How large B-factors can be in protein crystal structures

Accuracy of AlphaFold models: Comparison with short N…O contacts in atomic resolution protein crystal structures

Artificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with near-experimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental results at high resolution. T-tests and normal probability plots showed that these computational methods predict atomic position standard errors 3.5-6 times bigger than experimental errors. SYNOPSIS: Positional standard errors in AI-based protein 3D models are 3.5-6 times larger than in atomic resolution crystal structures.

Unravelling carbohydrate binding module 21 (CBM21) dynamics of interaction with amylose

The carbohydrate binding module 21 (CBM21) from Rhizopus oryzae is a dual-site CBM proposed to disrupt polysaccharide structures. Additionally, it serves as a purification tag in industry. CBM21 crystal structure features a Glc residue in an unusual 1S3 conformation, whose relevance for the CBM mechanism of action is unclear. In this context, we seek to contribute for the understanding of CBM21 mechanism of action by: i) investigating the role of the 1S3 conformation on carbohydrate recognition, and ii) characterize the protein-carbohydrate binding dynamics using molecular dynamics and metadynamics simulations at MM and QM/MM levels. Results indicate the 1S3 Glc conformation is unlikely to occur under biological conditions, being originated from the crystallographic environment. CBM21 binding to small ligands appears transient and unstable, while protein dimerization and polysaccharide chain size influence complex stability. In interactions with amylose, CBM21 exhibits a repeated unbinding followed by re-binding, while simultaneously alternating between binding sites I and II. These results suggest that CBM21 acts through transient interactions, directing carbohydrates to the catalytic center rather than forming strong and long-lasting bonds with carbohydrates. Accordingly, we expect such atomistic depiction of CBM21 mechanism could aid in CBM design targeting biotechnological applications.

Fragment library screening by X-ray crystallography and binding site analysis on thioredoxin glutathione reductase of Schistosoma mansoni

Schistosomiasis is caused by parasites of the genus Schistosoma, which infect more than 200 million people. Praziquantel (PZQ) has been the main drug for controlling schistosomiasis for over four decades, but despite that it is ineffective against juvenile worms and size and taste issues with its pharmaceutical forms impose challenges for treating school-aged children. It is also important to note that PZQ resistant strains can be generated in laboratory conditions and observed in the field, hence its extensive use in mass drug administration programs raises concerns about resistance, highlighting the need to search for new schistosomicidal drugs. Schistosomes survival relies on the redox enzyme thioredoxin glutathione reductase (TGR), a validated target for the development of new anti-schistosomal drugs. Here we report a high-throughput fragment screening campaign of 768 compounds against S. mansoni TGR (SmTGR) using X-ray crystallography. We observed 49 binding events involving 35 distinct molecular fragments which were found to be distributed across 16 binding sites. Most sites are described for the first time within SmTGR, a noteworthy exception being the "doorstop pocket" near the NADPH binding site. We have compared results from hotspots and pocket druggability analysis of SmTGR with the experimental binding sites found in this work, with our results indicating only limited coincidence between experimental and computational results. Finally, we discuss that binding sites at the doorstop/NADPH binding site and in the SmTGR dimer interface, should be prioritized for developing SmTGR inhibitors as new antischistosomal drugs.

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Using graphlet degree vectors to predict atomic displacement parameters in protein structures

In structural biology, atomic displacement parameters, commonly used in the form of B values, describe uncertainties in atomic positions. Their distribution over the structure can provide hints on local structural reliability and mobility. A spatial macromolecular model can be represented by a graph whose nodes are atoms and whose edges correspond to all interatomic contacts within a certain distance. Small connected subgraphs, called graphlets, provide information about the wiring of a particular atom. The multiple linear regression approach based on this information aims to predict a distribution of values of isotropic atomic displacement parameters (B values) within a protein structure, given the atomic coordinates and molecular packing. By modeling the dynamic component of atomic uncertainties, this method allows the B values obtained from experimental crystallographic or cryo-electron microscopy studies to be reproduced relatively well.

Analyzing Current Trends and Possible Strategies to Improve Sucrose Isomerases' Thermostability

Due to their ability to produce isomaltulose, sucrose isomerases are enzymes that have caught the attention of researchers and entrepreneurs since the 1950s. However, their low activity and stability at temperatures above 40 °C have been a bottleneck for their industrial application. Specifically, the instability of these enzymes has been a challenge when it comes to their use for the synthesis and manufacturing of chemicals on a practical scale. This is because industrial processes often require biocatalysts that can withstand harsh reaction conditions, like high temperatures. Since the 1980s, there have been significant advancements in the thermal stabilization engineering of enzymes. Based on the literature from the past few decades and the latest achievements in protein engineering, this article systematically describes the strategies used to enhance the thermal stability of sucrose isomerases. Additionally, from a theoretical perspective, we discuss other potential mechanisms that could be used for this purpose.

B-factor prediction in proteins using a sequence-based deep learning model

B factors provide critical insight into protein dynamics. Predicting B factors of an atom in new proteins remains challenging as it is impacted by their neighbors in Euclidean space. Previous learning methods developed have resulted in low Pearson correlation coefficients beyond the training set due to their limited ability to capture the effect of neighboring atoms. With the advances in deep learning methods, we develop a sequence-based model that is tested on 2,442 proteins and outperforms the state-of-the-art models by 30%. We find that the model learns that the B factor of a site is prominently affected by atoms within a 12-15 Å radius, which is in excellent agreement with cutoffs from protein network models. The ablation study revealed that the B factor can largely be predicted from the primary sequence alone. Based on the abovementioned points, our model lays a foundation for predicting other properties that are correlated with the B factor.

