Prediction of indirect interactions in proteins

Proteochemometrics modeling for prediction of the interactions between caspase isoforms and their inhibitors

Caspases (cysteine-aspartic proteases) play critical roles in inflammation and the programming of cell death in the form of necroptosis, apoptosis, and pyroptosis. The name of these enzymes has been chosen in accordance with their cysteine protease activity. They act as cysteines in nucleophilically active sites to attack and cleave target proteins in the aspartic acid and amino acid C-terminal. Based on the substrate's structure and the specificity, the physiological activity of caspases is divided. However, in apoptosis, the division of caspases into initiating caspases (caspase 2, 8, 9, and 10) and executive caspases (caspase 3, 6, and 7) is essential. The present study aimed to perform Proteochemometrics Modeling to generalize the data on caspases, which could predict ligand and protein interactions. In this study, we employed protein and ligand descriptors. Moreover, protein descriptors were computed using the Protr R package, while PADEL-Descriptor was employed for the computation of ligand descriptors. In addition, NCA (Neighborhood Component Analyses) was used for descriptor selection, and SVR, decision tree, and ensemble methods were utilized for the proteochemometrics modeling. This study shows that the ensemble model demonstrates superior performance compared with other models in terms of R², Q², and RMSE criteria.

Proteochemometrics-Based Prediction of Peptide Binding to HLA-DP Proteins

Human leukocyte antigens (HLA) class II proteins are involved in the antigen processing in the antigen presenting cells. They form complexes with antigen peptide fragments. The peptide-HLA protein complexes are presented on the cell surface where they are recognized by helper T cells (Th cells). HLA-DP is one of the three HLA class II loci. The HLA-DP proteins are associated with a significant number of autoimmune diseases, as well as with a susceptibility or resistance to a number of infectious agents. In the present study, we apply proteochemometrics-a method for bioactivity modeling of multiple ligands binding to multiple target proteins-to derive and validate a robust model for peptide binding prediction to the 7 most frequent HLA-DP proteins. The model is able to identify 86% of the binders in the top 10% of the best predicted nonamers generated from one protein.

Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure-based and Sequence-based Descriptors

Due to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors including sulfonamides, polyamines, coumarins and dithiocarbamates (DTCs). However, many of them hardly act as a selective inhibitor against a specific isoform. Therefore, finding highly selective inhibitors for different isoforms of CA is still an ongoing project. Proteochemometrics modeling (PCM) is able to model the bioactivity of multiple compounds against different isoforms of a protein. Therefore, it would be extremely applicable when investigating the selectivity of different ligands towards different receptors. Given the facts, we applied PCM to investigate the interaction space and structural properties that lead to the selective inhibition of CA isoforms by some dithiocarbamates. Our models have provided interesting structural information that can be considered to design compounds capable of inhibiting different isoforms of CA in an improved selective manner. Validity and predictivity of the models were confirmed by both internal and external validation methods; while Y-scrambling approach was applied to assess the robustness of the models. To prove the reliability and the applicability of our findings, we showed how ligands-receptors selectivity can be affected by removing any of these critical findings from the modeling process.

Origin of aromatase inhibitory activity via proteochemometric modeling

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.

Prediction of Cell-Penetrating Peptides

The in silico methods for the prediction of the cell-penetrating peptides are reviewed. Those include the multivariate statistical methods, machine-learning methods such as the artificial neural networks and support vector machines, and molecular modeling techniques including molecular docking and molecular dynamics.The applicability of the methods is demonstrated on the basis of the exemplary cases from the literature.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

Peptide Binding Prediction to Five Most Frequent HLA-DQ Proteins - a Proteochemometric Approach

Major histocompatibility complex (MHC) proteins class II, are glycoproteins binding within the cell to short peptides with foreign origin, called epitopes, and present them at the cell surface for inspection by T-cells. Apart from presenting foreign antigens, they are able to present also common self-antigens and trigger autoimmune diseases as coeliac disease and diabetes mellitus type 1. The MHC proteins are extremely polymorphic. The polymorphism is located mainly in the peptide binding site. In the present study, we apply a proteochemometric approach to derive a model for prediction of peptide binding to human MHC class II proteins from locus HLA-DQ. Proteochemometrics was applied on 2624 peptides binding to five most frequent HLA-DQ proteins. The sequences of peptides and proteins were described by three z-descriptors relating to hydrophobicity, steric effects and polarity of amino acids. Cross-terms accounting for the protein-peptide interactions also were included. The derived model was validated by external test set of 660 peptides and showed rpred (2) =0.808, AUC=0.965, 92.5 % accuracy at threshold of pIC50 =5.3 and average sensitivity of 83 % among the top 10 % best predicted nonamers. The model is implemented in the server for MHC binding prediction EpiTOP and is freely available at http://www.ddg-pharmfac.net/epitop.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

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 (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 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.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets

Background

While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA).

