Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Evolution of drug resistance drives destabilization of flap region dynamics in HIV-1 protease

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome. During the progression of the disease, some individual patients acquire drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the "flap region" of the HIV-1 protease, which is a critical dynamic region involved in nonspecific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning-assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and drug binding of wild-type and common drug-resistant versions of the main protease. We also use a multiagent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs. We find that a key conserved functional site in the flap region, a solvent-exposed isoleucine (Ile50) that controls flap dynamics is functionally targeted by drug resistance mutations, leading to amplified molecular dynamics affecting the functional ability of the flap region to hold the drugs. We conclude that better long-term patient outcomes may be achieved by designing drugs that target protease regions that are less dependent upon single sites with large functional binding effects.

Building Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins

Proteases are major drug targets for many viral diseases. However, mutations can render several antiprotease drugs inefficient rapidly even though these mutations may not alter protein structures significantly. Understanding relations between quickly mutating residues, protease structures, and the dynamics of the proteases is crucial for designing potent drugs. Due to this reason, we studied relations between the evolutionary information on residues in the amino acid sequences and protein dynamics for SARS-CoV-2 main protease. More precisely, we analyzed three dynamical quantities (Schlitter entropy, root-mean-square fluctuations, and dynamical flexibility index) and their relation to the amino acid conservation extracted from multiple sequence alignments of the main protease. We showed that a quantifiable similarity can be built between a sequence-based quantity called Jensen-Shannon conservation and those three dynamical quantities. We validated this similarity for a diverse set of 32 different proteins, other than the SARS-CoV-2 main protease. We believe that establishing these kinds of quantitative bridges will have larger implications for all viral proteases as well as all proteins.

Prediction of HIV-1 protease resistance using genotypic, phenotypic, and molecular information with artificial neural networks

Drug resistance is a primary barrier to effective treatments of HIV/AIDS. Calculating quantitative relations between genotype and phenotype observations for each inhibitor with cell-based assays requires time and money-consuming experiments. Machine learning models are good options for tackling these problems by generalizing the available data with suitable linear or nonlinear mappings. The main aim of this study is to construct drug isolate fold (DIF) change-based artificial neural network (ANN) models for estimating the resistance potential of molecules inhibiting the HIV-1 protease (PR) enzyme. Throughout the study, seven of eight protease inhibitors (PIs) have been included in the training set and the remaining ones in the test set. We have obtained 11,803 genotype-phenotype data points for eight PIs from Stanford HIV drug resistance database. Using the leave-one-out (LVO) procedure, eight ANN models have been produced to measure the learning capacity of models from the descriptors of the inhibitors. Mean R² value of eight ANN models for unseen inhibitors is 0.716, and the 95% confidence interval (CI) is [0.592-0.840]. Predicting the fold change resistance for hundreds of isolates allowed a robust comparison of drug pairs. These eight models have predicted the drug resistance tendencies of each inhibitor pair with the mean 2D correlation coefficient of 0.933 and 95% CI [0.930-0.938]. A classification problem has been created to predict the ordered relationship of the PIs, and the mean accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) values are calculated as 0.954, 0.791, 0.791, and 0.688, respectively. Furthermore, we have created an external test dataset consisting of 51 unique known HIV-1 PR inhibitors and 87 genotype-phenotype relations. Our developed ANN model has accuracy and area under the curve (AUC) values of 0.749 and 0.818 to predict the ordered relationships of molecules on the same strain for the external dataset. The currently derived ANN models can accurately predict the drug resistance tendencies of PI pairs. This observation could help test new inhibitors with various isolates.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.

A topological data analytic approach for discovering biophysical signatures in protein dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.

Structural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.

Antiretroviral Imprints and Genomic Plasticity of HIV-1 pol in Non-clade B: Implications for Treatment

Combinational antiretroviral therapy (cART) is the most effective tool to prevent and control HIV-1 infection without an effective vaccine. However, HIV-1 drug resistance mutations (DRMs) and naturally occurring polymorphisms (NOPs) can abrogate cART efficacy. Here, we aimed to characterize the HIV-1 pol mutation landscape in Cameroon, where highly diverse HIV clades circulate, and identify novel treatment-associated mutations that can potentially affect cART efficacy. More than 8,000 functional Cameroonian HIV-1 pol sequences from 1987 to 2020 were studied for DRMs and NOPs. Site-specific amino acid frequencies and quaternary structural features were determined and compared between periods before (≤2003) and after (2004-2020) regional implementation of cART. cART usage in Cameroon induced deep mutation imprints in reverse transcriptase (RT) and to a lower extent in protease (PR) and integrase (IN), according to their relative usage. In the predominant circulating recombinant form (CRF) 02_AG (CRF02_AG), 27 canonical DRMs and 29 NOPs significantly increased or decreased in RT during cART scale-up, whereas in IN, no DRM and only seven NOPs significantly changed. The profound genomic imprints and higher prevalence of DRMs in RT compared to PR and IN mirror the dominant use of reverse transcriptase inhibitors (RTIs) in sub-Saharan Africa and the predominantly integrase strand transfer inhibitor (InSTI)-naïve study population. Our results support the potential of InSTIs for antiretroviral treatment in Cameroon; however, close surveillance of IN mutations will be required to identify emerging resistance patterns, as observed in RT and PR. Population-wide genomic analyses help reveal the presence of selective pressures and viral adaptation processes to guide strategies to bypass resistance and reinstate effective treatment.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

Allosteric pockets and dynamic residue network hubs of falcipain 2 in mutations including those linked to artemisinin resistance