Binding of viral nuclear localization signal peptides to importin-α nuclear transport protein

Using all-atom replica-exchange molecular dynamics simulations, we mapped the mechanisms of binding of the nuclear localization signal (NLS) sequence from Venezuelan equine encephalitis virus (VEEV) capsid protein to importin-α (impα) transport protein. Our objective was to identify the VEEV NLS sequence fragment that confers native, experimentally resolved binding to impα as well as to study associated binding energetics and conformational ensembles. The two selected VEEV NLS peptide fragments, KKPK and KKPKKE, show strikingly different binding mechanisms. The minNLS peptide KKPK binds non-natively and nonspecifically by adopting five diverse conformational clusters with low similarity to the x-ray structure 3VE6 of NLS-impα complex. Despite the prevalence of non-native interactions, the minNLS peptide still largely binds to the impα major NLS binding site. In contrast, the coreNLS peptide KKPKKE binds specifically and natively, adopting a largely homogeneous binding ensemble with a dominant, highly native-like conformational cluster. The coreNLS peptide retains most of native binding interactions, including π-cation contacts and a tryptophan cage. While KKPK binding is governed by a complex multistate free energy landscape featuring transitions between multiple binding poses, the coreNLS peptide free energy map is simple, exhibiting a single dominant native-like bound basin. We argue that the origin of the coreNLS peptide binding specificity is several electrostatic interactions formed by the two C-terminal amino acids, Lys10 and Glu11, with impα. The coreNLS sequence is then sufficient for native binding, but none of the amino acids flanking minNLS, including Lys10 and Glu11, are strictly necessary for the native pose. Our analyses indicate that the VEEV coreNLS sequence is virtually unique among human and viral proteins interacting with impα making it a potential target for VEEV-specific inhibitors.

Cupressus sempervirens L. flavonoids as potent inhibitors to xanthine oxidase: in vitro, molecular docking, ADMET and PASS studies

Excessive intake of purine-rich foods such as seafood and red meat leads to excess xanthine oxidase activity and provokes gout attacks. The aim of this paper is to evaluate in vitro and in silico, the inhibition effect of Cupressus sempervirens plant extracts (flavonoids (Cae) and alkaloids (CaK)) and its six derivative compounds on bovine xanthine oxidase (BXO). The in silico study consists of molecular docking with GOLD v4.0 based on the best PLPchem score (PLP) and prediction of biological activity with the PASS server tool. The inhibitors used were lignan (cp1), Amentoflavone (cp2), Cupressuflavone (cp3), Isocryptomerin (cp4), Hinokiflavone (cp5), and Neolignan (cp6). The in vitro results showed that CaK gives an IC50 of 3.52 ± 0.04 μg/ml. Similarly, Cae saved an IC50 of 8.46 ± 1.98 μg/ml compared with the control (2.82 ± 0.10 μg/ml). The in silico results show that cp1 was the best inhibitor model (PLP of 88.09) with approved pharmacokinetics. These findings suggest that cp1 and cp2 may offer good alternatives for the treatment of hyperuricemia; cp3 was moderate, while the others (cp4 to cp6) were considered weak inhibitors according to their PLP.Communicated by Ramaswamy H. Sarma.

The SARS-CoV-2 spike protein structure: a symmetry tale on distortion trail

A preliminary step in the SARS-CoV-2 human infection process is a conformational change of the receptor binding domain (RBD) of its spike protein, characterized by a significant loss of symmetry. During this process, the residues which later on bind to the human angiotensin converting enzyme 2 (ACE2) receptor, become exposed at the surface of the protein. Symmetry analysis of a data set of 33 protein structures from experimental measurements and 32 structures from molecular dynamics simulation, show that the initial state carries clear indications on the structure of the final state, with respect to the local distortion along the sequence. This surprising finding implies that this type of analysis predicts the mechanism of change. We further show that the level of local distortion at the initial state increases with variant's transmissibility, for the wild type (WT) along with past and present variants of concern (WT ∼ alpha < beta < delta < Omicron BA.1), in accordance with the trend of their evolutionary path. In other words, the initial structure of the variant which is most infectious is also the most distorted, making its path to the final state shorter. It has been claimed that the RBD migration of the spike protein is allosterically controlled. Our analysis provides a quantitative support to a major theorem in this respect - that information about an allosteric process is encoded in the structure itself, suggesting that the path of local distortion is related to an allosteric information network.

Linking B-factor and temperature-induced conformational transition

The crystallographic B-factor, also called temperature factor or Debye-Waller factor, has long been used as a surrogate for local protein flexibility. However, the use of the absolute B-factor as a probe for protein motion requires reproducible validation against conformational changes against chemical and physical variables. Here we report the investigation of the thermal dependence of the crystallographic B-factor and its correlation with conformational changes of the protein. We obtained the crystal protein structure coordinates and B-factors at high resolution (1.5 Å) over a broad temperature range (100 K to 325 K). The exponential thermal dependence of B-factor as a function of temperature was equal for both the diffraction intensity data (Wilson B-factor) and for all modeled atoms of the system (protein and non-protein atoms), with a thermal diffusion constant of about 0.0045 K^-1, similar for all atoms. The extrapolated B-factor at zero Kelvin (or zero-point fluctuation) varies among the atoms, although with no apparent correlation with temperature-dependent protein conformational changes. These data suggest that the thermal vibration of the atom does not necessarily correlate with the conformational dynamics of the protein.

Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross-immunity against human visceral and cutaneous leishmaniasis: an immunoinformatics-based approach

CONTEXT

Leishmaniasis is a group of vector-borne infectious diseases caused by over 20 pathogenic Leishmania species that are endemic in many tropical and subtropical countries. The emergence of drug-resistant strains, the adverse side effects of anti-Leishmania drugs, and the absence of a preventative vaccination strategy threaten the sensitive population. Recently, many groups of researchers have exploited the field of reverse vaccinology to develop vaccines, focusing chiefly on inducing immunity against either visceral or cutaneous leishmaniasis.

METHODS

This present work involves retrieving twelve experimentally validated leishmanial antigenic protein sequences from the UniProt database, followed by their antigenicity profiling employing ANTIGENpro and Vaxijen 2.0 servers. MHC-binding epitopes for the same were predicted using both NetCTL 1.2 and SYFPEITHI servers, while epitopes for B cell were computed using ABCpred and BepiPred 2.0 servers. The screened epitopes with significantly higher scores were utilized for designing the vaccine construct with appropriate linkers and natural adjuvant. The secondary and tertiary structures of the synthetic peptide were determined by conditional random fields, shallow neural networks, and profile-profile threading alignment with iterative structure assembly simulations, respectively. The 3-D vaccine model was validated through CASP10-tested refinement and the MolProbity web server. Molecular docking and multi-scale normal mode analysis simulation were performed to analyze the best vaccine-TLR complex. Finally, computational immune simulation findings revealed promising cellular and humoral immune responses, suggesting that the engineered chimeric peptide is a potential broad-spectrum vaccine against visceral and cutaneous leishmaniasis.

Statistical Learning of Protein Elastic Network from Positional Covariance Matrix

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

GalaxyDock2-HEME: Protein-ligand docking for heme proteins

Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2-HEME, a new protein-ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation-dependent scoring term to describe heme iron-ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein-ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein-ligand complexes in which ligands do not bind iron show that GalaxyDock2-HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins.

Structural and biochemical characterization of Leptospira interrogans Lsa45 reveals a penicillin-binding protein with esterase activity

Leptospirosis is a bacterial disease that affects humans and animals and is caused by Leptospira. The recommended treatment for leptospirosis is antibiotic therapy, which should be given early in the course of the disease. Despite the use of these antibiotics, their role during the course of the disease is still not completely clear because of the lack of effective clinical trials, particularly for severe cases of the disease. Here, we present the characterization of L. interrogans Lsa45 protein by gel filtration, protein crystallography, SAXS, fluorescence and enzymatic assays. The oligomeric studies revealed that Lsa45 is monomeric in solution. The crystal structure of Lsa45 revealed the presence of two subdomains: a large α/β subdomain and a small α-helical subdomain. The large subdomain contains the amino acids Ser122, Lys125, and Tyr217, which correspond to the catalytic triad that is essential for β-lactamase or serine hydrolase activity in similar enzymes. Additionally, we also confirmed the bifunctional promiscuity of Lsa45, in hydrolyzing both the 4-nitrophenyl acetate (p-NPA) and nitrocefin β-lactam antibiotic. Therefore, this study provides novel insights into the structure and function of enzymes from L. interrogans, which furthers our understanding of this bacterium and the development of new therapies for the prevention and treatment of leptospirosis.

Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction

The COVID-19 pandemic has resulted in millions of deaths around the world. Multiple vaccines are in use, but there are many underserved locations that do not have adequate access to them. Variants may emerge that are highly resistant to existing vaccines, and therefore cheap and readily obtainable therapeutics are needed. Phytochemicals, or plant chemicals, can possibly be such therapeutics. Phytochemicals can be used in a polypharmacological approach, where multiple viral proteins are inhibited and escape mutations are made less likely. Finding the right phytochemicals for viral protein inhibition is challenging, but in-silico screening methods can make this a more tractable problem. In this study, we screen a wide range of natural drug products against a comprehensive set of SARS-CoV-2 proteins using a high-resolution computational workflow. This workflow consists of a structure-based virtual screening (SBVS), where an initial phytochemical library was docked against all selected protein structures. Subsequently, ligand-based virtual screening (LBVS) was employed, where chemical features of 34 lead compounds obtained from the SBVS were used to predict 53 lead compounds from a larger phytochemical library via supervised learning. A computational docking validation of the 53 predicted leads obtained from LBVS revealed that 28 of them elicit strong binding interactions with SARS-CoV-2 proteins. Thus, the inclusion of LBVS resulted in a 4-fold increase in the lead discovery rate. Of the total 62 leads, 18 showed promising pharmacokinetic properties in a computational ADME screening. Collectively, this study demonstrates the advantage of incorporating machine learning elements into a virtual screening workflow.Communicated by Ramaswamy H. Sarma.

Prominent role of cysteine residues C49 and C343 in regulating Plasmodiumfalciparum pyruvate kinase activity

The protozoan parasite Plasmodium falciparum causes the most severe form of malaria and is highly dependent on glycolysis. Glycolytic enzymes were shown to be massively redox regulated, inter alia via oxidative post-translational modifications (oxPTMs) of their cysteine residues. In this study, we identified P. falciparum pyruvate kinase (PfPK) C49 and C343 as amino acid residues essentially involved in maintaining structural and functional integrity of the enzyme. The mutation of these cysteines resulted in an altered substrate affinity, lower enzymatic activities, and, as studied by X-ray crystallography, conformational changes within the A-domain where the substrate binding site is located. Although the loss of a cysteine evoked an impaired catalysis in both mutants, the effects observed for mutant C49A were more severe: multiple conformational changes, caused by the loss of two hydrogen bonds, impeded proper substrate binding and thus the transfer of phosphate upon catalysis.

GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network

Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein-protein interfaces, and protein-compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.

A B-factor for NOEs?

Nuclear Overhauser effects (NOEs) are influenced by motion. Here, we derive exact, analytical results for a model of isotropic, harmonic fluctuations of atom positions that corresponds to the one underlying crystallographic B-factors. The model includes steric repulsion and yields closed-form expressions for the expected value of general invertible functions of the distance between two atoms, with the special case r^-6 for NOEs. We discuss the implications for the definition of an NOE-based B-factor in solution NMR.

Designing Anti-Microbial Peptides Against Major β-Lactamase Enzymes in Clinically Important Gram-Negative Bacterial Pathogens: An In-Silico Study

Anti-microbial resistance (AMR) creating healthcare concerns worldwide requires ardent exploration of therapeutic alternatives. Although anti-microbial peptides (AMP) are popular for broad-spectrum activity, recent evidence of increasing resistance to membrane-acting AMPs by ESKAPE pathogens has compelled us to design novel AMPs as therapeutic candidates. A library of 60 AMPs comprising natural AMPs and their mutants was constructed through in-silico methods. After physico-chemical property evaluations, each peptide in the library was subjected to flexible molecular docking against four major β-lactamases in Gram-negative ESKAPE pathogens. Among the potent AMP mutants, a Lactoferricin B-Mutant (M4) possessed uniformly high affinity with SHV1, OXA48, NDM1, and AmpC having energies -842.0Kcal/mol, -774.8Kcal/mol, -1103.3Kcal/mol, and -858.8Kcal/mol respectively. Coarse-grained clustering and flexibility analysis further accounted for the residue-level stable configurations of the protein-peptide complexes with high affinity. Highest affinity of Lactoferricin B_M4 was found with NDM1 due to H-bonds, salt-bridges, and hydrophobic interactions with the metallo-β-lactamase domain including crucial active-site residue Asp124. Molecular dynamics simulation further confirmed the stability of Lactoferricin B_M4-NDM1 complex having low residue-level root-mean square deviations (RMSD), atomic-level fluctuations, and radius of gyration (Rg). The study encourages experimental validations and similar methods to identify potential AMPs against drug-resistant pathogens.

Emergence of Meropenem Resistance Among Cefotaxime Non-susceptible Streptococcus pneumoniae: Evidence and Challenges

The principal causative agent of acute bacterial meningitis (ABM) in children and the elderly is Streptococcus pneumoniae, with a widespread increase in penicillin resistance. Resistance is due to non-synonymous single-nucleotide polymorphisms (nsSNPs) that alter the penicillin-binding proteins (PBPs), the targets for all β-lactam drugs. Hence, resistance against one β-lactam antibiotic may positively select another. Since meropenem is an alternative to cefotaxime in meningeal infections, we aim to identify whether nsSNPs in the PBPs causing penicillin and cefotaxime resistance can decrease the pneumococcal susceptibility to meropenem. Comparison of the nsSNPs in the PBPs between the cefotaxime-resistant Indian (n = 33) and global isolates (n = 28) revealed that nsSNPs in PBP1A alone elevated meropenem minimal inhibitory concentrations (MICs) to 0.12 μg/ml, and nsSNPs in both PBP2X and 2B combined with PBP1A increases MIC to ≥ 0.25 μg/ml. Molecular docking confirmed the decrease in the PBP drug binding affinity due to the nsSNPs, thereby increasing the inhibition potential and the MIC values, leading to resistance. Structural dynamics and thermodynamic stability pattern in PBPs as a result of mutations further depicted that the accumulation of certain nsSNPs in the functional domains reduced the drug affinity without majorly affecting the overall stability of the proteins. Restricting meropenem usage and promoting combination therapy with antibiotics having non-PBPs as targets to treat cefotaxime non-susceptible S. pneumoniae meningitis can prevent the selection of β-lactam resistance.

Widespread occurrence of covalent lysine–cysteine redox switches in proteins

We recently reported the discovery of a lysine–cysteine redox switch in proteins with a covalent nitrogen–oxygen–sulfur (NOS) bridge. Here, a systematic survey of the whole protein structure database discloses that NOS bridges are ubiquitous redox switches in proteins of all domains of life and are found in diverse structural motifs and chemical variants. In several instances, lysines are observed in simultaneous linkage with two cysteines, forming a sulfur–oxygen–nitrogen–oxygen–sulfur (SONOS) bridge with a trivalent nitrogen, which constitutes an unusual native branching cross-link. In many proteins, the NOS switch contains a functionally essential lysine with direct roles in enzyme catalysis or binding of substrates, DNA or effectors, linking lysine chemistry and redox biology as a regulatory principle. NOS/SONOS switches are frequently found in proteins from human and plant pathogens, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and also in many human proteins with established roles in gene expression, redox signaling and homeostasis in physiological and pathophysiological conditions.

A survey of protein structures identifies widespread lysine–cysteine cross-links in functionally diverse proteins across all domains of life and in various structural motifs, where these redox switches control enzyme catalysis and/or ligand binding.

