Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning..

Background: Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning. Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning. Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP. Results: The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively. Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

A (GCC) Repeat in the Untranslated Region of Human SBF1 Departs from Hardy-Weinberg Equilibrium in Human and Links to Late-onset Neurocognitive Disorder.

The human SBF1 (SET binding factor 1) gene, alternatively known as MTMR5, is predominantly expressed in the brain, and its epigenetic dysregulation is linked to late-onset neurocognitive disorders (NCDs), such as Alzheimer’s disease. This gene contains a (GCC)-repeat at the interval between +1 and +60 of the transcription start site (SBF1-202 ENST00000380817.8). Sequencing of the SBF1 (GCC)-repeat in a sample of 542 Iranian individuals, consisting of late-onset NCDs (N=260) and controls (N=282) revealed a predominantly bi-allelic locus for this STR, consisting of 8 and 9 repeats, with allele frequencies ranging from 0.39 to 0.55, and four other alleles with frequencies of <0.03 across the two groups. Overall heterozygosity for the observed alleles was significantly less than expected in the NCD and control groups, at 22.3% and 16.31%, respectively (p=0.000). Specifically, the heterozygous 8/9 genotype was significantly less than expected in both case and control groups (Hardy-Weinberg disequilibrium, p=0.000), and significantly enriched in the NCD group (Yates corrected p=0.001). Skewed heterozygous genotypes were also detected for other allele combinations, such as 6/8 vs 6/9 across groups (p=0.000). Bioinformatics studies revealed that the number of (GCC)-repeats may change the RNA secondary structure and interaction sites across human exon 1. This STR was specifically expanded beyond 2-repeats in primates. In conclusion, we report a novel biological phenomenon in which there is indication of purifying selection against heterozygous genotypes at a STR locus in human, and skewed genotype compartment in late-onset NCD vs. controls. In view of the location of this STR in the 5′ UTR, RNA/RNA or RNA/DNA heterodimer formation of the involved genotypes and possible deleterious downstream events should be considered.

An M protein coiled coil unfurls and exposes its hydrophobic core to capture LL-37

Surface-associated, coiled-coil M proteins of Streptococcus pyogenes (Strep A) disable human immunity through interaction with select proteins. However, coiled coils lack features typical of protein-protein interaction sites, and it is therefore challenging to understand how M proteins achieve specific binding, for example, with the human antimicrobial peptide LL-37, which results in its neutralization. The crystal structure of a complex of LL-37 with M87 protein, an antigenic variant from a strain that is an emerging threat, revealed a novel interaction mode. The M87 coiled coil unfurled and asymmetrically exposed its hydrophobic core to capture LL-37. A single LL-37 molecule bound M87 in the crystal, but in solution recruited additional LL-37 molecules, consistent with a protein trap neutralization mechanism. The interaction mode visualized crystallographically was verified to contribute significantly to LL-37 resistance in an M87 Strep A strain, and was identified to be conserved in a number of other M protein types that are prevalent in human populations. Our results provide specific detail for therapeutic inhibition of LL-37 neutralization by M proteins.

A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI.

Mapping functional regions of essential bacterial proteins with dominant-negative protein fragments

Massively-parallel measurements of dominant negative inhibition by protein fragments have been used to map protein interaction sites and discover peptide inhibitors. However, the underlying principles governing fragment-based inhibition have thus far remained unclear. Here, we adapt a high-throughput inhibitory fragment assay for use in Escherichia coli, applying it to a set of ten essential proteins. This approach yielded single amino acid resolution maps of inhibitory activity, with peaks localized to functionally important interaction sites, including oligomerization interfaces and folding contacts. Leveraging these data, we perform a systematic analysis to uncover principles of fragment-based inhibition. We determine a robust negative correlation between susceptibility to inhibition and cellular protein concentration, demonstrating that inhibitory fragments likely act primarily by titrating native protein interactions. We also characterize a series of trade-offs related to fragment length, showing that shorter peptides allow higher-resolution mapping but suffer from lower inhibitory activity. We employ an unsupervised statistical analysis to show that the inhibitory activities of protein fragments are largely driven not by generic properties such as charge, hydrophobicity, and secondary structure, but by the more specific characteristics of their bespoke macromolecular interactions. AlphaFold computational modeling of peptide complexes with one protein shows that the inhibitory activity of peptides is associated with their predicted ability to form native-like interactions. Overall, this work demonstrates fundamental characteristics of inhibitory protein fragment function and provides a foundation for understanding and controlling protein interactions in vivo.

Transcriptome-wide analysis of microRNA-mRNA correlations in unperturbed tissue transcriptomes identifies microRNA targeting determinants.

MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. Given the small size of the pairing region and the large number of mRNAs that each microRNA can control, the identification of biologically relevant targets is difficult. Since current knowledge of target recognition and repression has mainly relied on in vitro studies, we sought to determine if the interrogation of gene expression data of unperturbed tissues could yield new insight into these processes. The transcriptome-wide repression at the microRNA-mRNA canonical interaction sites (seed and 3'-supplementary region, identified by sole base complementarity) was calculated as a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA tissues (RNA-seq and small RNA-seq data of 546 samples). Using the repression values obtained we confirmed established properties or microRNA targeting efficacy, such as the preference for gene regions (3'UTR>CDS>5'UTR), the proportionality between repression and seed length (6mer<7mer<8mer) and the contribution to the repression exerted by the supplementary pairing at 13-16nt of the microRNA. Our results suggest that the 7mer-m8 seed could be more repressive than the 7mer-A1, while they have similar efficacy when they interact using the 3'-supplementary pairing. Strikingly, the 6mer+suppl sites yielded normalized Z-score of repression similar to the sole 7mer-m8 or 7mer-A1 seeds, which raise awareness of its potential biological relevance. We then used the approach to further characterize the 3'-supplementary pairing, using 39 microRNAs that hold repressive 3'-supplementary interactions. The analysis of the bridge between seed and 3'-supplementary pairing site confirmed the optimum +1 offset previously evidenced, but higher offsets appear to hold similar repressive strength. In addition, they show a low GC content at position 13-16, and base preferences that allow the selection of a candidate sequence motif. Overall, our study demonstrates that transcriptome-wide analysis of microRNA-mRNA correlations in large, matched RNA-seq and small-RNA-seq data has the power to uncover hints of microRNA targeting determinants operating in the in vivo unperturbed set. Finally, we made available a bioinformatic tool to analyze microRNA-target mRNA interactions using our approach.

Copy number variation of the putative speciation genes in the European house mouse hybrid zone

Hybrid zones have long been described as "windows on the evolutionary process", and studying them has become even more important since the advance in the genome analysis tools. The hybrid zone between two subspecies of the house mouse (Mus musculus musculus and Mus m. domesticus) is a unique model speciation system to study fine scale interactions of recently diverged genomes. Here, we explore the role of gene Copy Number Variation in shaping the barrier to introgression in the hybrid zone within a previously established transect in Central Europe. The CNV of seven pre-selected candidate genes was determined via droplet-digital PCR and analyzed in the context of ~500k SNPs, with the ancestral population (i.e. musculus or domesticus) of every SNP allele previously inferred in the admixed individuals (Baird et al., in prep.). The copy numbers of five genes were clearly associated with the prevalence of either musculus or domesticus genomes across the hybrid zone. In three cases, the highest and/or outlying levels of association were observed at or very close to the annotated positions of the respective gene amplicons, demonstrating the power of our approach in confirming the reference locations of copy number variants. Notably, several other reference locations were recognized as positive outliers in the association with particular CNV genes, possibly representing the extra gene copies and/or their epistatic interaction sites.

A Glance into MTHFR Deficiency at a Molecular Level

MTHFR deficiency still deserves an investigation to associate the phenotype to protein structure variations. To this aim, considering the MTHFR wild type protein structure, with a catalytic and a regulatory domain and taking advantage of state-of-the-art computational tools, we explore the properties of 72 missense variations known to be disease associated. By computing the thermodynamic ΔΔG change according to a consensus method that we recently introduced, we find that 61% of the disease-related variations destabilize the protein, are present both in the catalytic and regulatory domain and correspond to known biochemical deficiencies. The propensity of solvent accessible residues to be involved in protein-protein interaction sites indicates that most of the interacting residues are located in the regulatory domain, and that only three of them, located at the interface of the functional protein homodimer, are both disease-related and destabilizing. Finally, we compute the protein architecture with Hidden Markov Models, one from Pfam for the catalytic domain and the second computed in house for the regulatory domain. We show that patterns of disease-associated, physicochemical variation types, both in the catalytic and regulatory domains, are unique for the MTHFR deficiency when mapped into the protein architecture.

Orai1 Boosts SK3 Channel Activation

The interplay of SK3, a Ca2+ sensitive K+ ion channel, with Orai1, a Ca2+ ion channel, has been reported to increase cytosolic Ca2+ levels, thereby triggering proliferation of breast and colon cancer cells, although a molecular mechanism has remained elusive to date. We show in the current study, via heterologous protein expression, that Orai1 can enhance SK3 K+ currents, in addition to constitutively bound calmodulin (CaM). At low cytosolic Ca2+ levels that decrease SK3 K+ permeation, co-expressed Orai1 potentiates SK3 currents. This positive feedback mechanism of SK3 and Orai1 is enabled by their close co-localization. Remarkably, we discovered that loss of SK3 channel activity due to overexpressed CaM mutants could be restored by Orai1, likely via its interplay with the SK3–CaM binding site. Mapping for interaction sites within Orai1, we identified that the cytosolic strands and pore residues are critical for a functional communication with SK3. Moreover, STIM1 has a bimodal role in SK3–Orai1 regulation. Under physiological ionic conditions, STIM1 is able to impede SK3–Orai1 interplay by significantly decreasing their co-localization. Forced STIM1–Orai1 activity and associated Ca2+ influx promote SK3 K+ currents. The dynamic regulation of Orai1 to boost endogenous SK3 channels was also determined in the human prostate cancer cell line LNCaP.

