StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies.

Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r2 ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

Exploring the catalytic mechanism of dihydropteroate synthase: elucidating the differences between the substrate and inhibitor.

Dihydropteroate synthase (DHPS) catalyzes the condensation of 6-hydroxymethyl-7,8-dihydropterin pyrophosphate (DHPPP) with p-aminobenzoic acid (pABA) and is a well validated target for anti-malarial and anti-bacterial drugs. However, in recent years its utility as a therapeutic target has diminished considerably due to multiple mutations. As such, considerable structural biology and medicinal chemistry effort has been expended to understand and overcome this issue. To date no detailed computational analysis of the protein mechanism has been made despite the detailed crystal structures and multiple mechanistic proposals being made. In this study the mechanistic proposals for DHPS have been systematically investigated using a hybrid QM/MM method. We aimed to compare the energetics associated with SN1 and SN2 processes, whether the SN1 process involves a carbocation or neutral DHP intermediate, uncover the identity of the general base in the catalytic mechanism, and understand the differences in substrate vs. inhibitor reactivity. Our results suggest a reaction that follows an SN1 process with the rate determining step being C-O bond breaking to give a carbocation intermediate. Comparative studies on the inhibitor STZ confirm the experimental observations that it is also a DHPS substrate.

3D-QSAR and molecular docking studies of peptide-hybrids as dengue virus NS2B/NS3 protease inhibitors.

Global warming and climate change have made dengue disease a global health issue. More than 50 % of the world's population is at danger of dengue virus (DENV) infection, according to the World Health Organization (WHO). Therefore, a clinically approved dengue fever vaccination and effective treatment are needed. Peptide medication development is new pharmaceutical research. Here we intend to recognize the structural features inhibiting the DENV NS2B/NS3 serine protease for a series of peptide-hybrid inhibitors (R1-R2-Lys-R3-NH2) by the 3D-QSAR technique. Comparative molecular field analysis (q2 = 0.613, r2 = 0.938, r2pred = 0.820) and comparative molecular similarity indices analysis (q2 = 0.640, r2 = 0.928, r2pred = 0.693) were established, revealing minor, electropositive, H-bond acceptor groups at the R1 position, minor, electropositive, H-bond donor groups at the R2 position, and bulky, hydrophobic groups at the R3 position for higher inhibitory activity. Docking studies revealed extensive H-bond and hydrophobic interactions in the binding of tripeptide analogues to the NS2B/NS3 protease. This study provides an insight into the key structural features for the design of peptide-based inhibitors of DENV NS2B/NS3 protease.

What make malarial adenosine deaminase from PLASMODIUM VIVAX recognise adenosine and 5'-methylthioadenosine: simulation studies.

Malaria is a life-threatening disease in humans caused by Plasmodium parasites. Plasmodium vivax (P. vivax) is one of the prevalent species found worldwide. An increase in an anti-malarial drug resistance suggests the urgent need for new drugs. Zn2+-containing adenosine deaminase (ADA) is a promising drug target because the ADA inhibition is fatal to the parasite. Malarial ADA accepts both adenosine (ADN) and 5'-methylthioadenosine (MTA) as substrates. The understanding of the substrate binding becomes crucial for an anti-malarial drug development. In this work, ADA from P. vivax (pvADA) is of interest due to its prevalence worldwide. The binding of ADN and MTA are studied here using Molecular Dynamics (MD) simulations. Upon binding, the open and closed states of pvADA are captured. The displacement of α7, linking loops of ß3/α12, ß4/α13, ß5/α15, and α10/α11 is involved in the cavity closure and opening. Also, the inappropriate substrate orientation induces a failure in a complete cavity closure. Interactions with D46, D172, S280, D310, and D311 are important for ADN binding, whereas only hydrogen bonds with D172 and D311 are sufficient to anchor MTA inside the pocket. No Zn2+-coordinated histidine residues is acquired for substrate binding. D172 is found to play a role in ribose moiety recognition, while D311 is crucial for trapping the amine group of an adenine ring towards the Zn2+ site. Comparing between ADN and MTA, the additional interaction between D310 and an amine nitrogen on ADN supports a tighter fit that may facilitate the deamination.Communicated by Ramaswamy H. Sarma.

Structure-Activity Relationship Study of Xanthoxyline and Related Small Methyl Ketone Herbicides.

Xanthoxyline (1), a small natural methyl ketone, was previously reported as a plant growth inhibitor. In this research, related methyl ketones bearing electron-donating and electron-withdrawing groups, together with heteroaromatics, were investigated against seed germination and seedling growth of Chinese amaranth (Amaranthus tricolor L.) and barnyard grass [Echinochloa crus-galli (L.) Beauv]. The structure-activity relationships (SARs) of methyl ketone herbicides were clarified, and which types and positions of substituents were crucially important for activity were also clarified. Indole derivatives, namely, 3-acetylindole (43) and 3-acetyl-7-azaindole (44) were found to be the most active methyl ketones that highly suppressed plant growth at low concentrations. The molecular docking on the 4-hydroxyphenylpyruvate dioxygenase (HPPD) enzyme indicated that carbonyl, aromatic, and azaindole were key interactions of HPPD inhibitors. This finding would be useful for the development of small ketone herbicides.

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides.

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

QM/MM investigation of the reaction rates of substrates of 2,3-dimethylmalate lyase: A catabolic protein isolated from Aspergillus niger.

Aspergillus niger is an industrially important microorganism used in the production of citric acid. It is a common cause of food spoilage and represents a health issue for patients with compromised immune systems. Recent studies on Aspergillus niger have revealed details on the isocitrate lyase (ICL) superfamily and its role in catabolism, including (2R, 3S)-dimethylmalate lyase (DMML). Members of this and related lyase super families are of considerable interest as potential treatments for bacterial and fungal infections, including Tuberculosis. In our efforts to better understand this class of protein, we investigate the catalytic mechanism of DMML, studying five different substrates and two different active site metals configurations using molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. We show that the predicted barriers to reaction for the substrates show good agreement with the experimental kcat values. This results help to confirm the validity of the proposed mechanism and open up the possibility of developing novel mechanism based inhibitors specifically for this target.

Probing the Catalytic Mechanism Involved in the Isocitrate Lyase Superfamily: Hybrid Quantum Mechanical/Molecular Mechanical Calculations on 2,3-Dimethylmalate Lyase.

The isocitrate lyase (ICL) superfamily catalyzes the cleavage of the C(2)-C(3) bond of various α-hydroxy acid substrates. Members of the family are found in bacteria, fungi, and plants and include ICL itself, oxaloacetate hydrolase (OAH), 2-methylisocitrate lyase (MICL), and (2R,3S)-dimethylmalate lyase (DMML) among others. ICL and related targets have been the focus of recent studies to treat bacterial and fungal infections, including tuberculosis. The catalytic process by which this family achieves C(2)-C(3) bond breaking is still not clear. Extensive structural studies have been performed on this family, leading to a number of plausible proposals for the catalytic mechanism. In this paper, we have applied quantum mechanical/molecular mechanical (QM/MM) methods to the most recently reported family member, DMML, to assess whether any of the mechanistic proposals offers a clear energetic advantage over the others. Our results suggest that Arg161 is the general base in the reaction and Cys124 is the general acid, giving rise to a rate-determining barrier of approximately 10 kcal/mol.