Accelerating Cryptic Pocket Discovery Using AlphaFold

Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold's training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.

The nepenthesin insert in the Plasmodium falciparum aspartic protease plasmepsin V is necessary for enzyme function

Plasmepsin V (PM V) is a pepsin-like aspartic protease essential for growth of the malarial parasite Plasmodium falciparum. Previous work has shown PM V to be an endoplasmic reticulum-resident protease that processes parasite proteins destined for export into the host cell. Depletion or inhibition of the enzyme is lethal during asexual replication within red blood cells as well as during the formation of sexual stage gametocytes. The structure of the Plasmodium vivax PM V has been characterized by X-ray crystallography, revealing a canonical pepsin fold punctuated by structural features uncommon to secretory aspartic proteases; however, the function of this unique structure is unclear. Here, we used parasite genetics to probe these structural features by attempting to rescue lethal PM V depletion with various mutant enzymes. We found an unusual nepenthesin 1-type insert in the PM V gene to be essential for parasite growth and PM V activity. Mutagenesis of the nepenthesin insert suggests that both its amino acid sequence and one of the two disulfide bonds that undergird its structure are required for the insert's role in PM V function. Furthermore, molecular dynamics simulations paired with Markov state modeling suggest that mutations to the nepenthesin insert may allosterically affect PM V catalysis through multiple mechanisms. Taken together, these data provide further insights into the structure of the P. falciparum PM V protease.

Flap Dynamics in Pepsin-Like Aspartic Proteases: A Computational Perspective Using Plasmepsin-II and BACE-1 as Model Systems

The flexibility of β hairpin structure known as the flap plays a key role in catalytic activity and substrate intake in pepsin-like aspartic proteases. Most of these enzymes share structural and sequential similarity. In this study, we have used apo Plm-II and BACE-1 as model systems. In the apo form of the proteases, a conserved tyrosine residue in the flap region remains in a dynamic equilibrium between the normal and flipped states through rotation of the χ₁ and χ₂ angles. Independent MD simulations of Plm-II and BACE-1 remained stuck either in the normal or flipped state. Metadynamics simulations using side-chain torsion angles (χ₁ and χ₂ of tyrosine) as collective variables sampled the transition between the normal and flipped states. Qualitatively, the two states were predicted to be equally populated. The normal and flipped states were stabilized by H-bond interactions to a tryptophan residue and to the catalytic aspartate, respectively. Further, mutation of tyrosine to an amino-acid with smaller side-chain, such as alanine, reduced the flexibility of the flap and resulted in a flap collapse (flap loses flexibility and remains stuck in a particular state). This is in accordance with previous experimental studies, which showed that mutation to alanine resulted in loss of activity in pepsin-like aspartic proteases. Our results suggest that the ring flipping associated with the tyrosine side-chain is the key order parameter that governs flap dynamics and opening of the binding pocket in most pepsin-like aspartic proteases.

Collective variable discovery in the age of machine learning: reality, hype and everything in between

Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations.

Data driven collective variable discovery methods to capture conformational dynamics in biological macromolecules.

Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments

Pepsin-like aspartic proteases (PAPs) are a class of aspartic proteases which shares tremendous structural similarity with human pepsin. One of the key structural features of PAPs is the presence of a β-hairpin motif otherwise known as flap. The biological function of the PAPs is highly dependent on the conformational dynamics of the flap region. In apo PAPs, the conformational dynamics of the flap is dominated by the rotational degrees of freedom associated with χ1 and χ2 angles of conserved Tyr (or Phe in some cases). However it is plausible that dihedral order parameters associated with several other residues might play crucial roles in the conformational dynamics of apo PAPs. Due to their size, complexities associated with conformational dynamics and clinical significance (drug targets for malaria, Alzheimer's disease etc.), PAPs provide a challenging testing ground for computational and experimental methods focusing on understanding conformational dynamics and molecular recognition in biomolecules. The opening of the flap region is necessary to accommodate substrate/ligand in the active site of the PAPs. The BIG challenge is to gain atomistic details into how reversible ligand binding/unbinding (molecular recognition) affects the conformational dynamics. Recent reports of kinetics (K i, K d) and thermodynamic parameters (ΔH, TΔS, and ΔG) associated with macro-cyclic ligands bound to BACE1 (belongs to PAP family) provide a perfect challenge (how to deal with big ligands with multiple torsional angles and select optimum order parameters to study reversible ligand binding/unbinding) for computational methods to predict binding free energies and kinetics beyond typical test systems e.g. benzamide-trypsin. In this work, i reviewed several order parameters which were proposed to capture the conformational dynamics and molecular recognition in PAPs. I further highlighted how machine learning methods can be used as order parameters in the context of PAPs. I then proposed some open ideas and challenges in the context of molecular simulation and put forward my case on how biophysical experiments e.g. NMR, time-resolved FRET etc. can be used in conjunction with biomolecular simulation to gain complete atomistic insights into the conformational dynamics of PAPs.

Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling

Advanced molecular docking methods often aim at capturing the flexibility of the protein upon binding to the ligand. In this study, we investigate whether instead a simple rigid docking method can be applied, if combined with multiple target structures to model the backbone flexibility and molecular dynamics simulations to model the sidechain and ligand flexibility. The methods are tested for the binding of 35 ligands to FXR as part of the first stage of the Drug Design Data Resource (D3R) Grand Challenge 2 blind challenge. The results show that the multiple-target docking protocol performs surprisingly well, with correct poses found for 21 of the ligands. MD simulations started on the docked structures are remarkably stable, but show almost no tendency of refining the structure closer to the experimentally found binding pose. Reconnaissance metadynamics enhances the exploration of new binding poses, but additional collective variables involving the protein are needed to exploit the full potential of the method.

Resolving the problem of trapped water in binding cavities: prediction of host-guest binding free energies in the SAMPL5 challenge by funnel metadynamics

The funnel metadynamics method enables rigorous calculation of the potential of mean force along an arbitrary binding path and thereby evaluation of the absolute binding free energy. A problem of such physical paths is that the mechanism characterizing the binding process is not always obvious. In particular, it might involve reorganization of the solvent in the binding site, which is not easily captured with a few geometrically defined collective variables that can be used for biasing. In this paper, we propose and test a simple method to resolve this trapped-water problem by dividing the process into an artificial host-desolvation step and an actual binding step. We show that, under certain circumstances, the contribution from the desolvation step can be calculated without introducing further statistical errors. We apply the method to the problem of predicting host–guest binding free energies in the SAMPL5 blind challenge, using two octa-acid hosts and six guest molecules. For one of the hosts, well-converged results are obtained and the prediction of relative binding free energies is the best among all the SAMPL5 submissions. For the other host, which has a narrower binding pocket, the statistical uncertainties are slightly higher; longer simulations would therefore be needed to obtain conclusive results.

Flap Dynamics in Aspartic Proteases: A Computational Perspective

Recent advances in biochemistry and drug design have placed proteases as one of the critical target groups for developing novel small-molecule inhibitors. Among all proteases, aspartic proteases have gained significant attention due to their role in HIV/AIDS, malaria, Alzheimer's disease, etc. The binding cleft is covered by one or two β-hairpins (flaps) which need to be opened before a ligand can bind. After binding, the flaps close to retain the ligand in the active site. Development of computational tools has improved our understanding of flap dynamics and its role in ligand recognition. In the past decade, several computational approaches, for example molecular dynamics (MD) simulations, coarse-grained simulations, replica-exchange molecular dynamics (REMD) and metadynamics, have been used to understand flap dynamics and conformational motions associated with flap movements. This review is intended to summarize the computational progress towards understanding the flap dynamics of proteases and to be a reference for future studies in this field.

Insights on the structural perturbations in human MTHFR Ala222Val mutant by protein modeling and molecular dynamics