B-factor accuracy in protein crystal structures

The accuracy of B factors in protein crystal structures has been determined by comparing the same atoms in numerous, independent crystal structures of Gallus gallus lysozyme. Both B-factor absolute differences and normal probability plots indicate that the estimated B-factor errors are quite large, close to 9 Å2 in ambient-temperature structures and to 6 Å2 in low-temperature structures, and surprisingly are comparable to values estimated two decades ago. It is well known that B factors are not due to local movements only but reflect several, additional factors from crystal defects, large-scale disorder, diffraction data quality etc. It therefore remains essential to normalize B factors when comparing different crystal structures, although it has clearly been shown that they provide useful information about protein dynamics. Improved, quantitative analyses of raw B factors require novel experimental and computational tools that are able to disaggregate local movements from other features and properties that affect B factors.

Uses and Abuses of the Atomic Displacement Parameters in Structural Biology

B-factors determined with X-ray crystallographic analyses are commonly used to estimate the flexibility degree of atoms, residues, and molecular moieties in biological macromolecules. In this chapter, the most recent studies and applications of B-factors in protein engineering and structural biology are briefly summarized. Particular emphasis is given to the limitations in using B-factors, in order to prevent inappropriate applications. It is eventually predicted that future applications will involve anisotropically refined B-factors, deep learning, and data produced by cryo-EM.

Mutagenic Distinction between the Receptor-Binding and Fusion Subunits of the SARS-CoV-2 Spike Glycoprotein and Its Upshot

We observe that a residue R of the spike glycoprotein of SARS-CoV-2 that has mutated in one or more of the current variants of concern or interest, or under monitoring, rarely participates in a backbone hydrogen bond if R lies in the S1 subunit and usually participates in one if R lies in the S2 subunit. A partial explanation for this based upon free energy is explored as a potentially general principle in the mutagenesis of viral glycoproteins. This observation could help target future vaccine cargos for the evolving coronavirus as well as more generally. A related study of the Delta and Omicron variants suggests that Delta was an energetically necessary intermediary in the evolution from Wuhan-Hu-1 to Omicron.

Structural dynamics in the water and proton channels of photosystem II during the S2 to S3 transition

Light-driven oxidation of water to molecular oxygen is catalyzed by the oxygen-evolving complex (OEC) in Photosystem II (PS II). This multi-electron, multi-proton catalysis requires the transport of two water molecules to and four protons from the OEC. A high-resolution 1.89 Å structure obtained by averaging all the S states and refining the data of various time points during the S₂ to S₃ transition has provided better visualization of the potential pathways for substrate water insertion and proton release. Our results indicate that the O1 channel is the likely water intake pathway, and the Cl1 channel is the likely proton release pathway based on the structural rearrangements of water molecules and amino acid side chains along these channels. In particular in the Cl1 channel, we suggest that residue D1-E65 serves as a gate for proton transport by minimizing the back reaction. The results show that the water oxidation reaction at the OEC is well coordinated with the amino acid side chains and the H-bonding network over the entire length of the channels, which is essential in shuttling substrate waters and protons.

Molecular coevolution of nuclear and nucleolar localization signals inside basic domain of HIV-1 Tat

During evolution, viruses had to adapt to an increasingly complex environment of eukaryotic cells. Viral proteins that need to enter the cell nucleus or associate with nucleoli possess nuclear localization signals (NLSs) and nucleolar localization signals (NoLSs) for nuclear and nucleolar accumulation, respectively. As viral proteins are relatively small, acquisition of novel sequences seems to be a more complicated task for viruses than for eukaryotes. Here, we carried out a comprehensive analysis of the basic domain (BD) of HIV-1 Tat to show how viral proteins might evolve with NLSs and NoLSs without an increase in protein size. The HIV-1 Tat BD is involved in several functions, the most important being the transactivation of viral transcription. The BD also functions as an NLS, although it is substantially longer than a typical NLS. It seems that different regions in the BD could function as NLSs due to its enrichment with positively charged amino acids. Additionally, the high positive net charge inevitably causes the BD to function as an NoLS through a charge-specific mechanism. The integration of NLSs and NoLSs into functional domains enriched with positively charged amino acids might be a mechanism that allows the condensation of different functional sequences in small protein regions and, as a result, to reduce protein size, influencing the origin and evolution of NLSs and NoLSs in viruses. IMPORTANCE Here, we investigated the molecular mechanism of NLS and NoLS integration into the basic domain of HIV-1 Tat (⁴⁹RKKRRQRRR⁵⁷), and found that these two supplementary functions (i.e., function of NLS and NoLS) are embedded in the basic domain amino acid sequence. The integration of NLSs and NoLSs into functional domains of viral proteins enriched with positively charged amino acids is a mechanism that allows the concentration of different functions within small protein regions. Integration of NLS and NoLS into functional protein domains might have influenced the viral evolution, as this could prevent an increase in the protein size.

Low-Frequency Harmonic Perturbations Drive Protein Conformational Changes

Protein dynamics has been investigated since almost half a century, as it is believed to constitute the fundamental connection between structure and function. Elastic network models (ENMs) have been widely used to predict protein dynamics, flexibility and the biological mechanism, from which remarkable results have been found regarding the prediction of protein conformational changes. Starting from the knowledge of the reference structure only, these conformational changes have been usually predicted either by looking at the individual mode shapes of vibrations (i.e., by considering the free vibrations of the ENM) or by applying static perturbations to the protein network (i.e., by considering a linear response theory). In this paper, we put together the two previous approaches and evaluate the complete protein response under the application of dynamic perturbations. Harmonic forces with random directions are applied to the protein ENM, which are meant to simulate the single frequency-dependent components of the collisions of the surrounding particles, and the protein response is computed by solving the dynamic equations in the underdamped regime, where mass, viscous damping and elastic stiffness contributions are explicitly taken into account. The obtained motion is investigated both in the coordinate space and in the sub-space of principal components (PCs). The results show that the application of perturbations in the low-frequency range is able to drive the protein conformational change, leading to remarkably high values of direction similarity. Eventually, this suggests that protein conformational change might be triggered by external collisions and favored by the inherent low-frequency dynamics of the protein structure.

