A Look Inside HIV Resistance through Retroviral Protease Interaction Maps

Retroviruses affect a large number of species, from fish and birds to mammals and humans, with global socioeconomic negative impacts. Here the authors report and experimentally validate a novel approach for the analysis of the molecular networks that are involved in the recognition of substrates by retroviral proteases. Using multivariate analysis of the sequence-based physiochemical descriptions of 61 retroviral proteases comprising wild-type proteases, natural mutants, and drug-resistant forms of proteases from nine different viral species in relation to their ability to cleave 299 substrates, the authors mapped the physicochemical properties and cross-dependencies of the amino acids of the proteases and their substrates, which revealed a complex molecular interaction network of substrate recognition and cleavage. The approach allowed a detailed analysis of the molecular–chemical mechanisms involved in substrate cleavage by retroviral proteases

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking

Proteochemometric modeling of HIV protease susceptibility

<p>Abstract</p> <p>Background</p> <p>A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.</p> <p>Results</p> <p>The model provided excellent predictability (<it>R</it>²= 0.92, <it>Q</it>²= 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was <it>Q</it>²_{<it>inhibitors </it>}= 0.72.</p> <p>Conclusion</p> <p>Our results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.</p

Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors

BACKGROUND: Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens. METHODOLOGY/PRINCIPAL FINDINGS: We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2=0.89 and Q2ext=0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant. CONCLUSIONS/SIGNIFICANCE: Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants

The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web

Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA)

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets.

Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance ( 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side

Alfaviiruse mittestruktuurne proteaas ja tema liitvalgust substraat: täiuslikult korraldatud kooselu reeglid

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Alfaviirused (sugukond Togaviridae) on artriiti ja entsefaliiti põhjustavad RNA genoomsed viirused. Nende paljunemise strateegia aluseks on viiruse replikaasi valkude süntees ühe nn. mittestruktuurse eelvalgu P1234 kujul ning selle ajaliselt reguleeritud lõikamine valmis valkudeks nsP2 proteaasi abil. Käesoleva väitekirja aluseks olevad uurimistööd viisid nsP2 substraat-spetsiifilisust tagavate mehhanismide väljaselgitamiseni; muu hulgas kirjeldati uudset proteolüütiliste lõikamiste regulatsioonimehhanismi, mis põhineb liitvalgu erinevate regioonide vahelisel „suhtlemisel“ viiruse replikatsiooni kompleksi moodustamise käigus. Sellest saab järeldada, et P1234 lõikamise ajaline regulatsioon sõltub otseselt replikatsioonikompleksi konfiguratsioonidest, millised omakorda on määratud selle komponentide vaheliste interaktsioonide poolt. Seega tõuseb viiruse nsP2 proteaas esile kui keerulise signaalvõrgustiku keskne element, mille roll viirus infektsiooni regulatsioonis seisneb replikatsiooniga kaasnevate sündmuste „jälgimises“ ja nendele reageerimises. Viimane põhineb sellel, et kui viiruse paljunemine jõuab kindla vahe-etapini, siis kaasneb sellega lõikamiskohtade ja/või muude oluliste struktuuride „esitlemine“ proteaasile, mis reageerib toimunud muudatustele lokaalse signaalülekande, mis lõppkokkuvõttes viib replikaasi kompleksi struktuuri järjestikulistele muudatustele, käivitamisega. Kokkuvõttes, tõid läbiviidud uurimised välja asjaolu, et lisaks varem teada olnud lõikamisjärjestuste äratundmisele, omab ka makromolekulaarsete struktuuride moodustamine viiruse valkude poolt olulist (ja mitmel juhul isegi määravat) rolli viiruse proteaasi töö reguleerimisel. Veel enam, eeldati, et seesugune mitmetahuline regulatsioon võib olla paljukomponentsete proteolüütiliste süsteemide üldine omadus. Kirjeldatud avastused ja nende lahtimõtestamine omavad olulist rolli uurimistöödele, mille eesmärgiks on alfaviiruste paljunemist takistavate lähenemiste väljatöötamine. Nii võib saadud tulemuste põhjal järeldada, et lisaks proteaasi aktiivsuse otsesele mõjutamisele võib viiruse replikatsiooni takistada ka mõjutades proteolüüsi regulatsiooni tagavaid molekulide vahelised seoseid.Alphaviruses from the Togaviridae family are RNA viruses that may cause arthritic syndroms and encephalitis. The alphavirus replication strategy relies on the production of replicase proteins initially in the form of non-structural (ns) polyprotein precursor P1234, which during the course of replication becomes proteolytically processed by the virus-encoded nsP2 protease in a temporally regulated manner. The studies that constitute the basis of this thesis led to identification of the requirements for substrate specificity of nsP2 protease and revealed novel mechanism for the regulation of processing based on the specific communication between distant parts of the viral polyprotein brought together during assembly of replication complex. It was concluded that the order of alphaviral ns-polyprotein processing is mostly dependent on the configuration of the replication complex imposed by intermolecular interactions meant to guarantee timely cleavages. The alphaviral protease therefore emerges as an integral part of the sophisticated signaling mechanism, in which the regulatory task of the protease consists of monitoring the succession and completion of the events of viral infection. Once the respective replication status-induced conformational changes within replicase allow the presentation of the scissile bond and/or other essential determinants of substrate recognition like exosites, the local protease signaling is initiated, which apparently leads to further reconfiguration of the viral replication complex. Combined, the studies unveiled the decisive role played by the macromolecular assembly-dependent component of substrate recognition in addition to the sequence-dependent component, the combination of which may be expected to constitute the basis of regulation in multi-site proteolytic systems in general. Described findings and their interpretations are expected to provide with essential grounds and directions for further studies on the restriction of alphaviral replication through affecting the center of viral proteolytic activity or via intervention with its regulation by targeting intramolecular interactions