Methylenetetrahydrofolate reductase (MTHFR) protein catalyzes the only biochemical reaction which produces methyltetrahydrofolate, the active form of folic acid essential for several molecular functions. The Ala222Val polymorphism of human MTHFR encodes a thermolabile protein associated with increased risk of neural tube defects and cardiovascular disease. Experimental studies have shown that the mutation does not affect the kinetic properties of MTHFR, but inactivates the protein by increasing flavin adenine dinucleotide (FAD) loss. The lack of completely solved crystal structure of MTHFR is an impediment in understanding the structural perturbations caused by the Ala222Val mutation; computational modeling provides a suitable alternative. The three-dimensional structure of human MTHFR protein was obtained through homology modeling, by taking the MTHFR structures from Escherichia coli and Thermus thermophilus as templates. Subsequently, the modeled structure was docked with FAD using Glide, which revealed a very good binding affinity, authenticated by a Glide XP score of -10.3983 (kcal mol(-1)). The MTHFR was mutated by changing Alanine 222 to Valine. The wild-type MTHFR-FAD complex and the Ala222Val mutant MTHFR-FAD complex were subjected to molecular dynamics simulation over 50 ns period. The average difference in backbone root mean square deviation (RMSD) between wild and mutant variant was found to be ~.11 Å. The greater degree of fluctuations in the mutant protein translates to increased conformational stability as a result of mutation. The FAD-binding ability of the mutant MTHFR was also found to be significantly lowered as a result of decreased protein grip caused by increased conformational flexibility. The study provides insights into the Ala222Val mutation of human MTHFR that induces major conformational changes in the tertiary structure, causing a significant reduction in the FAD-binding affinity.

Effect of T68A/N126Y mutations on the conformational and ligand binding landscape of Coxsackievirus B3 3C protease

3C protease of Coxsackievirus B3 (CVB3) plays an essential role in the viral replication cycle, and therefore, emerged as an attractive therapeutic target for the treatment of human diseases caused by CVB3 infection. In this study, we report the first account of the molecular impact of the T68A/N126Y double mutant (Mutant(Bound)) using an integrated computational approach. Molecular dynamics simulation and post-dynamics binding free energy, principal component analysis (PCA), hydrogen bond occupancy, SASA, R(g) and RMSF confirm that T68A/N126Y instigated an increased conformational flexibility due to the loss of intra- and inter-molecular hydrogen bond interactions and other prominent binding forces, which led to a decreased protease grip on the ligand (3CPI). The double mutations triggered a distortion orientation of 3CPI in the active site and decreases the binding energy, ΔG(bind) (∼3 kcal mol(-1)), compared to the wild type (Wild(Bound)). The van der Waals and electrostatic energy contributions coming from residues 68 and 126 are lower for Mutant(Bound) when compared with Wild(Bound). In addition, variation in the overall enzyme motion as evident from the PCA, distorted hydrogen bonding network and loss of protein-ligand interactions resulted in a loss of inhibitor efficiency. The comprehensive molecular insight gained from this study should be of great importance in understanding the drug resistance against CVB3 3C protease; also, it will assist in the designing of novel Coxsackievirus B3 inhibitors with high ligand efficacy on resistant strains.

Heat-shock protein 90 (Hsp90) as anticancer target for drug discovery: an ample computational perspective

There are over 100 different types of cancer, and each is classified based on the type of cell that is initially affected. If left untreated, cancer can result in serious health problems and eventually death. Recently, the paradigm of cancer chemotherapy has evolved to use a combination approach, which involves the use of multiple drugs each of which targets an individual protein. Inhibition of heat-shock protein 90 (Hsp90) is one of the novel key cancer targets. Because of its ability to target several signaling pathways, Hsp90 inhibition emerged as a useful strategy to treat a wide variety of cancers. Molecular modeling approaches and methodologies have become 'close counterparts' to experiments in drug design and discovery workflows. A wide range of molecular modeling approaches have been developed, each of which has different objectives and outcomes. In this review, we provide an up-to-date systematic overview on the different computational models implemented toward the design of Hsp90 inhibitors as anticancer agents. Although this is the main emphasis of this review, different topics such as background and current statistics of cancer, different anticancer targets including Hsp90, and the structure and function of Hsp90 from an experimental perspective, for example, X-ray and NMR, are also addressed in this report. To the best of our knowledge, this review is the first account, which comprehensively outlines various molecular modeling efforts directed toward identification of anticancer drugs targeting Hsp90. We believe that the information, methods, and perspectives highlighted in this report would assist researchers in the discovery of potential anticancer agents.

