32 research outputs found
Analysis of X-ray Structures of Matrix Metalloproteinases via Chaotic Map Clustering
<p>Abstract</p> <p>Background</p> <p>Matrix metalloproteinases (MMPs) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. With this in mind, the perception of the intimate relationships among diverse MMPs could be a solid basis for accelerated learning in designing new selective MMP inhibitors. In this regard, decrypting the latent molecular reasons in order to elucidate similarity among MMPs is a key challenge.</p> <p>Results</p> <p>We describe a pairwise variant of the non-parametric chaotic map clustering (CMC) algorithm and its application to 104 X-ray MMP structures. In this analysis electrostatic potentials are computed and used as input for the CMC algorithm. It was shown that differences between proteins reflect genuine variation of their electrostatic potentials. In addition, the analysis has been also extended to analyze the protein primary structures and the molecular shapes of the MMP co-crystallised ligands.</p> <p>Conclusions</p> <p>The CMC algorithm was shown to be a valuable tool in knowledge acquisition and transfer from MMP structures. Based on the variation of electrostatic potentials, CMC was successful in analysing the MMP target family landscape and different subsites. The first investigation resulted in rational figure interpretation of both domain organization as well as of substrate specificity classifications. The second made it possible to distinguish the MMP classes, demonstrating the high specificity of the S<sub>1</sub>' pocket, to detect both the occurrence of punctual mutations of ionisable residues and different side-chain conformations that likely account for induced-fit phenomena. In addition, CMC demonstrated a potential comparable to the most popular UPGMA (Unweighted Pair Group Method with Arithmetic mean) method that, at present, represents a standard clustering bioinformatics approach. Interestingly, CMC and UPGMA resulted in closely comparable outcomes, but often CMC produced more informative and more easy interpretable dendrograms. Finally, CMC was successful for standard pairwise analysis (i.e., Smith-Waterman algorithm) of protein sequences and was used to convincingly explain the complementarity existing between the molecular shapes of the co-crystallised ligand molecules and the accessible MMP void volumes.</p
Insights into the Complex Formed by Matrix Metalloproteinase-2 and Alloxan Inhibitors: Molecular Dynamics Simulations and Free Energy Calculations
Matrix metalloproteinases (MMP) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. Hence, the development of potent and selective inhibitors targeting these enzymes continues to be eagerly sought. In this paper, a number of alloxan-based compounds, initially conceived to bias other therapeutically relevant enzymes, were rationally modified and successfully repurposed to inhibit MMP-2 (also named gelatinase A) in the nanomolar range. Importantly, the alloxan core makes its debut as zinc binding group since it ensures a stable tetrahedral coordination of the catalytic zinc ion in concert with the three histidines of the HExxHxxGxxH metzincin signature motif, further stabilized by a hydrogen bond with the glutamate residue belonging to the same motif. The molecular decoration of the alloxan core with a biphenyl privileged structure allowed to sample the deep S1′ specificity pocket of MMP-2 and to relate the high affinity towards this enzyme with the chance of forming a hydrogen bond network with the backbone of Leu116 and Asn147 and the side chains of Tyr144, Thr145 and Arg149 at the bottom of the pocket. The effect of even slight structural changes in determining the interaction at the S1′ subsite of MMP-2 as well as the nature and strength of the binding is elucidated via molecular dynamics simulations and free energy calculations. Among the herein presented compounds, the highest affinity (pIC50 = 7.06) is found for BAM, a compound exhibiting also selectivity (>20) towards MMP-2, as compared to MMP-9, the other member of the gelatinases
Defining Kawasaki disease and pediatric inflammatory multisystem syndrome-temporally associated to SARS-CoV-2 infection during SARS-CoV-2 epidemic in Italy: results from a national, multicenter survey
Background: There is mounting evidence on the existence of a Pediatric Inflammatory Multisystem Syndrome-temporally associated to SARS-CoV-2 infection (PIMS-TS), sharing similarities with Kawasaki Disease (KD). The main outcome of the study were to better characterize the clinical features and the treatment response of PIMS-TS and to explore its relationship with KD determining whether KD and PIMS are two distinct entities.