Development of Aptasensors for Steroidal Hormones

<p>Aptamers are synthetic nucleic acid single stranded (ss)DNAs or RNAs that can bind with high affinity and specificity to a broad range of targets, including proteins and low molecular weight molecules. This work presents the design, development and implementation of novel aptamer based sensors (aptasensors) for the detection of a target of environmental and medical significance - 17-β estradiol (E2). By combining a previously isolated E2 binding 75-mer ssDNA aptamer with a variety of different signal transducers, E2 was successfully detected and quantified below the environmental and biological relevant concentrations. By applying the same aptamer to different sensor formats, the advantages and disadvantages of each signal transduction mechanism were compared. Target-induced conformational switch within an aptamer molecule can be transduced via labelling different sections of the aptamer with pairs of fluorescent dyes or with a redox probe, however those strategies require detailed knowledge of specific aptamer conformations and target interaction sites. Herein, a label free method is developed - size based aptasensor described in Chapter 2. The new method only depends on the general property that small molecule binding aptamers adopt a more compact folded structure when they bind to their target. Dynamic light scattering (DLS) and tunable resistive pulse sensing (TRPS) were used to probe recognition events between E2 and aptamers conjugated to carboxylated polystyrene nanoparticles (NPs). Upon E2 recognition, a distinct reduction in size and a less negative surface potential of the conjugated particles were observed, which can be correlated to the concentration of E2 in the lower nanomolar range (as low as 5 nM). On-site monitoring of E2 requires rapid and sensitive screening methods with minimal instrumentation. Previously, gold nanoparticles (AuNPs) were exploited in the construction of colorimetric aptasensors for different targets. Aggregation assays produce colorimetric signals observed by naked-eye when target-bound aptamers dissociate from AuNP surfaces, triggering aggregation. However, it is unknown how the length of aptamer sequences affects their dissociation from AuNP surfaces and subsequent aggregation. Chapter 3 demonstrates the benefit of editing aptamer sequences with specific regard to the way signals are transduced in AuNP based colorimetric assays. The 20 flanking nucleotides to the 35-mer inner core of the parent 75-mer aptamer were eliminated. The 35-mer aptamer has a lower dissociation constant KD (14 nM vs. 25 nM), improved discrimination against other steroidal molecules and greatly improve the sensitivity for E2 detection from 5 nM to 200 pM. In fact, this simple strategy enabled facile detection of E2 in urine at 5 nM, approaching levels of biological relevance. There is a pressing demand for methods with accurate and rapid performance to detect and quantify E2, at levels comparable or even below the biological concentrations to eliminate pre-concentration and sample purification process. Existing electrochemical aptasensors feature DNA probes covalently tethered to various surfaces including gold and conducting polymer electrode. An electrochemical impedance spectroscopy (EIS) based sensor was created using nanoporous conducting polymer electrodes functionalized with the 75-mer aptamer. The one fM detection limit found is one order of magnitude lower than the recorded biological level. As a novel alternative approach, sensing electrodes were also created via the non-specific adsorption of the 35-mer onto Au and Au nanoparticle electrodes. This approach, described in Chapter 4, led to the same level of detection as the conducting polymer aptasensor, but via a mechanism with similarities to the colorimetric sensor. Non-specific adsorption of aptamers to Au was found to play additional favourable roles including self-passivation and stabilization of Au nanoparticle based electrodes. Sensing with this format might remove the need for laborious surface passivation with alkylthiol molecules encountered with the conventional covalent attachment of the DNAs through thiol-linkers. In general, the reported aptasensors provide efficient means to detect the steroidal molecule E2 as well as advance the understanding of aptasensors by comparing the performance of the same aptamer in various sensing platforms. Long aptamers sequences appeared to be more efficient in signal transduction when specific surface tethering is involved, as in the size-based assay, and the electrochemical assay with aptamers covalently tethered to the electrode. Here, the non-binding flanking nucleotides, i.e. nucleotides adjacent to the target binding pocket, appeared to amplify the sensing signals. However, shorter truncated sequences showed better performance when signal generation depends on surface dissociation of non-specifically adsorbed aptamer sequences, as in the colorimetric assay, and the electrochemical sensor constructed from adsorbed aptamers. These insights can be readily applied to aptasensors for the growing range of targets.</p>