There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.21256

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

https://www.advancedmedicine.ca/wp-content/uploads/2013/09/Nature-Chemical-Biology.pdf

Network pharmacology: the next paradigm in drug discovery.

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

Principles of docking: An overview of search algorithms and a guide to scoring functions

Allostery involves coupling of conformational changes between two widely separated binding sites. The common view holds that allosteric proteins are symmetric oligomers, with each subunit existing in "at least" two conformational states with a different affinity for ligands. Recent observations such as the allosteric behavior of myoglobin, a classical example of a nonallosteric protein, call into question the existing allosteric dogma. Here we argue that all (nonfibrous) proteins are potentially allosteric. Allostery is a consequence of re-distributions of protein conformational ensembles. In a nonallosteric protein, the binding site shape may not show a concerted second-site change and enzyme kinetics may not reflect an allosteric transition. Nevertheless, appropriate ligands, point mutations, or external conditions may facilitate a population shift, leading a presumably nonallosteric protein to behave allosterically. In principle, practically any potential drug binding to the protein surface can alter the conformational redistribution. The question is its effectiveness in the redistribution of the ensemble, affecting the protein binding sites and its function. Here, we review experimental observations validating this view of protein allostery.

Is allostery an intrinsic property of all dynamic proteins

Aβ(1–42) is the most pathogenic amyloid-β species in Alzheimer's disease (AD). The solid-state NMR–based atomic model of an Aβ(1–42) fibril elucidates the mechanism of fibril formation and propagation in AD and other amyloid diseases.

/pdf/ab-1-42-fibril-structure-illuminates-self-recognition-and-16esqkj0hs.pdf

Aβ(1–42) fibril structure illuminates self-recognition and replication of amyloid in Alzheimer's disease

Folding funnels have been the focus of considerable attention during the last few years. These have mostly been discussed in the general context of the theory of protein folding. Here we extend the utility of the concept of folding funnels, relating them to biological mechanisms and function. In particular, here we describe the shape of the funnels in light of protein synthesis and folding; flexibility, conformational diversity, and binding mechanisms; and the associated binding funnels, illustrating the multiple routes and the range of complexed conformers. Specifically, the walls of the folding funnels, their crevices, and bumps are related to the complexity of protein folding, and hence to sequential vs. nonsequential folding. Whereas the former is more frequently observed in eukaryotic proteins, where the rate of protein synthesis is slower, the latter is more frequent in prokaryotes, with faster translation rates. The bottoms of the funnels reflect the extent of the flexibility of the proteins. Rugged floors imply a range of conformational isomers, which may be close on the energy landscape. Rather than undergoing an induced fit binding mechanism, the conformational ensembles around the rugged bottoms argue that the conformers, which are most complementary to the ligand, will bind to it with the equilibrium shifting in their favor. Furthermore, depending on the extent of the ruggedness, or of the smoothness with only a few minima, we may infer nonspecific, broad range vs. specific binding. In particular, folding and binding are similar processes, with similar underlying principles. Hence, the shape of the folding funnel of the monomer enables making reasonable guesses regarding the shape of the corresponding binding funnel. Proteins having a broad range of binding, such as proteolytic enzymes or relatively nonspecific endonucleases, may be expected to have not only rugged floors in their folding funnels, but their binding funnels will also behave similarly, with a range of complexed conformations. Hence, knowledge of the shape of the folding funnels is biologically very useful. The converse also holds: If kinetic and thermodynamic data are available, hints regarding the role of the protein and its binding selectivity may be obtained. Thus, the utility of the concept of the funnel carries over to the origin of the protein and to its function.

https://onlinelibrary.wiley.com/doi/pdf/10.1110/ps.8.6.1181

Folding funnels, binding funnels, and protein function

Polar residue hot spots have been observed at protein–protein binding sites. Here we show that hot spots occur predominantly at the interfaces of macromolecular complexes, distinguishing binding sites from the remainder of the surface. Consequently, hot spots can be used to define binding epitopes. We further show a correspondence between energy hot spots and structurally conserved residues. The number of structurally conserved residues, particularly of high ranking energy hot spots, increases with the binding site contact size. This finding may suggest that effectively dispersing hot spots within a large contact area, rather than compactly clustering them, may be a strategy to sustain essential key interactions while still allowing certain protein flexibility at the interface. Thus, most conserved polar residues at the binding interfaces confer rigidity to minimize the entropic cost on binding, whereas surrounding residues form a flexible cushion. Furthermore, our finding that similar residue hot spots occur across different protein families suggests that affinity and specificity are not necessarily coupled: higher affinity does not directly imply greater specificity. Conservation of Trp on the protein surface indicates a highly likely binding site. To a lesser extent, conservation of Phe and Met also imply a binding site. For all three residues, there is a significant conservation in binding sites, whereas there is no conservation on the exposed surface. A hybrid strategy, mapping sequence alignment onto a single structure illustrates the possibility of binding site identification around these three residues.

https://www.pnas.org/content/pnas/100/10/5772.full.pdf

Buyong Ma

Papers

Principles of docking: An overview of search algorithms and a guide to scoring functions

Is allostery an intrinsic property of all dynamic proteins

Aβ(1–42) fibril structure illuminates self-recognition and replication of amyloid in Alzheimer's disease

Folding funnels, binding funnels, and protein function

Protein–protein interactions: Structurally conserved residues distinguish between binding sites and exposed protein surfaces