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\u27s 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\u27s 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

Molecular Recognition and Conformational Dynamics in Macromolecules

Computational methods gained a widespread use in drug discovery. Understanding conformational dynamics of protein and mechanisms of protein-ligand binding are two major areas in drug discovery. Molecular dynamics (MD) simulation have been routinely used to study conformational dynamics of protein and mechanisms of protein-ligand binding. In classical MD simulation, the system often remains stuck in a local free energy minimum for a long time. Hence, conformational changes associated with long timescales (e.g. loop motion, ligand binding/unbinding etc.) are beyond reach of classical MD simulation. Metadynamics is an enhanced sampling method which deposits bias along some chosen reaction coordinate and forces the system to escape local minimum thus, allows better sampling of the conformational space. In this thesis, I have used MD and metadynamics to study protein-ligand binding andconformational dynamics of globular proteins. We found that the presence of trapped water in the binding site of the protein plays a key role ligand binding. Further, we found that the side-chains of binding site residues and flexibility ofligands play a key role in the protein-ligand binding. We also studied how rotation of tyrosine dictates conformational dynamics in a class of protein known as pepsin-like aspartic protease. We found that apo protease remains in adynamic equilibrium between normal and flipped states due to rotation of tyrosine side-chain. Conformational dynamics also plays a crucial role in hydrogen exchange via solvent penetration. Local fluctuations in protein breaks the hydrogen bond interactions involving backbone amides which allows solvent penetration. We defined this metastable state as broken state. In the broken state, the backbone amide forms hydrogen bond interaction with water molecule. Using molecular dynamics and metadynamics we predicted free energy difference between the broken and ground state (backbone amide remains hydrogen bonded with neighboring residue) in a small globular protein

AlphaFold-SFA

AlphaFold-SFA: accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamic

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 withχ1 andχ2 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 diseaseetc.), 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 (Ki,Kd) 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 systemse.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 experimentse.g.NMR, time-resolved FRETetc.can be used in conjunction with biomolecular simulation to gain complete atomistic insights into the conformational dynamics of PAPs

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 (MutantBound) using an integrated computational approach. Molecular dynamics simulation and post-dynamics binding free energy, principal component analysis (PCA), hydrogen bond occupancy, SASA, Rg 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 (). The double mutations triggered a distortion orientation of in the active site and decreases the binding energy, ΔGbind (∼3 kcal mol(-1)), compared to the wild type (WildBound). The van der Waals and electrostatic energy contributions coming from residues 68 and 126 are lower for MutantBound when compared with WildBound. 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

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

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

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