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

Modeling and Experimental Validation of Algorithms for Maximum Quantity of Protein to be Immobilized on Solid Supports by Electrostatic Adsorption in the Strategy of Rational Design of Immobilized Derivatives

Protein immobilization by electrostatic adsorption to a support could represent a good option. On the other hand, lysozyme (EC 3.2.1.17) is a little and basic protein. The objective of this work was to test the functionality of the strategy of Rational Design of Immobilized Derivatives for the immobilization by electrostatic adsorption of egg white lysozyme on SP-Sepharose FastFlow support. The RDID_1.0 software was used to predict the superficial lysozyme clusters, the electrostatic configuration probability for each cluster, and the theoretical and estimated maximum quantity of protein to be immobilized. In addition, immobilization was performed and the experimental parameter practical maximum quantity of protein to be immobilized and the enzymatic activity of the immobilized derivative were assessed. The estimated maximum quantity of protein to be immobilized (9.49 protein mg/support g) was close to the experimental practical maximum quantity of protein to be immobilized (14.73 ± 0.09 protein mg/support g). The enzymatic activity assay with the chitosan substrate showed the catalytic functionality of the lysozyme-SP-Sepharose immobilized derivative (35.85 ± 3.07 U/support g), which preserved 78% functional activity. The used algorithm to calculate the estimated maximum quantity of protein to be immobilized works for other proteins, porous solid supports and immobilization methods, and this parameter has a high predictive value, useful for obtaining optimum immobilized derivatives. The applied methodology is valid to predict the most probable protein-support configurations and their catalytic competences, which concur with the experimental results. The produced biocatalyst had a high retention of functional activity. This indicates its functionality in enzymatic bioconversion processes.

How anisotropic and isotropic atomic displacement parameters monitor protein covalent bonds rigidity: isotropic B-factors underestimate bond rigidity

Under the assumption that covalent bonds are rigid, it is possible to compare the estimations of rigidity based on anisotropic and isotropic B-factors. This is done by computing the difference of the mean-square displacements (Delta-u) of atoms A and Z along the covalent bond A–Z, which must be close to zero for a rigid bond. The analysis of a high-quality set of protein structures, refined at a resolution better than (or equal to) 0.8 Angstroms, showed that Delta-u is significantly close to zero when anisotropic B-factors are used, with an average 60% Delta-u reduction. This reduction is larger for larger B-factors and this suggests that care should be taken in data-mining procedures that involve isotropic B-factors, especially at lower resolution, when anisotropic B-factors cannot be determined and when the average B-factor increases.

Comparison of cellooligosaccharide conformations in complexes with proteins with energy maps for cellobiose

Shapes (conformations) of cellulose molecules are described by their glycosidic linkage torsion angles ϕ and ψ. Although the torsions are known for cellulose in crystals, amorphous shapes are also interesting for understanding reactivity and physical properties. ϕ and ψ determination for unorganized matter is difficult; one approach is to study their range in many related molecules. For example, linkage torsions of cellulose should be similar to those in cellobiose. Herein, torsions were measured for cellooligosaccharides and lactose moieties complexed with proteins in the Protein Data Bank (PDB). These torsions were compared with ϕ/ψ maps based on quantum mechanics energies for solvated cellobiose and analogs lacking hydroxyl groups. Most PDB conformations corresponded to low map energies. Amorphous cellulose should be generally extended with individual linkages that would give 2- to 3-fold helices. The map for an analog lacking hydrogen bonding ability was more predictive for PDB linkages than the cellobiose map.

Spectroscopy, Dynamics, and Hydration of S-Nitrosylated Myoglobin

S-Nitrosylation, the covalent addition of NO to the thiol side chain of cysteine, is an important post-transitional modification that can alter the function of various proteins. The structural dynamics and vibrational spectroscopy of S-nitrosylation in the condensed phase are investigated for the methyl-capped cysteine model system and for myoglobin. Using conventional point charge and physically more realistic multipolar force fields for the -SNO group, it is found that the SN- and NO-stretch and the SNO-bend vibrations can be located and distinguished from the other protein modes for simulations of MbSNO at 50 K. The finding of stable cis- and trans-MbSNO agrees with experimental findings on other proteins as is the observation of buried -SNO. For MbSNO the observed relocation of the EF loop in the simulations by ∼3 Å is consistent with the available X-ray structure, and the conformations adopted by the -SNO label are in good overall agreement with the X-ray structure. Despite the larger size of the -SNO group compared with -SH, MbSNO recruits more water molecules in the first two hydration shells due to stronger electrostatic interactions. Similarly, when comparing the hydration between the A- and H-helices, they differ by up to 30% between WT and MbSNO. This suggests that local hydration can also be significantly modulated through nitrosylation.

