Allosteric Communication Pathways and Thermal Rectification in PDZ-2 Protein: A Computational Study

Allosteric communication in proteins is a central and yet unsolved problem of structural biochemistry. Previous findings, from computational biology (Ota and Agard, 2005), have proposed that heat diffuses in a protein through cognate protein allosteric pathways. This work studied heat diffusion in the well-known PDZ-2 protein, and confirmed that this protein has two cognate allosteric pathways and that heat flows preferentially through these. Also, a new property was also observed for protein structures - heat diffuses asymmetrically through the structures. The underling structure of this asymmetrical heat flow was a normal length hydrogen bond (~2.85 {\AA}) that acted as a thermal rectifier. In contrast, thermal rectification was compromised in short hydrogen bonds (~2.60 {\AA}), giving rise to symmetrical thermal diffusion. Asymmetrical heat diffusion was due, on a higher scale, to the local, structural organization of residues that, in turn, was also mediated by hydrogen bonds. This asymmetrical/symmetrical energy flow may be relevant for allosteric signal communication directionality in proteins and for the control of heat flow in materials science.Comment: 29 pages, 8 Figures. All Results Unchanged. Changed Title. Improved Grammar. Added references. Corrected typos. Elimination of the "Knocking" argument for Asp5-Lys91 Interaction in Results and in Discussion section

Structure and dynamics of the MAGUK core of PSD-95

Protein allostery plays central roles in regulation of enzyme catalysis, signaling conduction and cellular metabolism. In this research, the interdomain allostery of postsynaptic density protein 95 (PSD-95), a key component of the postsynapse, was studied using NMR and other biophysical methods. Previous research has identified numerous PSD-95 interaction partners and revealed many PSD-95 mediated biological functions. Interestingly, interdomain allostery within PSD-95 has been found between PDZ3 and the following SH3/GK, and these allosteric events are regulated by phosphorylation. However, the structural mechanism of interdomain allostery and phosphorylation regulation is not addressed by in vivo studies on biological functions or in vitro studies on excised domains. In this dissertation, we studied the structural and dynamic effects of phosphorylation at the PDZ3/SH3 linker (Y397, S415 and S418). Upon phosphorylation, we found that the C-extension α-helix of PDZ3 is unfolded and undocked. We further examined the PDZ3-SH3 construct and showed that phosphorylation interrupts the domain interaction between PDZ3 and SH3. Using chemical shift perturbation and paramagnetic relaxation enhancement, we identified the PDZ3-SH3 interface. These experiments also suggested that CRIPT binding moves PDZ3 away from SH3. To understand interdomain allostery, we modeled the PDZ3-SH3-GK structure using SAXS. Consistent with PRE results, we found PDZ3 is mainly docked to the Hook domain region of the SH3 domain, and CRIPT binding reshuffles the domain packing between PDZ3 and SH3-GK. To obtain a high resolution structural model of PDZ3-SH3-GK, we carried out Rosetta simulations in the presence of PRE and chemical shift perturbation constraints. We found that the PDZ3 domain uses its peptide binding groove to interact with the PDZ3-SH3 linker. This interaction brings PDZ3 close to SH3, whereas it can be disrupted by CRIPT binding. This Rosetta model also reveals that the positively charged face of the Hook domain, which is the binding interface for calmodulin, is masked by the PDZ3 domain. Therefore the model provides a basis for understanding interdomain allostery between SH3 and PDZ3. In this research we also discussed the possible mechanism by which PDZ3 and GK allostery is transferred.Doctor of Philosoph

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the “second secret of life.” The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets

Bridging Between Protein Dynamics and Evolution Through Simulations and Machine Learning Approaches

