In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science ...and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.
Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, ...kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning - especially deep learning - to molecular simulation. These techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.
State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data. In ...molecular dynamics simulations, these data-driven collective variables capture the slowest modes of the dynamics and are useful for enhanced sampling and free energy estimation. In this work, we employ SRV coordinates as a feature set for Markov state model (MSM) construction. Compared to the current state-of-the-art, MSMs constructed from SRV coordinates are more robust to the choice of input features, exhibit faster implied time scale convergence, and permit the use of shorter lagtimes to construct higher kinetic resolution models. We apply this methodology to study the folding kinetics and conformational landscape of the Trp-cage miniprotein. Folding and unfolding mean first passage times are in good agreement with the prior literature, and a nine macrostate model is presented. The unfolded ensemble comprises a central kinetic hub with interconversions to several metastable unfolded conformations and which serves as the gateway to the folded ensemble. The folded ensemble comprises the native state, a partially unfolded intermediate “loop” state, and a previously unreported short-lived intermediate that we were able to resolve due to the high time resolution of the SRV-MSM. We propose SRVs as an excellent candidate for integration into modern MSM construction pipelines.
Accelerating growth and global expansion of antimicrobial resistance has deepened the need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear advantages over conventional ...antibiotics which include slower emergence of resistance, broad-spectrum antibiofilm activity, and the ability to favourably modulate the host immune response. Broad bacterial susceptibility to antimicrobial peptides offers an additional tool to expand knowledge about the evolution of antimicrobial resistance. Structural and functional limitations, combined with a stricter regulatory environment, have hampered the clinical translation of antimicrobial peptides as potential therapeutic agents. Existing computational and experimental tools attempt to ease the preclinical and clinical development of antimicrobial peptides as novel therapeutics. This Review identifies the benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens, highlights advances in the deployment of novel promising antimicrobial peptides, and underlines the needs and priorities in designing focused development strategies taking into account the most advanced tools available.
Recent work has shown that polymeric catalysts can mimic some of the remarkable features of metalloenzymes by binding substrates in proximity to a bound metal center. We report here an unexpected ...role for the polymer: multivalent, reversible, and adaptive binding to protein surfaces allowing for accelerated catalytic modification of proteins. The catalysts studied are a group of copper-containing single-chain polymeric nanoparticles (CuI–SCNP) that exhibit enzyme-like catalysis of the copper-mediated azide–alkyne cycloaddition reaction. The CuI–SCNP use a previously observed “uptake mode”, binding small-molecule alkynes and azides inside a water-soluble amphiphilic polymer and proximal to copper catalytic sites, but with unprecedented rates. Remarkably, a combined experimental and computational study shows that the same CuI–SCNP perform a more efficient click reaction on modified protein surfaces and cell surface glycans than do small-molecule catalysts. The catalysis occurs through an “attach mode” where the SCNPs reversibly bind protein surfaces through multiple hydrophobic and electrostatic contacts. The results more broadly point to a wider capability for polymeric catalysts as artificial metalloenzymes, especially as it relates to bioapplications.
Full integration of green chemistry into the undergraduate curriculum is a necessity to prepare our students for a sustainable future. We discuss the reasons for the need to change the curriculum, ...the institutions in North America, Europe, and Asia that are leading the way towards integration with classroom resources, and the published textbooks that are currently available for both classroom and laboratory. We plead for more time for hard‐pressed college professors to revamp the curriculum, and for these efforts to be valued. We feel compelled by the urgency of this need to implore the chemistry education community to participate in these efforts now.
Give your chemistry classroom a green overhaul with resources from the American Chemical Society Green Chemistry Institute, Beyond Benign, and the Center for Green Chemistry & Green Engineering at Yale. Many green chemistry online resources, journals, and textbooks for classroom and laboratory are available. Urgent adoption of these resources is needed.
Multidrug-resistant, Gram-negative infection is a major global determinant of morbidity, mortality and cost of care. The advent of nanomedicine has enabled tailored engineering of macromolecular ...constructs, permitting increasingly selective targeting, alteration of volume of distribution and activity/toxicity. Macromolecules tend to passively and preferentially accumulate at sites of enhanced vascular permeability and are then retained. This enhanced permeability and retention (EPR) effect, whilst recognized as a major breakthrough in anti-tumoral targeting, has not yet been fully exploited in infection. Shared pathophysiological pathways in both cancer and infection are evident and a number of novel nanomedicines have shown promise in selective, passive, size-mediated targeting to infection. This review describes the similarities and parallels in pathophysiological pathways at molecular, cellular and circulatory levels between inflammation/infection and cancer therapy, where use of this principle has been established.
Application of 3D printed structures via stereolithography (SLA) is limited by imprecise dimensional control and inferior mechanical properties. These challenges is attributed to poor understanding ...of polymerization behavior during the printing process and inadequate post‐processing methods. The former via a modified version of Jacob's working curve equation that incorporates the resin's sub‐linear response to irradiation intensity is addressed by the authors. This new model provides a more accurate approach to select 3D printing parameters given a desired z‐resolution and conversion profile along the depth of the printed part. The authors use this improved model to motivate a novel material design that can be post‐processed to be indistinguishable from the polymer at 100% conversion. This approach employs a dual initiating system in which photo‐initiated printing is followed by a thermal post‐cure to achieve uniform conversion. The authors show that this approach enables fast printing times (10 s per layer), exceptional horizontal resolution (1–10 microns), precise control over vertical resolution, and decreased surface corrugations on a 10's of microns scale. The techniques described herein use an acrylate‐based SLA resin, but the approach can be extended to other monomer systems to simultaneously achieve predictable properties and dimensions that are critical for application of additive manufacturing in load‐bearing applications.
An acrylate based dual‐cure 3D‐printing resin is developed to increase the fidelity and mechanical properties of printed structures. The resin applies a dual initiating system in which photo‐initiated printing is followed by a thermal post‐cure to achieve uniform conversion. The resin design is motivated by a model that provides a more accurate approach to select 3D printing parameters given a desired z‐resolution.
The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public ...health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional “needles” in the vast “haystack” of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.
Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to ...predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = -0.74, p = 3.6×10-6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = -0.83, p = 3.7×10-12). Performance of the Potts model (r = -0.73, p = 9.7×10-9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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