Steroid disulfates - Sulfation double trouble

Sulfation pathways have recently come into the focus of biomedical research. For steroid hormones and related compounds, sulfation represents an additional layer of regulation as sulfated steroids are more water-soluble and tend to be biologically less active. For steroid diols, an additional sulfation is possible, carried out by the same sulfotransferases that catalyze the first sulfation step. The steroid disulfates that are formed are the focus of this review. We discuss both their biochemical production as well as their putative biological function. Steroid disulfates have also been linked to various clinical conditions in numerous untargeted metabolomics studies. New analytical techniques exploring the biosynthetic routes of steroid disulfates have led to novel insights, changing our understanding of sulfation in human biology. They promise a bright future for research into sulfation pathways, hopefully too for the diagnosis and treatment of several associated diseases.

MEDUSA: Prediction of Protein Flexibility from Sequence

Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.

Modal Analysis of the Lysozyme Protein Considering All-Atom and Coarse-Grained Finite Element Models

Proteins are the fundamental entities of several organic activities. They are essential for a broad range of tasks in a way that their shapes and folding processes are crucial to achieving proper biological functions. Low-frequency modes, generally associated with collective movements at terahertz (THz) and sub-terahertz frequencies, have been appointed as critical for the conformational processes of many proteins. Dynamic simulations, such as molecular dynamics, are vastly applied by biochemical researchers in this field. However, in the last years, proposals that define the protein as a simplified elastic macrostructure have shown appealing results when dealing with this type of problem. In this context, modal analysis based on different modelization techniques, i.e., considering both an all-atom (AA) and coarse-grained (CG) representation, is proposed to analyze the hen egg-white lysozyme. This work presents new considerations and conclusions compared to previous analyses. Experimental values for the B-factor, considering all the heavy atoms or only one representative point per amino acid, are used to evaluate the validity of the numerical solutions. In general terms, this comparison allows the assessment of the regional flexibility of the protein. Besides, the low computational requirements make this approach a quick method to extract the protein’s dynamic properties under scrutiny.

Comprehensive Structural and Molecular Comparison of Spike Proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and Their Interactions with ACE2

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has recently emerged in China and caused a disease called coronavirus disease 2019 (COVID-19). The virus quickly spread around the world, causing a sustained global outbreak. Although SARS-CoV-2, and other coronaviruses, SARS-CoV and Middle East respiratory syndrome CoV (MERS-CoV) are highly similar genetically and at the protein production level, there are significant differences between them. Research has shown that the structural spike (S) protein plays an important role in the evolution and transmission of SARS-CoV-2. So far, studies have shown that various genes encoding primarily for elements of S protein undergo frequent mutation. We have performed an in-depth review of the literature covering the structural and mutational aspects of S protein in the context of SARS-CoV-2, and compared them with those of SARS-CoV and MERS-CoV. Our analytical approach consisted in an initial genome and transcriptome analysis, followed by primary, secondary and tertiary protein structure analysis. Additionally, we investigated the potential effects of these differences on the S protein binding and interactions to angiotensin-converting enzyme 2 (ACE2), and we established, after extensive analysis of previous research articles, that SARS-CoV-2 and SARS-CoV use different ends/regions in S protein receptor-binding motif (RBM) and different types of interactions for their chief binding with ACE2. These differences may have significant implications on pathogenesis, entry and ability to infect intermediate hosts for these coronaviruses. This review comprehensively addresses in detail the variations in S protein, its receptor-binding characteristics and detailed structural interactions, the process of cleavage involved in priming, as well as other differences between coronaviruses.

Studying dynamics without explicit dynamics: A structure-based study of the export mechanism by AcrB

Resistance-nodulation-cell division family proteins are transmembrane proteins identified as large spectrum drug transporters involved in multidrug resistance. A prototypical case in this superfamily, responsible for antibiotic resistance in selected gram-negative bacteria, is AcrB. AcrB forms a trimer using the proton motive force to efflux drugs, implementing a functional rotation mechanism. Unfortunately, the size of the system (1049 amino acid per monomer and membrane) has prevented a systematic dynamical exploration, so that the mild understanding of this coupled transport jeopardizes our ability to counter it. The large number of crystal structures of AcrB prompts studies to further our understanding of the mechanism. To this end, we present a novel strategy based on two key ingredients, which are to study dynamics by exploiting information embodied in the numerous crystal structures obtained to date, and to systematically consider subdomains, their dynamics, and their interactions. Along the way, we identify the subdomains responsible for dynamic events, refine the states (A, B, E) of the functional rotation mechanism, and analyze the evolution of intramonomer and intermonomer interfaces along the functional cycle. Our analysis shows the relevance of AcrB's efflux mechanism as a template within the HAE1 family but not beyond. It also paves the way to targeted simulations exploiting the most relevant degrees of freedom at certain steps, and to a targeting of specific interfaces to block the drug efflux. Our work shows that complex dynamics can be unveiled from static snapshots, a strategy that may be used on a variety of molecular machines of large size.

Backbone Free Energy Estimator Applied to Viral Glycoproteins

Earlier analysis of the Protein Data Bank derived the distribution of rotations from the plane of a protein hydrogen bond donor peptide group to the plane of its acceptor peptide group. The quasi Boltzmann formalism of Pohl-Finkelstein is employed to estimate free energies of protein elements with these hydrogen bonds, pinpointing residues with a high propensity for conformational change. This is applied to viral glycoproteins as well as capsids, where the 90th+ percentiles of free energies determine residues that correlate well with viral fusion peptides and other functional domains in known cases and thus provide a novel method for predicting these sites of importance as antiviral drug or vaccine targets in general. The method is implemented at https://bion-server.au.dk/hbonds/ from an uploaded Protein Data Bank file.

