Proteins, the machinery of life, perform a vast array of essential biochemical functions, evolving over time to acquire diverse roles within biological systems. This evolution, primarily driven by mutations within protein sequences, can profoundly impact protein function, potentially leading to various diseases. This thesis aims to dissect the intricate mechanisms through which genetic mutations influence protein functionality, focusing on the dynamic alterations induced by single and combined mutations. Employing a suite of computational tools, including molecular dynamics (MD) simulations and proven analysis metrics like the Dynamic Flexibility Index (DFI) and Dynamic Coupling Index (DCI), I analyze protein dynamics to uncover the common dynamic effects associated with disease causation and compensatory mechanisms. This analysis extends to exploring the concept of epistasis through the lens of protein dynamics, showing how combinations of mutations interact within the protein's 3D structure to either exacerbate or mitigate the functional impacts of individual mutations. The use of EpiScore, a computational tool designed to quantify the epistatic effects of mutations, provides insight on the combined dynamic effects two mutations might have. This is particularly evident in the analysis of rare alleles within human populations, where certain allele combinations, despite their individual rarity, frequently co-occur, suggesting a mechanism of dynamic compensation. This phenomenon is further investigated in the context of the SARS-CoV-2 spike protein, providing insights into viral evolution and the adaptive significance of specific mutations. Additionally, I delve into the role of Intrinsically Disordered Regions (IDRs) in protein function and mutation compensation, highlighting the need for sophisticated dynamics analysis tools to capture the full spectrum of mutation effects. By integrating these analyses, this thesis unveils a complex picture of how proteins' dynamic properties, shaped by mutations, underpin their functional evolution and disease outcomes.

Natures hardworking machines, proteins, are dynamic beings. Comprehending the role of dynamics in mediating allosteric effects is paramount to unraveling the intricate mechanisms underlying protein function and devising effective protein design strategies. Thus, the essential objective of this thesis is to elucidate ways to use protein dynamics based tools integrated with evolution and docking techniques to investigate the effect of distal allosteric mutations on protein function and further rationally design proteins. To this end, I first employed molecular dynamics (MD) simulations, Dynamic Flexibility Index (DFI) and Dynamic Coupling Index (DCI) on PICK1 PDZ, Butyrylcholinesterase (BChE), and Dihydrofolate reductase (DHFR) to uncover how these proteins utilize allostery to tune activity. Moreover, a new classification technique (“Controller”/“Controlled”) based on asymmetry in dynamic coupling is developed and applied to DHFR to elucidate the effect of allosteric mutations on enzyme activity. Subsequently, an MD driven dynamics design approach is applied on TEM-1 β-lactamase to tailor its activity against β-lactam antibiotics. New variants were created, and using a novel analytical approach called "dynamic distance analysis" (DDA) the degree of dynamic similarity between these variants were quantified. The experimentally confirmed results of these studies showed that the implementation of MD driven dynamics design holds significant potential for generating variants that can effectively modulate activity and stability. Finally, I introduced an evolutionary guided molecular dynamics driven protein design approach, integrated co-evolution and dynamic coupling (ICDC), to identify distal residues that modulate binding site dynamics through allosteric mechanisms. After validating the accuracy of ICDC with a complete mutational data set of β-lactamase, I applied it to Cyanovirin-N (CV-N) to identify allosteric positions and mutations that can modulate binding affinity. To further investigate the impact of mutations on the identified allosteric sites, I subjected putative mutants to binding analysis using Adaptive BP-Dock. Experimental validation of the computational predictions demonstrated the efficacy of integrating MD, DFI, DCI, and evolution to guide protein design. Ultimately, the research presented in this thesis demonstrates the effectiveness of using evolutionary guided molecular dynamics driven design alongside protein dynamics based tools to examine the significance of allosteric interactions and their influence on protein function.

Transportation of material across a cell membrane is a vital process for maintaininghomeostasis. Na+/H+ antiporters, for instance, help maintain cell volume and regulate intracellular sodium and proton concentrations. They are prime drug targets, since dysfunction of these crucial proteins in humans is linked to heart and neurodegenerative diseases. Due to their placement in a cell membrane, their study is particularly difficult compared to globular proteins, which is likely the reason the transport mechanisms for these proteins are not entirely known. This work focuses on the electrogenic bacterial homologs Thermus thermophilus NapA (TtNapA) and Echerichia coli NhaA (EcNhaA), each transporting one sodium from the interior of the cell for two protons on outside of the cell. Even though X-ray crystal structures for both of these systems have been resolved, their study through molecular dynamics (MD) simulations is limited. The dynamic protonation and deprotonation of the binding site residues is a fundamental process in the transport cycle, which currently cannot be explored intuitively with standard MD methodologies. Apart from this limitation, simulation performance is only a fraction of what is needed to understand the full transport process, particularly when it comes to global conformational changes. This work seeks to overcome these limitations through the development and application of a multiscale thermodynamic and kinetic framework for constructing models capable of predicting experimental observables, such as the dependence of transporter turnover on membrane voltage. These models allow interpretation of the effects of individual processes on the function as a whole. This procedure is demonstrated for TtNapA and the connection between structure and function is shown by computing cycle turnover across a range of non-equilibrium conditions.

Molecular docking serves as an important tool in modeling protein-ligand interactions. Most of the docking approaches treat the protein receptor as rigid and move the ligand in the binding pocket through an energy minimization, which is an incorrect approach as proteins are flexible and undergo conformational changes upon ligand binding. However, modeling receptor backbone flexibility in docking is challenging and computationally expensive due to the large conformational space that needs to be sampled.

A novel flexible docking approach called BP-Dock (Backbone Perturbation docking) was developed to overcome this challenge. BP-Dock integrates both backbone and side chain conformational changes of a protein through a multi-scale approach. In BP-Dock, the residues along a protein chain are perturbed mimicking the binding induced event, with a small Brownian kick, one at a time. The fluctuation response profile of the chain upon these perturbations is computed by Perturbation Response Scanning (PRS) to generate multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, this approach was applied to a large and diverse dataset of unbound structures as receptors. Furthermore, the protein-peptide docking of PICK1-PDZ proteins was investigated. This study elucidates the determinants of PICK1-PDZ binding that plays crucial roles in numerous neurodegenerative disorders. BP-Dock approach was also extended to the challenging problem of protein-glycan docking and applied to analyze the energetics of glycan recognition in Cyanovirin-N (CVN), a cyanobacterial lectin that inhibits HIV by binding to its highly glycosylated envelope protein gp120. This study provide the energetic contribution of the individual residues lining the binding pocket of CVN and explore the effect of structural flexibility in the hinge region of CVN on glycan binding, which are also verified experimentally. Overall, these successful applications of BP-Dock highlight the importance of modeling backbone flexibility in docking that can have important implications in defining the binding properties of protein-ligand interactions.

Finally, an induced fit docking approach called Adaptive BP-Dock is presented that allows both protein and ligand conformational sampling during the docking. Adaptive BP-Dock can provide a faster and efficient docking approach for the virtual screening of novel targets for rational drug design and aid our understanding of protein-ligand interactions.