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This thesis presents two papers addressing important biochemical prediction challenges. The first paper focuses on accurate protein distance predictions and introduces updates to the ProSPr network. We evaluate its performance in the Critical Assessment of techniques for Protein Structure Prediction (CASP14) competition, investigating its accuracy dependence on sequence length and multiple sequence alignment depth. The ProSPr network, an ensemble of three convolutional neural networks (CNNs), demonstrates superior performance compared to individual networks. The second paper addresses the issue of accurate ligand ranking in virtual screening for drug discovery. We propose MILCDock, a machine learning consensus docking tool that leverages predictions from five traditional molecular docking tools. MILCDock, an ensemble of eight neural networks, outperforms single-network approaches and other consensus docking methods on the DUD-E dataset. However, we find that LIT-PCBA targets remain challenging for all methods tested. Furthermore, we explore the effectiveness of training machine learning tools on the biased DUD-E dataset, emphasizing the importance of mitigating its biases during training. Collectively, this work emphasizes the power of ensembling in deep learning-based biochemical prediction problems, highlighting improved performance through the combination of multiple models. Our findings contribute to the development of robust protein distance prediction tools and more accurate virtual screening methods for drug discovery.

The type VII secretion system is a protein export pathway linked to diverse phenotypes in both Actinobacteria and Firmicutes. The Actinobacterial subtype of the T7SS, referred to as T7SSa, has been shown to play a critical role in various aspects of Mycobacterial life including virulence, conjugation, and metal homeostasis. The T7SSb of Firmicutes bacteria on the other hand has similarly been shown to influence virulence but by the direct growth inhibition of competitor bacteria. Structure-function analyses of the T7SSa apparatus as well as various effectors and chaperones have begun to build a more mechanistic understanding of how T7SSa functions. In contrast, we know little of how the T7SSb functions despite its noted importance to both pathogens and environmental bacteria such as Bacillus, Staphylococcus, Enterococcus, and Streptococcus. During my thesis work, I have addressed several gaps in our understanding of T7SSb function. The three major questions that I have studied are: (1) how do T7SSb immunity proteins inhibit the toxicity of their cognate toxins, (2) how does the T7SSb export effectors through the thick Gram-positive cell wall, and (3) what is the role of chaperone proteins in facilitating T7SSb effector export? / Thesis / Doctor of Philosophy (PhD) / Bacteria require space and various nutrients to survive and grow and must therefore compete against other bacteria for access to these resources. To gain advantage over their competitors, many bacteria have developed molecular weapons that target and kill other closely related bacteria. Some of these weapons take the form of protein secretion machines that export antibacterial toxins. Gram-positive bacteria use the type VIIb secretion system (T7SSb) to inhibit the growth of other Gram-positive bacteria. In this work, I explore several aspects of T7SSb including: (1) how toxins are inhibited by immunity proteins, (2) how toxins are secreted through the cell envelope, and (3) how toxins are recognized by the secretion apparatus. The goal of this work is to better understand how T7SSb functions at the molecular level.

The random-coil model has been dominant for unfolded proteins since the 1950’s; however, some experiments showed that the unfolded proteins were biased toward specific conformations in conflict with the random-coil model. Recently, residual dipolar couplings (RDCs) and paramagnetic relaxation enhancement (PRE) were applied to obtain a large amount of structural information on unfolded proteins. Typically, these data were interpreted in a framework of random-coil ensembles with a good agreement between experimental data and theoretical predictions. In this thesis, it was tested whether locally organized nonrandom ensembles could describe this agreement equally as well. Using a complete set of RDC and PRE data for denatured ubiquitin, it was revealed that there was no distinguishable difference between random-coil ensembles and ensembles containing 50% native structure. Thus, while it is important to measure as many RDCs or PRE as possible, even the best datasets may be insensitive to local organization in unfolded proteins.

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