Bioinformatics and variability in drug response: a protein structural perspective

Molecular modelling, docking and network analysis of phytochemicals from Haritaki churna: role of protein cross-talks for their action

Phytochemicals are bioactive agents present in medicinal plants with therapeutic values. Phytochemicals isolated from plants target multiple cellular processes. In the current work, we have used fractionation techniques to identify 13 bioactive polyphenols in ayurvedic medicine Haritaki Churna. Employing the advanced spectroscopic and fractionation, structure of bioactive polyphenols was determined. Blasting the phytochemical structure allow us to identify a total of 469 protein targets from Drug bank and Binding DB. Phytochemicals with their protein targets from Drug bank was used to create a phytochemical-protein network comprising of 394 nodes and 1023 edges. It highlights the extensive cross-talk between protein target corresponding to different phytochemicals. Analysis of protein targets from Binding data bank gives a network comprised of 143 nodes and 275 edges. Taking the data together from Drug bank and binding data, seven most prominent drug targets (HSP90AA1, c-Src kinase, EGFR, Akt1, EGFR, AR, and ESR-α) were found to be target of the phytochemicals. Molecular modelling and docking experiment indicate that phytochemicals are fitting nicely into active site of the target proteins. The binding energy of the phytochemicals were better than the inhibitors of these protein targets. The strength and stability of the protein ligand complexes were further confirmed using molecular dynamic simulation studies. Further, the ADMET profiles of phytochemicals extracted from HCAE suggests that they can be potential drug targets. The phytochemical cross-talk was further proven by choosing c-Src as a model. HCAE down regulated c-Src and its downstream protein targets such as Akt1, cyclin D1 and vimentin. Hence, network analysis followed by molecular docking, molecular dynamics simulation and in-vitro studies clearly highlight the role of protein network and subsequent selection of drug candidate based on network pharmacology.Communicated by Ramaswamy H. Sarma.

Effects of genetic variation on the structure of RNA and protein

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

Structural and Phylogenetic Analysis of CXCR4 Protein Reveals New Insights into Its Role in Emerging and Re-Emerging Diseases in Mammals

Chemokine receptor type 4 (CXCR4) is a G protein-coupled receptor that plays an essential role in immune system function and disease processes. Our study aims to conduct a comparative structural and phylogenetic analysis of the CXCR4 protein to gain insights into its role in emerging and re-emerging diseases that impact the health of mammals. In this study, we analyzed the evolution of CXCR4 genes across a wide range of mammalian species. The phylogenetic study showed species-specific evolutionary patterns. Our analysis revealed novel insights into the evolutionary history of CXCR4, including genetic changes that may have led to functional differences in the protein. This study revealed that the structural homologous human proteins and mammalian CXCR4 shared many characteristics. We also examined the three-dimensional structure of CXCR4 and its interactions with other molecules in the cell. Our findings provide new insights into the genomic landscape of CXCR4 in the context of emerging and re-emerging diseases, which could inform the development of more effective treatments or prevention strategies. Overall, our study sheds light on the vital role of CXCR4 in mammalian health and disease, highlighting its potential as a therapeutic target for various diseases impacting human and animal health. These findings provided insight into the study of human immunological disorders by indicating that Chemokines may have activities identical to or similar to those in humans and several mammalian species.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol^-1 on an independent test set with an R² value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

First report of computational protein-ligand docking to evaluate susceptibility to HIV integrase inhibitors in HIV-infected Iranian patients

Background

Iran has recently included integrase (INT) inhibitors (INTIs) in the first-line treatment regimen in human immunodeficiency virus (HIV)-infected patients. However, there is no bioinformatics data to elaborate the impact of resistance-associated mutations (RAMs) and naturally occurring polymorphisms (NOPs) on INTIs treatment outcome in Iranian patients.

Method

In this cross-sectional survey, 850 HIV-1-infected patients enrolled; of them, 78 samples had successful sequencing results for INT gene. Several analyses were performed including docking screening, genotypic resistance, secondary/tertiary structures, post-translational modification (PTM), immune epitopes, etc.

Result

The average docking energy (E value) of different samples with elvitegravir (EVG) and raltegravir (RAL) was more than other INTIs. Phylogenetic tree analysis and Stanford HIV Subtyping program revealed HIV-1 CRF35-AD was the predominant subtype (94.9%) in our cases; in any event, online subtyping tools confirmed A1 as the most frequent subtype. For the first time, CRF-01B and BF were identified as new subtypes in Iran. Decreased CD4 count was associated with several factors: poor or unstable adherence, naïve treatment, and drug user status.