Chikungunya virus (CHIKV) inhibitors from natural sources: a medicinal chemistry perspective

Abstract

Chikungunya virus (CHIKV) is one of the re-emerging “neglected” tropical diseases whose recent outbreak affected not only Africa and South-East Asia but also several parts of America and Europe. To date, despite its serious nature, no antivirals or vaccines were developed in order to counter this resurgent infectious disease. The recent advancement in crystallography and availability of crystal structures of certain domains of CHIKV initiates the development of anti-CHIKV agents using structure-based drug design or synthetic medicinal chemistry approach. Despite the fact that almost 50 % of the new chemical entities against several biological targets were either obtained from natural products or natural product analogues, a very humble effort was directed towards identification of novel CHIKV inhibitors from natural products. In this review, besides a brief overview on CHIKV as well as the nature as a source of medicines, we highlight the current progress and future steps towards the discovery of CHIKV inhibitors from natural products. This report could pave the road towards the design of novel semi-synthetic derivatives with enhanced anti-viral activities.

Graphical Abstract

Multi-drug resistance profile of PR20 HIV-1 protease is attributed to distorted conformational and drug binding landscape: molecular dynamics insights

The PR20 HIV-1 protease, a variant with 20 mutations, exhibits high levels of multi-drug resistance; however, to date, there has been no report detailing the impact of these 20 mutations on the conformational and drug binding landscape at a molecular level. In this report, we demonstrate the first account of a comprehensive study designed to elaborate on the impact of these mutations on the dynamic features as well as drug binding and resistance profile, using extensive molecular dynamics analyses. Comparative MD simulations for the wild-type and PR20 HIV proteases, starting from bound and unbound conformations in each case, were performed. Results showed that the apo conformation of the PR20 variant of the HIV protease displayed a tendency to remain in the open conformation for a longer period of time when compared to the wild type. This led to a phenomena in which the inhibitor seated at the active site of PR20 tends to diffuse away from the binding site leading to a significant change in inhibitor-protein association. Calculating the per-residue fluctuation (RMSF) and radius of gyration, further validated these findings. MM/GBSA showed that the occurrence of 20 mutations led to a drop in the calculated binding free energies (ΔGbind) by ~25.17 kcal/mol and ~5 kcal/mol for p2-NC, a natural peptide substrate, and darunavir, respectively, when compared to wild type. Furthermore, the residue interaction network showed a diminished inter-residue hydrogen bond network and changes in inter-residue connections as a result of these mutations. The increased conformational flexibility in PR20 as a result of loss of intra- and inter-molecular hydrogen bond interactions and other prominent binding forces led to a loss of protease grip on ligand. It is interesting to note that the difference in conformational flexibility between PR20 and WT conformations was much higher in the case of substrate-bound conformation as compared to DRV. Thus, developing analogues of DRV by retaining its key pharmacophore features will be the way forward in the search for novel protease inhibitors against multi-drug resistant strains.

An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of M184V mutation on HIV reverse transcriptase resistance to lamivudine

The emergence of different drug resistant strains of HIV-1 reverse transcriptase (HIV RT) remains of prime interest in relation to viral pathogenesis as well as drug development. Amongst those mutations, M184V was found to cause a complete loss of ligand fitness. In this study, we report the first account of the molecular impact of M184V mutation on HIV RT resistance to 3TC (lamivudine) using an integrated computational approach. This involved molecular dynamics simulation, binding free energy analysis, principle component analysis (PCA) and residue interaction networks (RINs). Results clearly confirmed that M184V mutation leads to steric conflict between 3TC and the beta branched side chain of valine, decreases the ligand (3TC) binding affinity by ∼7 kcal mol(-1) when compared to the wild type, changes the overall conformational landscape of the protein and distorts the native enzyme residue-residue interaction network. The comprehensive molecular insight gained from this study should be of great importance in understanding drug resistance against HIV RT as well as assisting in the design of novel reverse transcriptase inhibitors with high ligand efficacy on resistant strains.

Reaching beyond HIV/HCV: nelfinavir as a potential starting point for broad-spectrum protease inhibitors against dengue and chikungunya virus

Re-purposing HIV/HCV inhibitors against DENV and CHIKV using computer aided drug design.

Flap dynamics of plasmepsin proteases: insight into proposed parameters and molecular dynamics

Herein, for the first time, we report the flap opening and closing in Plasmepsin proteases – plasmepsin II (PlmII) was used as a prototype model.