliability and the applicability of our findings, we showed how ligands-receptors selectivity can be affected by removing any of these critical findings from the modeling process., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Human protein disulfide isomerase (hPDI) is a key redox-regulated thiol-containing protein operating as both oxidoreductase and molecular chaperone in the endoplasmic reticulum of cells. hPDI thiol-disulfide interchange reactions lead to the adoption of two distinct red/ox conformations with different substrate preferences. hPDI also displays high binding capacity for some endogenous steroid hormones including 17 beta-estradiol (E2) and thus contributes to the regulation of their intracellular concentration, storage and actions. The primary focus of this study was to investigate the impact of E2 binding on functional activity of recombinant hPDI. Then, we examined the effect of E2 binding on structural alteration of hPDI red/ox conformations and its influence on affinity and position of interaction using experimental and computational analysis. Our results revealed that interaction of one E2 per each hPDI molecule led to the inhibition of hPDI reductase activity and conformational changes in both oxidation states. Mutually, E2-binding position were also redox-regulated with higher affinity in oxidized hPDI compare to the reduced form. The importance of histidine-256 protonation states in distinct binding preferences of E2 were also demonstrated in hPDI red/ox conformations. These findings might pave the way for better understanding of the mechanisms behind the redox-dependent hormone-binding activity of hPDI., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The critical role of carbonic anhydrases in different physiological processes has put this protein family at the center of attention, challenging major diseases like glaucoma, neurological disorders such as epilepsy and Alzheimer's disease, obesity, and cancers. Many QSAR/QSPR (quantitative structure-activity/property relationship) researches have been carried out to design potent carbonic anhydrase inhibitors (CAIs); however, using inhibitors with no selectivity for different isoforms can lead to major side-effects. Given that QSAR/QSPR methods are not capable of covering multiple targets in a unified model, we have applied the proteochemometric approach to model the interaction space that governs selective inhibition of different CA isoforms by some mono-/dihydroxybenzoic acid esters. Internal and external validation methods showed that all models were reliable in terms of both validity and predictivity, whereas Y-scrambling assessed the robustness of the models. To prove the applicability of our models, we showed how structural changes of a ligand can affect the selectivity. Our models provided interesting information that can be useful for designing inhibitors with selective behavior toward isoforms of carbonic anhydrases, aiding in their selective inhibition., (© 2016 John Wiley & Sons A/S.)

Bisphosphonates are important therapeutic agents in bone-related diseases and exhibit complex H-bonding networks. To assess the role of H-bonds in biophosphonate stability, a full conformational search was performed for methylenebisphosphonate (MBP) and 1-hydroxyethylidene-1,1-diphosphonate (HEDP) using the MP2 method in conjunction with the continuum solvation model. The most stable structures and their equilibrium populations were analyzed at two protonation states via assignment of H-bonding motifs to each conformer. Geometrical and topological approaches for the identification and characterization of H-bonds were compared with each other, and some of the important correlations between H-bond features were described over the entire conformational space of a hydroxy-bisphosphonate moiety. The topologically derived H-bond energy obtained from the local density of potential energy at bond critical points shows consistent correlations with other measures such as H-bond frequency shift. An inverse power form without an intercept predicts topological H-bond energies from hydrogen-acceptor distances with an RMS error of less than 1 kcal mol(-1). The consistency of this measure was further checked by building a model that reasonably reproduces the relative stabilities of different conformers from their hydrogen-acceptor distances. In all systems, the predictions of this model are improved by the consideration of weak H-bonds that have no bond critical point.