Conclusion

As the first bioinformatic report on HIV-integrase from Iran, this study indicates that EVG and RAL are the optimal INTIs in first-line antiretroviral therapy (ART) in Iranian patients. Some conserved motifs and specific amino acids in INT-protein binding sites have characterized that mutation(s) in them may disrupt INT-drugs interaction and cause a significant loss in susceptibility to INTIs. Good adherence, treatment of naïve patients, and monitoring injection drug users are fundamental factors to control HIV infection in Iran effectively.

In silico analyses of acetylcholinesterase (AChE) and its genetic variants in interaction with the anti-Alzheimer drug Rivastigmine

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Despite causing great social and economic impact, there is currently no cure for AD. The most effective therapy to manage AD symptoms is based on acetylcholinesterase inhibitors (AChEi), from which rivastigmine presented numerous benefits. However, mutations in AChE, which affect approximately 5% of the population, can modify protein structure and function, changing the individual response to Alzheimer's treatment. In this study, we performed computer simulations of AChE wild type and variants R34Q, P135A, V333E, and H353N, identified by one or more genome-wide association studies, to evaluate their effects on protein structure and interaction with rivastigmine. The functional effects of AChE variants were predicted using eight machine learning algorithms, while the evolutionary conservation of AChE residues was analyzed using the ConSurf server. Autodock4.2.6 was used to predict the binding modes for the hAChE-rivastigmine complex, which is still unknown. Molecular dynamics (MD) simulations were performed in triplicates for the AChE wild type and mutants using the GROMACS packages. Among the analyzed variants, P135A was classified as deleterious by all the functional prediction algorithms, in addition to occurring at highly conserved positions, which may have harmful consequences on protein function. The molecular docking results suggested that rivastigmine interacts with hAChE at the upper active-site gorge, which was further confirmed by MD simulations. Our MD findings also suggested that the complex hAChE-rivastigmine remains stable over time. The essential dynamics revealed flexibility alterations at the active-site gorge upon mutations P135A, V333E, and H353N, which may lead to strong and nonintuitive consequences to hAChE binding. Nonetheless, similar binding affinities were registered in the MMPBSA analysis for the hAChE wild type and variants when complexed to rivastigmine. Finally, our findings indicated that the rivastigmine binding to hAChE is an energetically favorable process mainly driven by negatively charged amino acids.

Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images

Analysis of protein subcellular localization is a critical part of proteomics. In recent years, as both the number and quality of microscopic images are increasing rapidly, many automated methods, especially convolutional neural networks (CNN), have been developed to predict protein subcellular location(s) based on bioimages, but their performance always suffers from some inherent properties of the problem. First, many microscopic images have non-informative or noisy sections, like unstained stroma and unspecific background, which affect the extraction of protein expression information. Second, the patterns of protein subcellular localization are very complex, as a lot of proteins locate in more than one compartment. In this study, we propose a new label-correlation enhanced deep neural network, laceDNN, to classify the subcellular locations of multi-label proteins from immunohistochemistry images. The model uses small representative patches as input to alleviate the image noise issue, and its backbone is a hybrid architecture of CNN and recurrent neural network, where the former network extracts representative image features and the latter learns the organelle dependency relationships. Our experimental results indicate that the proposed model can improve the performance of multi-label protein subcellular classification.

Opposite genotype-specific effects of serotoninergic treatments on Pavlovian Conditioned Approach in mice of two inbred strains C57 BL/6J and DBA/2J

Individual variability in the response to pharmacological therapies is a major problem in the treatment of psychiatric disorders. Comparative studies of phenotypes expressed by mice of the C57BL/6J (C57) and DBA/2J (DBA) inbred strains can help identify neurobiological determinants of this variability at preclinical levels. We have recently demonstrated that whereas young adult mice of both strains develop sign-tracking in a Pavlovian Conditioned Approach (PCA), a trait associated with dysfunctional behavior in rat models, in full adult C57 mice acquisition of this phenotype is inhibited by pre-frontal cortical (PFC) serotonin (5HT) transmission. These findings suggest a different role of 5HT transmission on sign-tracking development in mice of the two genotypes. In the present experiments, we tested the effects of the 5-HT synthesis booster 5-hydroxytryptophan (5-HTP) and of the selective 5HT reuptake inhibitor (SSRI) fluoxetine on the development and expression of sign-tracking in young adult mice from both inbred strains. In mice of the C57 strain, administration of 5-HTP before each training session blocked the training-induced shift to positive PCA scores which indicates the development of sign-tracking, whereas the same treatment was ineffective in mice of DBA strain. On the other hand, a single administration of fluoxetine was ineffective in unhandled saline- and 5-HTP-treated C57 mice, whereas it enhanced the expression of positive PCA scores by mice of DBA strain treated with 5-HTP during training. These findings confirm the strain-specific inhibitory role of PFC 5-HT transmission on sign-tracking development by mice of the C57 strain and support the hypothesis that different genotype-specific neurobiological substrates of dysfunctional phenotypes contribute to variable effects of pharmacotherapies.

