Structural and binding insights into HIV-1 protease and P2-ligand interactions through molecular dynamics simulations, binding free energy and principal component analysis

Substitution Models of Protein Evolution with Selection on Enzymatic Activity

Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme-substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Exploring the mechanism of C473D mutation on CDC25B causing weak binding affinity with CDK2/CyclinA by molecular dynamics study

CDC25B belongs to the CDC25 family, and it plays an important part in regulating the activity of CDK/CyclinA. Studies have shown that CDC25B is closely related to cancer development. When CYS473 on CDC25B is mutated into ASP, the affinity between CDC25B and CDK2/CyclinA weakens, and their dissociation speed is greatly improved. However, the mechanism by which the CDC25BC473D mutant weakens its binding to CDK2/CyclinA is unclear. In order to study the effect of CDC25BC473D mutants on CDK2/CyclinA substrates, we constructed and verified the rationality of the CDC25BWT:CDK2/CyclinA system and CDC25BC473D:CDK2/CyclinA system and conducted molecular dynamics (MD) simulation analysis. In the post-analysis, the fluctuations of residues ARG488-SER499, LYS541-TRP550 on CDC25B and residues ASP206-ASP210 on CDK2 were massive in the mutant CDC25BC473D:CDK2/CyclinA system. And the interactions between residue ARG492 and residue GLU208, residue ARG544 and residue GLU42, residue ARG544 and TRP550 were weakened in the mutant CDC25BC473D:CDK2/CyclinA system. The results showed that when CYS473 on CDC25B was mutated into ASP473, the mutant CDC25BC473D:CDK2/CyclinA system was less stable than the wild-type CDC25BWT:CDK2/CyclinA system. Finally, active site CYS473 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25BCYS473 and CDK2 in the CDC25BC473D:CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately provided a useful understanding of the weak interactions between CDC25BCYS473D and CDK2/CyclinA.Communicated by Ramaswamy H. Sarma.

Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations

Mutations V32I, I50V and I84V in the HIV-1 protease (PR) induce drug resistance towards drug amprenavir (APV). Multiple short molecular dynamics (MSMD) simulations and molecular mechanics generalized Born surface area (MM-GBSA) method were utilized to investigate drug-resistant mechanism of V32I, I50V and I84V towards APV. Dynamic information arising from MSMD simulations suggest that V32I, I50V and I84V highly affect structural flexibility, motion modes and conformational behaviours of two flaps in the PR. Binding free energies calculated by MM-GBSA method suggest that the decrease in binding enthalpy and the increase in binding entropy induced by mutations V32I, I50V and I84V are responsible for drug resistance of the mutated PRs on APV. The energetic contributions of separate residues on binding of APV to the PR show that V32I, I50V and I84V highly disturb the interactions of two flaps with APV and mostly drive the decrease in binding ability of APV to the PR. Thus, the conformational changes of two flaps in the PR caused by V32I, I50V and I84V play key roles in drug resistance of three mutated PR towards APV. This study can provide useful dynamics information for the design of potent inhibitors relieving drug resistance.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison

Understanding non-covalent biomolecular recognition, which includes drug-protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand-protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand-protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand-protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand-protein recognition mechanisms and drug discovery.

Molecular dynamics study of CDC25BR492L mutant causing the activity decrease of CDC25B

Cell division cycle 25B (CDC25B) was responsible for regulating the various stages of cell division in the cell cycle. R492L was one of the common types of CDC25B mutants. Researches showed that compared to CDC25B^WT, CDC25B^R492L mutant had a ∼100-fold reduction in the rate constant for forming phosphatase intermediate (k₂). However, the molecular basis of how the CDC25B^R492L mutant influenced the process of binding between CDC25B and CDK2/CyclinA was not yet known. Therefore, the optimizations of three-dimensional structure of the CDC25B^WT-CDK2/CyclinA system and the CDC25B^R492L-CDK2/CyclinA system were constructed by ZDOCK and RDOCK, and five methods were employed to verify the reasonability of the docking structure. Then the molecular dynamics simulations on the two systems were performed to explore the reason why CDC25B^R492L mutant caused the weak interactions between CDC25B^R492L and CDK2/CyclinA, respectively. The remote docking site (Arg488-Tyr497) and the second active site (Lys538-Arg544) of CDC25B were observed to have high fluctuations in the CDC25B^R492L-CDK2/CyclinA system with post-analysis, where the high fluctuation of these two regions resulted in weak interactions between CD25B and CDK2. In addition, Asp38-Glu42 and Asp206-Asp210 of CDK2 showed the slightly descending fluctuation, and CDK2 revealed an enhanced the self-interaction, which made CDK2 keep a relatively stable state in the CDC25B^R492L-CDK2/CyclinA system. Finally, Leu492 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25B^R492L and CDK2 in the CDC25B^R492L-CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately offered a helpful understanding of the weak interactions between CDC25B^R492L and CDK2.

Binding Insight of Anti-HIV Phytocompounds with Prime Targets of HIV: A Molecular Dynamics Simulation Analysis

BACKGROUND

Despite intense efforts, AIDS is difficult to tackle by current anti-retroviral therapy (ART) due to its side effects; therefore, there is an urgent need to discover potential, multitarget and low-cost anti-HIV compounds.

