Multiscale modeling of bio-nano interactions of zero-valent silver nanoparticles.

Understanding the specifics of interaction between protein and nanomaterial is crucial for designing efficient, safe, and selective nanoplatforms, such as biosensor or nanocarrier systems. Routing experimental screening for the most suitable complementary pair of biomolecule and nanomaterial used in such nanoplatforms might be a resource-intensive task. While a variety of computational tools is available for pre-screening libraries of small drug molecules interacting with proteins, options for high-throughput screening of protein libraries for binding affinities to new and existing nanomaterials are limited. In the current work, we present the results of a systematic computational study of protein interaction with zero-valent silver nanoparticles using a multiscale approach. A variety of blood plasma and dietary proteins, namely, bovine and human serum albumins, bovine and human hemoglobin, papain, bromelain, lysozyme, and bovine lactoferrin, were examined. Selected combinations of nanomaterial and proteins can serve as a starting model for developing noble metal-based nanocarriers and biosensors. The computed binding (adsorption) characteristics for selected proteins were validated by experimental data reported in the literature. An advanced in silico nano-QSAR/QSPR interfacial descriptor 〖log⁡P〗^NM was also introduced to characterize the relative hydrophobicity/hydrophilicity of the nanomaterial.

Structured proteins are abundant in unevolved sequence space

Natural proteins represent numerous but tiny structure/function islands in a vast ocean of possible protein sequences, most of which has not been explored by either biological evolution or research. Recent studies have suggested this uncharted sequence space possesses surprisingly high structural propensity, but development of an understanding of this phenomenon has been awaiting a systematic high-throughput approach.Here, we designed, prepared, and characterized two combinatorial protein libraries consisting of randomized proteins, each 105 residues in length. The first library constructed proteins from the entire canonical alphabet of 20 amino acids. The second library used a subset of only 10 residues (A,S,D,G,L,I,P,T,E,V) that represent a consensus view of plausibly available amino acids through prebiotic chemistry. Our study shows that compact structure occurrence (i) is abundant (up to 40%) in random sequence space, (ii) is independent of general Hsp70 chaperone system activity, and (iii) is not granted solely by “late” and complex amino acid additions. The Hsp70 chaperone system effectively increases solubility and stability of the canonical alphabet but has only a minor impact on the “early” library. The early alphabet proteins are inherently more stable and soluble, possibly assisted by salts and cofactors in the cell-like environment in which these assays were performed.Our work indicates that natural protein space may have been selected to some extent by chance rather than unique structural characteristics.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

An enhanced yeast display platform demonstrates the binding plasticity under various selection pressures

Yeast surface display is popularin vitroevolution method. Here, we enhanced the method by multiple rounds of DNA and protein engineering, resulting in increased protein stabilities, surface expression, and enhanced fluorescence. The pCTcon2 yeast display vector was rebuild, introducing surface exposure tailored reporters – eUnaG2 and DnbALFA, creating a new platform of C and N terminal fusion vectors. In addition to gains in simplicity, speed, and cost, new applications were included to monitor protein surface exposure and protein retention in the secretion pathway. The enhanced methodologies were applied to investigatede-novoevolution of protein-protein interaction sites. Selecting binding from a mix of 6 protein-libraries towards two targets using high stringency selection led to the isolations of single high-affinity binders to each of the targets, without the need for high complexity libraries. Conversely, low-stringency selection resulted in the creation of many solutions for weak binding, demonstrating the plasticity of weakde-novointeractions.