Methods: The Rheumatology Study Group of the Italian Pediatric Society launched a survey to enroll patients diagnosed with KD (Kawasaki Disease Group - KDG) or KD-like (Kawacovid Group - KCG) disease between February 1st 2020, and May 31st 2020. Demographic, clinical, laboratory data, treatment information, and patients' outcome were collected in an online anonymized database (RedCAP®). Relationship between clinical presentation and SARS-CoV-2 infection was also taken into account. Moreover, clinical characteristics of KDG during SARS-CoV-2 epidemic (KDG-CoV2) were compared to Kawasaki Disease patients (KDG-Historical) seen in three different Italian tertiary pediatric hospitals (Institute for Maternal and Child Health, IRCCS "Burlo Garofolo", Trieste; AOU Meyer, Florence; IRCCS Istituto Giannina Gaslini, Genoa) from January 1st 2000 to December 31st 2019. Chi square test or exact Fisher test and non-parametric Wilcoxon Mann-Whitney test were used to study differences between two groups.
Results: One-hundred-forty-nine cases were enrolled, (96 KDG and 53 KCG). KCG children were significantly older and presented more frequently from gastrointestinal and respiratory involvement. Cardiac involvement was more common in KCG, with 60,4% of patients with myocarditis. 37,8% of patients among KCG presented hypotension/non-cardiogenic shock. Coronary artery abnormalities (CAA) were more common in the KDG. The risk of ICU admission were higher in KCG. Lymphopenia, higher CRP levels, elevated ferritin and troponin-T characterized KCG. KDG received more frequently immunoglobulins (IVIG) and acetylsalicylic acid (ASA) (81,3% vs 66%; p = 0.04 and 71,9% vs 43,4%; p = 0.001 respectively) as KCG more often received glucocorticoids (56,6% vs 14,6%; p < 0.0001). SARS-CoV-2 assay more often resulted positive in KCG than in KDG (75,5% vs 20%; p < 0.0001). Short-term follow data showed minor complications. Comparing KDG with a KD-Historical Italian cohort (598 patients), no statistical difference was found in terms of clinical manifestations and laboratory data.
Conclusion: Our study suggests that SARS-CoV-2 infection might determine two distinct inflammatory diseases in children: KD and PIMS-TS. Older age at onset and clinical peculiarities like the occurrence of myocarditis characterize this multi-inflammatory syndrome. Our patients had an optimal response to treatments and a good outcome, with few complications and no deaths
Pharmacophore Binding Motifs for Nicotinamide Adenine Dinucleotide Analogues Across Multiple Protein Families: A Detailed Contact-Based Analysis of the Interaction between Proteins and NAD(P) Cofactors
We have analyzed the protein-binding
pharmacophore of NAD and its
close analogues in all protein–ligand structures available
in the RCSB database as of February 2012; this analysis has then been
used to assess the novelty of structures emerging after that date.
We show that proteins have evolved diverse pharmacophore motifs for
binding the adenine moiety, fewer, but still diverse, motifs for nicotinamide,
and a very limited set of motifs for binding the pyrophosphate linker.
Our exhaustive analysis includes a pharmacophore contact analysis
for over 1900 protein–ligand structures containing NAD analogues;
we have benchmarked this set of contacts against nearly 27 000
protein–ligand structures to demonstrate that the diversity
of interactions seen with NAD is very similar to that seen for all
other ligands. Hence, variation in binding motifs for NAD is not distinct
from that observed for other ligands and they show significant variation
across protein families
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Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations.