Global spectrum of population-specific common missense variation in cytochrome P450 pharmacogenes

Next-generation sequencing technology has afforded the discovery of many novel variants that are of significance to inheritable pharmacogenomics (PGx) traits but a large proportion of them have unknown consequences. These include missense variants resulting in single amino acid substitutions in cytochrome P450 (CYP) proteins that can impair enzyme function, leading to altered drug efficacy and toxicity. While most unknown variants are rare, an overlooked minority are variants that are collectively rare but enriched in specific populations. Here, we analyzed sequence variation data in 141,456 individuals from across eight study populations in gnomAD for 38 CYP genes to identify such variants in addition to common variants. By further comparison with data from two PGx-specific databases (PharmVar and PharmGKB) and ClinVar, we identified 234 missense variants in 35 CYP genes, of which 107 were unknown to these databases. Most unknown variants (n = 83) were population-specific common variants and several (n = 7) were found in important CYP pharmacogenes (CYP2D6, CYP4F2, and CYP2C19). Overall, 29% (n = 31) of 107 unknown variants were predicted to affect CYP enzyme function although further biochemical characterization is necessary. These variants may elucidate part of the unexplained interpopulation differences observed in drug response.

Analysis of the Biochemical Reaction Status by Real-Time Monitoring Molecular Diffusion Behaviors Using a Transistor Biosensor Integrated with a Microfluidic Channel

Traditional methods of monitoring biochemical reactions measure certain detectable reagents or products while assuming that the undetectable species follow the stoichiometry of the reactions. Here, based upon the metal-oxide thin-film transistor (TFT) biosensor, we develop a real-time molecular diffusion model to benchmark the concentration of the reagents and products. Using the nicotinamide adenine dinucleotide (NADH)-oxaloacetic acid with the enzyme of malate dehydrogenase as an example, mixtures of different reagent concentrations were characterized to extract the ratio of remaining concentrations between NAD⁺ and NADH. We can thus obtain the apparent equilibrium constant of the reaction, (8.06 ± 0.61) × 10⁴. Because the whole analysis was conducted using a TFT sensor fabricated using a semiconductor process, our approach has the advantages of exploring biochemical reaction kinetics in a massively parallel manner.

Synthesis, DNA/RNA-interaction and biological activity of benzo[k,l]xanthene lignans

Interactions of two newly synthesized and six previously reported benzoxanthene lignans (BXLs), analogues of rare natural products, with DNA/RNA, G-quadruplex and HSA were evaluated by a set of spectrophotometric methods. Presence/absence of methoxy and hydroxy groups on the benzoxanthene core and minor modifications at C-1/C-2 side pendants - presence/absence of phenyl ring and presence/absence of methoxy and hydroxy groups on phenyl ring - influenced the fluorescence changes and the binding strength to double-stranded (ds-) and G-quadruplex structures. In general, compounds without phenyl ring showed stronger fluorescence changes upon binding than phenyl-substituted BXLs. On the other hand, BXLs with an unsubstituted phenyl ring showed the best stabilization effects of G-quadruplex. Circular dichroism spectroscopy results suggest mixed binding mode, groove binding and partial intercalation, to ds-DNA/RNA and end-stacking to top or bottom G-tetrads as the main binding modes of BXLs to those targets. All compounds exhibited micromolar binding affinities toward HSA and an increased protein thermal stability. Moderate to strong antiradical scavenging activity was observed for all BXLs with hydroxy groups at C-6, C-9 and C-10 positions of the benzoxanthene core, except for derivative bearing methoxy groups at these positions. BXLs with unsubstituted or low-substituted phenyl ring and one derivative without phenyl ring showed strong growth inhibition of Gram-positive Staphylococcus aureus. All compounds showed moderate to strong tumor cell growth-inhibitory activity and cytotoxicity.

