Quantitative Biology

Cataract is one of the most prevalent protein aggregation disorders and still the biggest cause of vision loss worldwide. The human lens, in its core region, lacks turnover of any cells or cellular components; it has therefore evolved remarkable mechanisms for resisting protein aggregation for a lifetime. We now report that one such mechanism relies on an unusually abundant metabolite, myo-inositol, to suppress light-scattering aggregation of lens proteins. We quantified aggregation suppression by in vitro turbidimetry and characterized both macroscopic and microscopic mechanisms of myo-inositol action using negative-stain electron microscopy, differential scanning fluorometry, and a thermal scanning Raman spectroscopy apparatus. Given recent metabolomic evidence that it is dramatically depleted in human cataractous lenses compared to age-matched controls, we suggest that maintaining or restoring healthy levels of myo-inositol in the lens may be a simple, safe, and widely available strategy for reducing the global burden of cataract.

Accurate prediction of protein stability changes upon single-site variations (DDG) is important for protein design, as well as our understanding of the mechanism of genetic diseases. The performance of high-throughput computational methods to this end is evaluated mostly based on the Pearson correlation coefficient between predicted and observed data, assuming that the upper bound would be 1 (perfect correlation). However, the performance of these predictors can be limited by the distribution and noise of the experimental data. Here we estimate, for the first time, a theoretical upper-bound to the DDG prediction performances imposed by the intrinsic structure of currently available DDG data. Given a set of measured DDG protein variations, the theoretically best predictor is estimated based on its similarity to another set of experimentally determined DDG values. We investigate the correlation between pairs of measured DDG variations, where one is used as a predictor for the other. We analytically derive an upper bound to the Pearson correlation as a function of the noise and distribution of the DDG data. We also evaluate the available datasets to highlight the effect of the noise in conjunction with DDG distribution. We conclude that the upper bound is a function of both uncertainty and spread of the DDG values, and that with current data the best performance should be between 0.7-0.8, depending on the dataset used; higher Pearson correlations might be indicative of overtraining. It also follows that comparisons of predictors using different datasets are inherently misleading.

In the recent years, therapeutic use of antibodies has seen a huge growth, due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody-antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR or mutagenesis analysis can be applied, but these are usually expensive and time consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterisation of antibody-antigen complexes. Here we describe a protocol for the prediction of the 3D structure of antibody-antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of: 1) The identification of the antibody residues belonging to the hyper variable loops which are known to be crucial for the binding and can be used to guide the docking; 2) The detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues.

Manipulation of temperature can be used to actuate DNA origami nano-hinges containing gold nanoparticles. We develop a physical model of this system that uses partition function analysis of the interaction between the nano-hinge and nanoparticle to predict the probability that the nano-hinge is open at a given temperature. The model agrees well with experimental data and predicts experimental conditions that allow the actuation temperature of the nano-hinge to be tuned over a range of temperatures from 30 ??C to 45 ??C . Additionally, the model reveals surprising physical constraints on the system. This combination of physical insight and predictive potential is likely to inform future designs that integrate nanoparticles into dynamic DNA origami structures. Furthermore, our modeling approach could be expanded to consider the incorporation, stability, and actuation of other types of functional elements or actuation mechanisms integrated into nucleic acid devices.

Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on the protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energy, toxicity, partition coefficient, protein folding stability changes upon mutation, etc.

A new coronavirus identified as SARS-CoV-2 virus has brought the world to a state of crisis, causing a major pandemic, claiming more than 433,000 lives and instigating major financial damage to the global economy. Despite current efforts, developing safe and effective treatments remains a major challenge. Moreover, new strains of the virus are likely to emerge in the future. To prevent future pandemics, several drugs with various mechanisms of action are required. Drug discovery efforts against the virus fall into two main categories: (a) monoclonal antibodies targeting the spike protein of the virus and blocking it from entry; (b) small molecule inhibitors targeting key proteins of the virus, interfering with replication and translation of the virus. In this study, we are presenting a computational investigation of a potential drug candidate that targets SARS-CoV-2 protease, a viral protein critical for replication and translation of the virus.

We propose a mesoscale model structure for the coronavirus nucleocapsid, assembled from the high resolution structures of the basic building blocks of the N-protein, CryoEM imaging and mathematical constraints for an overall quasi-spherical particle. The structure is a truncated octahedron that accommodates two layers: an outer shell composed of triangular and quadrangular lattices of the N-terminal domain and an inner shell of equivalent lattices of coiled parallel helices of the C-terminal domain. The model is consistent with the dimensions expected for packaging large viral genomes and provides a rationale to interpret the apparent pleomorphic nature of coronaviruses.

Membrane transporters contribute to the regulation of the internal environment of cells by translocating substrates across cell membranes. Like all physical systems, the behaviour of membrane transporters is constrained by the laws of thermodynamics. However, many mathematical models of transporters, especially those incorporated into whole-cell models, are not thermodynamically consistent, leading to unrealistic behaviour. In this paper we use a physics-based modelling framework, in which the transfer of energy is explicitly accounted for, to develop thermodynamically consistent models of transporters. We then apply this methodology to model two specific transporters: the cardiac sarcoplasmic/endoplasmic Ca 2+ ATPase (SERCA) and the cardiac Na + /K + ATPase.

The physical chemistry of liquid-liquid phase separation (LLPS) of polymer solutions bears directly on the assembly of biologically functional droplet-like bodies from proteins and nucleic acids. These biomolecular condensates include certain extracellular materials, and intracellular compartments that are characterized as "membraneless organelles". Analytical theories are a valuable, computationally efficient tool for addressing general principles. LLPS of neutral homopolymers are quite well described by theory; but it has been a challenge to develop general theories for the LLPS of heteropolymers involving charge-charge interactions. Here we present a novel theory that combines a random-phase-approximation treatment of polymer density fluctuations and an account of intrachain conformational heterogeneity based upon renormalized Kuhn lengths to provide predictions of LLPS properties as a function of pH, salt, and charge patterning along the chain sequence. Advancing beyond more limited analytical approaches, our LLPS theory is applicable to a wide variety of charged sequences ranging from highly charged polyelectrolytes to neutral or nearly neutral polyampholytes. The new theory should be useful in high-throughput screening of protein and other sequences for their LLPS propensities and can serve as a basis for more comprehensive theories that incorporate non-electrostatic interactions. Experimental ramifications of our theory are discussed.

The evolution of drug-resistant microbial species is one of the major challenges to global health. The development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemical assays. Computational techniques can be used for rapid screening of promising antimicrobial peptide candidates prior to testing in the wet lab. The vast majority of existing antimicrobial peptide predictors are non-targeted in nature, i.e., they can predict whether a given peptide sequence is antimicrobial, but they are unable to predict whether the sequence can target a particular microbial species. In this work, we have developed a targeted antimicrobial peptide activity predictor that can predict whether a peptide is effective against a given microbial species or not. This has been made possible through zero-shot and few-shot machine learning. The proposed predictor called AMP0 takes in the peptide amino acid sequence and any N/C-termini modifications together with the genomic sequence of a target microbial species to generate targeted predictions. It is important to note that the proposed method can generate predictions for species that are not part of its training set. The accuracy of predictions for novel test species can be further improved by providing a few example peptides for that species. Our computational cross-validation results show that the pro-posed scheme is particularly effective for targeted antimicrobial prediction in comparison to existing approaches and can be used for screening potential antimicrobial peptides in a targeted manner especially for cases in which the number of training examples is small. The webserver of the method is available at this http URL.