Mutations Suppressing the Lack of Prepilin Peptidase Provide Insights Into the Maturation of the Major Pilin Protein in Cyanobacteria.

Type IV pili are bacterial surface-exposed filaments that are built up by small monomers called pilin proteins. Pilins are synthesized as longer precursors (prepilins), the N-terminal signal peptide of which must be removed by the processing protease PilD. A mutant of the cyanobacterium Synechocystis sp. PCC 6803 lacking the PilD protease is not capable of photoautotrophic growth because of the impaired function of Sec translocons. Here, we isolated phototrophic suppressor strains of the original ΔpilD mutant and, by sequencing their genomes, identified secondary mutations in the SigF sigma factor, the γ subunit of RNA polymerase, the signal peptide of major pilin PilA1, and in the pilA1-pilA2 intergenic region. Characterization of suppressor strains suggests that, rather than the total prepilin level in the cell, the presence of non-glycosylated PilA1 prepilin is specifically harmful. We propose that the restricted lateral mobility of the non-glycosylated PilA1 prepilin causes its accumulation in the translocon-rich membrane domains, which attenuates the synthesis of membrane proteins.

Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models.

We present a multistep protocol, combining Monte Carlo and molecular dynamics simulations, for the estimation of absolute binding free energies, one of the most significant challenges in computer-aided drug design. The protocol is based on an initial short enhanced Monte Carlo simulation, followed by clustering of the ligand positions, which serve to identify the most relevant states of the unbinding process. From these states, extensive molecular dynamics simulations are run to estimate an equilibrium probability distribution obtained with Markov State Models, which is subsequently used to estimate the binding free energy. We tested the procedure on two different protein systems, the Plasminogen kringle domain 1 and Urokinase, each with multiple ligands, for an aggregated molecular dynamics length of 760 µs. Our results indicate that the initial sampling of the unbinding events largely facilitates the convergence of the subsequent molecular dynamics exploration. Moreover, the protocol is capable to properly rank the set of ligands examined, albeit with a significant computational cost for the, more realistic, Urokinase complexes. Overall, this work demonstrates the usefulness of combining enhanced sampling methods with regular simulation techniques as a way to obtain more reliable binding affinity estimates.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Adaptive simulations, towards interactive protein-ligand modeling.

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.