Structural Comparison of Diverse HIV-1 Subtypes using Molecular Modelling and Docking Analyses of Integrase Inhibitors

The process of viral integration into the host genome is an essential step of the HIV-1 life cycle. The viral integrase (IN) enzyme catalyzes integration. IN is an ideal therapeutic enzyme targeted by several drugs; raltegravir (RAL), elvitegravir (EVG), dolutegravir (DTG), and bictegravir (BIC) having been approved by the USA Food and Drug Administration (FDA). Due to high HIV-1 diversity, it is not well understood how specific naturally occurring polymorphisms (NOPs) in IN may affect the structure/function and binding affinity of integrase strand transfer inhibitors (INSTIs). We applied computational methods of molecular modelling and docking to analyze the effect of NOPs on the full-length IN structure and INSTI binding. We identified 13 NOPs within the Cameroonian-derived CRF02_AG IN sequences and further identified 17 NOPs within HIV-1C South African sequences. The NOPs in the IN structures did not show any differences in INSTI binding affinity. However, linear regression analysis revealed a positive correlation between the Ki and EC50 values for DTG and BIC as strong inhibitors of HIV-1 IN subtypes. All INSTIs are clinically effective against diverse HIV-1 strains from INSTI treatment-naïve populations. This study supports the use of second-generation INSTIs such as DTG and BIC as part of first-line combination antiretroviral therapy (cART) regimens, due to a stronger genetic barrier to the emergence of drug resistance.

Peptide/Receptor Co-evolution Explains the Lipolytic Function of the Neuropeptide TLQP-21

Structural and functional diversity of peptides and GPCR result from long evolutionary processes. Even small changes in sequence can alter receptor activation, affecting therapeutic efficacy. We conducted a structure-function relationship study on the neuropeptide TLQP-21, a promising target for obesity, and its complement 3a receptor (C3aR1). After having characterized the TLQP-21/C3aR1 lipolytic mechanism, a homology modeling and molecular dynamics simulation identified the TLQP-21 binding motif and C3aR1 binding site for the human (h) and mouse (m) molecules. mTLQP-21 showed enhanced binding affinity and potency for hC3aR1 compared with hTLQP-21. Consistently, mTLQP-21, but not hTLQP-21, potentiates lipolysis in human adipocytes. These findings led us to uncover five mutations in the C3aR1 binding pocket of the rodent Murinae subfamily that are causal for enhanced calculated affinity and measured potency of TLQP-21. Identifying functionally relevant peptide/receptor co-evolution mechanisms can facilitate the development of innovative pharmacotherapies for obesity and other diseases implicating GPCRs.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Vitamin D receptor (VDR) non-synonymous single nucleotide polymorphisms (nsSNPs) affect the calcitriol drug response - A theoretical insight

Pharmacogenetics and pharmacogenomics have become presumptive with advancements in next-generation sequencing technology. In complex diseases, distinguishing the feasibility of pathogenic and neutral disease-causing variants is a time consuming and expensive process. Recent drug research and development processes mainly rely on the relationship between the genotype and phenotype through Single nucleotide polymorphisms (SNPs). The SNPs play an indispensable role in elucidating the individual's vulnerability to disease and drug response. The understanding of the interplay between these leads to the establishment of personalized medicine. In order to address this issue, we developed a computational pipeline of vitamin D receptor (VDR) for SNP centered study by application of elegant molecular docking and molecular dynamics simulation approaches. In a few SNPs the volume of the binding cavities has increased in mutant structures when compared to the wild type, indicating a weakening in interaction (699.1 ų in wild type Vs. 738.8 in Leu230Val, 820.7 ų in Arg247Leu). This also differently reflected in the H-bond interactions and binding free energies -169.93 kcal/mol (wild type) Vs -156.43 kcal/mol (R154W), -105.49 kcal/mol (R274L) in Leu230Val and Arg247Leu respectively. Although we could not find noteworthy changes in the binding free energies and binding pocket in the remaining mutations, the H-bond interactions made these SNPs deleterious. Thus, we further analyzed the H-bond interactions and distances using molecular dynamics (MD) simulation studies.

Simultaneous Binding of Folic Acid and Methotrexate to Human Serum Albumin: Insights into the Structural Changes of Protein and the Location and Competitive Displacement of Drugs