Results

In describing amino acid similarities, MSWHIM, T-scales and ST-scales show related behavior, as do the VHSE, FASGAI, and ProtFP (PCA3) descriptor sets. Conversely, the ProtFP (PCA5), ProtFP (PCA8), Z-Scales (Binned), and BLOSUM descriptor sets show behavior that is distinct from one another as well as both of the clusters above. Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data.

Conclusion

In this work a comparison is provided of how similar (and differently) currently available amino acids descriptor sets behave when converting structure to property space. The results obtained enable molecular modelers to select suitable amino acid descriptor sets for structure-activity analyses, e.g. those showing complementary behavior.

A rigid network of long-range contacts increases thermostability in a mutant endoglucanase

Thermodynamic stability of a protein at elevated temperatures is a key factor for thermostable enzymes to catalyze their specific reactions. Yet our understanding of biological determinants of thermostability is far from complete. Many different atomistic factors have been suggested as possible means for such proteins to preserve their activity at high temperatures. Among these factors are specific local interatomic interactions or enrichment of specific amino acid types. The case of glycosyl hydrolase family endoglucanase of Trichoderma reesei defies current hypotheses for thermostability because a single mutation far from the active site (A35 V) converts this mesostable protein into a thermostable protein without significant change in the protein structure. This substantial change in enzymatic activity cannot be explained on the basis of local intramolecular interactions alone. Here we present a more global view of the induced thermostability and show that the A35 V mutation affects the underlying structural rigidity of the whole protein via a number of long-range, non-local interactions. Our analysis of this structure reveals a precisely tuned, rigid network of atomic interactions. This cooperative, allosteric effect promotes the transformation of this mesostable protein into a thermostable one.

Involvement of the first transmembrane segment of human α(2) -adrenoceptors in the subtype-selective binding of chlorpromazine, spiperone and spiroxatrine

BACKGROUND AND PURPOSE

Some large antagonist ligands (ARC239, chlorpromazine, prazosin, spiperone, spiroxatrine) bind to the human α(2A) -adrenoceptor with 10- to 100-fold lower affinity than to the α(2B)- and α(2C)-adrenoceptor subtypes. Previous mutagenesis studies have not explained this subtype selectivity.

EXPERIMENTAL APPROACH

The possible involvement of the extracellular amino terminus and transmembrane domain 1 (TM1) in subtype selectivity was elucidated with eight chimaeric receptors: six where TM1 and the N-terminus were exchanged between the α(2)-adrenoceptor subtypes and two where only TM1 was exchanged. Receptors were expressed in CHO cells and tested for ligand binding with nine chemically diverse antagonist ligands. For purposes of interpretation, molecular models of the three human α(2)-adrenoceptors were constructed based on the β(2)-adrenoceptor crystal structure.

KEY RESULTS

The affinities of three antagonists (spiperone, spiroxatrine and chlorpromazine) were significantly improved by TM1 substitutions of the α(2A)-adrenoceptor, but reciprocal effects were not seen for chimaeric receptors based on α(2B)- and α(2C)-adrenoceptors. Molecular docking of these ligands suggested that binding occurs in the orthosteric ligand binding pocket.

CONCLUSIONS AND IMPLICATIONS

TM1 is involved in determining the low affinity of some antagonist ligands at the human α(2A)-adrenoceptor. The exact mechanism is not known, but the position of TM1 at a large distance from the binding pocket indicates that TM1 does not participate in specific side-chain interactions with amino acids within the binding pocket of the receptor or with ligands bound therein. Instead, molecular models suggest that TM1 has indirect conformational effects related to the charge distribution or overall shape of the binding pocket.

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

Proteochemometric modeling is founded on the principles of QSAR but is able to benefit from additional information in model training due to the inclusion of target information.

An eScience-Bayes strategy for analyzing omics data

BACKGROUND

The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.

RESULTS

We present a general approach to omics data analysis that alleviates these problems. By combining eScience and Bayesian methods, we retrieve scientific information and data from multiple sources and coherently incorporate them into large models. These models improve the accuracy of predictions and offer new insights into the underlying mechanisms. This "eScience-Bayes" approach is demonstrated in two proof-of-principle applications, one for breast cancer prognosis prediction from transcriptomic data and one for protein-protein interaction studies based on proteomic data.

