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1. Active Learning of the Conformational Ensemble of Proteins Using Maximum Entropy VAMPNets

   https://doi.org/10.1021/acs.jctc.3c00040  

   Kleiman, Diego E. ; Shukla, Diwakar ( April 2023 , Journal of Chemical Theory and Computation)

   Rapid computational exploration of the free energy landscape of biological molecules remains an active area of research due to the difficulty of sampling rare state transitions in molecular dynamics (MD) simulations. In recent years, an increasing number of studies have exploited machine learning (ML) models to enhance and analyze MD simulations. Notably, unsupervised models that extract kinetic information from a set of parallel trajectories have been proposed including the variational approach for Markov processes (VAMP), VAMPNets, and time-lagged variational autoencoders (TVAE). In this work, we propose a combination of adaptive sampling with active learning of kinetic models to accelerate the discovery of the conformational landscape of biomolecules. In particular, we introduce and compare several techniques that combine kinetic models with two adaptive sampling regimes (least counts and multiagent reinforcement learning- based adaptive sampling) to enhance the exploration of conformational ensembles without introducing biasing forces. Moreover, inspired by the active learning approach of uncertainty-based sampling, we also present MaxEnt VAMPNet. This technique consists of restarting simulations from the microstates that maximize the Shannon entropy of a VAMPNet trained to perform the soft discretization of metastable states. By running simulations on two test systems, the WLALL pentapeptide and the villin headpiece subdomain, we empirically demonstrate that MaxEnt VAMPNet results in faster exploration of conformational landscapes compared with the baseline and other proposed methods. 

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2. Conformational dynamics and interfacial interactions of peptide-appended pillar[5]arene water channels in biomimetic membranes

   https://doi.org/10.1039/C9CP04408F  

   Liu, Yong ; Vashisth, Harish ( January 2019 , Physical Chemistry Chemical Physics)

   Peptide appended pillar[5]arene (PAP) is an artificial water channel resembling biological water channel proteins, which has shown a significant potential for designing bioinspired water purification systems. Given that PAP channels need to be incorporated at a high density in membrane matrices, it is critical to examine the role of channel–channel and channel–membrane interactions in governing the structural and functional characteristics of channels. To resolve the atomic-scale details of these interactions, we have carried out atomistic molecular dynamics (MD) simulations of multiple PAP channels inserted in a lipid or a block-copolymer (BCP) membrane matrix. Classical MD simulations on a sub-microsecond timescale showed clustering of channels only in the lipid membrane, but enhanced sampling MD simulations showed thermodynamically-favorable dimerized states of channels in both lipid and BCP membranes. The dimerized configurations of channels, with an extensive buried surface area, were stabilized via interactions between the aromatic groups in the peptide arms of neighboring channels. The conformational metrics characterizing the orientational and structural changes in channels revealed a higher flexibility in the lipid membrane as opposed to the BCP membrane although hydrogen bonds between the channel and the membrane molecules were not a major contributor to the stability of channels in the BCP membrane. We also found that the channels undergo wetting/dewetting transitions in both lipid and BCP membranes with a marginally higher probability of undergoing a dewetting transition in the BCP membrane. Collectively, these results highlight the role of channel dynamics in governing channel–channel and channel–membrane interfacial interactions, and provide atomic-scale insights needed to design stable and functional biomimetic membranes for efficient separations. 

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3. A generalized deep learning approach for local structure identification in molecular simulations

   https://doi.org/10.1039/c9sc02097g  

   DeFever, Ryan S. ; Targonski, Colin ; Hall, Steven W. ; Smith, Melissa C. ; Sarupria, Sapna ( January 2019 , Chemical Science)

   Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop ( e.g. , ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor ( e.g. , crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e. , atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations. 

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4. Practical guide to replica exchange transition interface sampling and forward flux sampling

   https://doi.org/10.1063/5.0080053  

   Hall, Steven W. ; Díaz Leines, Grisell ; Sarupria, Sapna ; Rogal, Jutta ( May 2022 , The Journal of Chemical Physics)

   Path sampling approaches have become invaluable tools to explore the mechanisms and dynamics of the so-called rare events that are characterized by transitions between metastable states separated by sizable free energy barriers. Their practical application, in particular to ever more complex molecular systems, is, however, not entirely trivial. Focusing on replica exchange transition interface sampling (RETIS) and forward flux sampling (FFS), we discuss a range of analysis tools that can be used to assess the quality and convergence of such simulations, which is crucial to obtain reliable results. The basic ideas of a step-wise evaluation are exemplified for the study of nucleation in several systems with different complexities, providing a general guide for the critical assessment of RETIS and FFS simulations.

    

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5. Physics‐based protein structure refinement in the era of artificial intelligence

   https://doi.org/10.1002/prot.26161  

   Heo, Lim ; Janson, Giacomo ; Feig, Michael ( June 2021 , Proteins: Structure, Function, and Bioinformatics)

   Protein structure refinement is the last step in protein structure prediction pipelines. Physics‐based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine‐learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine‐learning, a detailed analysis via MD simulations and Markov state modeling is presented here. We continue to find that MD‐based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter‐domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics‐based refinement could continue to play a role in the future for improving initial predictions based on machine learning‐based methods.

    

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