Protein structure can be flexible to adopt multiple conformations to house small molecules. In this paper, we have attempted to experimentally figure out how the structure of a transport protein steer the drug-drug competition (DDC) by maintaining the equilibrium distribution of the bound and unbound fractions of drugs. This understanding is an important facet in biophysical and medicinal chemistry to ascertain the effectiveness of drugs. It is important to note that majority of studies involving small-molecule-transport protein interaction aimed at characterizing the binding process, and because these proteins can interact with thousands of molecules, there are hundreds of reports on such interactions. This ultimately led to an impression among the readers that any studies involving serum albumin may not lead to any new findings except for traditional binding explorations. However, in the present paper, we would like to draw the attention of the readers that our findings are very surprising, new, and important, involving the phenomenon of ligand-protein interaction. Here, we have studied two structurally similar drugs methotrexate (MTX) and folic acid (FA), which attempt to bind the primary binding site (subdomain IIA), one at a time, of human serum albumin. Details of binding analyses reveal that when both of the drugs are present, the single-site binding mode of FA prefers to occupy the primary binding site and hence pushes the primary-site-bound MTX to another location (subdomain IIIA), which is the second binding site of MTX. The structural analysis indicates that DDC has occurred in a cooperative fashion so that the loss of the protein secondary structure is minimum when both drugs are bound to the protein, which means that in the case of duo-drug binding, the conventional interaction between the drug and the protein is altered to undergo restoration of the protein structure. This can indeed regulate the DDC by modifying the bound and unbound fractions of MTX in plasma. The present study emphasizes that in vitro structural characterizations of the transport protein provide important information to improve the molecular-level understanding of DDC for further therapeutic applications with combination drug.

Alanine mutation of the catalytic sites of Pantothenate Synthetase causes distinct conformational changes in the ATP binding region

The enzyme Pantothenate synthetase (PS) represents a potential drug target in Mycobacterium tuberculosis. Its X-ray crystallographic structure has demonstrated the significance and importance of conserved active site residues including His44, His47, Asn69, Gln72, Lys160 and Gln164 in substrate binding and formation of pantoyl adenylate intermediate. In the current study, molecular mechanism of decreased affinity of the enzyme for ATP caused by alanine mutations was investigated using molecular dynamics (MD) simulations and free energy calculations. A total of seven systems including wild-type + ATP, H44A + ATP, H47A + ATP, N69A + ATP, Q72A + ATP, K160A + ATP and Q164A + ATP were subjected to 50 ns MD simulations. Docking score, MM-GBSA and interaction profile analysis showed weak interactions between ATP (substrate) and PS (enzyme) in H47A and H160A mutants as compared to wild-type, leading to reduced protein catalytic activity. However, principal component analysis (PCA) and free energy landscape (FEL) analysis revealed that ATP was strongly bound to the catalytic core of the wild-type, limiting its movement to form a stable complex as compared to mutants. The study will give insight about ATP binding to the PS at the atomic level and will facilitate in designing of non-reactive analogue of pantoyl adenylate which will act as a specific inhibitor for PS.

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features

Motivation

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models.

Results

In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell.

Availability and Implementation

www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/.

Contacts

hbshen@sjtu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

The impact of structural biology in medicine illustrated with four case studies

The contributions of structural biology to drug discovery have expanded over the last 20 years from structure-based ligand optimization to a broad range of clinically relevant topics including the understanding of disease, target discovery, screening for new types of ligands, discovery of new modes of action, addressing clinical challenges such as side effects or resistance, and providing data to support drug registration. This expansion of scope is due to breakthroughs in the technology, which allow structural information to be obtained rapidly and for more complex molecular systems, but also due to the combination of different technologies such as X-ray, NMR, and other biophysical methods, which allows one to get a more complete molecular understanding of disease and ways to treat it. In this review, we provide examples of the types of impact molecular structure information can have in the clinic for both low molecular weight and biologic drug discovery and describe several case studies from our own work to illustrate some of these contributions.

On the importance of composite protein multiple ligand interactions in protein pockets

Conventional small molecule drug-discovery approaches target protein pockets. However, the limited number of geometrically distinct pockets leads to widespread promiscuity and deleterious side-effects. Here, the idea of COmposite protein LIGands (COLIG) that interact with each other as well as the protein within a single ligand binding pocket is examined. As a practical illustration, experimental evidence that E. coli Dihydrofolate reductase inhibitors are COLIGs is presented. Then, analysis of a non-redundant set of all holo PDB structures indicates that almost 47-76% of proteins (based on different sequence identity thresholds) can simultaneously bind multiple, interacting ligands in the same pocket. Moreover, most ligands that are either Singletons and COLIGs bind at the bottom of ligand binding pocket and occupy 30% and 43% of the volume of the bottom of the pocket. This suggests the use of COLIGs as a potential new class of small molecule drugs. © 2016 Wiley Periodicals, Inc.