CONCLUSIONS

Bayesian statistics provide the flexibility to tailor statistical models to the complex data structures in omics biology as well as permitting coherent integration of multiple data sources. However, Bayesian methods are in general computationally demanding and require specification of possibly thousands of prior distributions. eScience can help us overcome these difficulties. The eScience-Bayes thus approach permits us to fully leverage on the advantages of Bayesian methods, resulting in models with improved predictive performance that gives more information about the underlying biological system.

From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions

A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area.

Critical review of general guidelines for membrane proteins model building and analysis

Membrane proteins play major roles in many biological processes such as signalling, transport, etc. They have been shown to be involved in the development of many diseases and have become important drug targets per se. The understanding of their functional properties may be facilitated if a 3D structure is available. However, in the case of membrane proteins, only a few 3D structures have been solved to date. Bioinformatics and molecular modelling approaches are thus powerful alternatives to fill the gap between the sequence and the structure. Here, a review of the most recent approaches is proposed together with guidelines on how to use them. In addition, insofar as important biological processes require conformational changes, we discuss some interesting methods aimed at exploring the dynamic behaviour of proteins in their membrane environment. The paper ends with a brief description of useful approaches for determining oligomerisation or ligand binding sites.

Peptide binding to the HLA-DRB1 supertype: a proteochemometrics analysis

A proteochemometrics approach was applied to a set of 2666 peptides binding to 12 HLA-DRB1 proteins. Sequences of both peptide and protein were described using three z-descriptors. Cross terms accounting for adjacent positions and for every second position in the peptides were included in the models, as well as cross terms for peptide/protein interactions. Models were derived based on combinations of different blocks of variables. These models had moderate goodness of fit, as expressed by r2, which ranged from 0.685 to 0.732; and good cross-validated predictive ability, as expressed by q2, which varied from 0.678 to 0.719. The external predictive ability was tested using a set of 356 HLA-DRB1 binders, which showed an r2(pred) in the range 0.364-0.530. Peptide and protein positions involved in the interactions were analyzed in terms of hydrophobicity, steric bulk and polarity.

Real-time monitoring of peptide cleavage using a nanopore probe

Here we report a rapid, label-free method for monitoring peptide cleavage. Monitoring peptide translocation through an engineered ion channel in the absence and the presence of an enzyme allowed quantitative chemical kinetics information on enzymatic processes to be obtained. In addition to its potential application in disease diagnostics and drug discovery, this peptide/protein cleavage approach is envisioned for further development as a novel rapid, label-free protein sequencing technique.

Proteochemometrics mapping of the interaction space for retroviral proteases and their substrates

Understanding the complex interactions of retroviral proteases with their ligands is an important scientific challenge in efforts to achieve control of retroviral infections. Development of drug resistance because of high mutation rates and extensive polymorphisms causes major problems in treating the deadly diseases these viruses cause, and prompts efforts to identify new strategies. Here we report a comprehensive analysis of the interaction of 63 retroviral proteases from nine different viral species with their substrates and inhibitors based on publicly available data from the past 17years of retroviral research. By correlating physico-chemical descriptions of retroviral proteases and substrates to their biological activities we constructed a highly statistically valid 'proteochemometric' model for the interactome of retroviral proteases. Analysis of the model indicated amino acid positions in retroviral proteases with the highest influence on ligand activity and revealed general physicochemical properties essential for tight binding of substrates across multiple retroviral proteases. Hexapeptide inhibitors developed based on the discovered general properties effectively inhibited HIV-1 proteases in vitro, and some exhibited uniformly high inhibitory activity against all HIV-1 proteases mutants evaluated. A generalized proteochemometric model for retroviral proteases interactome has been created and analysed in this study. Our results demonstrate the feasibility of using the developed general strategy in the design of inhibitory peptides that can potentially serve as templates for drug resistance-improved HIV retardants.

Targeting melanocortin receptors: an approach to treat weight disorders and sexual dysfunction

The melanocortin system has multifaceted roles in the control of body weight homeostasis, sexual behaviour and autonomic functions, and so targeting this pathway has immense promise for drug discovery across multiple therapeutic areas. In this Review, we first outline the physiological roles of the melanocortin system, then discuss the potential of targeting melanocortin receptors by using MC3 and MC4 agonists for treating weight disorders and sexual dysfunction, and MC4 antagonists to treat anorectic and cachectic conditions. Given the complexity of the melanocortin system, we also highlight the challenges and opportunities for future drug discovery in this area.

Proteochemometric modeling of HIV protease susceptibility

BACKGROUND

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.

RESULTS

The model provided excellent predictability (R2 = 0.92, Q2 = 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 Q2 inhibitors = 0.72.

CONCLUSION

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.