Antibiotics resistance posed a serious threat to the public health and caused huge economic cost. β-Lactamases, which are enzymes produced by bacteria to hydrolyze β-lactam based antibiotics, are one of the driving forces behind antibiotic resistance. To explore the antibiotic resistance effect, understanding the mechanistic and dynamical features of β-lactamases through their interactions with antibiotics is critical. In my doctoral research, I applied both molecular dynamic (MD) simulations and machine learning approaches to explore these crucial interactions. Vancomycin is a typical glycopeptide antibiotic, which inhibits the bacterial cell wall through binding with peptidoglycan (PG). The key interactions of vancomycin and cell wall structure are identified by the conformational distributions of vancomycin and its three derivatives with PG complexes. TEM-1 is a serine-based β-lactamase and can hydrolyze the benzyl penicillin antibiotic. The key residues on TEM-1 are identified by random forest classification models. Moreover, the dynamical motions of four antibiotic resistance related proteins TEM-1, TOHO-1, PBP-A and DD-transpeptidase with a benzyl penicillin are analyzed and compared to explore their evolutionary correlation. I also investigated the petroleum thermal cracking mechanism through quantum chemistry calculations, and provided a quantitative and insightful understanding of thermal cracking processes

Approaches for studying allostery using network theory

Allostery is the process whereby binding of a substrate at a site other than the active site modulates the function of a protein. Allostery is thus one of the myriad of biological processes that keeps cells under tight regulatory control, specifically one that acts at the level of the protein rather than through changes in gene transciption or translation of mRNA. Despite over 50 years of investigation, allostery has remained a difficult phenomenon to elucidate. Structural changes are often too subtle for many experimental methods to capture and it has become increasingly obvious that a range of timescales are involved, from extremely fast pico- to nanosecond local fluctuations all the way up to the millisecond or even second timescales over which the biological effects of allostery are observed. As a result, computational methods have arisen to become a powerful means of studying allostery, aided greatly by the staggering increases in computational power over the last 70 years. A field that has experienced a surge in interest over the last 20 years or so is \emph{network theory}, perhaps stimulated by the development of the internet and the Web, two examples of immensely important networks in our everyday life. One of the reasons for the popularity of networks in modelling is their comparative simplicity: a network consists of \emph{nodes}, representing a set of objects in a system, and \emph{edges}, that capture the relations between them. In this thesis, we both apply existing ideas and methods from network theory and develop new computational network methods to study allostery in proteins. We attempt to tackle this problem in three distinct ways, each representing a protein using a different form of a network. Our initial work follows on logically from previous work in the group, representing proteins as \emph{graphs} where atoms are nodes and bonds are energy weighted edges. In effect we disregard the 3-dimensional structure of the protein and instead focus on how the bond \emph{connectivity} can be used to explain potential long range communication between allosteric and active sites in a multimeric protein. We then focus on a class of protein models known as \emph{elastic network models}, in which our edges now correspond to mechanical Hooke springs between either atoms or residues, in order to attempt to understand the physical, mechanistic basis of allostery.Open Acces

Accurate prediction of the dynamical changes within the second PDZ domain of PTP1e.

Experimental NMR relaxation studies have shown that peptide binding induces dynamical changes at the side-chain level throughout the second PDZ domain of PTP1e, identifying as such the collection of residues involved in long-range communication. Even though different computational approaches have identified subsets of residues that were qualitatively comparable, no quantitative analysis of the accuracy of these predictions was thus far determined. Here, we show that our information theoretical method produces quantitatively better results with respect to the experimental data than some of these earlier methods. Moreover, it provides a global network perspective on the effect experienced by the different residues involved in the process. We also show that these predictions are consistent within both the human and mouse variants of this domain. Together, these results improve the understanding of intra-protein communication and allostery in PDZ domains, underlining at the same time the necessity of producing similar data sets for further validation of thses kinds of methods.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Accurate prediction of peptide-induced dynamical changes within the second PDZ domain of PTP1e

Accepted for a highlight presentation of the published paper.Accepted for a highlight presentation of the published paper.info:eu-repo/semantics/publishe

Accurate prediction of peptide-induced dynamical changes within the second PDZ domain of PTP1e

info:eu-repo/semantics/publishe

Accurate prediction of peptide-induced dynamical changes within the second PDZ domain of PTP1e

info:eu-repo/semantics/publishe

Accurate prediction of peptide-induced dynamical changes within the second PDZ domain of PTP1e.

info:eu-repo/semantics/publishe