Double Mutants in DNA Gyrase Lead to Ofloxacin Resistance in Mycobacterium tuberculosis

Fluoroquinolones are among the most important classes of highly effective antibacterial drugs, exhibiting wide range of activity to cure infectious diseases. Ofloxacin is second generation fluoroquinolone approved by FDA for the treatment of tuberculosis by selectively inhibiting DNA gyrase. However, the emergence of drug resistance owing to mutations in DNA gyrase poses intimidating challenge for the effective therapy of this drug. The double mutants GyrA^A90V GyrB^D500N and GyrA^A90V GyrB^T539N are reported to be implicated in conferring higher levels of OFX resistance. The present study was designed to unravel the molecular principles behind development of resistance by the bug against fluoroquinolones. Our results highlighted that polar interactions play critical role in the development of drug resistance and highlight the significant correlation between the free energy calculations predicted by MM-PBSA and stability of the ligand-bound complexes. Modifications at the OFX binding pocket due to amino acid substitution leads to fewer hydrogen bonds in mutants DNA gyrase-OFX complex, which determined the low susceptibility of the ligand in inhibiting the mutant protein. This study provides a structural rationale to the mutation-based resistance to ofloxacin and will pave way for development potent fluoroquinolone-based resistant-defiant drugs. J. Cell. Biochem. 118: 2950-2957, 2017. © 2017 Wiley Periodicals, Inc.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

Patient-Level Adverse Event Patterns in a Single-Institution Study of the Multi-Kinase Inhibitor Sorafenib

Novel characterization of patterns of adverse events (AEs) of kinase inhibitors (KIs) could reveal new insights on human molecular physiology and methods to improve the therapeutic index of KIs. Incidence and severity of AEs for each of 157 patients enrolled in sorafenib clinical trials were determined for three clinically relevant treatment intervals: weeks 0–3, weeks 3–7, and after 7 weeks. The most common within patient co‐occurrences were mucositis with dermatologic events: hand‐foot syndrome (HFS; odds ratio [OR] = 4.36; p = 0.0017) and rash (OR = 5.32; p < 0.001). Prevalence of severe: alopecia (p = 0.02), diarrhea (p < 0.001), and fatigue (p = 0.005) increased over the course of therapy. Incidence of HFS (60%) and diarrhea (25%) increased up to a minimum steady‐state concentration (approximately 5 mcg mL^‐1) and plateaued thereafter. Common AEs of sorafenib occur in distinct temporal and tissue distribution patterns and this analysis identified unrecognized relationships among mechanism‐dependent and independent effects of a KI.

Polymorphism in ion channel genes of Dirofilaria immitis: Relevant knowledge for future anthelmintic drug design

Dirofilaria immitis, a filarial parasite, causes cardiopulmonary dirofilariasis in dogs, cats and wild canids. The macrocyclic lactone (ML) class of drugs has been used to prevent heartworm infection. There is confirmed ML resistance in D. immitis and thus there is an urgent need to find new anthelmintics that could prevent and/or control the disease. Targeting ion channels of D. immitis for drug design has obvious advantages. These channels, present in the nematode nervous system, control movement, feeding, mating and respond to environmental cues which are necessary for survival of the parasite. Any new drug that targets these ion channels is likely to have a motility phenotype and should act to clear the worms from the host. Many of the successful anthelmintics in the past have targeted these ion channels and receptors. Knowledge about genetic variability of the ion channel and receptor genes should be useful information for drug design as receptor polymorphism may affect responses to a drug. Such information may also be useful for anticipation of possible resistance development. A total of 224 ion channel genes/subunits have been identified in the genome of D. immitis. Whole genome sequencing data of parasites from eight different geographical locations, four from ML-susceptible populations and the other four from ML-loss of efficacy (LOE) populations, were used for polymorphism analysis. We identified 1762 single nucleotide polymorphic (SNP) sites (1508 intronic and 126 exonic) in these 224 ion channel genes/subunits with an overall polymorphic rate of 0.18%. Of the SNPs found in the exon regions, 129 of them caused a non-synonymous type of polymorphism. Fourteen of the exonic SNPs caused a change in predicted secondary structure. A few of the SNPs identified may have an effect on gene expression, function of the protein and resistance selection processes.

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In the Dirofilaria immitis genome, 126 ion channel genes were identified.

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Within 126 ion channel genes, 1762 polymorphic loci were identified.

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Fourteen exonic SNPs caused a change in predicted secondary structure.

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SNPs may effect gene expression, protein function or resistance selection.

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D. immitis populations have low genetic variability among ion channel genes.

Drugging Membrane Protein Interactions

The majority of therapeutics target membrane proteins, accessible on the surface of cells, to alter cellular signaling. Cells use membrane proteins to transduce signals into cells, transport ions and molecules, bind cells to a surface or substrate, and catalyze reactions. Newly devised technologies allow us to drug conventionally "undruggable" regions of membrane proteins, enabling modulation of protein-protein, protein-lipid, and protein-nucleic acid interactions. In this review, we survey the state of the art of high-throughput screening and rational design in drug discovery, and we evaluate the advances in biological understanding and technological capacity that will drive pharmacotherapy forward against unorthodox membrane protein targets.