Continually emerging resistant strains of malarial parasites to current drugs present challenges. Understanding the underlying resistance mechanisms, especially those linked to allostery is, thus, highly crucial for drug design. This forms the main concern of the paper through a case study of falcipain 2 (FP-2) and its mutations, some of which are linked to artemisinin (ART) drug resistance. Here, we applied a variety of in silico approaches and tools that we developed recently, together with existing computational tools. This included novel essential dynamics and dynamic residue network (DRN) analysis algorithms. We identified six pockets demonstrating dynamic differences in the presence of some mutations. We observed striking allosteric effects in two mutant proteins. In the presence of M245I, a cryptic pocket was detected via a unique mechanism in which Pocket 2 fused with Pocket 6. In the presence of the A353T mutation, which is located at Pocket 2, the pocket became the most rigid among all protein systems analyzed. Pocket 6 was also highly stable in all cases, except in the presence of M245I mutation. The effect of ART linked mutations was more subtle, and the changes were at residue level. Importantly, we identified an allosteric communication path formed by four unique averaged BC hubs going from the mutated residue to the catalytic site and passing through the interface of three identified pockets. Collectively, we established and demonstrated that we have robust tools and a pipeline that can be applicable to the analysis of mutations.

MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics

The web server, MDM-TASK-web, combines the MD-TASK and MODE-TASK software suites, which are aimed at the coarse-grained analysis of static and all-atom MD-simulated proteins, using a variety of non-conventional approaches, such as dynamic residue network analysis, perturbation-response scanning, dynamic cross-correlation, essential dynamics and normal mode analysis. Altogether, these tools allow for the exploration of protein dynamics at various levels of detail, spanning single residue perturbations and weighted contact network representations, to global residue centrality measurements and the investigation of global protein motion. Typically, following molecular dynamic simulations designed to investigate intrinsic and extrinsic protein perturbations (for instance induced by allosteric and orthosteric ligands, protein binding, temperature, pH and mutations), this selection of tools can be used to further describe protein dynamics. This may lead to the discovery of key residues involved in biological processes, such as drug resistance. The server simplifies the set-up required for running these tools and visualizing their results. Several scripts from the tool suites were updated and new ones were also added and integrated with 2D/3D visualization via the web interface. An embedded work-flow, integrated documentation and visualization tools shorten the number of steps to follow, starting from calculations to result visualization. The Django-powered web server (available at https://mdmtaskweb.rubi.ru.ac.za/) is compatible with all major web browsers. All scripts implemented in the web platform are freely available at https://github.com/RUBi-ZA/MD-TASK/tree/mdm-task-web and https://github.com/RUBi-ZA/MODE-TASK/tree/mdm-task-web.

Crystal Structure of a Retroviral Polyprotein: Prototype Foamy Virus Protease-Reverse Transcriptase (PR-RT)

In most cases, proteolytic processing of the retroviral Pol portion of the Gag-Pol polyprotein precursor produces protease (PR), reverse transcriptase (RT), and integrase (IN). However, foamy viruses (FVs) express Pol separately from Gag and, when Pol is processed, only the IN domain is released. Here, we report a 2.9 Å resolution crystal structure of the mature PR-RT from prototype FV (PFV) that can carry out both proteolytic processing and reverse transcription but is in a configuration not competent for proteolytic or polymerase activity. PFV PR-RT is monomeric and the architecture of PFV PR is similar to one of the subunits of HIV-1 PR, which is a dimer. There is a C-terminal extension of PFV PR (101-145) that consists of two helices which are adjacent to the base of the RT palm subdomain, and anchors PR to RT. The polymerase domain of PFV RT consists of fingers, palm, thumb, and connection subdomains whose spatial arrangements are similar to the p51 subunit of HIV-1 RT. The RNase H and polymerase domains of PFV RT are connected by flexible linkers. Significant spatial and conformational (sub)domain rearrangements are therefore required for nucleic acid binding. The structure of PFV PR-RT provides insights into the conformational maturation of retroviral Pol polyproteins.

Determining the unbinding events and conserved motions associated with the pyrazinamide release due to resistance mutations of Mycobacterium tuberculosis pyrazinamidase

Pyrazinamide (PZA) is the only first-line antitubercular drug active against latent Mycobacterium tuberculosis (Mtb). It is activated to pyrazinoic acid by the pncA-encoded pyrazinamidase enzyme (PZase). Despite the emergence of PZA drug resistance, the underlying mechanisms of resistance remain unclear. This study investigated part of these mechanisms by modelling a PZA-bound wild type and 82 mutant PZase structures before applying molecular dynamics (MD) with an accurate Fe2+ cofactor coordination geometry. After observing nanosecond-scale PZA unbinding from several PZase mutants, an algorithm was developed to systematically detect ligand release via centre of mass distances (COM) and ligand average speed calculations, before applying the statistically guided network analysis (SGNA) method to investigate conserved protein motions associated with ligand unbinding. Ligand and cofactor perspectives were also investigated. A conserved pair of lid-destabilising motions was found. These consisted of (1) antiparallel lid and side flap motions; (2) the contractions of a flanking region within the same flap and residue 74 towards the core. Mutations affecting the hinge residues (H51 and H71), nearby residues or L19 were found to destabilise the lid. Additionally, other metal binding site (MBS) mutations delocalised the Fe2+ cofactor, also facilitating lid opening. In the early stages of unbinding, a wider variety of PZA poses were observed, suggesting multiple exit pathways. These findings provide insights into the late events preceding PZA unbinding, which we found to occur in some resistant PZase mutants. Further, the algorithm developed here to identify unbinding events coupled with SGNA can be applicable to other similar problems.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.