Tight-binding "dihedral orbitals" approach to the degree of folding of macromolecular chains

We develop a tight-binding molecular approach to quantify the degree of folding of a macromolecular chain. This approach is based on the linear combination of "dihedral" orbitals to give molecular orbitals (LCDO-MO). The dihedral orbitals are a set of orbitals situated in each dihedral angle of the chain. The LCDO-MO approach remains basically topological, and we display its direct relation to known graph theoretical concepts. Using this approach, we define the dihedral electronic energy and the dihedral electronic partition function of a linear macromolecular chain. We show that the partition function per dihedral angle quantifies the degree of folding of the dihedral graph. We analyze the empirical relationship between these two functions by using a series of 100 proteins. We also study the relation between these two functions and the percentages of secondary structure for these proteins. Finally, we illustrate the use of the dihedral energy and the partition function in structure-property studies of proteins by analyzing the binding of steroids to DB3 antibody.

Proteochemometric analysis of small cyclic peptides' interaction with wild-type and chimeric melanocortin receptors

The melanocortin (MC) system confines unique G-protein coupled receptor pathways, which include the MC(1-5) receptors and their endogenous agonists and antagonists, the MCs and the agouti and agouti-related proteins. The MC4 receptor is an important target for development of drugs for treatment of obesity and cachexia. While natural MC peptides are selective for the MC1 receptor, some cyclic pentapeptides, such as the HS-129 peptide, show high selectivity for the MC4 receptor. Here we gained insight into the mechanisms for its recognition by MC receptors. To this end we correlated the interaction data of four HS peptide analogues with four wild-type and 14 multiple chimeric MC receptors to the binary and physicochemical descriptions of the studied entities by use of partial least squares regression, which resulted in highly valid proteochemometric models. Analysis of the models revealed that the recognition sites of the HS peptides are different from the earlier proteochemometrically mapped linear MSH peptides' recognitions sites, although they overlap partially. The analysis also revealed important amino acids that explain the selectivity of the HS-129 peptide for the MC4 receptor.

Determining membrane protein structures: still a challenge!

Determination of structures and dynamics events of transmembrane proteins is important for the understanding of their function. Analysis of such events requires high-resolution 3D structures of the different conformations coupled with molecular dynamics analyses describing the conformational pathways. However, the solution of 3D structures of transmembrane proteins at atomic level remains a particular challenge for structural biochemists--the need for purified and functional transmembrane proteins causes a 'bottleneck'. There are various ways to obtain 3D structures: X-ray diffraction, electron microscopy, NMR and modelling; these methods are not used exclusively of each other, and the chosen combination depends on several criteria. Progress in this field will improve knowledge of ligand-induced activation and inhibition of membrane proteins in addition to aiding the design of membrane-protein-targeted drugs.

Proteochemometric modeling reveals the interaction site for Trp9 modified α-MSH peptides in melanocortin receptors

The interactions of alpha-MSH peptides with melanocortin receptors (MCRs) were located by proteochemometric modeling. Nine alpha-MSH peptide analogues were constructed by exchanging the Trp9 residue in the alpha-MSH core with the natural or artificial amino acids Arg, Asp, Cys, Gly, Leu, Nal, d-Nal, Pro, or d-Trp. The nine peptides created, and alpha-MSH itself, were evaluated for their interactions with the 4 wild-type MC(1,3-5)Rs and 15 multichimeric MCRs, each of the latter being constructed from three sequence segments, each taken from a different wild-type MC(1,3-5)R. The segments of the chimeric MCRs were selected according to the principles of statistical molecular design and were arranged so as to divide the receptors into five parts. By this approach, a set of 19 maximally diverse MC receptor proteins was obtained for which the interaction activity with the 10 peptides were measured by radioligand binding thus creating data for 190 ligand-protein pairs, which were subsequently analyzed by use of proteochemometric modeling. In proteochemometrics, the structural or physicochemical properties of both interaction partners, which represent the complementarity of the interacting entities, are used to create multivariate mathematical descriptions. (Here, physicochemical property descriptors of the receptors' and peptides' amino acids were used). A valid, highly predictive (Q2 = 0.74) and easily interpretable model was then obtained. The model was further validated by its ability to correctly predicting the affinity of alpha-MSH for new point and cassette-mutated MC4/MC1Rs, and it was then used to identify the receptor residues that are important for affording the high affinity and selectivity of alpha-MSH for the MC1R. It was revealed that these residues are located in several quite distant parts of the receptors' transmembrane cavity and must therefore cause their influence at various stages of the dynamic ligand-binding process, such as by affecting the conformation of the ligand at the vicinity of the receptor and taking part in the path of the ligand's entry into its binding pocket. Our study can be used as a template how to create high resolution proteochemometric models when there are a limited number of natural proteins and ligands available.

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.