Extensive DFT and ab initio calculations were performed to characterize the conformational space of pamidronate, a typical pharmaceutical for bone diseases. Mono-, di- and tri-protic states of molecule, relevant for physiological pH range, were investigated for both canonical and zwitterionic tautomers. Semiempirical PM6 method were used for prescreening of the single bond rotamers followed by geometry optimizations at the B3LYP/6-31++G(d,p) and B3LYP/6-311++G(d,p) levels. For numerous identified low energy conformers the final electronic energies were determined at the MP2/6-311++G(2df,2p) level and corrected for thermal effects at B3LYP level. Solvation effects were also considered via the COSMO and C-PCM implicit models. Reasonable agreement was found between bond lengths and angle values in comparison with X-ray crystal structures. Relative equilibrium populations of different conformers were determined from molecular partition functions and the role of electronic, vibrational and rotational degrees of freedom on the stability of conformers were analyzed. For no level of theory is a zwitterionic structure stable in the gas-phase while solvation makes them available depending on the protonation state. Geometrically identified intramolecular hydrogen bonds were analyzed by QTAIM approach. All conformers exhibit strong inter-phosphonate hydrogen bonds and in most of them the alkyl-amine side chain is folded on the P-C-P backbone for further hydrogen bond formation.

Based on definition of angular central moments, a quantitative measure is proposed for comparative assessment of the anisotropy of different intermolecular potential energy surfaces at different intermolecular distances. Angular spreadness, skewness and peakedness are three features of anisotropy that are used here to describe the distribution of values of interaction energy around its isotropic component. In agreement with qualitative interpretations, the proposed measure exhibits a sharp change in the R-dependent pattern of anisotropy at an intermediate distance where the repulsive forces on the average overcome the attractive ones. The R-dependence of anisotropy of available N(2)-N(2) potentials is examined in comparison with bare ab initio data and considerable discrepancies are found at distances shorter than the onset of repulsion. It is shown that the full experimentally derived potentials with simplified functional forms do not reproduce the correct anisotropy of interaction energy., (This journal is © the Owner Societies 2011)

In this work, for the first time, an analytical four-dimensional representation for the intermolecular potential of the N(2)-CO dimer is constructed from ab initio calculations. The most stable structure of dimer is found to be a distorted T-shape conformation with CO forming the top and N(2) the leg of T. Important structures of the dimer are characterized, and surprisingly, it is found that in contrast with general assumptions, the potential energy surface of the N(2)-CO dimer has a single symmetry unique minimum. The energy profile of a minimum energy path that connects two T-shaped saddle points to the minimum structure is derived. Important structures are characterized along this path to represent the concerted internal rotation of monomers within the complex. The second virial coefficient is calculated from the fitted PES, and reasonable agreement is found with recent experimental results.

The intermolecular potential energy surface (PES) of the Cl2 dimer is calculated at the MP2/aTZ + b level of ab initio theory. A quantitative measure is proposed for comparison of the anisotropy of PESs of different systems at different intermolecular distances. A high degree of anisotropy at short and intermediate distances results in the failure of fitting strategies that are based on the angular expansion of the potential energy. To tackle this problem, a step-by-step fitting strategy is designed for analytical representation of the PES. The global minimum energy configuration of the dimer is found to be a distorted L-shape structure with a well depth of around 615 cm(-1). The PES is finally scaled to minimize deviations between calculated and experimental second virial coefficients.

Two analytical representations for the potential energy surface of the F(2) dimer were constructed on the basis of ab initio calculations up to the fourth-order of Møller-Plesset (MP) perturbation theory. The best estimate of the complete basis set limit of interaction energy was derived for analysis of basis set incompleteness errors. At the MP4/aug-cc-pVTZ level of theory, the most stable structure of the dimer was obtained at R = 6.82 au, theta(a) = 12.9 degrees , theta(b) = 76.0 degrees , and phi = 180 degrees , with a well depth of 716 microE(h). Two other minima were found for canted and X-shaped configurations with potential energies around -596 and -629 microE(h), respectively. Hexadecapole moments of monomers play an important role in the anisotropy of interaction energy that is highly R-dependent at intermediate intermolecular distances. The quality of potentials was tested by computing values of the second virial coefficient. The fitted MP4 potential has a more reasonable agreement with experimental values.