Identification of HIV protease mutants with improved specificities toward an Alzheimer's Disease associated peptide sequence

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.Includes bibliographical references (leaves 119-121).We have developed the first protease specificity engineering technology intended to serve as a general-purpose source of target-specific protease mutants with potential therapeutic applications. We used this E. coli-based screening system to isolate multiple HIV protease mutants with improved proteolytic specificities and activities toward an octapeptide taken from the beta-amyloid peptide (Abeta) implicated in Alzheimer's Disease. Quantitative Western blotting of E. coli extracts revealed approximately 20- and 30-fold increases in proteolytic specificity and activity, respectively, toward the Abeta octapeptide relative to a native octameric substrate of the wild type HIV protease. Our HIV protease specificity engineering system requires no expensive reagents or equipment and can be conveniently adopted by any laboratory. The system employs the E. coli betagalactosidase protein as a reporter for protease activity. The employment of Pgal as a reporter gene allows facile blue/white E. coli colony screening of mutant protease libraries. Additionally, mutant protease specificity profiles are readily assessed via E. coli liquid culture Pgal activity assays.(cont.) Mutant protease library screening stringency is conveniently controlled by the amount of arabinose added to blue/white screening plates. We have identified Alzheimer's Disease, Parkinson's Disease, HIV and Hepatitis C as maladies that may be treated with target-specific proteases obtained by using our system. We are eager to observe the future pursuit of both these and additional biomedical applications for target-specific proteases.by Pete Heinzelman.Ph.D

SCHEMA Recombination of a Fungal Cellulase Uncovers a Single Mutation That Contributes Markedly to Stability

A quantitative linear model accurately (R^2 = 0.88) describes the thermostabilities of 54 characterized members of a family of fungal cellobiohydrolase class II (CBH II) cellulase chimeras made by SCHEMA recombination of three fungal enzymes, demonstrating that the contributions of SCHEMA sequence blocks to stability are predominantly additive. Thirty-one of 31 predicted thermostable CBH II chimeras have thermal inactivation temperatures higher than the most thermostable parent CBH II, from Humicola insolens, and the model predicts that hundreds more CBH II chimeras share this superior thermostability. Eight of eight thermostable chimeras assayed hydrolyze the solid cellulosic substrate Avicel at temperatures at least 5 °C above the most stable parent, and seven of these showed superior activity in 16-h Avicel hydrolysis assays. The sequence-stability model identified a single block of sequence that adds 8.5 °C to chimera thermostability. Mutating individual residues in this block identified the C313S substitution as responsible for the entire thermostabilizing effect. Introducing this mutation into the two recombination parent CBH IIs not featuring it (Hypocrea jecorina and H. insolens) decreased inactivation, increased maximum Avicel hydrolysis temperature, and improved long time hydrolysis performance. This mutation also stabilized and improved Avicel hydrolysis by Phanerochaete chrysosporium CBH II, which is only 55–56% identical to recombination parent CBH IIs. Furthermore, the C313S mutation increased total H. jecorina CBH II activity secreted by the Saccharomyces cerevisiae expression host more than 10-fold. Our results show that SCHEMA structure-guided recombination enables quantitative prediction of cellulase chimera thermostability and efficient identification of stabilizing mutations

Discovery of human ACE2 variants with altered recognition by the SARS-CoV-2 spike protein.

Understanding how human ACE2 genetic variants differ in their recognition by SARS-CoV-2 can facilitate the leveraging of ACE2 as an axis for treating and preventing COVID-19. In this work, we experimentally interrogate thousands of ACE2 mutants to identify over one hundred human single-nucleotide variants (SNVs) that are likely to have altered recognition by the virus, and make the complementary discovery that ACE2 residues distant from the spike interface influence the ACE2-spike interaction. These findings illuminate new links between ACE2 sequence and spike recognition, and could find substantial utility in further fundamental research that augments epidemiological analyses and clinical trial design in the contexts of both existing strains of SARS-CoV-2 and novel variants that may arise in the future

Nanoscale Extracellular Vesicle Analysis in Alzheimer’s Disease Diagnosis and Therapy

Diagnostic assays that leverage bloodborne neuron-derived (neuronal) nanoscale extracellular vesicles (nsEVs) as “windows into the brain” can predict incidence of Alzheimer’s Disease (AD) many years prior to onset. Beyond diagnostics, bloodborne neuronal nsEVs analysis may have substantial translational impact by revealing mechanisms of AD pathology; such knowledge could enlighten new drug targets and lead to new therapeutic approaches. The potential to establish three-dimensional nsEV analysis methods that characterize highly purified bloodborne nsEV populations in method of enrichment, cell type origin, and protein or RNA abundance dimensions could bring this promise to bear by yielding nsEV “omics” datasets that uncover new AD biomarkers and enable AD therapeutic development. In this review we provide a survey of both the current status of and new developments on the horizon in the field of neuronal nsEV analysis. This survey is supplemented by a discussion of the potential to translate such neuronal nsEV analyses to AD clinical diagnostic applications and drug development

Neural network extrapolation to distant regions of the protein fitness landscape

Abstract Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks’ capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models’ extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture’s inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust

Efficient screening of fungal cellobiohydrolase class I enzymes for thermostabilizing sequence blocks by SCHEMA structure-guided recombination

We describe an efficient SCHEMA recombination-based approach for screening homologous enzymes to identify stabilizing amino acid sequence blocks. This approach has been used to generate active, thermostable cellobiohydrolase class I (CBH I) enzymes from the 390 625 possible chimeras that can be made by swapping eight blocks from five fungal homologs. Constructing and characterizing the parent enzymes and just 32 ‘monomeras’ containing a single block from a homologous enzyme allowed stability contributions to be assigned to 36 of the 40 blocks from which the CBH I chimeras can be assembled. Sixteen of 16 predicted thermostable chimeras, with an average of 37 mutations relative to the closest parent, are more thermostable than the most stable parent CBH I, from the thermophilic fungus Talaromyces emersonii. Whereas none of the parent CBH Is were active >65°C, stable CBH I chimeras hydrolyzed solid cellulose at 70°C. In addition to providing a collection of diverse, thermostable CBH Is that can complement previously described stable CBH II chimeras (Heinzelman et al., Proc. Natl Acad. Sci. USA 2009;106:5610–5615) in formulating application-specific cellulase mixtures, the results show the utility of SCHEMA recombination for screening large swaths of natural enzyme sequence space for desirable amino acid blocks