The present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i) covalent interactions in biomolecular systems; (ii) the mathematical framework of covalent molecular docking; (iii) implementation of covalent docking protocol in drug design workflows; (iv) applications covalent docking: case studies and (v) shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.

Herein, for the first time, we report the flap opening and closing in Plasmepsin proteases - plasmepsin II (PlmII) was used as a prototype model. We proposed different combined parameters to define the asymmetric flap motion; the distance, d1, between the flap tip residues (Val78 and Leu292); the dihedral angle, ϕ; in addition to TriCα angles Val78-Asp34-Leu292, θ1, and Val78-Asp214-Leu292, θ2. Only three combined parameters, the distance, d1, the dihedral angle, ϕ, and the TriCα angle, θ1, were found to appropriately define the observed "twisting' motion during the flap opening and closing. The coordinated motions of the proline-rich loop adjacent to the binding cavity rim appeared to exert steric hindrance on the flap residues, driving the flap away from the active site cavity. This loop may also have increased movements around the catalytic dyad residue, Asp214, making TriCα, θ2, unreliable in describing the flap motion. The full flap opening at d1, 23.6 Å, corresponded to the largest TriCα angle, θ1, at 78.6° on a ∼46 ns time scale. Overall the average θ1 and θ2 for the bound was ∼46° and ∼53°, respectively, compared to ∼50° and ∼59° for the Apo PlmII, indicating a drastic increase in TriCα as the active site cavity opens. Similar trends in the distance, d1, and the dihedral angle, ϕ, were observed during the simulation. The asymmetrical opening of the binding cavity was best described by the large shift in ϕ from -33.91° to +21.00° corresponding to the partial opening of the flap in the range of 22-31 ns. Though, the dihedral angle described the twisting of the flap, the extent of flap opening can appropriately be defined by combining d1 and θ1. The results presented here, on the combined parameters, will certainly augment current efforts in designing potent structure-based inhibitors against plasmepsins.

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.

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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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9948-6) contains supplementary material, which is available to authorized users.

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

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.

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.