Molecular principles behind pyrazinamide resistance due to mutations in panD gene in Mycobacterium tuberculosis

The latest resurrection of drug resistance poses serious threat to the treatment and control of the disease. Mutations have been detected in panD gene in the Mycobacterium tuberculosis (Mtb) strains. Mutation of histidine to arginine at residue 21 (H21R) and isoleucine to valine at residue 29 (I49V) in the non-active site of panD gene has led to PZA resistance. This study will help in reconnoitering the mechanism of pyrazinamide (PZA) resistance caused due to double mutation identified in the panD gene of M. tuberculosis clinical isolates. It is known that panD gene encodes aspartate decarboxylase essential for β-alanine synthesis that makes it a potential therapeutic drug target for tuberculosis treatment. The knowledge about the molecular mechanism conferring drug resistance in M. tuberculosis is scarce, which is a significant challenge in designing successful therapeutic drug. In this study, structural and dynamic repercussions of H21R-I49V double mutation in panD complexed with PZA have been corroborated through docking and molecular dynamics based simulation. The double mutant (DM) shows low docking score and thus, low binding affinity for PZA as compared to the native protein. It was observed that the mutant protein exhibits more structural fluctuation at the ligand binding site in comparison to the native type. Furthermore, the flexibility and compactness analyses indicate that the double mutation influence interaction of PZA with the protein. The hydrogen-bond interaction patterns further supported our results. The covariance and PCA analysis elucidated that the double mutation affects the collective motion of residues in phase space. The results have been presented with an explanation for the induced drug resistance conferred by the H21R-I49V double mutation in panD gene and gain valuable insight to facilitate the advent of efficient therapeutics for combating resistance against PZA.

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

In silico analysis and molecular modeling of RNA polymerase, sigma S (RpoS) protein in Pseudomonas aeruginosa PAO1

BACKGROUND

Sigma factors are proteins that regulate transcription in bacteria. Sigma factors can be activated in response to different environmental conditions. The rpoS (RNA polymerase, sigma S) gene encodes sigma-38 (σ38, or RpoS), a 37.8 kDa protein in Pseudomonas aeruginosa (P. aeruginosa) strains. RpoS is a central regulator of the general stress response and operates in both retroactive and proactive manners; not only does it allow the cell to survive environmental challenges; it also prepares the cell for subsequent stresses (cross-protection).

METHODS

The significance of RpoS for stress resistance and protein expression in stationary-phase P. aeruginosa cells was assessed. The goal of the current study was to characterize RpoS of P. aeruginosa PAO1 using bioinformatics tools.

RESULTS

The results showed that RpoS is an unstable protein that belongs to the sigma-70 factor family. Secondary structure analysis predicted that random coil is the predominant structure followed by extended alpha helix. The three-dimensional (3D) structure was modeled using SWISS-MODEL Workspace.

CONCLUSION

Determination of sequence, function, structure, and predicted epitopes of RpoS is important for modeling of inhibitors that will help in the design of new drugs to combat multi-drug-resistant (MDR) strains. Such information may aid in the development of new diagnostic tools, drugs, and vaccines for treatment in endemic regions.

Computational methods and resources for the interpretation of genomic variants in cancer

The recent improvement of the high-throughput sequencing technologies is having a strong impact on the detection of genetic variations associated with cancer. Several institutions worldwide have been sequencing the whole exomes and or genomes of cancer patients in the thousands, thereby providing an invaluable collection of new somatic mutations in different cancer types. These initiatives promoted the development of methods and tools for the analysis of cancer genomes that are aimed at studying the relationship between genotype and phenotype in cancer. In this article we review the online resources and computational tools for the analysis of cancer genome. First, we describe the available repositories of cancer genome data. Next, we provide an overview of the methods for the detection of genetic variation and computational tools for the prioritization of cancer related genes and causative somatic variations. Finally, we discuss the future perspectives in cancer genomics focusing on the impact of computational methods and quantitative approaches for defining personalized strategies to improve the diagnosis and treatment of cancer.

Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest

Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

Parsing interindividual drug variability: an emerging role for systems pharmacology

There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype-drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug-specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically-orientated systems biology and pharmacometrics. Pharmacologically-orientated systems biology utilizes high throughput omics technologies, including next-generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data-derived models of sufficient detail will be required to help realize the goal of precision medicine.

