In financial markets, an excess of buying tends to drive prices up, and an excess of selling tends to drive them down. This is called market impact. Based on a simplified model for market making, it is possible to derive a unique functional form for market impact. This can be used to formulate a non-equilibrium theory for price formation. Commonly used trading strategies, such as value investing, and trend following induce characteristic dynamics in the price. Although there is a tendency for self-fulfilling prophesies, this is not always the case; in particular, many value-investing strategies fail to make prices reflect values. When there is a diversity of perceived values, nonlinear strategies give rise to excess volatility. Many market phenomena such as trends and temporal correlations in volume and volatility have simple explanations. The theory is both simple and experimentally testable. Under this theory there is an emphasis on the interrelationships of strategies that makes it natural to regard a market as a financial ecology. A variety of examples show how diversity emerges automatically as new strategies exploit the inefficiencies of old strategies. This results in capital reallocations that evolve on longer time scales and cause apparent non-stationarities on shorter time scales. The drive toward market efficiency can be studied in the dynamical context of pattern evolution. The evolution of the capital of a strategy is analogous to the evolution of the population of a biological species. Several different arguments suggest that the time scale for market efficiency is years to decades.

pdf/market-force-ecology-and-evolution-22klbobeau.pdf

Market Force, Ecology, and Evolution

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.

/pdf/satpdb-a-database-of-structurally-annotated-therapeutic-miidhqdiow.pdf

SATPdb: a database of structurally annotated therapeutic peptides

Self-binding peptides (SBPs) represent those short peptide segments within monomeric proteins to fulfill their biological functions by dynamically binding to/unbinding from their target domains in the same monomers. They are frequently found in disordered or unstructured regions of proteins that are now known to be not simply helices, loops, or linkers and adopt a well-defined structure upon binding. In this study, we systematically explored the structural basis, energy landscape, and thermodynamic properties of SBP-mediated biological mechanisms by dissecting dynamic interactions of SBPs with their cognate targets. In order to identify whether the formation of the SBP–target bound state is naturally a folding or a binding, we carried out atomistic molecular dynamics (MD) simulations to investigate both the native bound state of eight representative SBPs in complex with their cognate domains and the same bound state but where the SBPs were split artificially from the domains in primary sequence. Results s...

Self-binding peptides: folding or binding?

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling. We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically. All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

/pdf/the-scoring-of-poses-in-protein-protein-docking-current-1t1l7doprq.pdf

The scoring of poses in protein-protein docking: current capabilities and future directions.

Self-binding peptides (SBPs) represent a novel biomolecular phenomenon spanning between folding and binding, where a short peptide segment within a monomeric protein fulfills biological functions by dynamically binding to/unbinding from its target domain in the same monomer. Here, we were able to quantitatively reconstruct the complete structural dynamics picture of binding of free SBPs to their target domains for five representative SBP systems by carrying out the state-of-the-art molecular dynamics (MD) simulations. In the picture, a two-step binding mechanism for SBP–domain recognition and association was proposed, which includes a fast, nonspecific diffusive phase and a slow, specific organizational phase. The electrostatic interactions and desolvation effects play a predominant role in the first phase that leads to the formation of a metastable encounter complex, while conformational rearrangement is observed in the second phase to optimize the exquisite network of nonbonded chemical forces such as hydrogen bonds and salt bridges across the complex interface. From an energetic point of view, a funnel-shape enthalpy landscape steers these SBPs towards their native bound state and thus facilitates the binding process. However, the binding exhibits typical enthalpy–entropy compensation due to the high flexibility of peptides that results in a relatively low affinity for SBP–domain binding and forces the SBP systems to rapidly switch between the bound and unbound states. In addition, slight conformational changes in the target domain and/or in the polypeptide linker between the domain and peptide can significantly affect the energetic properties and dynamic behavior of the fine-tuned binding process of SBP–domain recognition.

A two-step binding mechanism for the self-binding peptide recognition of target domains

The recent focus on protein-protein interaction networks has increasingly been shifted towards the disruption of protein complexes, which either are mediated by the binding of a globular domain in one protein to a short peptide stretch in another, or involve flat, large, and hydrophobic interfaces that classical small-molecule agents are not always ideally suited. Rational design of therapeutic peptides with high affinity targeting such interactions has emerged as a new and promising tool in discovery of potential drug candidates against associated diseases. The design is commonly based on bioinformatics methods or molecular modeling techniques, indirectly exploiting structure-activity relationship at the level of peptide sequence or directly deriving lead entities from protein complex architecture. Here, a newly rising subfield called computational peptidology that focuses on the use of computational and theoretical approaches to treat peptide-related problems is comprehensively reviewed on the design and discovery of peptide agents targeting protein-protein interactions. We address a systematic discussion on several representative cases in which the computational peptidology is successfully employed to develop peptide therapeutics. Besides, some problems and pitfalls accompanied with the current use of computational methods in peptide modeling and design are also present.

Computational Peptidology: A New and Promising Approach to Therapeutic Peptide Design

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Computational Peptidology: A New and Promising Approach to Therapeutic Peptide Design