Anharmonic Conformational Analysis of Biomolecular Simulations

Anharmonicity in time-dependent conformational fluctuations is noted to be a key feature of functional dynamics of biomolecules. While anharmonic events are rare, long timescale ($\mu s - ms$ and beyond) simulations facilitate probing of such events. However, automated analysis and visualization of anharmonic events from these long timescale simulations is proving to be a significant bottleneck. Traditional analysis tools for biomolecular simulations have focused on spatial second order statistics. Previous work involved resolving \emph{higher order spatial correlations} through quasi-anharmonic analysis (QAA). In this thesis, we extend this analysis to spatio-temporal domain in the form of anharmonic conformational analysis (ANCA). We demonstrate ANCA on a publicly available millisecond long trajectory data of the protein Bovine pancreatic trypsin inhibitor (BPTI) using cartesian coordinates of the individual atoms selected for analysis. To overcome the limitation of finding a good reference structure through trajectory alignment, we propose ANCA in the dihedral space to make use of the internal angles derived from the backbone of a fluctuating biomolecule. We test this dihedral angle extension of ANCA on a microsecond long simulation of Drew-Dickerson Dodecamer B-DNA data. Our results indicate that ANCA provides a biophysically meaningful organizational framework for long timescale biomolecular simulations. We have additionally built a scalable Python package for ANCA, namely pyANCA, with modules that can: (1) measure for anharmonicity in the form of higher order statistics and show its variation as a function of time, (2) output a story board representation of the simulations to identify key anharmonic conformational events, and (3) identify putative anharmonic conformational substates and visualize transitions between these substates. ANCA is available as an open-source Python package under the BSD 3-Clause license. Python tutorial notebooks, documentation and examples can be downloaded from http://csb.pitt.edu/anca

Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s

Integrating AI-based applications in anatomic pathology workflows

Traditional pathological diagnosis is considered as the gold standard by clinicians. However, this manual practice can be inefficient, error-prone, and highly subjective. To mitigate these issues, digital pathology is gaining traction which has attracted researchers to build black-box AI-based approaches intended to assist anatomic pathology workflows. The success of such approaches is dependent on large-scale generation of pathologist annotated high quality training data which is a serious bottleneck in computational pathology. Additionally, the AI systems must be interpretable and minimize the time-to-decision to achieve clinical adoption and possibly facilitate regulatory agency approvals. We hypothesize that building computational models of already established anatomic pathology knowledge will alleviate the training data generation bottleneck and develop clinically interpretable models. In addition, implementing computational pathology workflows on the emerging customizable computing AI-based architectures will satisfy high-throughput and minimal time-to-decision requirements. In this thesis, we tested our hypothesis on differential diagnoses of breast biopsies. We invoke analytical models to provide a quantitative assessment of the structural changes in the breast tissue along a diagnostic continuum triggered by atypia and other malignancies. We further combine the analytical models with a prototype-driven learning strategy to provide interpretability and achieve a superior classification performance in diagnosing breast biopsies over the state-of-the-art methods. To showcase the potential for seamless integration of our computational pathology framework into clinical workflows, we use a next generation high performance AI-based computing architecture to detect histological structures in breast tissue and classify them as high-risk vs low-risk. A key contribution of our framework is in building a communication platform for pathologists and computational scientists to interact and develop AI-based applications and to enhance patient care in a clinical setting