Personalized biochemistry and biophysics

Whole human genome sequencing of individuals is becoming rapid and inexpensive, enabling new strategies for using personal genome information to help diagnose, treat, and even prevent human disorders for which genetic variations are causative or are known to be risk factors. Many of the exploding number of newly discovered genetic variations alter the structure, function, dynamics, stability, and/or interactions of specific proteins and RNA molecules. Accordingly, there are a host of opportunities for biochemists and biophysicists to participate in (1) developing tools to allow accurate and sometimes medically actionable assessment of the potential pathogenicity of individual variations and (2) establishing the mechanistic linkage between pathogenic variations and their physiological consequences, providing a rational basis for treatment or preventive care. In this review, we provide an overview of these opportunities and their associated challenges in light of the current status of genomic science and personalized medicine, the latter often termed precision medicine.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets

There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm. marseille.inserm.fr/fileadmin/rf-score-3.tgz.

Evolutionary Diagnosis of non-synonymous variants involved in differential drug response

Background

Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.

Results

We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.

Conclusions

The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.

Recent progress on structural bioinformatics research of cytochrome P450 and its impact on drug discovery

Cytochrome P450 is predominantly responsible for human drug metabolism, which is of critical importance for drug discovery and development. Structural bioinformatics focuses on analysis and prediction of three-dimentional structure of biological macromolecules and elucidation of structure-function relationship as well as identification of important binding interactions. Rapid advancement of structural bioinformatics has been made over the last decade. With more information available for CYP structures, the methods of structural bioinformatics may be used in the CYP field. In this review, we demonstrate three previous studies on CYP using the methods of structural bioinformatics, including the investigation of reasons for decrease of enzymatic activity of CYP1A2 caused by a peripheral mutation, the construction of a pharmacophore model specific to active site of CYP1A2 and the prediction of the functional consequences of single residue mutation in CYP. By illustrating these studies we attempt to show the potential role of structural bioinformatics in CYP research and help better understanding the importance of structural bioinformatics in drug designing.

Computer-aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and therapeutic opportunities

The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often lead to complications, such as tumorigenesis, type 2 diabetes and cardiovascular diseases. Molecular targeted therapy, aimed at modulating the deregulated pathway, holds great promise for controlling these diseases, though side effects may be inevitable, given the ubiquity of the pathway in cell functions. Here, we review a variety of factors found to modulate the PI3K/Akt/mTOR pathway, including gene mutations, certain metabolites, inflammatory factors, chemical toxicants, drugs found to rectify the pathway, as well as viruses that hijack the pathway for their own synthetic purposes. Furthermore, this evidence of PI3K/Akt/mTOR pathway alteration and related pathogenesis has inspired the exploration of computer-aided targeting of this pathway to optimize therapeutic strategies. Herein, we discuss several possible options, using computer-aided targeting, to reduce the toxicity of molecularly-targeted therapy, including mathematical modeling, to reveal system-level control mechanisms and to confer a low-dosage combination therapy, the potential of PP2A as a therapeutic target, the formulation of parameters to identify patients who would most benefit from specific targeted therapies and molecular dynamics simulations and docking studies to discover drugs that are isoform specific or mutation selective so as to avoid undesired broad inhibitions. We hope this review will stimulate novel ideas for pharmaceutical discovery and deepen our understanding of curability and toxicity by targeting the PI3K/Akt/mTOR pathway.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype.

In silico comparative characterization of pharmacogenomic missense variants

BACKGROUND

Missense pharmacogenomic (PGx) variants refer to amino acid substitutions that potentially affect the pharmacokinetic (PK) or pharmacodynamic (PD) response to drug therapies. The PGx variants, as compared to disease-associated variants, have not been investigated as deeply. The ability to computationally predict future PGx variants is desirable; however, it is not clear what data sets should be used or what features are beneficial to this end. Hence we carried out a comparative characterization of PGx variants with annotated neutral and disease variants from UniProt, to test the predictive power of sequence conservation and structural information in discriminating these three groups.

RESULTS

126 PGx variants of high quality from PharmGKB were selected and two data sets were created: one set contained 416 variants with structural and sequence information, and, the other set contained 1,265 variants with sequence information only. In terms of sequence conservation, PGx variants are more conserved than neutral variants and much less conserved than disease variants. A weighted random forest was used to strike a more balanced classification for PGx variants. Generally structural features are helpful in discriminating PGx variant from the other two groups, but still classification of PGx from neutral polymorphisms is much less effective than between disease and neutral variants.

CONCLUSIONS

We found that PGx variants are much more similar to neutral variants than to disease variants in the feature space consisting of residue conservation, neighboring residue conservation, number of neighbors, and protein solvent accessibility. Such similarity poses great difficulty in the classification of PGx variants and polymorphisms.

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.

Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?

Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.