Local and global analysis of macromolecular atomic displacement parameters

This paper describes the global and local analysis of atomic displacement parameters (ADPs) of macromolecules in X-ray crystallography. The distribution of ADPs is shown to follow the shifted inverse-gamma distribution or a mixture of these distributions. The mixture parameters are estimated using the expectation-maximization algorithm. In addition, a method for the resolution- and individual ADP-dependent local analysis of neighbouring atoms has been designed. This method facilitates the detection of mismodelled atoms, heavy-metal atoms and disordered and/or incorrectly modelled ligands. Both global and local analyses can be used to detect errors in atomic models, thus helping in the (re)building, refinement and validation of macromolecular structures. This method can also serve as an additional validation tool during PDB deposition.

Structural Basis and Binding Kinetics of Vaborbactam in Class A β-Lactamase Inhibition

Class A β-lactamases are a major cause of β-lactam resistance in Gram-negative bacteria. The recently FDA-approved cyclic boronate vaborbactam is a reversible covalent inhibitor of class A β-lactamases, including CTX-M extended-spectrum β-lactamase and KPC carbapenemase, both frequently observed in the clinic. Intriguingly, vaborbactam displayed different binding kinetics and cell-based activity for these two enzymes, despite their similarity. A 1.0-Å crystal structure of CTX-M-14 demonstrated that two catalytic residues, K73 and E166, are positively charged and neutral, respectively. Meanwhile, a 1.25-Å crystal structure of KPC-2 revealed a more compact binding mode of vaborbactam versus CTX-M-14, as well as alternative conformations of W105. Together with kinetic analysis of W105 mutants, the structures demonstrate the influence of this residue and the unusual conformation of the β3 strand on the inactivation rate, as well as the stability of the reversible covalent bond with S70. Furthermore, studies of KPC-2 S130G mutant shed light on the different impacts of S130 in the binding of vaborbactam versus avibactam, another recently approved β-lactamase inhibitor. Taken together, these new data provide valuable insights into the inhibition mechanism of vaborbactam and future development of cyclic boronate inhibitors.

Impact of protein dynamics on secondary structure prediction

Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction. Interestingly, a direct link is observed between the quality of the prediction and the stability of the assignment to the secondary structure state. The more stable a local protein conformation is, the better the prediction will be. The secondary structure assignment not taken from the crystallized structures but from the conformations observed during the dynamics slightly increase the quality of the secondary structure prediction. These results show that evaluation of PSSPs can be done differently, but also that the notion of dynamics can be included in development of PSSPs and other approaches such as de novo approaches.

A Computational and Modeling Study of the Reaction Mechanism of Staphylococcus aureus Monoglycosyltransferase Reveals New Insights on the GT51 Family of Enzymes

Bacterial glycosyltransferases of the GT51 family are key enzymes in bacterial cell wall synthesis. Inhibiting cell wall synthesis is a very effective approach for development of antibiotics, as this can lead to either bacteriostatic or bactericidal effects. Even though the existence of this family has been known for over 50 years, only one potent inhibitor exists, which is an analog of the lipid IV product and derived from a natural product. Drug development focused on bacterial transglycosylase has been hampered due to little being know about its structure and reaction mechanism. In this study, Staphylococcus aureus monoglycosyltransferase was investigated at an atomistic level using computational methods. Classical molecular dynamics simulations were used to reveal information about the large-scale dynamics of the enzyme-substrate complex and the importance of magnesium in structure and function of the protein, while mixed mode quantum mechanics/molecular mechanics calculations unveiled a novel hypothesis for the reaction mechanism. From these results, we present a new model for the binding mode of lipid II and the reaction mechanism of the GT51 glycosyltransferases. A metal-bound hydroxide catalyzed reaction mechanism yields an estimated free energy barrier of 16.1 ± 1.0 kcal/mol, which is in line with experimental values. The importance of divalent cations is also further discussed. These findings could significantly aid targeted drug design, particularly the efficient development of transition state analogues as potential inhibitors for the GT51 glycosyltransferases.

The Sequence and Three-Dimensional Structure Characterization of Snake Venom Phospholipases B

Snake venom phospholipases B (SVPLBs) are the least studied enzymes. They constitute about 1% of Bothrops crude venoms, however, in other snake venoms, it is present in less than 1%. These enzymes are considered the most potent hemolytic agent in the venom. Currently, no structural information is available about these enzymes from snake venom. To better understand its three-dimensional structure and mechanisms of envenomation, the current work describes the first model-based structure report of this enzyme from Bothrops moojeni venom named as B. moojeni phospholipase B (PLB_Bm). The structure model of PLB_Bm was generated using model building software like I-TESSER, MODELLER 9v19, and Swiss-Model. The build PLB_Bm model was validated using validation tools (PROCHECK, ERRAT, and Verif3D). The analysis of the PLB_Bm modeled structure indicates that it contains 491 amino acid residues that form a well-defined four-layer αββα sandwich core and has a typical fold of the N-terminal nucleophile aminohydrolase (Ntn-hydrolase). The overall structure of PLB_Bm contains 18 β-strands and 17 α-helices with many connecting loops. The structure divides into two chains (A and B) after maturation. The A chain is smaller and contains 207 amino acid residues, whereas the B chain is larger and contains 266 amino acid residues. The sequence and structural comparison among homologous snake venom, bacterial, and mammals PLBs indicate that differences in the length and sequence composition may confer variable substrate specificity to these enzymes. Moreover, the surface charge distribution, average volume, and depth of the active site cavity also vary in these enzymes. The present work will provide more information about the structure-function relationship and mechanism of action of these enzymes in snakebite envenomation.