OBJECTIVE

We have shown that few phytocompounds can potentially inhibit the prime targets of HIV namely GP120 envelope protein, reverse transcriptase, protease, integrase and ribonulcease. In this study, top ranked prioritized compounds were subjected to Molecular Dynamics (MD) simulation in order to study the conformational dynamics and integrity of crucial interaction in the receptor sites.

METHODS

The system was built for selected protein-ligand complex using TIP3P water model and OPLS_2005 force field. Trajectories were recorded up to 20 ns simulation time in Desmond module of Schrödinger software.

RESULTS

As a result of a comprehensive analysis of molecular properties and dynamics of the complexes, it has been concluded that Chebulic acid, Curcumin and Mulberroside C could be developed as envelope glycoprotein GP120 inhibitor, reverse transcriptase inhibitor and protease inhibitor respectively. However, the fluctuation of Chebulic acid with respect to integrase and ribonuclease protein was higher during the simulation.

CONCLUSION

These findings can aid in the designing of the structural properties for more effective anti-HIV compounds against the given targets.

Acquired HIV-1 Protease Conformational Flexibility Associated with Lopinavir Failure May Shape the Outcome of Darunavir Therapy after Antiretroviral Therapy Switch

Understanding the underlying molecular interaction during a therapy switch from lopinavir (LPV) to darunavir (DRV) is essential to achieve long-term virological suppression. We investigated the kinetic and structural characteristics of multidrug-resistant South African HIV-1 subtype C protease (HIV-1 PR) during therapy switch from LPV to DRV using enzyme activity and inhibition assay, fluorescence spectroscopy, and molecular dynamic simulation. The HIV-1 protease variants were from clinical isolates with a combination of drug resistance mutations; MUT-1 (M46I, I54V, V82A, and L10F), MUT-2 (M46I, I54V, L76V, V82A, L10F, and L33F), and MUT-3 (M46I, I54V, L76V, V82A, L90M, and F53L). Enzyme kinetics analysis shows an association between increased relative resistance to LPV and DRV with the progressive decrease in the mutant HIV-1 PR variants' catalytic efficiency. A direct relationship between high-level resistance to LPV and intermediate resistance to DRV with intrinsic changes in the three-dimensional structure of the mutant HIV-1 PR as a function of the multidrug-resistance mutation was observed. In silico analysis attributed these structural adjustments to the multidrug-resistance mutations affecting the LPV and DRV binding landscape. Though DRV showed superiority to LPV, as a lower concentration was needed to inhibit the HIV-1 PR variants, the inherent structural changes resulting from mutations selected during LPV therapy may dynamically shape the DRV treatment outcome after the therapy switch.

Effective estimation of the inhibitor affinity of HIV-1 protease via a modified LIE approach

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses. Computer-aided drug design (CADD) would enhance the discovery of new therapies, through which the estimation of ligand-binding affinity is critical to predict the most efficient inhibitor. A time-consuming binding free energy method would reduce the usefulness of CADD. The modified linear interaction energy (LIE) approach emerges as an appropriate protocol that performs this task. In particular, the polar interaction free energy, which is obtained via numerically resolving the linear Poisson–Boltzmann equation, plays as an important role in driving the binding mechanism of the HIV-1 PR + inhibitor complex. The electrostatic interaction energy contributes to the attraction between two molecules, but the vdW interaction acts as a repulsive factor between the ligand and the HIV-1 PR. Moreover, the ligands were found to adopt a very strong hydrophobic interaction with the HIV-1 PR. Furthermore, the results obtained corroborate the high accuracy and precision of computational studies with a large correlation coefficient value R = 0.83 and a small RMSE δ_RMSE = 1.25 kcal mol⁻¹. This method is less time-consuming than the other end-point methods, such as the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) and free energy perturbation (FEP) approaches. Overall, the modified LIE approach would provide ligand-binding affinity with HIV-1 PR accurately, precisely, and rapidly, resulting in a more efficient design of new inhibitors.

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses.

Exploration of the effects of sequence variations between HIV-1 and HIV-2 proteases on their three-dimensional structures

HIV protease inhibitors (PIs) approved by the FDA (US Food and Drug Administration) are a major class of antiretroviral. HIV-2 protease (PR2) is naturally resistant to most of them as PIs were designed for HIV-1 protease (PR1). In this study, we explored the impact of amino-acid substitutions between PR1 and PR2 on the structure of protease (PR) by comparing the structural variability of 13 regions using 24 PR1 and PR2 structures complexed with diverse ligands. Our analyses confirmed structural rigidity of the catalytic region and highlighted the important role of three regions in the conservation of the catalytic region conformation. Surprisingly, we showed that the flap region, corresponding to a flexible region, exhibits similar conformations in PR1 and PR2. Furthermore, we identified regions exhibiting different conformations in PR1 and PR2, which could be explained by the intrinsic flexibility of these regions, by crystal packing, or by PR1 and PR2 substitutions. Some substitutions induce structural changes in the R2 and R4 regions that could have an impact on the properties of PI-binding site and could thus modify PI binding mode. Substitutions involved in structural changes in the elbow region could alter the flexibility of the PR2 flap regions relative to PR1, and thus play a role in the transition from the semi-open form to the closed form, and have an impact on ligand binding. These results improve the understanding of the impact of sequence variations between PR1 and PR2 on the natural resistance of HIV-2 to commercially available PIs.Communicated by Ramaswamy H. Sarma.