CSF Levels of Elongation Factor Tu Is Associated With Increased Mortality in Malawian Adults With Streptococcus pneumoniae Meningitis

BackgroundMortality from bacterial meningitis, predominately caused by Streptococcus pneumoniae, exceeds 50% in sub-Saharan African countries with high HIV prevalence. Underlying causes of high mortality are poorly understood. We examined the host and pathogen proteome in the CSF of adults with proven pneumococcal meningitis (PM), testing if there was an association between differentially expressed proteins and outcome.Materials/MethodsCSF proteomes were analyzed by quantitative Mass-Spectrometry. Spectra were identified using the Swissprot human and TIGR4 pneumococcal protein libraries. Proteins were quantitated and analyzed against mortality. Unique proteins in PM were identified against published normal CSF proteome. Random-Forest models were used to test for protein signatures discriminating outcome. Proteins of interest were tested for their effects on growth and neutrophil opsonophagocytic killing of S. pneumoniae.ResultsCSF proteomes were available for 57 Adults with PM (median age 32 years, 60% male, 70% HIV-1 co-infected, mortality 63%). Three hundred sixty individual human and 23 pneumococcal proteins were identified. Of the human protein hits, 30% were not expressed in normal CSF, and these were strongly associated with inflammation and primarily related to neutrophil activity. No human protein signature predicted outcome. However, expression of the essential S. pneumoniae protein Elongation Factor Tu (EF-Tu) was significantly increased in CSF of non-survivors [False Discovery Rate (q) <0.001]. Expression of EF-Tu was negatively co-correlated against expression of Neutrophil defensin (r 0.4 p p < 0.002), but not against complement proteins C3 or Factor H. In vitro, addition of EF-Tu protein impaired S. pneumoniae neutrophil killing in CSF.ConclusionsExcessive S. pneumoniae EF-Tu protein in CSF was associated with reduced survival in meningitis in a high HIV prevalence population. We show EF-Tu may inhibit neutrophil mediated killing of S. pneumoniae in CSF. Further mechanistic work is required to better understand how S. pneumoniae avoids essential innate immune responses during PM through production of excess EF-Tu.

High-throughput discovery and characterization of human transcriptional effectors

SummaryThousands of proteins localize to the nucleus; however, it remains unclear which contain transcriptional effectors. Here, we develop HT-recruit - a pooled assay where protein libraries are recruited to a reporter, and their transcriptional effects are measured by sequencing. Using this approach, we measure gene silencing and activation for thousands of domains. We find a relationship between repressor function and evolutionary age for the KRAB domains, discover Homeodomain repressor strength is collinear with Hox genetic organization, and identify activities for several Domains of Unknown Function. Deep mutational scanning of the CRISPRi KRAB maps the co-repressor binding surface and identifies substitutions that improve stability/silencing. By tiling 238 proteins, we find repressors as short as 10 amino acids. Finally, we report new activator domains, including a divergent KRAB. Together, these results provide a resource of 600 human proteins containing effectors and demonstrate a scalable strategy for assigning functions to protein domains.

Antha-guided Automation of Darwin Assembly for the Construction of Bespoke Gene Libraries

Protein engineering through directed evolution facilitates the screening and characterization of protein libraries. Efficient and effective methods for multiple site-saturation mutagenesis, such as Darwin Assembly, can accelerate the sampling of relevant sequence space and the identification of variants with desired functionalities. Here, we present the automation of the Darwin Assembly method, using a Gilson PIPETMAX™ liquid handling platform under the control of the Antha software platform, which resulted in the accelerated construction of complex, multiplexed gene libraries with minimal hands-on time and error-free, while maintaining flexibility over experimental parameters through a graphical user interface rather than requiring user-driven library-dependent programming of the liquid handling platform. We also present an approach for barcoding libraries that overcomes amplicon length limitations in next generation sequencing and enables fast reconstruction of library reads.

