Biochemistry, Biophysics, and Structural Biology Commons^™

From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …

Structural And Computational Studies Of The Sars-Cov-2 Spike Protein Binding Mechanisms With Nanobodies: From Structure And Dynamics To Avidity-Driven Nanobody Engineering, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Nanobodies provide important advantages over traditional antibodies, including their smaller size and robust biochemical properties such as high thermal stability, high solubility, and the ability to be bioengineered into novel multivalent, multi-specific, and high-affinity molecules, making them a class of emerging powerful therapies against SARS-CoV-2. Recent research efforts on the design, protein engineering, and structure-functional characterization of nanobodies and their binding with SARS-CoV-2 S proteins reflected a growing realization that nanobody combinations can exploit distinct binding epitopes and leverage the intrinsic plasticity of the conformational landscape for the SARS-CoV-2 S protein to produce efficient neutralizing and mutation resistant characteristics. Structural …

Allosteric Determinants Of The Sars-Cov-2 Spike Protein Binding With Nanobodies: Examining Mechanisms Of Mutational Escape And Sensitivity Of The Omicron Variant, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against the SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with single nanobodies Nb6, VHH E, and complex with VHH E/VHH V nanobody combination. We combined coarse-grained and all-atom molecular simulations and collective dynamics analysis with binding free energy scanning, perturbation-response scanning, and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric …

Ionophoric Polyphenols Are Permeable To The Blood Brain Barrier, Interact With Human Serum Albumin And Calf Thymus Dna, And Inhibit Ache Enzymatic Activity, Alberto Martinez, Mai Zahran, Miguel Gomez, Johnny Guevara, Rosemary Pichardo-Bueno, Junaid Asim, Gabriel Ortiz, Yaa Andoh, Sinji Shibutani, Baljit Kaur

Publications and Research

Alzheimer’s disease (AD) is the most common form of dementia that affects more than 40 million people around the world. The incidence is expected to rapidly increase due to the lack of any effective treatment. In previous work we synthesized a family of five ionophoric polyphenols (compounds 1–5) that targeted important aspects related to AD, such as the toxic aggregation of amyloid-β peptides, the production of reactive oxygen species, or the excessive presence of Cu²⁺ ions. Here, in order to gain insights into their potential therapeutic value, we have tested the ability of compounds 1– …

Network Approaches To Elucidate The Determinants Of Protein Topology And Stability, Zeinab Haratipour

Chemistry & Biochemistry Theses & Dissertations

Predicting three-dimensional structures of proteins from sequence information alone, remains one of the most profoundly challenging and intensely studied problems in basic science. It has uniquely garnered the interdisciplinary efforts of biologists, biochemists, computer scientists, mathematicians and physicists. The advancement of computational methods to study fundamental features of proteins also enables insights that are either difficult to explore experimentally or complimentary to further interpret experimental data. In the present research and through the combined development and application of molecular dynamics and network science approaches we aimed to elucidate the role of geographically important amino acids and evolutionarily conserved long-range interactions …

Improving The Thermal Stability Of Cellobiohydrolase Cel7a From Hypocrea Jecorina By Directed Evolution, Frits Goedegebuur, Lydia Dankmeyer, Peter Gualfetti, Saeid Karkehabadi, Henrik Hansson, Suvamay Jana, Vicky Huynh, Bradley R. Kelemen, Paulien Kruithof, Edmund A. Larenas, Pauline J. M. Teunissen, Jerry Ståhlberg, Christina M. Payne, Colin Mitchinson, Mats Sandgren

Chemical and Materials Engineering Faculty Publications

Secreted mixtures of Hypocrea jecorina cellulases are able to efficiently degrade cellulosic biomass to fermentable sugars at large, commercially relevant scales. H. jecorina Cel7A, cellobiohydrolase I, from glycoside hydrolase family 7, is the workhorse enzyme of the process. However, the thermal stability of Cel7A limits its use to processes where temperatures are no higher than 50 °C. Enhanced thermal stability is desirable to enable the use of higher processing temperatures and to improve the economic feasibility of industrial biomass conversion. Here, we enhanced the thermal stability of Cel7A through directed evolution. Sites with increased thermal stability properties were combined, and …

Computational Modeling Of Rna-Small Molecule And Rna-Protein Interactions, Lu Chen

Dissertations & Theses (Open Access)

The past decade has witnessed an era of RNA biology; despite the considerable discoveries nowadays, challenges still remain when one aims to screen RNA-interacting small molecule or RNA-interacting protein. These challenges imply an immediate need for cost-efficient while predictive computational tools capable of generating insightful hypotheses to discover novel RNA-interacting small molecule or RNA-interacting protein. Thus, we implemented novel computational models in this dissertation to predict RNA-ligand interactions (Chapter 1) and RNA-protein interactions (Chapter 2).

Targeting RNA has not garnered comparable interest as protein, and is restricted by lack of computational tools for structure-based drug design. To test the potential …

Rescuing Acetylcholinesterase From Nerve Agent Inhibition: Protein Dynamics Driven Drug Discovery, Aiyana M. Emigh, Brian Bennion

STAR Program Research Presentations

Severe morbidity and mortality consequences result from irreversible inhibition of human acetylcholinesterase by organophosphates (OPs). Oxime-based reactivators are currently the only available treatments but lack efficacy in the central nervous system (CNS) where the most damage occurs. Computational docking and molecular dynamics (MD) simulations reveal complex structural barriers that may reduce oxime efficacy. These results may guide future drug designs of more effective countermeasures.