Acknowledgements: The authors would like to thank the University of Cambridge and the Cambridge Crystallographic Data Centre for funding this work.Funder: Cambridge Crystallographic Data Centre; doi: http://dx.doi.org/10.13039/501100022029Funder: University of Cambridge; doi: http://dx.doi.org/10.13039/501100000735Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations
Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
Abstract Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations
Alloxan Derivatives as Inhibitors of Matrix Metalloproteinase-2: Theoretical Calculations and Experimental Results
Matrix metalloproteinases (MMPs) are a family of structurally related zinc-containing endopeptidases involved in tissue remodelling and degradation of the extracellular matrix. The failure of common synthetic inhibitors makes the design of new selective and potent MMP inhibitors an extreme challenge in health care for the treatment of various pathological states such as inflammation, arthritis, and cancer. In this view, an over-expression of MMP-2 is supposed to be responsible for the occurrence of many different human tumours and inflammatory processes involving the hydrolysis of the type IV collagen, the main component of the basement membrane. A series of studies therefore focused on the design of new potential inhibitors biased towards MMP-2: campaigns of molecular virtual screening of several large chemical libraries resulted in a number of attractive hits. Interestingly, a shortlist of alloxan-like structures was selected with inhibition constants in the nM range. In this respect, we investigated a series of complexes of MMP-2 with alloxan inhibitors by thermodynamic integration in all atoms molecular dynamics simulations. We thus obtained quantitative differences in binding free energies for a list of alloxan compounds. On this basis, we were able to elucidate the molecular rationale for the remarkable inhibition exerted by these compounds with the ultimate aim of driving the synthesis of new more potent and selective derivatives that are at present awaiting for further experimental investigations through enzymatic assays
Mining the Cambridge Structural Database for Matched Molecular Crystal Structures: A Systematic Exploration of Isostructurality
The
Cambridge Structural Database (CSD) is the world leading collection
of small-molecule crystal structures and represents an invaluable
resource for crystal engineers. It enables structures to be readily
compared and new insights to be gained from the comparison. In order
to search the database for pairs of structures that are related by
the same chemical transformation, and to systematically investigate
the effect of this transformation on crystal packing, a repository
of matched molecular crystal structures has been derived from the
CSD. This makes it easy to find all pairs of structures differing
by the same chemical change or, alternatively, all available chemical
modifications to a given CSD entry. Our analysis shows one of the
many possible applications of these data. An extensive, yet not exhaustive,
exploration of isostructurality across the entire CSD has been carried
out with the aim of identifying packing features within crystals that
maintain isostructurality. With particular focus on terminal chemical
modifications observed between single-component structures with <i>Z</i>′ equal to 1, packing similarity has been calculated
with an enhanced version of existing software. Across the entire data
set of approximately 125 000 matched molecular pairs, 4% of
the pairs were isostructural. Several cases showed an enrichment with
respect to this baseline value, and examples have been discussed to
illustrate some of the questions which can be asked and how they can
be answered using the data set. This will open up avenues of research
for the future and increase our understanding of the impact of functional
groups on crystal packing
An Extensive and Diverse Set of Molecular Overlays for the Validation of Pharmacophore Programs
The pharmacophore hypothesis plays
a central role in both the design
and optimization of drug-like ligands. Pharmacophore patterns are
invoked to explain the binding affinity of ligands and to enable the
design of chemically distinct scaffolds that show affinity for a protein
target of interest. The importance of pharmacophores in rationalizing
ligand affinity has led to numerous algorithms that seek to overlay
ligands based on their pharmacophoric features. All such algorithms
must be validated with respect to known ligand overlays, usually by
extracting ligand overlay sets from the Protein Data Bank (PDB). This
validation step creates the problem of which of the known overlays
to select and from which proteins. The large number of structures
and protein families in the PDB makes it difficult to establish a
definitive overlay set; as a result, validation studies have rarely
employed the same data sets. We have therefore undertaken an exhaustive
analysis of the RCSB PDB to identify 121 distinct ligand overlay sets.
We have defined a robust protein overlay protocol, which is free from
subjective interpretation over which residues to include, and we have
analyzed each overlay set on the basis of whether they provide evidence
for the pharmacophore hypothesis. Our final data set spans a broad
range of structural types and degrees of difficulty and includes overlays
that any algorithm should be able to reproduce, as well as some for
which there is very weak evidence for a conserved pharmacophore at
all. We provide this set in the hope that it will prove definitive,
at least until the PDB is greatly enriched with further structures
or with radically different protein folds and families. Upon publication,
the data set will be available for free download from the Web site
of the Cambridge Crystallographic Data Centre