A microplate screen to estimate metal-binding affinities of metalloproteins

Solute-binding proteins (SBPs) from ATP-binding cassette (ABC) transporters play crucial roles across all forms of life in transporting compounds against chemical gradients. Some SBPs have evolved to scavenge metal substrates from the environment with nanomolar and micromolar affinities (KD). There exist well established techniques like isothermal titration calorimetry for thoroughly studying these metalloprotein interactions with metal ions, but they are low-throughput. For protein libraries comprised of many metalloprotein homologues and mutants, and for collections of buffer conditions and potential ligands, the throughput of these techniques is paramount. In this study, we describe an improved method termed the microITFQ-LTA and validated it using CjNikZ, a well-characterized nickel-specific SBP (Ni-BP) from Campylobacter jejuni. We then demonstrated how the microITFQ-LTA can be designed to screen through a small collection of buffers and ligands to elucidate the binding profile of a putative Ni-BP from Clostridium carboxidivorans that we call CcSBPII. Through this study, we showed CcSBPII can bind to various metal ions with KD ranged over 3 orders of magnitude. In the presence of L-histidine, CcSBPII could bind to Ni2+ over 2000-fold more tightly, which was 11.6-fold tighter than CjNikZ given the same ligand.Highlightsan improved version of a high-throughput screen (microITFQ-LTA) is described for multiplexed elucidation of metalloprotein binding profilesvalidation was accomplished with the previously characterized CjNikZ; testing was accomplished with an uncharacterized homologue herein named CcSBPIICcSBPII is shown to bind to multiple transition metal ions with a large range of affinities, and potentially overcome mismetallation using a simple histidine metallophore

DeCoDe: degenerate codon design for complete protein-coding DNA libraries

MotivationHigh-throughput protein screening is a critical technique for dissecting and designing protein function. Libraries for these assays can be created through a number of means, including targeted or random mutagenesis of a template protein sequence or direct DNA synthesis. However, mutagenic library construction methods often yield vastly more non-functional than functional variants and, despite advances in large-scale DNA synthesis, individual synthesis of each desired DNA template is often prohibitively ex-pensive. Consequently, many protein screening libraries rely on the use of degenerate codons (DCs), mixtures of DNA bases incorporated at specific positions during DNA synthesis, to generate highly diverse protein variant pools from only a few low-cost synthesis reactions. However, selecting DCs for sets of sequences that covary at multiple positions dramatically increases the difficulty of designing a DC library and leads to the creation of many undesired variants that can quickly outstrip screening capacity.ResultsWe introduce a novel algorithm for total DC library optimization, DeCoDe, based on integer linear programming. DeCoDe significantly outperforms state-of-the-art DC optimization algorithms and scales well to more than a hundred proteins sharing complex patterns of covariation (e.g. the lab-derived avGFP lineage). Moreover, DeCoDe is, to our knowledge, the first DC design algorithm with the capability to encode mixed-length protein libraries. We anticipate DeCoDe to be broadly useful for a variety of library generation problems, ranging from protein engineering attempts that leverage mutual information to the reconstruction of ancestral protein states.Availabilitygithub.com/OrensteinLab/[email protected]

An improved platform for functional assessment of large protein libraries in mammalian cells

Multiplex genetic assays can simultaneously test thousands of genetic variants for a property of interest. However, limitations of existing multiplex assay methods in cultured mammalian cells hinder the breadth, speed and scale of these experiments. Here, we describe a series of improvements that greatly enhance the capabilities of a Bxb1 recombinase-based landing pad system for conducting different types of multiplex genetic assays in various mammalian cell lines. We incorporate the landing pad into a lentiviral vector, easing the process of generating new landing pad cell lines. We also develop several new landing pad versions, including one where the Bxb1 recombinase is expressed from the landing pad itself, improving recombination efficiency more than 2-fold and permitting rapid prototyping of transgenic constructs. Other versions incorporate positive and negative selection markers that enable drug-based enrichment of recombinant cells, enabling the use of larger libraries and reducing costs. A version with dual convergent promoters allows enrichment of recombinant cells independent of transgene expression, permitting the assessment of libraries of transgenes that perturb cell growth and survival. Lastly, we demonstrate these improvements by assessing the effects of a combinatorial library of oncogenes and tumor suppressors on cell growth. Collectively, these advancements make multiplex genetic assays in diverse cultured cell lines easier, cheaper and more effective, facilitating future studies probing how proteins impact cell function, using transgenic variant libraries tested individually or in combination.