No abstract

The recently developed GROMOS 54A7 force field, a modification of the 53A6 force field, is validated by simulating the folding equilibrium of two β-peptides which show different dominant folds, i.e., a 314-helix and a hairpin, using three different force fields, i.e., GROMOS 45A3, 53A6, and 54A7. The 54A7 force field stabilizes both folds, and the agreement of the simulated NOE atom-atom distances with the experimental NMR data is slightly improved when using the 54A7 force field, while the agreement of the (3)J couplings with experimental results remains essentially unchanged when varying the force field. The 54A7 force field developed to improve the stability of α-helical structures in proteins can thus be safely used in simulations of β-peptides.

No abstract

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.a., one-step or unified) alchemical transformations commonly used in the prediction of hydration and relative binding free energies (RBFEs). We first classify several known challenges in these calculations into three categories: endpoint catastrophes, particle collapse, and large gradient-jumps. While endpoint catastrophes have long been addressed using softcore potentials, the remaining two problems occur much more sporadically and can result in either numerical instability (i.e., complete failure of a simulation) or inconsistent estimation (i.e., stochastic convergence to an incorrect result). The particle collapse problem stems from an imbalance in short-range electrostatic and repulsive interactions and can, in principle, be solved by appropriately balancing the respective softcore parameters. However, the large gradient-jump problem itself arises from the sensitivity of the free energy to large values of the softcore parameters, as might be used in trying to solve the particle collapse issue. Often, no satisfactory compromise exists with the existing softcore potential form. As a framework for solving these problems, we developed a new family of smoothstep softcore (SSC) potentials motivated by an analysis of the derivatives along the alchemical path. The smoothstep polynomials generalize the monomial functions that are used in most implementations and provide an additional path-dependent smoothing parameter. The effectiveness of this approach is demonstrated on simple yet pathological cases that illustrate the three problems outlined. With appropriate parameter selection, we find that a second-order SSC(2) potential does at least as well as the conventional approach and provides vast improvement in terms of consistency across all cases. Last, we compare the concerted SSC(2) approach against the gold-standard stepwise (a.k.a., decoupled or multistep) scheme over a large set of RBFE calculations as might be encountered in drug discovery.

Idiopathic, post-infectious and immunodeficiency constitute major bronchiectasis aetiologies in Guangzhou. Clinical characteristics of patients between known aetiologies and idiopathic bronchiectasis were similar. Ethnicity and geography only account for limited differences in aetiologic spectra. These findings will offer rationales for early diagnosis and management of bronchiectasis in future studies and clinical practice in China.

No abstract

Free energy perturbation (FEP) has become widely used in drug discovery programs for binding affinity prediction between candidate compounds and their biological targets. However, limitations of FEP applications also exist, including, but not limited to, high cost, long waiting time, limited scalability, and breadth of application scenarios. To overcome these problems, we have developed XFEP, a scalable cloud computing platform for both relative and absolute free energy predictions using optimized simulation protocols. XFEP enables large-scale FEP calculations in a more efficient, scalable, and affordable way, for example, the evaluation of 5000 compounds can be performed in 1 week using 50–100 GPUs with a computing cost roughly equivalent to the cost for the synthesis of only one new compound. By combining these capabilities with artificial intelligence techniques for goal-directed molecule generation and evaluation, new opportunities can be explored for FEP applications in the drug discovery stages of hit identification, hit-to-lead, and lead optimization based not only on structure exploitation within the given chemical series but also including evaluation and comparison of completely unrelated molecules during structure exploration in a larger chemical space. XFEP provides the basis for scalable FEP applications to become more widely used in drug discovery projects and to speed up the drug discovery process from hit identification to preclinical candidate compound nomination.

Computer simulation using long molecular dynamics (MD) can be used to simulate the folding equilibria of peptides and small proteins. However, a systematic investigation of the influence of the side-chain composition and position at the backbone on the folding equilibrium is computationally as well as experimentally too expensive because of the exponentially growing number of possible side-chain compositions and combinations along the peptide chain. Here, we show that application of the one-step perturbation technique may solve this problem, at least computationally; that is, one can predict many folding equilibria of a polypeptide with different side-chain substitutions from just one single MD simulation using an unphysical reference state. The methodology reduces the number of required separate simulations by an order of magnitude.